Undergraduate Courses

ISE Courses

All undergraduate students need to complete three technical electives in addition to the required core courses of their program. Options for technical electives can be found here. Students should refer to the major requirements and guidesheet for their term of entry to the program to ensure they are on-track for completion. Progress will be reviewed at least once per semester with an academic advisor within the department.

• ISE 211 - Engineering Economics

  The fundamentals of engineering economy are introduced in this course. Topics covered include time value of money, equivalence, cash flows all at varying interest rates and points of time. The factors commonly used in industry for economic evaluation of alternatives are introduced and applied in this course. The analysis includes both a before tax and after tax evaluation of competing investment alternatives. Prerequisites: Math 227 and EDD 112 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 212 - Engineering Computing

  The objectives of the course are to: demonstrate the ability to design systems for automating processes in manufacturing, demonstrate problem-solving skills in automation, and to demonstrate the skill of using the LabVIEW and MATLAB software packages. Prerequisite: EDD 112 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 231 - Human Factors

  Review of the concepts involved in the application of scientific principles, methods, and history to the development of engineering systems in which people play a significant role. Primary focus is on the man/machine interface and how to design for the human being as part of an overall system. Prerequisite: MATH 226/227 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 261 - Probabilistic Systems I

  This course provides an introduction to probability models and statistical methods most likely to be encountered and used by students in their careers in engineering and the natural sciences. This introduction will emphasize, from the outset, that variation is the source from which all statistical methodology flows. Discussion includes the practical aspects of data collection and descriptive statistics with an introduction to the basic concepts of probability theory and probability distributions, correlation, point estimation, confidence intervals, and test of hypothesis. Prerequisites: Math 227 and EDD 112 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 295 - Seminar Course

  Development of the non-technical skills essential to effective engineering. Focus is on the overview of ISE curriculum and review of technical elective options. Review of internships, resume building, issues relevant to careers in ISE (e.g., typical tasks done by ISEs) are explored. Discussion and exploration of opportunities within program. Prerequisites: Sophomore Standing. Offered in the Fall semester. 1 credit. Levels: Undergraduate

• ISE 311 - Enterprise Systems

  Course introduces the concepts, design and planning of operating systems, with particular emphasis on manufacturing systems. Topics include introduction to lean manufacturing, JIT, Kanban, value stream mapping, standard times, MRP, inventory control, etc. The course includes plant tours to local industries that practice the concepts of the Toyota production system. Prerequisite: ISE 211 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 312 - Manufacturing Systems

  This course has three main areas of focus: production and inventory control, planning and design of manufacturing facilities, and understanding the physical fundamentals of processes and is designed mainly for engineering students intent on following an engineering career in a manufacturing industry. This course covers the models, networking, and systems needed to design and manage a manufacturing enterprise. Topics include facility design and material handling, forecasting techniques, demand management, economic lot size, inventory management, and scheduling methods. This is considered a technical elective. Prerequisite: ISE 311 or consent of department chair. Prerequisite: Junior standing in the Watson School or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Undergraduate

• ISE 314 - Computer Program for Engineers

  This course provides an introduction to computer programming and its applications for industrial and systems engineering (ISE) students emphasizing modern software engineering techniques in the context of industrial systems. Topics will include the fundamental concepts and applications of computer programming, software engineering, database management, computational problem solving, and statistical techniques for data mining. In this course, programming languages such as Python, VB.Net, or Matlab will be taught along with Excel VBA and SQL. Students will learn how to apply computer-programming techniques to solve different ISE problems. Prerequisite: ISE 261 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 315 - Enterprise Resource Planning

  This course introduces the student to the main areas of production, including production planning, control, master scheduling, finance and human resources. It provides a hands on approach to these areas through an Enterprise Resource planning software. Prerequisites: Junior standing in ISE or consent of instructor. Term offered varies. 3 credits Levels: Undergraduate

• ISE 320 - Optimiz & Operation Research I

  Operations research (OR) is devoted to determination of the optimal course of action of a decision problem given resource restrictions. This course primarily covers deterministic optimization and operations research techniques. Following a review of linear algebra, students learn how to mathematically model an engineering problem, how to solve the problem to optimality and how to perform sensitivity analyses on the results. Students learn linear programming (LP), integer programming (IP), branch-and-bound (B & B), and other optimization techniques. Special emphasis on the solution of engineering decision making includes the following areas: transportation models; network models; inventory models; assignment problems; decision making under risk and uncertainty; and game theory. For non-ISE students using this course as an elective for the Sustainability Engineering minor, application of these techniques as applied to decision-making for sustainability are included. Prerequisite: Math 304 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 362 - Probabilistic Systems II & DOE

  Methods of inference involving two independent samples and paired data are presented. The analysis of variance is examined for single-factor and multi-factor experiments. Regression analysis for simple linear models and correlation are discussed followed by non-linear and multiple regression models. A practical, yet fundamental, approach for building quality control charts from statistical concepts, as well as a goodness-of-fit test for testing discrete and continuous underlying distributions, are reviewed. Prerequisites: ISE 261 Probabilistic Systems I or permission of instructor. Offered in the Fall semester. 4 credits Levels: Undergraduate

• ISE 363 - Quality Engineering

  This course is designed to introduce the student to techniques required to maintain and improve quality within manufacturing organizations and the service sector through the use of statistical methodologies. The course covers concepts of quality, quality management and assurance, product quality, design of quality, six sigma, lean (using quality to eliminate waste), control charting, statistical process control, and quality improvement through design by considering concept development and implementation. Traditional and modern quality systems will be discussed including the work of such quality gurus like Taguchi, Deming, Juran, and Shewhart. Prerequisites: ISE 362 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 364 - Eng Project Management

  Effective Engineering Project Management is critical to business and organizational success. Through in-class practice of concepts and processes, learn proven techniques, enhanced skills and best practice principles to lay a solid Engineering Project Management foundation. Through the discipline of initiating, planning, executing, controlling, and closing a project, you will grasp a thorough understanding of how to make your engineering projects successful. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the critical path method with a case study of contemporary engineering project. Prerequisites: ISE 211 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 411 - Production & Schedule Control

  This course covers general concepts of production planning and control but goes more into detail of the concepts and mathematics of scheduling applications and theory. Upon completion of the course, the student will be able to apply mathematical techniques in developing solutions to “production scheduling” problems. The “production scheduling” terminology may seem like these techniques are applicable only to manufacturing, however, they have broad applications across many industries and sectors including, but not limited, to healthcare, service industries, food processing, etc. Prerequisites: ISE 311 and senior-level standing, or permission of the instructor. Offered for the Fall semester. 3 credits. Levels: Undergraduate

• ISE 415 - Operations Mgmt Supply Chains

  Course deals with management of supply chains, in particular, with the operational aspects. A broad overview of supply chains of a company is introduced, together with performance measures and needed critical success factors. The course concentrates on supplies, inventories, manufacturing, and logistics of distribution. Managerial aspects as well as mathematical modeling for better planning and control will be covered. This course is considered a technical elective for undergraduate students. Prerequisite: ISE 311 and ISE 320 or consent of department chair. Crosslisted with SSIE 515. Prerequisite: Senior standing in ISE or consent of instructor. Offered in the spring semester. 3 credits. Levels: Undergraduate

• ISE 416X - Fund. of Energy Sys. Analytics

  This course provides fundamental knowledge of the application of Industrial and Systems Engineering in the Energy field. It focuses on the modeling, design, simulation, and optimization of alternative and conventional energy technologies, the societal and environmental impact of technology developments, and the economic benefits of those developments. Recent literature that covers a wide range of ISE topics related to energy systems such as transportation energy, building energy, manufacturing energy, solar energy, wind energy, bioenergy, greenhouse gas emission, and smart grid operations will be discussed in this course. Students will gain breadth and depth of knowledge, and an interdisciplinary understanding of the contemporary need for energy solutions. 3 Credits. Prerequisites: Senior level standing. Offered in fall or spring. Levels: Undergraduate

• ISE 418 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisite: ISE 314 or permission of instructor. Cross listed with SSIE 523. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 419 - Applied Soft Computing

  Covers relatively new approaches to machine intelligence known collectively as "soft computing". Introduces various types of fuzzy inference systems, neural networks, and genetic algorithms, along with several synergistic approaches for combining them as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction, design, control, databases and data mining. This course is considered a technical elective for undergraduate students. Prerequisites: Basic knowledge of calculus and discrete mathematics and competence in at least one programming language, or permission of instructor. Crosslisted with SSIE 519. Offered in the fall semester. 3 credits. Levels: Undergraduate

• ISE 420 - Optimiz & Operations Res II

  Operations research (OR) is devoted to the determination of the optimal course of action of a decision problem given resource restrictions. This course is intended as a second course in an Optimization and OR sequence and builds upon the material presented in ISE 320. ISE 320 primarily restricts attention to deterministic OR models. In addition to covering additional deterministic techniques (e.g., deterministic dynamic programming and additional inventory problems not covered in ISE 320, among others), ISE 420 covers probabilistic and advanced OR topics such as Monte Carlo simulation, fundamentals of queueing theory, probabilistic dynamic programming, and others. The course also introduces the student to emerging optimization techniques including, but not limited to, tabu search, simulated annealing, and genetic algorithms. Prerequisite: ISE 320 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 421 - Modeling And Simulation

  Model building, nature of simulation and material on the full range of simulation activities, such as input analysis, output analysis, verification and validation, and model animation. Includes random number generation; distribution functions and random variates; applications of discrete event simulation methods to queueing, inventory control and production planning problems; Markov processes, queueing theory and decision analysis. Prerequisites: ISE 362 and ISE 320 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 422 - Advanced Decision Modeling

  Course provides a broad foundation in decision models and techniques used in industry and research for technical and managerial problems. Topics include decision theory, risk and uncertainty, value of information, preference measurements, prioritization of alternatives, multiple objectives and hierarchical decisions. Prerequisite: ISE 362 or permission of the instructor. Cross-listed with SSIE 522. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 423 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisite: ISE 314 or permission of instructor. Crosslisted with SSIE 523. Term offered varies. 3 credits. . Levels: Undergraduate

• ISE 426X - Sys Dynamics & Sys Thinking

  In this course, the basic concepts of systems thinking and system dynamics modeling will be introduced and students will understand, evaluate, and improve upon problems that manifest themselves in complex systems, such as diffusion of new technologies; global power consumption; fluctuating sales, production and earnings; environmental challenges such as urban development and poverty; healthcare and public health challenges such as disease spread. System dynamics is a simulation modeling process to quantify problems that unfold through time. In this iterative process, a problem that is pervasive throughout a system is defined, the structure of the system that generates the problem will be studied, and policies or practices that can change the system structure and behavior and therefore alleviate/solve the problem will be studied. Prerequisites: Senior level standing or permission of the instructor. Crosslisted with SSIE 526X. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 430 - Healthcare Policy Analysis

  This course provides an overview of issues and policies related to health care systems, with emphasis on health care systems within the United States. The course will apply various engineering tools and approaches for policy analysis and program planning in this area. Some potential future policy changes will also be discussed, as well as challenges with implementation and change management techniques. Cross-listed with SSIE 530. Prerequisites: Senior standing or permission of the instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 434 - Fundamentals of Health Systems

  One of the growing systems in our society is that of the healthcare delivery system. The purpose of this course is to introduce the concepts behind the healthcare delivery systems and to focus upon the systems improvement or continuous improvement techniques available for complex systems. Topics would include improvement to, and problems with: organizational structure, managing change, the financial structure, the responsibility structure, quality data and implications of quality measures, use of clinical decision support systems and the caregiver's role in the system. There will also be a focus upon suppliers to the healthcare delivery system and the unique requirements placed upon their products and processes. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing or consent of department chair. Cross-listed with SSIE 534. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 437 - Ind & Sys Eng in Healthcare

  Introduction to health systems and healthcare delivery. The application of industrial and systems engineering principles to continuous process improvement in the healthcare domain will be studied. Concepts that will be addressed will include, but not be limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. The course is considered a technical elective for undergraduate students. Prerequisites: ISE 311 or permission of instructor. Crosslisted with SSIE 537. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 438 - Healthcare Financial Eng

  This course is intended to provide an overview of healthcare finance and the current financial environment for the healthcare industry and to learn how to apply engineering economics to healthcare financial management. It will provide information on financial and managerial accounting and how the use of Systems Science and Industrial and Systems Engineering principles can be applied to financial management concepts to allow for health-related organizations to make sound business decisions. Prerequisite: Senior standing or permission of instructor. Crosslisted with SSIE 538. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 439 - Human Factors Eng Healthcare

  This course introduces and emphasizes the role that human factors engineering/ergonomics plays in healthcare systems, with a focus on its applications to help improve quality, safety, efficiency, and effectiveness of patient care. Focused topics include human factors in workflow models; work system design for patient safety; human error analysis/taxonomies to reduce medical errors; task analysis and data collection methods in healthcare environments; clinical staff workload and patient safety; physical ergonomics in healthcare and human performance modeling; and diffusion and adoption of technology in healthcare, with emphasis on the usability and design of medical devices and information systems. Prerequisite: Senior standing or permission of instructor. Crosslisted with SSIE 539. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 440 - Intro to Systems Science

  Includes the following: a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions and historical development of the field; an overview of fundamental systems concepts, principles and laws; and a survey of application areas of systems science and its implications for other fields of study. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing or permission of the department chair. Cross-listed with SSIE 501. Offered in the fall semester. 3 credits. Levels: Undergraduate

• ISE 448 - Healthcare Data Sci&Analytics

  Data analytics plays an increasingly greater role in healthcare organizations. However, many healthcare professionals lack training in this vital area. This course will cover advanced data analytics with applications in healthcare systems engineering. In specific, the course will cover the concepts and methods of the data analytics in three particular areas: (i) descriptive analytics, which includes data description and aggregation; (ii) predictive analytics, which uses machine learning and statistical modeling for prediction and forecasting; and (iii) prescriptive analytics, which makes use of the results of predictive analysis to optimize the decision making process. All introduced methods will be demonstrated along with a variety of data modalities collected from healthcare applications such as pervasive health, disease diagnosis, fraud detection, and pharmaceutical discovery along with medical decision support systems. Through the provided coursework, students will be able to learn a variety of methodologies on data processing and modeling and make the best use of healthcare data to facilitate medical decision making. Prerequisites: Senior level standing, ISE 314 or equivalent, or permission of the instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 456 - Expert Systems In Manufacturin

  Computer-based decision-making tools in which a domain expert's knowledge is embedded are known as knowledge-based expert systems (KBESs). Expert systems allow such expert knowledge to be widely disseminated (e.g., throughout a company or throughout different facilities that a company has). This course introduces the student to all steps of development (e.g., knowledge representations, knowledge acquisition, and expert system development, among others) with special emphasis on manufacturing applications. The course will culminate with the students developing functioning prototype expert systems in a manufacturing area of their choice. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing in ISE or permission of instructor. Crosslisted with SSIE 556. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 462 - Cost Estimating For Engineers

  Changes in our society have resulted in major changes in manufacturing to the point at which labor costs are no longer the controlling cost, or even the major cost. Major costs have shifted to the material and material-related costs, with the overhead and burden costs almost as significant. While innovative design is critical to engineering, being profitable is also critical, and profit starts with determining the proper costs for a product or idea. Topics include costs of labor, equipment, material, overhead or burden, volume/cost relationships, use rates, collection, build-up of costs, costing of manufacturing operations, standard costs and variances. This course is considered a technical elective for undergraduate students. Prerequisite: ISE 211 or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Undergraduate

• ISE 463 - Project Analysis And Control

  Course covers the topics of project planning, economic design making, costing and pricing. The topics include network planning, present worth, annual cost, rate of return, activity based costing, inflation, price change, etc. Prerequisite: ISE 211 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 464 - Elem Of Fuz Log & Fuz Set Thry

  Simple introduction to basic elements of fuzzy logic and fuzzy set theory, including an overview of classical logic and classical set theory. Included are basic concepts and properties of classical sets and fuzzy sets, classical relations and fuzzy relations, classical logic and fuzzy logic, and fuzzy arithmetic. The practical utility of fuzzy logic and fuzzy set theory is illustrated by describing selected applications in various areas of human affairs. This course is considered a technical elective for undergraduate students. Prerequisite: ISE 261 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 465 - Engineering Project Management

  Effective Engineering Project Management is critical to business and organizational success. Through in-class practice of concepts and processes, learn proven techniques, enhanced skills and best practice principles to lay a solid Engineering Project Management foundation. Through the discipline of initiating, planning, executing, controlling, and closing a project, you will grasp a thorough understanding of how to make your engineering projects successful. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the critical path method with a case study of contemporary engineering project. Prerequisites: ISE 261 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 468X - Flexible & Hybrid Electronics

  Flexible and hybrid electronics integrate ultra-thin silicon semiconductor technology with non-traditional thin skin-like substrates establishing wearable devices for both human and machine. This course will cover design, materials, fabrication methods and evaluation of printed and thinned silicon-based electronics for applications such as smart energy, human health, and industrial sensing. Emphasis will be placed on fundamental understanding of the hard-soft materials interface, advances in materials printing, and approaches to make these systems robust for use. The course will promote understanding of the engineering requirements that lead to systems design for both manufacturing and reliability. 3 credits. Levels: Undergraduate

• ISE 469 - Materials for Manufacturing

  This course provides an overview of the materials used in manufacturing that are of significant economic, technical and industrial relevance. A special emphasis will be given to polymeric materials currently used in electronics manufacturing as well as those materials emerging for use in additive manufacturing and 3D printing. Selected materials will be classified by composition, properties, manufacturing methods, applications, economics and related supply chains. Prerequisites: Senior standing in engineering or science or permission of the instructor. Crosslisted with SSIE 569. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 470 - Industrl Automation&Ctrl

  Industrial automation is a major field in the application of computer controls and the many advances in computer systems. The objectives of this course are to: demonstrate the ability to design systems for automating processes in manufacturing, demonstrate problem-solving skills in automation, and safely use the machines in the engineering laboratory to complete designed experiments. Lectures and laboratories include exploring the use of sensors, industrial robotics, numerical control, programmable logic controllers, machine vision, electrical circuits and the fundamentals of common electrical devices, fuzzy control, the implementation of online computer control, and the ability to use industrial technical software including Pro-Engineer and AutoCAD. Laboratory work and technical reports are required. Prerequisites: ISE 311 or consent of department chair. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 470X - International STEM Trip

  This course will explore Germany's approach to science, engineering, technology development and manufacturing. We will study, observe and discuss before, during and after organized travel: Germany and European history, the German culture and work ethic, the impact government organizations on economic development. We will learn why Germany is still considered a leader in manufacturing and the economic powerhouse of the European Union. We will answer: what can we learn from Germany and how will it influence our own contributions as scientists and engineers in the United States. Offered in the Summer term. 3 credits Levels: Undergraduate

• ISE 473 - Processes for Electr Mfg.

  The purpose is for the students to gain a broad knowledge and understanding of the basics of printed circuit board manufacturing and assembly. The course offers an introduction to surface mount and insertion mount components, materials and processes as well as to PCB design and manufacturing. Lectures will introduce assembly process flows and component types, PCB construction and defects solder paste printing and equipment, placement processes and equipment, reflow and ovens, flip chip assembly and underfilling, defects and mitigation, reliability optimization and testing. Efforts will be made to include visits to local industrial assembly facilities as well as equipment on campus. The overall goal is to provide the students with a basis for communicating and working with subject matter experts. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing or permission of instructor. Crosslisted with SSIE 578. Offered in the fall semester. 3 credits. Levels: Undergraduate

• ISE 483X - Evolutionary Sys & Bio-Inspire

  In this course, the fundamental concepts of bio-inspired computing are introduced with a focus on understanding how evolutionary systems are different from other complex systems in nature and society. Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, evolvable, resilient, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course is strongly grounded on the foundations of complex systems science and theoretical biology. It aims to provide an understanding of the distributed architectures of evolving complex systems, and how those can be used to produce artificial intelligence and computational tools, as well as engineering designs, with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, physics, complexity, computer science, informatics, cognitive science, robotics, optimization, control, and cybernetics. Bio-inspired computing at the core of modern artificial intelligence, and thus applied throughout science and engineering, but understanding the mechanisms behind resilience, evolvability, and adaptation are essential to tackle the most vexing biomedical and social complexity problems we currently face. Therefore, the course should interest students in various programs. Students will be introduced to fundamental topics in bio-inspired computing and build up their proficiency in the application of various algorithms in real-world problems developed in lab assignments developed for Python (Jupyter) notebooks. 3 credits Prerequisites: Senior level standing or permission of the instructor / department chair. Basic knowledge of Python. Levels: Graduate, Undergraduate

• ISE 485 - Additive Mfg Processes & Sys

  This course aims to inculcate a comprehensive understanding of additive manufacturing (AM), and will address the following consequential aspects of AM: (1) Materials and Process Mechanics; (2) Machines, Machine Design, and Mechanisms; (3) Quality control, Sensing, and Process Monitoring; and (4) Design for AM and novel trans-disciplinary applications. Prerequisites: ISE 311 or permission of the instructor. Crosslisted with SSIE 585. Offered in the Fall semester. 3 credits. Levels: Undergraduate

• ISE 491 - Systems Design

  Covers the design process from the definition of requirements through the final output. Focus is on the design principles and design methodologies used to ensure a quality outcome. Prerequisite: ISE 311 and 362 or consent of department chair. Course fee applies. Refer to the Schedule of Classes. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 492 - Systems Design Project

  The capstone project for the undergraduate degree. Students are expected to work in multi-disciplinary teams to provide solutions through design. Prerequisite: ISE 491 or consent of department chair. Course fee applies. Offered in the Spring semester. 4 credits. Levels: Graduate, Undergraduate

• ISE 496 - Industrial Internship

  Special topics in industrial and systems engineering. Semester offered varies. Credits vary. Levels: Undergraduate

• ISE 497 - Independent Study

  Available every semester. Credits vary. Levels: Undergraduate

• ISE 497A - Independent Study

  Available every semester. Credits vary. Levels: Undergraduate

Please note: There is a mandatory $250 per course fee attached to the two-course sequence ISE 491 (Systems Design) and ISE 492 (Systems Design Project).

Pre-approved tech electives for BS ISE

• Only one non-ISE (SSIE) technical elective may be allowed
• This also applies to students pursuing minors (such as sustainability)

Undergraduate courses

• CS 101 - Prof Skills Ethics & CS Trends

  Introduction to and discussion of topics of interest to computer science majors: social, ethical and professional issues; university, school and department resources; current developments in computer science. Prerequisite: none. Offered in the Fall semester. 1 credit Levels: Undergraduate

• CS 105 - Intro To Computing

  Computing and its place in our society, including ethics and privacy. Basic concepts of computer hardware and systems. Data flow in computer systems. Understanding and using common application programs: word processors, spreadsheets and databases. Computers in communications. Basic concepts of algorithms, programming and the programming process. CS majors may only use this as free-elective credit. Does not provide any prerequisites for courses in the CS major or minor. Prerequisite: none Offered every semester. 4 credits Levels: Undergraduate

• CS 106 - Everyday Computing

  Translating human logic to computer logic; program flow, data structures and functions; graphics; libraries; networks; and basic data analytics. Provides the basic digital literacy required for responsible digital communications in professional environments. Introduces essential computing concepts and programming skills in a widely used scripting language. Students follow protocols and use tools to promote secure practices in the workplace, at school, and at home. Intended for non-majors; cannot be used toward the fulfillment of a computer science degree. Prerequisites: none. Term offered varies. 4 credits Levels: Undergraduate

• CS 110 - Pgming Concepts&Applic

  An introductory course for students with little or no programming experience. Basic control flow, data types, simple data structures and functions using a scripting language. Developing code using an integrated environment. The basics of directories, files and file types, including text files. Simple examples of the applications enabled by a modern, platform-independent scripting language such as GUIs, event handling, and database access. This course is open to all students who have not taken any other CS courses (with the exception of CS 105) and under these conditions, can count as free-elective credit for CS majors. Prerequisite: Math 225 (May be taken concurrently. ) (All prerequisites must have a grade of C- or better).Offered every semester. 4 credits Levels: Undergraduate

• CS 115 - FRI Image & Acoustic Signal I

  This is one of two lecture-lab courses of a 2-semester sequence for FRI students in Image and Acoustic Signals Analysis. Computing skills in MATLAB and Unix and introduction to computer programming. Technical writing using LaTeX. Students will learn research techniques while gaining understanding of research problems in this area. Prerequisites: HARP 170 and enrollment in Freshman Research Immersion (FRI) program (All prerequisites must have a grade of C- or better). Offered in the Spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• CS 120 - Prog & Hardware Fundamentals

  Introduction to the C programming language, including local and global variables, basic control structures, function calls, pointers and the stack; use of command-line C development environments and development tools such as gdb and make; assembly language connection to higher-level C; building blocks of the Von Neumann machine (ALU, registers, control unit, RAM, decoders, program counters) and the underlying basic logic elements; simple non-pipelined processor architectures. Supervised laboratory work involves programming in C and low-level languages, interfacing with hardware, and the design and simulation of small circuits and simplified microprocessors. Prerequisite MATH 225 that may be taken concurrently. CS 110, CS Majors may request a waiver from the Undergraduate Director based on prior programming experience (All prerequisites must have a grade of C- or better) Offered every semester. 4 credits Levels: Undergraduate

• CS 140 - Prog with Obj & Data Struct

  Assumes a foundation in procedural programming as covered in CS 110. Provides the foundations of software development using Java and the data structures provided by Java. Problem solving using object-oriented programming techniques is emphasized. Topics include primitive and reference data types, variables, expressions, assignment, functions/methods, parameters, selection, iteration, recursion, exception handling, generic linear data structures and maps, file types, file I/O, simple GUIs, programming to an interface, use of inheritance, javadoc documentation, and introduction to Java threads. Required laboratory provides supervised problem solving, programming using the command line as well as Eclipse or Netbeans development environments, code backup in a version control repository, debugging and JUnit testing techniques. Prerequisite: CS 110,CS Majors may request a waiver from the Undergraduate Director based on prior programming experience. Math 225 (All prerequisites must have a grade of C- or better). Offered every semester. 4 credits Levels: Undergraduate

• CS 185A - Living/Learning Computer Proj

  Projects developed in the context of Residential Life's Learning Communities. Projects minimally include technology, community service and group learning and depend on the interests of instructor and needs of the sponsoring Living Community. Only counts as free-elective credit for CS majors. Semester offered Spring. 1 credits. Levels: Undergraduate

• CS 207 - Introduction to Data Science

  This course will provide a broad overview of data science's different areas, from statistics, machine learning to data engineering and many data science applications. The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. This course is designed to provide a non-technical introduction to the data science approach. It is intended both for students from non-quantitative fields and those from a quantitative field who are interested in data science. The pre-requisite for this course includes working knowledge in high school math (Math 108 or equivalent), a course in introductory statistics (at the level of Math 147, Math 148 or AP statistics with grade 3+, or equivalent). It is recommended that a student have some understanding of scientific research. Prior programming experience is NOT required. Software: it is expected that both R and python programming will be introduced in this course. CS majors may only use this as free-elective credit. Does not provide any prerequisites for courses in the CS major or minor. Term offered varies. 4 credits Levels: Undergraduate

• CS 211 - Programming I Engineers

  Introduction to computer programming with engineering applications. Programming in the procedural language C, control structures, functions, arrays and pointers. Introduction to abstract data types and object-oriented programming using C++. This course is intended for Engineering Students. Not applicable toward a major or minor in computer science. Offered in the Fall semester. 4 credits Levels: Undergraduate

• CS 212 - Programming II for Engineers

  Development tools and methodologies for modular programming with an emphasis on engineering applications using the C language. Software design using functional and data abstraction. Specification, use and implementation of abstract data types including stacks, queues, lists, trees and graphs. Programming language features such as recursion, dynamically allocated data structures and separate compilation. Introduction to algorithm analysis, searching and sorting. Exposure to C++ classes for implementing abstract data types. Prerequisite: CS 211. This course is intended for Engineering Students. Not applicable toward major or minor in computer science. Offered in the Spring semester. 4 credits Levels: Undergraduate

• CS 215 - FRI Image & Acoustic Signal II

  This is the second of two lecture-lab courses of a 2-semester sequence for FRI students in Image and Acoustic Signals Analysis. Computer programming in C, and selected topics in multimedia signal processing and computer vision. Students will learn research techniques while gaining understanding of research problems in this area. Prerequisites: HARP 170 and EECE 115/CS 115, and enrollment in Freshman Research Immersion (FRI) program (All prerequisites must have a grade of C- or better). Offered in the fall semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• CS 220 - Arch from a Prog Perspective

  The architecture and programming of computer systems. Data representation and computer arithmetic. Processor and memory organization. Assembly and machine language programming. Advanced C programming language constructs and their implementation in assembly language. Introduction to system software (assemblers, linkers, loaders, compilers). Supervised laboratory work involves programming and debugging using machine language, assembly language and C. Prerequisite: CS 120 and CS 140 (All prerequisites must have a grade of C- or better). Offered every semester. 4 credits Levels: Undergraduate

• CS 240 - Data Struct & Algorithms

  Analysis of the design, implementation, and properties of basic and advanced data structures, including lists, stacks, queues, hash tables, trees, heaps, and graphs. Design and time-space analysis of basic and advanced algorithms, including searching, sorting, insert/delete, hash table collision resolution techniques, recursive functions, balanced tree maintenance, and graph algorithms. Weekly required laboratory programming and three or more additional programming projects in C++. Practical programming techniques including C++ templates and the Standard Template Library (STL), operator overloading, C++ stream I/O, separate compilation using makefiles, debugging tools and techniques, dynamic memory management. Prerequisites: CS 120 and CS 140, and either MATH 226 (may be taken concurrently) OR MATH 230 (All prerequisites must have a grade of C- or better). Offered every semester. 4 credits Levels: Undergraduate

• CS 301 - Eth Soc & Global Issues Comput

  Communications course with required writing and oral presentations. Understanding the local and global implications of computing in society, including ethical, legal, security and social issues. Developing professional skills related to computing, including effective communication and productive teamwork. Fostering an appreciation for continuing professional development. Should be taken at the same time as or before any junior-level Computer Science courses. Prerequisites: Any General Education "C" course, CS 101, and CS 210, and either CS 140 or CS 210 and may be taken concurrently (All prerequisites must have a grade of C- or better). Offered every semester. 4 credits Levels: Undergraduate

• CS 311 - Operating Systems Concepts

  Introduction to fundamental concepts for the design and implementation of operating systems: hardware/software interfaces; processes and threads; scheduling; synchronization techniques and primitives; memory management and virtual memory; file systems; input/output subsystems; resource and system virtualization; protection and security; introduction to distributed systems. Not open to CS majors. Prerequisites: CS 212 and EECE 287 (All prerequisites must have a grade of C- or better). Offered in the Fall semester. 4 credits Levels: Undergraduate

• CS 320 - Advanced Computer Architecture

  Performance metrics and analysis; instruction set architecture and its implications; high-performance computer arithmetic; instruction pipelines and pipelined datapath implementation; out-of-order execution, register renaming, branch prediction and superscalar processors; caches and memory systems; memory hierarchy; the I/O subsystem; reliable storage systems; introduction to multicore and multithreaded architectures; hardware and architectural support for security. Required lab includes programming projects. Prerequisite: CS 220 (All prerequisites must have a grade of C- or better). Offered every semester. Credits 4 Levels: Undergraduate

• CS 350 - Operating Systems

  Introduction to the design and implementation of operating systems: hardware/software interface; processes and threads; CPU scheduling; virtual memory; memory management; concurrency, race conditions, deadlocks, and synchronization; file and storage systems; input/output; protection and security; virtualization and hypervisors; multi-processor operating systems. Required lab includes programming exercises and presentations. Prerequisites: CS 220 and either CS 240 (All prerequisites must have a grade of C- or better). Prerequisite (May be taken concurrently.): CS 301. Offered every semester. 4 credits. Levels: Undergraduate

• CS 373 - Automata Theory & Formal Lg.

  Theory and application of automata and the languages they recognize. Regular languages, deterministic and non-deterministic finite automata, regular expressions, context-free languages, context-free grammars, pushdown automata, normal forms, context-sensitive languages, linear bounded automata, Turing recognizable languages, Turing decidable languages, Turing machines, computability, decidability, reducibility. Students will utilize an automata simulator to program finite automata, pushdown automata, and Turing machines. Application of concepts. Required activity includes student presentations. Prerequisites: CS 140 and either MATH 314 or MATH 330 (All prerequisites must have a grade of C- or better). Offered every semester 4 credits Levels: Undergraduate

• CS 375 - Design & Analysis of Algorithm

  Analysis of common algorithms for processing strings, trees, graphs and networks. Comparison of sorting and searching algorithms. Algorithm design strategies: divide and conquer, dynamic, greedy, back tracking, branch and bound. Introduction to NP-completeness. Required activity includes student presentations. Prerequisites: CS 240, MATH 227 and MATH 314 or MATH 330, CS 301 (may be taken concurrently). (All prerequisites must have a grade of C- or better). Offered every semester. 4 credits Levels: Undergraduate

• CS 395 - Computer Science Internship

  On-the-job, unpaid experience in computer science. Student interns have opportunities to work in local industrial, commercial or not-for-profit institutions and to apply their knowledge to practical professional problems. Formal classroom meetings in which interns share their experiences and discuss job-search techniques. Prerequisites: four courses in computer science; open to computer science minors or majors. Registration competitive and by consent of instructor. To count as a computer science elective, this course must be taken for a total of 4 credits (168 hours). Prerequisites: CS 220 and either CS 240 or CS 310 and Junior or Senior standing in Computer Science (All prerequisites must have a grade of C- or better). To count as a CS elective student must have a total of 4 Credits of CS 395. Offered every semester. Variable credits. Levels: Undergraduate

• CS 396 - Computer Science Co-Op

  On-the-job experience in computer science. Co-op students work 20 hours per week for a total of 560 hours, September through May, in local industrial, commercial or not-for-profit organizations and apply their knowledge to practical, professional problems. Students share experiences and discuss job search techniques in formal class meetings. Alternatively, students work full-time for a total of 560 hours outside the local area during one semester. Compensation provided by sponsor organization. Prerequisites: CS 220 and either CS 240 or CS 310 and Junior or Senior standing in Computer Science (All prerequisites must have a grade of C- or better). Registration, by consent of instructor, is competitive and requires sponsor interview. To count as a CS elective student must have a total of 4 Credits of CS 396. Offered every semester. Variable Credit Levels: Undergraduate

• CS 397 - Independent Study

  Individual study under direct supervision of faculty member investigating topic of interest to student. Special registration form required with signature of supervising faculty member. Can only count as free-elective credit for CS majors. Can not be counted for the CS minor. Variable credit Levels: Undergraduate

• CS 402 - Software & Eng. Project Mgmt

  Information is traveling faster and being shared by more individuals than ever before. Although project management has been an established field for many years, managing Software Development and Information Technology Projects requires ideas and information that go beyond standard project management. This course presents an understandable, integrated view of the many concepts skills, tools, and techniques involved in software project management. The Project Management Knowledge areas (from PMI?s PMBOK) are used to guide the student through the concepts of Software Project Management techniques and their application to the management of software and IT projects. Specifically, students will learn how to develop a software development plan including its associated tasks, milestones and deliverables, software project scheduling and how/why to establish relationships among the different tasks. Prerequisites: CS 220 and CS 240 Junior or Senior standing in Computer Science or Information Systems, Junior or Senior standing in Computer Engineering (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

• CS 415 - Social Media Data Sci Pipeline

  The focus of this course is on applying data science techniques to large-scale social media. The topics covered include large-scale data collection and management, exploratory analysis and measurement techniques, hypothesis testing and statistical modeling, and predictive, real time analytics. Students will build an end-to-end analysis pipeline and use it to answer questions about online events as they occur. The goal of the class is to provide students with a methodological toolbox, the technical skills to make use of these tools, and the experience of using them on real world data. Prerequisite: CS 350 Operating Systems, CS 375 Design & Analysis Algorithm, MATH 327 Probability with Stat Methods or equivalent. (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

• CS 424 - Intelligent Mobile Robotics

  The focus of this course is on intelligent mobile robots that can autonomously operate in indoor environments with limited human guidance. The topics covered in this course include mapping, localization, navigation, planning, reasoning, and human-robot interaction (language-based and vision-based). The students will learn to develop software in Robot Operating System (ROS) on real mobile robots. The goal of this course is to help students learn entry level algorithms and programming skills that are required to conduct research in the area of intelligent mobile robotics. Prerequisite: CS 350, CS 375. (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

• CS 426 - Internet of Things

  This course covers the applied area of Internet of Things (IoT). IoT are pervasive computing systems consisting of smart sensors embedded in physical environments with many promising applications. IoT challenge many classical approaches to computing and networking. Students will learn about key techniques in IoT through this course. A substantial part of the material will cover wireless sensor networks, embedded operating systems, and network protocols. A variety of different IoT applications will be introduced, including clinical monitoring, structural health monitoring, and industrial process automation. In this course, students will also have an opportunity to obtain hands-on experience on programming embedded devices to sense and communicate. Prerequisite: Either CS 320 Advanced Computer Architecture or CS350 Operating Systems (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

• CS 427 - Mobile Systems Security

  This course discusses cybersecurity issues in various aspects of mobile systems, including mobile networks, mobile communications, mobile OSes, mobile applications, and mobile devices. It presents technical details of how mobile systems work and explains their vulnerabilities with potential security risks. The course uses a combination of real-world mobile devices and high-fidelity emulation testbeds to demonstrate common types of attacks in mobile systems as well as how to defend against these attacks. In this course, students are expected to gain hands-on experiences with mobile systems and learn basic techniques to tackle their cybersecurity problems. Prerequisite: CS 350 Operating Systems, or equivalents, CS 375 Design and Analysis of Algorithms. Term offered varies. 4 credits. Levels: Undergraduate

• CS 428 - Computer Networks

  Communication protocols and layering, hardware-software infrastructures for networking, MAC protocols, data link protocols, switching, inter- and intra-domain routing, the TCP/IP protocol suite, transport protocols, application layer protocols, local and system area networks, wireless and sensor networks, overlay and virtual networks, client-server and peer-to-peer models, network programming with sockets, protocol design and implementation issues, network security. Prerequisite: CS 350 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits. Levels: Undergraduate

• CS 432 - Database Systems

  Associations between data elements and data models: entity-relationship, relational and object-oriented. Relational database design techniques. Formal and commercial query languages. Introduction to query processing, transaction management and concurrency control. Prerequisite: CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 433 - Information Retrieval

  Indexing and data structures for storing and searching the index. Boolean, statistical, inference nets and knowledge-based models. Thesaurus construction. Query expansion. Natural language and linguistic techniques. Evaluation. Distributed information retrieval. Information integration and fusion. Dissemination of information. Summaries, themes and reading tours. Hypertext. Internet tools. Intelligent agents. Digital libraries. Prerequisite: CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 435 - Introduction To Data Mining

  Basic topics of data mining, including data preprocessing, mining association rules, classification rules, clustering rules, post processing and mining in unstructured data. Prerequisites: CS 375, MATH 304 and MATH 327 or MATH 448 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 436 - Intro to Machine Learning

  This course provides a broad introduction to machine learning and its applications. Major topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); computational learning theory (bias/variance tradeoffs, VC theory, large margins); unsupervised learning; semi-supervised learning; reinforcement learning. The course will give students the basic ideas and intuition behind different techniques as well as a more formal understanding of how and why they work. The course will also discuss recent applications of machine learning, such as to data mining, bioinformatics, and information retrieval. Prerequisites: CS 375 and MATH 327 or MATH 448 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 440 - Adv Topics - Obj Oriented Prog

  Object-oriented programming and its concomitant design patterns provide rich abstractions for program development. These programs will eventually execute on real hardware, however. This course will investigate advanced object-oriented techniques and how they interact with hardware and operating system issues. We will ground our topics in C++, but the goal of the course will be to develop understanding that can be applied across languages. We will examine different design techniques for things such as memory management, and explore how and why they differ in performance and robustness. We will also cover idioms such as ""Resource Acquisition Is Initialization"" (RAII) and how they can be used to provide robust resource management for exceptions (exception safety). We will also devote time to covering generic programming and related topics such as expression templates. This is a growing area that seeks to decouple algorithms and data structures through the use of templates and other meta-programming techniques. These techniques exploit the fact that the C++ template mechanism is a language-within-a-language that is executed at compile-time rather than run-time. Additional topics include dynamic linking for techniques such as ""plug-ins"", template instantiation mechanisms, template specialization, idioms for memory management, thread-safety issues, thread-safety, C++ reflection. Prerequisites: CS 240 and CS 350. Term offered varies. 4 credits. Levels: Undergraduate

• CS 441 - Game Dev For Mobile Platforms

  This course focuses software development for mobile computing platforms, such as smartphones and tables, with an emphasis on games. Students will develop interactive applications, and utilize the wide variety of sensors and networking features available on the platform, along with basic elements of graphics programming and animation. The course also covers the mechanics of distributing software for mobile computing platforms. Both iOS and Android operating systems will be covered. The course will feature a mix of individual and team projects. Prerequisite: CS 140 , CS 375. (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits. Levels: Undergraduate

• CS 442 - Design Patterns

  Patterns for program design including examples of patterns used in existing software libraries. Exercises in programming with design patterns and communicating designs to other programmers using the language of patterns. Use of an object-oriented programming language to implement patterns and principles for common design problems. Design patterns are applied to problems involving features including concurrency, sockets, streams, reflection, and dynamic proxies. The course also discusses automating software build processes with build tools. Prerequisites: CS 140 or CS 210 and CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 444 - Programming for the Web

  An in-depth understanding of programming for the World Wide Web: detailed coverage of widely used language(s) for web programming, asynchronous programming, principles of web architecture, web protocols, web design patterns, client-side programming, templating, server-side programming, a technical history of the web, web security. Students are expected to have experience with a modern programming language and will be assigned programming projects using current state-of- the-art web technologies. Prerequisite: either CS 140 or CS 210 and, CS 320 or CS 350 or CS 375. (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits Levels: Undergraduate

• CS 445 - Software Engineering

  Software engineering practice applied to the life cycle of software applications and engineering projects. Software project planning and management: risk management, estimation, scheduling, trade studies, CM and SQA. Software development: process model selection, domain analysis, requirements gathering, analysis and design modeling, user interface design, architectural and detailed design, documentation, testing strategies/methods, test plan generation, and reuse. Advanced topics include formal methods and cleanroom software engineering. Requires a major team project. Prerequisites: CS 350 or CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 447 - High Performance Computing

  This course covers the applied area of high performance computing for machine learning, big data, and scientific computing. Students will learn about techniques for programs where the amount of computation is substantial enough that performance is a major concern. A substantial part of the material will cover parallel computation, including message-passing, multicore, and vectorization. A variety of different algorithms and applications will be considered, including machine learning, big data, and more traditional scientific computing. Prerequisite: CS 220, CS 240, either CS 320 or CS350 (All prerequisites must have a grade of C- or better). Term offered varies. 4 Credits Levels: Undergraduate

• CS 451 - Systems Programming

  A detailed study of the application program interface of a modern operating system. File operations, concurrency, processes, threads, inter-process communication, synchronization, client-server programming, multi-tier programming. Prerequisite: CS 350 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 452 - Intro to Cloud Computing

  This course will provide students with topics in cloud computing with coverage of core cloud components such as virtualization techniques, distributed systems, cloud service models, and representative cloud computing systems. It will not only include the in-depth study of the fundamental enabling techniques such as server, network, and data storage virtualization, but also provide the demonstration of how these techniques work inside today’s widely-used cloud-based systems such as MapReduce, key-value stores, and Openstack. The course will include weekly assignments in the form of homework, labs, and projects. Prerequisite: CS 350. Term offered varies. 4 credits. Levels: Undergraduate

• CS 453 - Software Security

  This hands-on course covers offensive and defensive technologies in the area of software security. Particularly, students will learn about various vulnerabilities that lead to software compromise, attacks that exploit such vulnerabilities, and defenses that defend against such attacks. Topics covered include simple control-flow corruption attacks, slightly harder buffer overflow and return-to-libc attacks, and advanced ROP attacks. Students are expected to not only learn the concepts behind each attack, but also execute them in a controlled environment. Prerequisite: CS350 Operating Systems (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits Levels: Undergraduate

• CS 455 - Intro to Visual Info Processin

  The course focuses on fundamental topics, including visual information acquisition, representation, description, enhancement, restoration, transformations and compressions, and reconstruction from projections. The second focus is on Computer Science applications, including algorithms developed in applications such as statistical and syntactic pattern recognition, robotic vision, multimedia indexing, visual data mining, and bio-informatics. Prerequisite: CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 456 - Intro to Computer Vision

  Course has two parts. Part one focuses on an introduction to the fundamental topics of computer vision, including low-level vision, intermediate-level vision, high-level vision, vision systems, visual knowledge representation, motion analysis, shape from shading and 3D reconstruction, as well as image retrieval. Part two introduces the applications of the fundamental computer vision techniques. Examples include robotic vision, pattern recognition and medical imaging. Pre req CS 375 (All prerequisites must have a grade of C- or better). 4 credits Levels: Undergraduate

• CS 457 - Intro To Distributed Systems

  Fundamental issues in distributed systems. Distributed synchronization and concurrency control. Distributed process management (scheduling, remote invocation, task forces, load balancing). Protection and security. Robust distributed systems. Case studies. Prerequisites: CS 350 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 458 - Intro to Computer Security

  The course provides an introduction to the principles and practices of network, computer, and information security. Topics include authentication and cryptographic techniques, intrusion detection, access control, security policies, and program/policy analysis techniques. Prerequisites: CS 350 and CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 459 - Science of Cyber Security

  This course focuses on techniques that approach cyber security problems in a principled manner using concepts from data mining, game theory, graph theory, and psychology. The intent of this course is to permit students to bridge the divide between real-world cyber threats and formal, scientific foundations of solutions that address such threats. Real-world cyber security issues, such as spamming, phishing attacks, malware, sybil attacks in social networks, and DDoS attacks, are used to illustrate how cyber threats can be modeled with abstract representations that are amenable to rigorous analysis and formal reasoning. The course also emphasizes the development of cyber defense mechanisms that are rooted in scientific foundations. Prerequisite: CS 350 and CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits. Levels: Undergraduate

• CS 460 - Computer Graphics

  Concepts, structure, techniques and algorithms for use of modern interactive computer graphics systems. Graphics hardware, software system structure. Techniques and algorithms for basic graphics input/output functions. Matrix techniques for transformations and projections. Techniques for two- and three-dimensional modeling, rendering, animation and visualization. Prerequisite: CS 375 (All prerequisites must have a grade of C- or better). Prerequisite or Corequisite: MATH 304. Term offered varies. 4 credits Levels: Undergraduate

• CS 465 - Intro to Artificial Intelligen

  This course will cover the basic ideas and techniques underlying the design of artificial intelligence (AI) agents. Topics include search, knowledge representation (and reasoning), planning, reasoning under uncertainty, machine learning (including reinforcement learning), and applications (natural language processing, vision, robotics, etc). Prerequisite: CS 375 (All prerequisites must have a grade of C- or better) Term offered varies. 4 credits. Levels: Undergraduate

• CS 471 - Programming Languages

  Introduction to the design and implementation of programming languages: linguistic features for expressing algorithms; formal syntax specification; introduction to language semantics and parsing; declarative programming (functional and goal-driven); scripting languages; imperative programming (procedural and object-oriented); comparative design and implementation issues across languages and paradigms. Assignments emphasize languages such as Prolog, Haskell, Python, and Ruby. Required lab includes student presentations. Prerequisites: CS 373 and 375 (All prerequisites must have a grade of C- or better). Offered every semester. 4 credits Levels: Undergraduate

• CS 472 - Compiler Design

  Fundamentals of programming language translation. Compiler design concepts. General aspects of lexical analysis and parsing of context-free languages. Grammars and parsing techniques. Syntax-directed translation. Declarations and symbol management. Semantic processing and code generation. Principles, methods and examples of code optimization. Prerequisite: CS 373 and CS 375 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 476 - Program Models Emerg Platforms

  The landscape of computation platforms has changed dramatically in recent years. Computing devices such as Unmanned Aerial Vehicles (UAVs) are on the horizon. Big data processing becomes an indispensable part of numerous applications. Multi-core CPUs are commonly deployed in computer systems. Programming on these emerging platforms remains a challenging task. This course introduces a number of state-of-the-art programming models on these platforms, and further explores the frontier of next-generation programming language design that may potentially impact the future programming practice for emerging platforms. In particular, the course investigates UAV programming, Big Data programming, and multi-core programming, with additional presentations on other platforms on the rise. Applications of these programming models range from high-performance computing, cyber-physical systems, databases, to energy-conscious systems. Prerequisites: CS 240 and 320 or CS 350 (All prerequisites must have a grade of C- or better). Term offered varies. 4 credits Levels: Undergraduate

• CS 480A - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480B - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480C - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480E - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480F - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480G - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480I - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480K - Special Topics

  This course will provide students with advanced topics in cloud computing with coverage of core cloud components such as virtualization techniques, distributed systems, cloud service models, and representative cloud computing systems. It will not only include the in-depth study of the fundamental enabling techniques such as server, network, and data storage virtualization, but also provide the demonstration of how these techniques work inside today’s widely-used cloud-based systems such as MapReduce, key-value stores, and Openstack. The course will include weekly assignments in the form of homework, labs, and projects. Students will also be required to read, critique and present papers on research topics in the domain of cloud computing. Prerequisite: CS 350. 4 credits. Offered annually. Levels: Undergraduate

• CS 480L - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480N - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480R - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480S - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480T - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480V - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480W - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480Y - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 480Z - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Undergraduate

• CS 485 - Info Systems Senior Proj I

  First semester of two-semester-long team projects involving analysis, specification, design, implementation and documentation of large-scale information systems. Project teams work to bring structure to a loosely formulated business/organizational problem. Previously learned concepts and techniques are applied in a real-world environment. Interpersonal (including communication) skills are enhanced. Host organizations and the instructor supervise the projects. Oral and written reports are required. Prerequisite: senior standing in the Information Systems Dual-Diploma program. Offered in the Fall semester. 3 credits Levels: Undergraduate

• CS 486 - Info Systems Senior Proj II

  Continuation of CS 485, Information Systems Project I. Prerequisite: CS 485 and senior standing in the Information Systems Dual-Diploma program. Offered in Spring semester. 3 credits Levels: Undergraduate

• CS 499 - Undergraduate Research

  Participation in research under supervision of a faculty member. Written report and oral presentation required. Levels: Undergraduate

• CS 515 - Social Media Data Sci Pipeline

  The focus of this course is on applying data science techniques to large-scale social media. The topics covered include large-scale data collection, cleaning, and management, exploratory analysis and measurement techniques, hypothesis testing and statistical modeling, and predictive, real time analytics. Students will build an end-to-end analysis pipeline and use it to answer questions about online events as they occur. The goal of the class is to provide students with a methodological toolbox, the technical skills to make use of these tools, and the experience of using them on real world data. Prerequisite: Undergraduate Operating Systems, Undergraduate Algorithms, Probability with Stat Methods or equivalent. Term offered varies. 3 credits Levels: Graduate

• CS 520 - Computer Architecture & Organ

  Pipelined processors: basic theory, instruction pipelines, multifunction units, dynamic instruction scheduling, branch handling, precise interrupts. Compiler techniques for enhancing ILP. Pipelined vector machines. Superscalar, VLIW and EPIC architectures. High-speed memory system design. Overview of parallel/multiprocessor architectures: SIMD/MIMD systems, interconnection networks, synchronization and cache coherence. Prerequisite: Computer Architecture. Offered every semester when possible. 3 credits Levels: Graduate, Undergraduate

• CS 524 - Intelligent Mobile Robotics

  The focus of this course is on intelligent mobile robots that can autonomously operate in indoor environments with limited human guidance. The topics covered in this course include mapping, localization, navigation, planning, reasoning, and human-robot interaction (language-based and vision-based). The students will learn to develop software in Robot Operating System (ROS) on real mobile robots. The goal of this course is to help students learn entry-level algorithms and programming skills that are required to conduct research in the area of intelligent mobile robotics. Prerequisite: Undergraduate Operating Systems and Algorithms. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 526 - Internet of Things

  This course covers the applied area of Internet of Things (IoT). IoT are pervasive computing systems consisting of smart sensors embedded in physical environments with many promising applications. IoT challenge many classical approaches to computing and networking. Students will learn about key techniques in IoT through this course. A substantial part of the material will cover wireless sensor networks, embedded operating systems, and network protocols. A variety of different IoT applications will be introduced, including clinical monitoring, structural health monitoring, and industrial process automation. In this course, students will also have an opportunity to obtain hands-on experience on programming embedded devices to sense and communicate. Prerequisite: Either Undergraduate Operating Systems or Advanced Computer Architecture. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 527 - Mobile Systems Security

  This course discusses cybersecurity issues in various aspects of mobile systems, including mobile networks, mobile communications, mobile OSes, mobile applications, and mobile devices. It presents technical details of how mobile systems work and explains their vulnerabilities with potential security risks. The course uses a combination of real-world mobile devices and high-fidelity emulation testbeds to demonstrate common types of attacks in mobile systems as well as how to defend against these attacks. In this course, students are expected to gain hands-on experiences with mobile systems and learn basic techniques to tackle their cybersecurity problems. Credits 3 Prerequisite: CS 350 Operating Systems, or equivalents When offered: Expected to be offered at least once every two years Levels: Graduate, Undergraduate

• CS 527X - Mobile Systems Security

  This course discusses cybersecurity issues in various aspects of mobile systems, including mobile networks, mobile communications, mobile OSes, mobile applications, and mobile devices. It presents technical details of how mobile systems work and explains their vulnerabilities with potential security risks. The course uses a combination of real-world mobile devices and high-fidelity emulation testbeds to demonstrate common types of attacks in mobile systems as well as how to defend against these attacks. In this course, students are expected to gain hands-on experiences with mobile systems and learn basic techniques to tackle their cybersecurity problems. Prerequisite: Undergraduate Operating Systems, or equivalents, Undergraduate Algorithms. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 528 - Computer Networks

  Communication protocols and layering, hardware-software infrastructures for networking, MAC protocols, data link protocols, switching, inter- and intra-domain routing, the TCP/IP protocol suite, transport protocols, application layer protocols, local and system area networks, wireless and sensor networks, overlay and virtual networks, client-server and peer-to-peer models, network programming with sockets, protocol design and implementation issues, network security. Prerequisite: Undergraduate Operating Systems. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 532 - Database Systems

  Associations between data elements and data models: entity-relationship, relational and object-oriented. Relational database design techniques. Formal and commercial query languages. Introduction to query processing, transaction management and concurrency control. Prerequisite: Undergraduate Algorithms. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 533 - Information Retrieval

  Indexing and data structures for storing and searching the index. Boolean, statistical, inference nets and knowledge-based models. Thesaurus construction. Query expansion. Natural language and linguistic techniques. Evaluation. Distributed information retrieval. Information integration and fusion. Dissemination of information. Summaries, themes and reading tours. Hypertext. Internet tools. Intelligent agents. Digital libraries. Prerequisite: Undergraduate Algorithms. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 535 - Introduction To Data Mining

  Basic topics of data mining, including data preprocessing, mining association rules, classification rules, clustering rules, post processing, and mining in unstructured data. Prerequisite: Undergraduate Algorithms and Probability & Statistics or equivalent. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 536 - Intro to Machine Learning

  This course provides a broad introduction to machine learning and its applications. Major topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); computational learning theory (bias/variance tradeoffs, VC theory, large margins); unsupervised learning; semi-supervised learning; reinforcement learning. The course will give students the basic ideas and intuition behind different techniques as well as a more formal understanding of how and why they work. The course will also discuss recent applications of machine learning, such as to data mining, bioinformatics, and information retrieval. Prerequisites: Undergraduate Algorithms, Probability with Statistical Methods. Term offered varies. 3 credits.. Levels: Graduate, Undergraduate

• CS 540 - Adv Topics - Obj Oriented Prog

  Object-oriented programming and its concomitant design patterns provide rich abstractions for program development. These programs will eventually execute on real hardware, however. This course will investigate advanced object-oriented techniques and how they interact with hardware and operating system issues. We will ground our topics in C++, but the goal of the course will be to develop understanding that can be applied across languages. We will examine different design techniques for things such as memory management, and explore how and why they differ in performance and robustness. We will also cover idioms such as "Resource Acquisition Is Initialization" (RAII) and how they can be used to provide robust resource management for exceptions (exception safety). We will also devote time to covering generic programming and related topics such as expression templates. This is a growing area that seeks to decouple algorithms and data structures through the use of templates and other meta-programming techniques. These techniques exploit the fact that the C++ template mechanism is a language-within-a-language that is executed at compile-time rather than run-time. Additional topics include dynamic linking for techniques such as "plug-ins", template instantiation mechanisms, template specialization, idioms for memory management, thread-safety issues, thread-safety, C++ reflection. Prerequisites: Undergraduate Operating Systems. Term offered varies. 3 credits. Levels: Graduate

• CS 541 - Game Dev For Mobile Platforms

  This course focuses software development for mobile computing platforms, such as smartphones and tablets, with an emphasis on games. Students will develop interactive applications and utilize the wide variety of sensors and networking features available on the platform, along with basic elements of graphics programming and animation. The course also covers the mechanics of distributing software for mobile computing platforms. Both iOS and Android operating systems will be covered. The course will feature a mix of individual and team projects. Prerequisite: Programming with Objects & Data Structures, Java Programming, Undergraduate Algorithms or equivalents. Term offered varies. 3 Credits Levels: Graduate, Undergraduate

• CS 542 - Design Patterns

  Patterns for program design including examples of patterns used in existing software libraries. Exercises in programming with design patterns and communicating designs to other programmers using the language of patterns. Use of an object-oriented programming language to implement patterns and principles for common design problems. Design patterns are applied to problems involving features such as concurrency, sockets, streams, reflection, and dynamic proxies. The course also discusses automating software build processes with build tools. Prerequisites: Java Programming, Undergraduate Algorithms or equivalents. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 544 - Programming for the Web

  An in-depth understanding of programming for the World Wide Web: detailed coverage of widely used language(s) for web programming, asynchronous programming, principles of web architecture, web protocols, web design patterns, client-side programming, templating, server-side programming, a technical history of the web, web security. Students are expected to have experience with a modern programming language and will be assigned programming projects using current state-of-the-art web technologies. Prerequisite: Undergraduate Operating Systems or Undergraduate Algorithms, or equivalents. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 545 - Software Engineering

  Software engineering practice applied to the life cycle of large software applications and engineering projects. Software project planning and management: risk management, estimation, scheduling, trade studies, CM and SQA. Software development: process model selection, domain analysis, requirements gathering, analysis and design modeling, user interface design, architectural and detailed design, documentation, testing strategies/methods, test plan generation, and reuse. Advanced topics include formal methods and cleanroom software engineering. Requires a major team project. Prerequisite: Undergraduate Algorithms Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 547 - High Performance Computing

  This course covers the applied area of high performance computing for machine learning, big data, and scientific computing. Students will learn about techniques for programs where the amount of computation is substantial enough that performance is a major concern. A substantial part of the material will cover parallel computation, including message-passing, multicore, and vectorization. A variety of different algorithms and applications will be considered, including machine learning, big data, and more traditional scientific computing. Prerequisite: Computer Architecture or Undergraduate Operating Systems and C programming. Term offered varies. 3 Credits Levels: Graduate, Undergraduate

• CS 550 - Operating Systems

  Advanced topics in operating systems. Process synchronization, linguistic support for concurrency, virtual memory, deadlock theory, robustness, security, mathematical models and correctness of concurrent programs. Treatment of selected topics in distributed and multiprocessor operating systems. Prerequisite: Undergraduate Operating Systems. Offered every semester when possible. 3 credits Levels: Graduate, Undergraduate

• CS 551 - Systems Programming

  A detailed study of the application program interface of a modern operating system. File operations, concurrency, processes, threads, inter-process communication, synchronization, client-server programming, multi-tier programming. Prerequisite: Undergraduate Operating Systems. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 552 - Intro to Cloud Computing

  This course will provide students with topics in cloud computing with coverage of core cloud components such as virtualization techniques, distributed systems, cloud service models, and representative cloud computing systems. It will not only include the in-depth study of the fundamental enabling techniques such as server, network, and data storage virtualization, but also provide the demonstration of how these techniques work inside today’s widely-used cloud-based systems such as MapReduce, key-value stores, and Openstack. The course will include weekly assignments in the form of homework, labs, and projects. Prerequisite: Undergraduate Operating Systems. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 553X - Software Security

  This hands-on course covers offensive and defensive technologies in the area of software security. Particularly, students will learn about various vulnerabilities that lead to software compromise, attacks that exploit such vulnerabilities, and defenses that defend against such attacks. Topics covered include simple control-flow corruption attacks, slightly harder buffer overflow and return-to-libc attacks, advanced ROP attacks, Meltdown and Spectre attacks on the hardware. Students are expected to not only learn the concepts behind each attack, but also execute them in a controlled environment. Prerequisite: Undergraduate Operating Systems. Term offered varies. 3 credits. Levels: Graduate

• CS 554 - Intro Real-Time Embedded Syst

  This course covers important topics related to developing next generation real-time embedded systems such as real-time scheduling, real-time operating systems, and sensor networks. It will begin with an introduction to classical real-time systems. Following the introduction, research papers envisioning future real-time systems will be discussed. The class will include not only lectures but also discussions and brainstorming. In this way, students can develop a solid understanding of real-time computing, while improving research skills. Prerequisites: Undergraduate Operating Systems and Computer Networks. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 555 - Intro to Visual Info Processin

  The course focuses on fundamental topics, including visual information acquisition, representation, description, enhancement, restoration, transformations and compressions, and reconstruction from projections. The second focus is on Computer Science applications, including algorithms developed in applications such as statistical and syntactic pattern recognition, robotic vision, multimedia indexing, visual data mining, and bio-informatics. Prerequisite: Undergraduate Algorithms. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 556 - Intro to Computer Vision

  Course has two parts. Part one focuses on an introduction to the fundamental topics of computer vision, including low-level vision, intermediate-level vision, high-level vision, vision systems, visual knowledge representation, motion analysis, shape from shading and 3D reconstruction, as well as image retrieval. Part two introduces the applications of the fundamental computer vision techniques. Examples include robotic vision, pattern recognition and medical imaging. Prerequisite: Undergraduate Algorithims. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 557 - Intro To Distributed Systems

  Fundamental issues in distributed systems. Distributed synchronization and concurrency control. Distributed process management (scheduling, remote invocation, task forces, load balancing). Protection and security. Robust distributed systems. Case studies. Prerequisite: Undergraduate Operating Systems. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 558 - Intro to Computer Security

  The course provides an introduction to the principles and practices of network, computer, and information security. Topics include authentication and cryptographic techniques, intrusion detection, access control, security policies, and program/policy analysis techniques. Prerequisite: Undergraduate Operating Systems and Algorithms. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 559 - Science of Cyber Security

  This course focuses on techniques that approach cyber security problems in a principled manner using concepts from data mining, game theory, graph theory, and psychology. The intent of this course is to permit students to bridge the divide between real-world cyber threats and formal, scientific foundations of solutions that address such threats. Real-world cyber security issues, such as spamming, phishing attacks, malware, sybil attacks in social networks, and DDoS attacks, are used to illustrate how cyber threats can be modeled with abstract representations that are amenable to rigorous analysis and formal reasoning. The course also emphasizes the development of cyber defense mechanisms that are rooted in scientific foundations. Prerequisite: Undergraduate Operating Systems and Algorithms. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 560 - Computer Graphics

  Concepts, structure, techniques, algorithms for use of modern interactive computer graphics systems. Graphics hardware, software system structure. Techniques and algorithms for basic graphics input-output functions. Matrix techniques for transformations and projections. Techniques for two- and three-dimensional modeling, rendering, animation and visualization. Prerequisites: Undergraduate Algorithms and Linear Algebra. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 565 - Intro to Artificial Intelligen

  This course will cover the basic ideas and techniques underlying the design of artificial intelligence (AI) agents. Topics include search, knowledge representation (and reasoning), planning, reasoning under uncertainty, machine learning (including reinforcement learning), and applications (natural language processing, vision, robotics, etc). Prerequisite: Undergraduate Algorithims. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• CS 571 - Programming Languages

  Selected topics in programming languages and alternative programming paradigms. Functional and imperative languages. Logic programming and object-oriented programming paradigms. Languages for concurrent computation. Semantics of programming languages. Prerequisite: Undergraduate Algorithims. Offered every semester when possible. 3 credits Levels: Graduate, Undergraduate

• CS 572 - Compiler Design

  Fundamentals of programming language translation. Compiler design concepts. General aspects of lexical analysis and parsing of context-free languages. Grammars and parsing techniques. Syntax-directed translation. Declarations and symbol management. Semantic processing and code generation. Principles, methods and examples of code optimization. Prerequisite: Programming Languages. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 575 - Design & Analysis Comp Algorit

  Analysis of programs and review of design techniques. Lower bound theory and NP-completeness. Heuristic, approximation, probabilistic and parallel algorithms. Prerequisites: Undergraduate Algorithms. Offered every semester when possible. 3 credits Levels: Graduate, Undergraduate

• CS 576 - Program Models Emerg Platforms

  The landscape of computation platforms has changed dramatically in recent years. Computing devices such as Unmanned Aerial Vehicles (UAVs) are on the horizon. Big data processing becomes an indispensable part of numerous applications. Multi-core CPUs are commonly deployed in computer systems. Programming on these emerging platforms remains a challenging task. This course introduces a number of state-of-the-art programming models on these platforms, and further explores the frontier of next-generation programming language design that may potentially impact the future programming practice for emerging platforms. In particular, the course investigates UAV programming, Big Data programming, and multi-core programming, with additional presentations on other platforms on the rise. Applications of these programming models range from high-performance computing, cyber-physical systems, databases, to energy-conscious systems. Prerequisites: Java programming, Computer Architecture or Undergraduate Operating Systems. Term offered varies. 3 credits Levels: Graduate, Undergraduate

• CS 580A - Special Topics

  Special topics in Computer Science. 4 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580B - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 580C - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580E - Special Topics

  Special Topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580F - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580G - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580H - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580I - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580K - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580L - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 580M - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580N - Special Topics

  SPECIAL TOPICS Levels: Graduate, Undergraduate

• CS 580P - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580R - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580S - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580T - Special Topics

  SPECIAL TOPICS Levels: Graduate, Undergraduate

• CS 580U - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580W - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580Y - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 580Z - Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate, Undergraduate

• CS 592 - Tech. Dev. Curriculum I

  This course is a 32-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to a broad review of engineering fundamentals, with emphasis on interdisciplinary topics related to Electronic Systems products and processes, technologies, applications, and problem solving techniques. Coursework includes a team-project and presentation to engineering management. Term offered varies. Levels: Graduate, Undergraduate

• CS 593 - Tech. Dev. Curriculum II

  This course is a 16-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to challenging students with problems very similar to those frequently facing Electronic Systems engineers. Coursework includes a technical project requiring the application of systems, software, and hardware engineering skills. Term offered varies. Levels: Graduate, Undergraduate

• CS 594 - Industrial Internship

  Computer science, engineering and other professional experience. Daily log book memo progress reports and a formal report required. May replace no more than one lecture course for the MSCS or MEng degree. Prerequisite: consent of department chair. Variable credit Levels: Graduate, Undergraduate

• CS 595 - Termination Project

  A theoretical or practical project carried out under the supervision of a member of the Computer Science Department. Project documentation must be submitted to the department library and a public presentation is required. Further information is available in the department office. Prerequisites: consent of instructor and committee members. Variable credit. Levels: Graduate, Undergraduate

• CS 597 - Independent Study

  Independent study supervised by a computer science faculty member. Student must obtain consent of instructor, who then determines description of study program, number of credits, frequency of meetings and location. Variable credit Levels: Graduate, Undergraduate

• CS 599 - Masters Thesis

  Research for and preparation of thesis. Must be approved by department chair. Levels: Graduate, Undergraduate

• CS 601X - CS Research Methodology Sem

  This seminar course covers topics that are important for students who need to perform serious research to complete their degree. Specific topics include research paper review, literature survey of research topics, identification of research problems, research ethics, research methodologies, experiment design, performance evaluation, research paper writing and revision, response to paper review comments, faculty life, research proficiency examination (RPE), PhD prospectus, PhD dissertation, and research presentation. Credits: 3 Prerequisite: Computer Science PhD students. Instructor’s approval for master’s students. When offered: Expected to be offered once a year Levels: Graduate, Undergraduate

• CS 620X - Advanced Computer Architecture

  Levels: Graduate

• CS 634 - Web Data Management

  Advanced topics in Web data management. New techniques for retrieving documents from search engines, including the use of links and user-behavior knowledge. Meta-search engine techniques, including resource discovery and result fusion. Database approaches for Web data management. Semi-structured data management, including data models, query languages and XML. Topics may vary when offered in different years. Prerequisite: Database Systems or equivalent. Corequisite: Information Retrieval or equivalent. (All prerequisites must have a grade of C- or better). Term offered varies. 3 credits Levels: Graduate

• CS 635 - Advanced Data Mining Seminar

  This course is a graduate seminar focusing on the state-of-the-art research issues in the area of data mining. Each time this course is taught, the specific focus will be on a research topic related to the instructor's research projects and may be different for each offering. Students taking this course will be exposed to major data mining research topics as well as the current literature. Levels: Graduate

• CS 660 - Advanced Computer Graphics

  A comprehensive review of the techniques needed to produce computer-generated shaded images of three-dimensional scenes. Recent research results are presented. Students design and implement portions of a three-dimensional graphics package. Topics selected from modern graphics standards (PHIGS, X-Windows), user interface issues, 3-D viewing, geometric modeling, image synthesis, image manipulation, animation, scientific visualization. Prerequisite: Computer Graphics or equivalent. Term offered varies. 3 credits Levels: Graduate

• CS 680C - Advanced Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 680E - Advanced Special Topics

  Levels: Graduate

• CS 680H - Special Topics

  Levels: Graduate

• CS 680M - Advanced Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 680P - Advanced Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 680R - Advanced Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 680S - Special Topics

  Levels: Graduate

• CS 680V - Advanced Special Topics

  Special topics in Computer Science. 3 credits. Semester offered varies. Levels: Graduate

• CS 688 - Comp Sci Graduate Seminar

  Weekly seminar presentation by invited speakers, department faculty and graduate students on contemporary topics in computer science and related fields. May not be used toward the MSCS. Prerequisites: graduate standing in computer science. Levels: Graduate

• CS 697 - Advanced Independent Study

  Reading and research on special advanced topics under direction of computer science advisor. Student must obtain consent of professor, who then determines description of study program, number of credits, frequency of meetings, location. Levels: Graduate

• CS 698 - Predissertation Research

  Reserved for exploratory research oriented toward dissertation. Levels: Graduate

• CS 699 - Dissertation

  Research for and preparation of dissertation. Registration restricted to those admitted to candidacy. Levels: Graduate

• CS 700 - Continuous Registration

  every semester. 1 Credit Levels: Graduate

• CS 701 - Pract/Teaching &Research Asst

  Required for all funded graduate assistants. Research or teaching supervised by faculty advisor. Levels: Graduate

• CS 707 - Research Skills

  Development of research skills required within graduate programs. May not be applied toward course credits for any graduate degree. Prerequisite: approval of relevant graduate program directors or department chairs. Levels: Graduate

• EECE 115 - FRI Image & Acoustic Signal I

  This is one of two lecture-lab courses of a 2-semester sequence for FRI students in Image and Acoustic Signals Analysis. Computing skills in MATLAB and Unix and introduction to computer programming. Technical writing using LaTeX. Students will learn research techniques while gaining understanding of research problems in this area. Prerequisites: HARP 170 and enrollment in Freshman Research Immersion (FRI) program. Offered in the Spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 212 - Linear Algebra&Eng Programming

  An introduction to linear algebra for engineers with computer analysis, visualization, and mathematical programming. Linear algebra topics include matrix operations, systems of linear equations, determinants, solution to matrix equations, vector space, eigenvalues and eigenvectors. Introduction to engineering programming includes variables and arrays, matrix operation, functions, data representation, user defined functions and a brief introduction on graphical programming. Engineering applications such as the matrix representation of graphs, matrices applied to electric circuits and linear systems will be introduced during the class. Laboratory exercises. Prerequisite: Calculus I. Offered every spring semester. 4 credits. Levels: Undergraduate

• EECE 215 - FRI Image & Acoustic Signal II

  This is the second of two lecture-lab courses of a 2-semester sequence for FRI students in Image and Acoustic Signals Analysis. Computer programming in C, and selected topics in multimedia signal processing and computer vision. Students will learn research techniques while gaining understanding of research problems in this area. Prerequisites: HARP 170 and EECE 115/CS 115, and enrollment in Freshman Research Immersion (FRI) program. Offered in the Fall semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 251 - Digital Logic Design

  Fundamental and advanced concepts of digital logic. Boolean algebra and functions. Design and implementation of combinational and sequential logic, minimization techniques, number representation, and basic binary arithmetic. Logic families and digital integrated circuits and use of CAD tools for logic design. Laboratory exercises. Offered every fall semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 260 - Electric Circuits

  Units and definitions. Ohm's Law and Kirchhoff's Laws. Analysis of resistive circuits. Circuit analysis using: Nodal and mesh methods, Norton and Thevenin theorems, and voltage divider. Transient and sinusoidal steady-state response of circuits containing resistors, capacitors, and inductors. Laboratory exercises. Prerequisite: PHYS 132. Offered every spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. . Levels: Undergraduate

• EECE 281 - EECE Seminar I

  Overview of the fields of electrical engineering and computer engineering. Various sub-fields within EE and CoE are explored, with emphasis on how they are interrelated. Issues relevant to careers in EE and CoE (e.g., typical tasks done by EEs and CoEs) are explored. Offered every fall semester. 1 credit. Levels: Undergraduate

• EECE 287 - Sophomore Design

  Design-based introduction to embedded computer systems. Organization and composition of computer processors, memory, and peripherals. Introduction to assembly-language and embedded C programming. Design of hardware and software for embedded processor applications. Laboratory exercises and design projects. Prerequisites: CS 211 and EECE 251. Offered every spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 301 - Signals And Systems

  Provides an introduction to continuous-time and discrete-time signals and linear systems. Topics covered include time-domain descriptions (differential and difference equations, convolution) and frequency-domain descriptions (Fourier series and transforms, transfer function, frequency response, Z transforms and Laplace transforms). Prerequisites: EECE 212 and 260 and MATH 324. Offered every fall semester. 4 credits. Levels: Undergraduate

• EECE 315 - Electronics I

  Introduction to electronics, concentrating on the fundamental devices (diode, transistor, operational amplifier, logic gate) and their basic applications; modeling techniques; elementary circuit design based on devices. Laboratory exercises. Prerequisites: EECE 260 and EECE 251. Offered every fall semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 323 - Electromagnetics

  Physics and applications of electromagnetic fields as encapsulated in the vector form of Maxwell's equations. Static electrical and magnetic fields, time-varying electromagnetic fields, Poisson's equations, fundamental laws of electromagnetic fields (including Gauss's law, Ampere's law, Faraday's law, and Biot-Savart's law), time-harmonic fields, wave propagation, and transmission lines. Numerical techniques. Prerequisites: EECE 260 and MATH 323. Offered every spring semester. 4 credits. . Levels: Undergraduate

• EECE 332 - Semiconductor Devices

  Basic theory of semiconductors, p-n junctions, bipolar junction transistors, junction and MOS field effect devices; device design and modeling; and fabrication. Prerequisite: PHYS 132. Corequisite: EECE 315. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 351 - Digital Systems Design

  Synchronous sequential circuit design. Algorithmic state machine method; state reduction; control-datapath circuit partitioning. Design of sequential arithmetic circuits. Memory interfacing; bus-based design. Specification and synthesis of digital systems using hardware description language and implementation using programmable logic devices. Simulation, analysis, testing and verification of digital systems. Laboratory exercises. Prerequisite: EECE 251. Offered every fall semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 359 - Computer Comm and Networking

  Theoretical basis for and practical foundations of modern data communications within and between computing systems. Topics include: properties of signals and transmission media; data encoding and modulation, multiplexing, and multiple access; data integrity, error detection and error control, forward error correction; protocol concepts and design, flow control, sliding window protocols, data link control; local area networking, LAN standards, and interconnecting LANs; networking and inter-networking devices, bridges, repeaters, routers; inter-networking protocols. Lab exercises. Prerequisites: EECE 301 and EECE 351. Offered every spring semester. 4 credits. Levels: Undergraduate

• EECE 361 - Control Systems

  "Introduction to analysis, design and modeling of control systems. Fourier and Laplace transforms, frequency response, transfer functions static and transient analysis. Systems block diagrams and signal-flow graphs. Concepts of stability. Numerical simulation and design of simple control systems. Introduction of discrete-time control. Prerequisite: EECE 301. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 377 - Communications Systems

  Fundamentals of communications systems. Modulation and demodulation methods. Characteristics of modern analog and digital communications methods. Prerequisites: EECE 301 and ISE 261. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 382 - EECE Seminar II

  Provides an overview of the professional aspects of the fields of electrical engineering and computer engineering. Topics to be covered include: typical career paths in EECE, engineering ethics, resume writing and job search techniques, preparing for graduate school, professional engineer license, etc. Offered every fall semester. 1 credit. Levels: Undergraduate

• EECE 387 - Design Lab

  This course provides experience with the engineering design process, which spans significant areas of electrical and computer engineering. Lecture will focus on various aspects of the design process as well as discussions of component characteristics. EE student prerequisites: EECE 301 and EECE 315. CoE student prerequisites: EECE 315 and EECE 351. Offered every spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. . Levels: Undergraduate

• EECE 402 - Digital Signal Processing I

  Covers the general area of discrete-time signals and the analysis and design of discrete time systems. Topics include time domain analysis, solutions of difference equations, Z-transform analysis, sampling of continuous-time signals, discrete Fourier transforms, Fast Fourier Transforms, and spectral analysis. Processing of discrete-time signals using the DFT and FFT. Design and implementation of discrete-time filters. Extensive use of software simulations in a high-level language such as Matlab. Technical elective. Prerequisite: EECE 301. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 403 - Digital Audio SignalProcessing

  This course covers the fundamentals of digital audio signal processing. Topics include analog-to-digital converters and digital-to-analog converters, models for characterizing and analyzing quantization errors in digital audio, sampling rate conversion, design of filters and equalizers, algorithms for simulating room acoustics, and algorithms for implementing digital audio effects. Implementation of algorithms using Matlab is emphasized. 3 credits Prerequisites: EECE 301 and ISE 261, or instructor approval. Levels: Undergraduate

• EECE 405 - Cryptography & Info Security

  Introduction to codes and ciphers, information security, and mathematical basis of encryption. Cryptanalysis (code-breaking), modern block and stream ciphers, public-key cryptography, protocols, security engineering and threat management. Technical elective. Prerequisite: ISE 261 or MATH 327 and CS 211. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 407 - Network Computer Security

  Key topics in network and host level security. Introduction to practical aspects of networking in physical and virtual environments. Lab exercises using enterprise-level networking equipment, such as switches, routers, and firewalls. Common attacks on computer networks and defense mechanisms. Deployment, detection and mitigation of common attacks using a variety of software tools. Technical elective. Prerequisites: Programming skills (Preferably Python). Offered every semester. 3 credits. Levels: Undergraduate

• EECE 414 - Software-Defined Radio

  This course explores and analyzes the individual elements required to design a software-defined radio (SDR) receiver. Optimizations and tradeoffs are discussed as the receiver elements are integrated together. For the final project, the functional elements are integrated into a digital receiver system and the performance of the receiver is assessed. Topics include: modulation and demodulation, signal sampling, pulse shaping, bandwidth and data rate, synchronization, linear equalization, coding and decoding, timing recovery, and carrier recovery. Matlab is used to model the receiver elements and to implement the final project. Technical elective. Prerequisites: EECE 359 or EECE 377 or Equivalent. Offered every spring semester. 3 credits. Levels: Undergraduate

• EECE 416 - Analog Circuit Design

  Introduction to analog circuit design. Course topics include large and small-signal analysis, circuit characteristics (gain, input/output resistance, etc.), amplifier structures, frequency/time response, feedback and stability, multistage amplifier and active filters. This course includes several assignments requiring the use of software tools for circuit simulation. Technical elective. Prerequisite: EECE 315. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 418 - Power Sysl: Equip & Power Flow

  The purpose of this course is to form a solid basis for analyzing power systems. The course gives an overview of a power system and is focused on the basic concepts and elements that are essential in power system planning, operation and control. The course introduces modeling methods for major components in a power system and the methods for solving power flow problems. Specifically, the course addresses the following topics: an overview of power system and structure of electric power industry; the 3 phase circuits in power systems; steady state modeling of generators and transformers; transmission line modeling; power flow analysis. Technical elective. Prerequisites: EECE 260 and EECE 323, or approval of instructor. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 419 - Power Electronics

  Electronic processing of electrical energy. Overview of power electronics devices such as DMOSFET, IGBT and Thyristor. Power supply circuits from AC or DC sources as used in computers, inverters and variable-speed motor drives. Analytical and numerical techniques for simulation. Technical elective. Prerequisites: EECE 315 and EECE 301. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 421 - Electric Drives

  Fundamentals of electric drive systems with applications emphasis. The course offers an integrative treatment of multiple components that make up electric drives, including electrical machines, power-electronics-based converters, mechanical systems, feedback controller design, and the interaction of the drives with the utility grid. Technical elective. Prerequisites: EECE 301 and EECE 323. Offered every spring semester. 3 credits. Levels: Undergraduate

• EECE 423 - Power SysII: Protect & Control

  The purpose of this course is to help students develop skills for analyzing electric power systems beyond power flow solutions. The following topics are covered: basics of economic dispatch; basics of fault analysis and fault current calculations; generator transients induced by grid faults; basics of power system protection; basics of stability concepts and stability analysis; basics of load frequency control, automatic generation control, reactive power and voltage control. Technical elective. Prerequisites: EECE418. Offered every spring semester. 3 credits. Levels: Undergraduate

• EECE 431 - Intro to Microfabrication

  Multidisciplinary Introduction to Microfabrication: Introduction to clean room tools, procedures, and theory through the fabrication and characterization of various devices from the fields of electrical engineering, mechanical engineering, physics and chemistry. Fabrication of the devices will cover most clean room tools and techniques, including lithography-based patterning methodologies; chemical vapor deposition; sputtering; thermal and e-beam evaporation; thermal oxidation; reactive ion etching; ion implantation; and wet chemical processing. The accompanying lecture will cover the theory of the tools used. Technical elective. Prerequisite: EECE 332 or an equivalent course in basic semiconductor device physics. Offered every spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 432 - Physics & Tech of Solar Cells

  This course focuses on the science and engineering fundamentals of photovoltaic solar energy devices and systems. The lectures cover solar radiation, semiconductor properties, p-n junction theory, solar cell operating principles, solar cell device designs and fabrication of traditional crystalline silicon and thin film solar cells. Students will learn the advanced concepts for high efficiency solar cells and emerging photovoltaic devices like organic (plastic) solar cells and quantum solar cells. The course covers solar module interconnections, engineering design of solar electricity systems, and power conditioning at the systems level. Technical elective. Prerequisites: EECE 332 or equivalent is desirable but not essential. Offered every spring semester. 3 credits. Levels: Undergraduate

• EECE 433 - Advanced Semiconductor Lasers

  Physics, device structure, and operation of semiconductor lasers. Band structure in solids, optical processes in semiconductors, p-n junctions, and quasi-Fermi levels; rate equation model for semiconductor lasers, static and dynamic characteristics; characteristics of distributed feedback, Bragg reflector, vertical cavity and Fabry-Perot lasers. Technical Elective. Prerequisite: EECE 332. Offered every third semester. 3 credits. Levels: Undergraduate

• EECE 438 - System On A Chip Design

  This course will provide an overview of the components of system-on-a-chip (SOC) design from initial technology and architectural choices, to hardware/software design techniques (e.g., data/control flow modeling and implementation, embedded cores and coprocessors, hardware/software communication) as well as SOC implementation issues (e.g., performance, core selection, on-chip communication networks, power management, package constraints and cost). Also covered are SOC design and implementation processes (e.g., functional integration, simulation, clocking strategies, timing, design for test, and debug strategies). Technical elective. Prerequisites: EECE 287 and EECE 315. Offered every spring semester. 3 credits. Levels: Undergraduate

• EECE 451 - Digital Systems Design II

  VLSI design, synthesis and test using hardware description language (HDL) at the register-transfer level (RTL). HDL programming and simulation basics, followed by advanced HDL programming for synthesis and test benching. Principles of RTL synthesis introduced. Design and synthesis of digital systems to meet constraints. Pre-synthesis and post-synthesis verification using simulation. Commercial synthesis tool discussed in detail. Technical elective. Prerequisite: EECE 351. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 452 - Computer Design

  This course will cover several topics in the area of computer architecture. We will discuss various concepts behind the designs of current microprocessors. In particular, the topics that will be covered in the course are: RISC machines and instruction set architectures, performance evaluation, pipelined architecture, static and dynamic scheduling, instruction-level parallelism, advanced pipelining, speculative execution, memory hierarchy and organization, multi-processing, compiler techniques (and more). Technical elective. Prerequisites: EECE 351 or equivalent. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 453 - Computer Network Architecture

  This is an introduction level computer network architecture course for senior/graduate students. The goal is to provide the students with a comprehensive picture of the technologies behind the Internet and applications on top of it. Starting with a brief review of some basics of networking, we will discuss from the lowest levels of packet transmissions through the highest levels of application software, under the umbrella of the TCP/IP architecture. Emphasis will be given on network architecture, components and design issues. Technical elective. Prerequisite: Basic knowledge of probability and statistics. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 455 - CMOS VLSI Circuits & Arch.

  The topics include the principles of MOSFET transistors, characteristics of CMOS digital circuits, layout design and process, performance analysis of CMOS gates, circuit design styles using MOSFET, performance, area and power optimization of CMOS circuits. Commercial design and simulation tools will be used in the class. Laboratory assignments include design, layout, extraction and simulation. Technical elective. Prerequisite: EECE 351. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 457 - Fundamentals of Comp Security

  Introduction to security engineering, systemic analysis and common design principles. Cryptographic protocols, multilevel security, system evaluation, real-world vulnerabilities and attacks. Technical elective. Prerequisites: EECE 287 or CS 211, familiarity with C or C++ or similar programming language. Offered every spring semester. 3 credits. Levels: Undergraduate

• EECE 458 - Embedded Systems Design

  Microcontroller based embedded systems design and analysis. Bare metal assembly and C software development for hardware and software integration. Driver based hardware abstraction and input/output synchronization. Sensor hardware integration, information extraction and fusion. Microcontroller applications and project. Technical elective. Prerequisites: EECE 251, EECE 315, and CS 211 or equivalent. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 462 - Control Systems II

  Conventional and state variable techniques for the analysis and design of digital and analog control systems. Z-transform. Sampled data systems. Discrete state variable. Numerical simulation and computer-aided design of control systems. Technical elective. Prerequisite: EECE 361. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 474 - Electro-Optics

  Electro-optic devices and systems. Blackbody, LED and laser sources, photodetectors, modulators, fiber optics, Fourier optics. Design of electro-optic systems. Technical elective. Prerequisite: EECE 323. Offered every other fall semester. 3 credits. Levels: Undergraduate

• EECE 477 - Digital Communications

  Fundamentals of digital communication systems; baseband modulation and demodulation; digital passband modulation and demodulation; channel coding, error detection; spread spectrum; signal space representation; bit error rate. Technical elective. Prerequisite: EECE 377 or equivalent. Offered every fall semester. 3 credits. Levels: Undergraduate

• EECE 480A - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 480B - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 480C - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 480D - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 480E - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 480F - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 480G - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Undergraduate

• EECE 486 - Senior Project I lab

  Faculty Advisor sections for ECE Senior Project I. Register after being assigned your project. Co-requisite: EECE 487 . Offered every fall semester. 1 credit. Levels: Undergraduate

• EECE 487 - Senior Project I

  Design projects in cooperation with local industry, other external clients, and university sponsored projects - outlining specifications, proposals, time schedules, and paper designs. Periodic design reviews with client, written and oral progress reports, final presentation. Evaluation based on individual and team performance. Prerequisites: EECE 387 and senior standing. Co-requisite: After being assigned your project, register for your Faculty Advisor's lab section of EECE 486. Offered every fall semester. 2 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 488 - Senior Project II

  Continuation of team development project using lightweight version of industrial approach. Covers hardware and software builds, integration and test, and project completion; scope, cost, and time. Individual assignments; team written and oral progress reports, periodic reviews, integration and test plans, system verification methods, project reports, and project presentations. Evaluation based primarily on team performance. Prerequisites: EECE 487 and senior standing. Co-requisite: Register for your Project Advisor's lab section of EECE 489. Offered every spring semester. 2 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Undergraduate

• EECE 489 - Senior Project II lab

  Faculty Advisor sections for ECE Senior Project II. Evaluation based primarily on individual performance. Register for your Project Advisor's lab section. Co-requisite: EECE 488. Offered every spring semester. 1 credit. Levels: Undergraduate

• EECE 491 - Teaching Practicum

  Assist with undergraduate instruction of a formal course under the direct supervision of the course instructor. May count for one professional elective but may not be used to satisfy a technical elective unless significant work is done and always at the discretion of the Chair or Undergraduate Program Director. Taken with normal (letter) grade option ONLY; NOT P/F or S/U. Prerequisite: permission of course instructor. Offered every semester. Variable credit. Levels: Undergraduate

• EECE 496 - Industrial Internship

  Engineering work experience in industry. Daily log book, memo progress reports and formal final report required. May satisfy, at most, one professional elective. Internships require course registration during the same term or semester as the internship is being completed (e.g., internships completed during the summer months would require summer registration). Taken with normal (letter) grade option ONLY; NOT P/F or S/U. Prerequisite: permission of faculty sponsor for internship. Offered every semester. Variable credit. Levels: Undergraduate

• EECE 497 - Independent Study

  Individual study under direct supervision of a faculty member. Approval of proposed subject by the faculty member and plan of study must be obtained prior to registration. Can be used to satisfy either a technical or professional elective. If used to satisfy a technical or professional elective, it must be taken with normal (letter) grade option ONLY; NOT P/F or S/U. Offered every semester. Variable credit. Levels: Graduate, Undergraduate

• EECE 499 - Undergraduate Research

  Assist with faculty research. Approval of proposed subject by the faculty member and plan of research must be obtained prior to registration. Can be used to satisfy either a technical or professional elective. If used to satisfy a technical elective or professional elective, it must be taken with normal (letter) grade option ONLY: NOT P/F or S/U. Offered every semester. Variable credit. Levels: Undergraduate

• EECE 501 - Analog Circuit Design

  Introduction to analog circuit design. Course topics include large and small-signal analysis, circuit characteristics (gain, input/output resistance, etc.), amplifier structures, frequency/time response, feedback and stability, multistage amplifiers and active filters. This course includes several assignments requiring the use of software tools for circuit simulation and a design project. Lecture portion meets with EECE 416. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 315. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 502 - Power Sysl: Equip & Power Flow

  The purpose of this course is to form a solid basis for analyzing power systems. The course gives an overview of a power system and is focused on the basic concepts and elements that are essential in power system planning, operation and control. The course introduces modeling methods for major components in a power system and the methods for solving power flow problems. Specifically, the course addresses the following topics: an overview of power system and structure of electric power industry; the 3 phase circuits in power systems; steady state modeling of generators and transformers; transmission line modeling; power flow analysis. Lecture portion meets with EECE 418. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE260 and EECE323, or approval of instructor. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 503 - Electric Drives

  Fundamentals of electric drive systems with applications emphasis. The course offers an integrative treatment of multiple components that make up electric drives, including electrical machines, power-electronics-based converters, mechanical systems, feedback controller design, and the interaction of the drives with the utility grid. Lecture portion meets with EECE 421. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE 260, EECE 301 and EECE 323. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 504 - Power Electronics

  Electronic processing of electrical energy. Overview of power electronics devices such as DMOSFET, IGBT and Thyristor. Power supply circuits from AC or DC sources as used in computers, inverters and variable-speed motor drives. Analytical and numerical techniques for simulation. Lecture portion meets with EECE 419. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 315 and 301. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 505 - BioMEMS and Bioelectronics

  This course is meant to provide an overview of the field of bioMEMS and bioelectronics. It will familiarize students with micro/nanofabrication techniques, Lab-on-a-chip, microfluidics, various microfluidic components, biofuel cells, drug delivery, neural interface, microsystems for cellular studies and tissue engineering. The objective of this course is to link engineering principles to biology, medicine, health, or bioenergy. Prerequisites: EECE 332. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 506 - Mathematical Methods In EE

  Selected topics in the advanced engineering mathematics, with special focus on their electrical engineering applications. Topics include ordinary and partial differential equations, Laplace transform, Fourier transform, linear algebra, matrix theory, numerical methods, complex analysis, optimization, probability and statistics. Prerequisites: calculus and differential equations. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 507 - Mathematical Methods In COE

  This course provides fundamental computer engineering knowledge for the design and analysis of digital systems. Includes applications of Discrete Math; Groups; Group Codes; Semi-groups; Synthesis of Networks; Reliable Design and Fault Diagnosis; Graphs; and Finite State Machines. Prerequisites: EECE 351 or equivalent and MATH 314 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 508 - Physics & Tech of Solar Cells

  This course focuses on the science and engineering fundamentals of photovoltaic solar energy devices and systems. The lectures will cover solar radiation, semiconductor properties, p-n junction theory, solar cell operating principles, solar cell device designs and fabrication of traditional crystalline silicon and thin film solar cells. Students will learn the advanced concepts for high efficiency solar cells and emerging photovoltaic devices like organic (plastic) solar cells and quantum solar cells. The course covers solar module interconnections, engineering design of solar electricity systems and power conditioning at systems level. Lecture portion meets with EECE 432. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE 332 or equivalent is desirable but not essential. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 509 - Power SysII: Protect & Control

  The purpose of this course is to help students develop skills for analyzing electric power systems beyond power flow solutions. The following topics are covered: basics of economic dispatch; basics of fault analysis and fault current calculations; generator transients induced by grid faults; basics of power system protection; basics of stability concepts and stability analysis; basics of load frequency control, automatic generation control, reactive power and voltage control. Lecture portion meets with EECE 423. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE 502 or approval of instructor. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 510 - Control Systems II

  Conventional and state variable techniques for the analysis and design of digital and analog control systems. Z-transform. Sampled data systems. Discrete state variable. Numerical simulation and computer-aided design of control systems. Lecture portion meets with EECE 462. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE 361 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 511 - Power Gen, Operation & Control

  The purpose of this course is to introduce and study a number of engineering and economic problems in planning, operating and controlling power generation and transmission systems in electric utilities. The course will also introduce some mathematical optimization methods and apply them to solve power system problems. Specifically, the course will address the following topics: Power generation characteristics; Economic dispatch; Optimization with constraints; Unit commitment; Generation with limited energy supply; Transmission system effects; Power system security; Optimal power flow; Introduction of state estimation. The course targets graduate students with power/energy concentration. Prerequisites: EECE 418 or 502 or approval of instructor. Offered every other spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 512 - Renewable Energy & Distributio

  Working principles and modeling of distribution grids, renewable energy resources, grid-tie battery system, microgrid, as well as analysis of integrated renewable energy systems. Prerequisites: EECE 418, EECE 361, or equivalent, or approval of instructor. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 514 - Elect Energy Storage Sys& Appl

  : Introduction to various electrical energy storage technologies focusing on supercapacitor and battery. Electrical design of storage systems and performance analysis. Electric energy storage application in photovoltaic and wind power based renewable energy systems. Modeling of electric energy storage in power and electronic systems such as in power conversion, electric drives, low power embedded systems and electrical traction. Broad impact of this course will be in understanding efficient and effective use of electrical energy in electronic, renewable energy and power conversion systems through supercapacitor array and battery based electric energy storage. 3 credits. Prerequisite: EECE315 (Electronics I), or equivalents, or approval of Instructor Levels: Graduate, Undergraduate

• EECE 516 - Intro to Bioenergy

  This course provides an overview of the field of renewable energy and fuel, made available from biologically derived materials with focus on its system implementations. In particular, the course will focus on innovative systems for bioelectricity, biofuel, biorefinery, and environmental applications. The objective of this course is to link engineering principles to biology and bioenergy. Prerequisites: EECE 315 or EECE 332 or approval of Instructor. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 517 - Adaptive Control Systems

  Techniques for the mathematical description, analysis and design of adaptive control systems. Concept of adaptation, model reference and self-tuning approaches to system identification. Computer simulation. Prerequisite: EECE 462. Offered every other spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 518 - Data-Driven Methods forControl

  Applications of statistical, optimization and advanced control techniques for analysis, mathematical description, optimization and control of multivariable processes. Topics include regression analysis, linear, non-linear and dynamic programming, adaptive control. Prerequisite: EECE 361 or equivalent. Offered every other spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 519 - Control of Networked Systems

  Methods and tools for modeling, control, and performance analysis of asynchronous systems driven by random events. Main topics include stochastic processes, Markov chain models, queuing systems, simulation of event- and time-driven systems, security and optimal control of networked systems as controlled Markov chains. Prerequisites: A course in basic probability (probability space; conditional probability; random variables; probability mass and probability density functions; conditional distributions; expectation, variance, and moment computation.) Offered every other fall semester. 3 credits Levels: Graduate, Undergraduate

• EECE 520 - Digital Signal Processing I

  Covers the general area of discrete-time signals and the analysis and design of discrete time systems. Topics include time domain analysis, solutions of difference equations, Z-transform analysis, sampling of continuous-time signals, discrete Fourier transforms, Fast Fourier Transforms, and spectral analysis. Processing of discrete-time signals using the DFT and FFT. Design and implementation of discrete-time filters. Extensive use of software simulations in a high-level language such as Matlab. Lecture portion meets with EECE 402. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 301. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 521 - Digital Signal Processing II

  Advanced topics in digital signal processing. Bandpass signals and bandpass sampling, multi-rate processing and filterbanks, random signals and spectrum estimation, modern DSP applications. Prerequisites: EECE 402 or equivalent and MATH 327 or ISE 261 or equivalent. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 522 - Estimation Theory

  Theory and practice of estimating parameters for discrete-time signals embedded in noise. Application to problems in radar, sonar, emitter location and communication systems. Topics include: Cramer-Rao lower bound, minimum variance unbiased estimation, least squares estimation, maximum likelihood estimation, Bayesian estimation, and Wiener filtering. Prerequisites: EECE 402 or equivalent and ISE 261 or equivalent. Offered every other spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 525 - Digital Audio SignalProcessing

  Catalog Description: This course covers the fundamentals of digital audio signal processing. Topics include analog-to-digital converters and digital-to-analog converters, models for characterizing and analyzing quantization errors in digital audio, sampling rate conversion, design of filters and equalizers, algorithms for simulating room acoustics, and algorithms for implementing digital audio effects. Implementation of algorithms using Matlab is emphasized. 3 credits. Prerequisites: EECE301 Signals and Systems, ISE261 Probabilistic Systems I; or equivalents, or approval of instructor. Levels: Graduate

• EECE 527 - Information Theory

  An Introduction to information theory for signal processing and communications theory. Entropy, mutual information, divergence, source coding, channel capacity, hypothesis testing and types. Prerequisite: EECE 301 or equivalent and ISE 261 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 530 - Electro-Optics

  Electro-optic devices and systems. Black-body, LED and laser sources, photo detectors, modulators, fiber optics, Fourier optics. Design of electro-optic systems. Lecture portion meets with EECE 474. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 323. Offered every other fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 532 - Advanced Semiconductor Lasers

  Physics, device structure, and operation of semiconductor lasers. Band structure in solids, optical processes in semiconductors, p-n junctions, and quasi-Fermi levels; rate equation model for semiconductor lasers, static and dynamic characteristics; characteristics of distributed feedback, Bragg reflector, vertical cavity and Fabry-Perot lasers. Lecture portion meets with EECE 433. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 332. Offered every third semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 542 - Wireless Communication Systems

  Topics in wireless communications such as cellular radio, PCS and wireless LAN. Cellular system design, frequency reuse, channel assignment, handoff, power control, cell splitting, sectorization, system capacity. Radio propagation, multi-path and fading, signal design principles, spread-spectrum modulation techniques, receiver/transmitter architectures. Multiple access for wireless systems: FDMA, TDMA, CDMA, SDMA. Wireless networking. Prerequisite: EECE 377 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 545 - Digital Communications

  Fundamentals of information in digital communication systems; digital passband modulation and demodulation; channel coding; error detection; spread spectrum; signal space representation; bit error rate. Digital modem design and implementation and final project. Lecture portion meets with EECE 477. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 377 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 549 - Free-Space Laser Communication

  Introduction to the phenomena related to optical communications. Laser crosslinks, optomechanical, laser and detector technologies, acquisition and tracking. System configuration and design. Prerequisite: familiarity with electromagnetic theory, basic concepts of optics and electronics, elementary differential equations and fundamental principles of communications theory. Offered every other fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 552 - Computer Design

  This course will cover several topics in the area of computer architecture. We will discuss various concepts behind the designs of current mircroprocessors. In particular, the topics that will be covered in the course are: RISC machines and instruction set architectures, performance evaluation, pipelined architecture, static and dynamic scheduling, instruction-level parallelism, advanced pipelining, speculative execution, memory hierarchy and organization, multi-processing, compiler techniques (and more). Lecture portion meets with EECE 452. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 351 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 553 - Computer Network Architecture

  This is an introduction level computer network architecture course for senior/graduate students. The goal is to provide the students with a comprehensive picture of the technologies behind the Internet and applications on top of it. Starting with a brief review of some basics of networking, we will discuss from the lowest levels of packet transmissions through the highest levels of application software under the umbrella of the TCP/IP architecture. Emphasis will be given on the network architecture, components and design principles. Lecture portion meets with EECE 453. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: Basic knowledge of probability and statistics. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 558 - Embedded Systems Design

  Microcontroller based embedded systems design and analysis. Bare metal assembly and C software development for hardware and software integration. Driver based hardware abstraction and input/output synchronization. Sensor hardware integration, information extraction and fusion. Microcontroller applications and project. Lecture portion meets with EECE 458. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE 351 or equivalent and CS 211. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 559 - Software-Defined Radio

  This course explores and analyzes the individual elements required to design a software-defined radio (SDR) receiver. Optimizations and tradeoffs are discussed as the receiver elements are integrated together. For the final project, the functional elements are integrated into a digital receiver system and the performance of the receiver is assessed. Topics include: modulation and demodulation, signal sampling, pulse shaping, bandwidth and data rate, synchronization, linear equalization, coding and decoding, timing recovery, and carrier recovery. Matlab is used to model the receiver elements and to implement the final project. Lecture portion meets with EECE 414. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 459 or EECE 377 or equivalent. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 560 - Cryptography & Info Security

  Introduction to codes and ciphers, information security, and mathematical basis of encryption. Cryptanalysis (code-breaking), modern block and stream ciphers, public-key cryptography, protocols, security engineering and threat management. Lecture portion meets with EECE 405. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: Familiarity with a computer programming language is necessary to complete assignments. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 562 - Fundamentals Of Steganography

  An introduction to the modern field of covert communication using digital media. General principles of covert communications in digital images and detection of hidden data in digital media files (statistical tests, signal estimation). Steganographic security, capacity and detectability. Universal blind steganayzers based on machine learning. Forensic steganalysis. Prerequisites: Knowledge of MATLAB and basics of signal processing. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 564 - Convex Optimization

  Convex sets, functions, and problems; basics of convex analysis; least-squares, linear, quadratic, geometric, and semi-definite programs; duality theory; numerical linear algebra and algorithms; MATLAB-based tools for convex optimization; applications to in signal processing, networking, control, circuit design, etc. Prerequisites: EECE 506 or working knowledge of linear algebra, exposure to probability, and some familiarity with MATLAB, or Instructor permission. Offered every third spring semester. 3 credits Levels: Graduate, Undergraduate

• EECE 565 - Network Computer Security

  Key topics in network and host level security. Introduction to practical aspects of networking in physical and virtual environments. Lab exercises using enterprise-level networking equipment, such as switches, routers, and firewalls. Common attacks on computer networks and defense mechanisms. Deployment, detection and mitigation of common attacks using a variety of software tools. Meets with EECE 407. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: CS211, Python programming (preferred) or approval of instructor. Offered every semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 566 - Detection Theory

  Course on statistical signal detection techniques and their applications in digital forensics. Lectures are interleaved with hands-on projects to show the students the usefulness of theoretical concepts introduced during lectures. Students will become familiar with an important forensic method for digital media that identifies digital cameras and manipulated images using the so-called sensor fingerprint. From the technical point of view, the course contains the following topics: Binary and multiple hypothesis testing, Neyman-Pearson theorem, Bayesian risk minimization, likelihood ratio test. Detection of deterministic signals and random signals, matched and generalized matched filter. Gaussian and non-Gaussian noise. Detection of signals with unknown parameters and unknown noise parameters, generalized likelihood ratio test, universally most powerful test. Basics of parameter estimation theory. The techniques will be demonstrated via hands-on projects drawn from the field of digital forensics (digital watermarking and sensor fingerprints). Prerequisite: Basic statistics. Offered every spring semester. 3 credits. Levels: Graduate

• EECE 567 - Cyber-Physical Sys Security

  Cyber-physical systems (CPS) represent a new class of systems that bring together sensing, computation, communication and control to enable continuous interactions with physical processes. Application areas are as diverse as aerospace, civil infrastructure, energy, manufacturing, transportation most of which are safety-critical. The availability of cheap communication technologies such as the Internet, makes such infrastructures to susceptible to cyber-threats. The goal of this class is to provide a unified overview of CPS security issues, from the perspective of control, communication and game theories, relating to various CPS including industrial control systems and those considered critical infrastructure systems. It will also introduce students to concepts in optimization, decentralized decision making in teams and games, and the interconnections between information and decision making, and networked control systems. Prerequisites: EECE212, ISE261, or approval of instructor. Offered every other fall semester. 3 Credits. Levels: Graduate, Undergraduate

• EECE 568 - Fund of Reinforcement Learning

  This course will provide an introduction to the field of reinforcement learning (RL). The topics that will be covered (time permitting) include but not limited to Markov Chains; MDPs; Value Functions; Policy Iteration and Value Iteration; Monte Carlo Methods; Temporal Difference (TD) Learning; (Linear) Function Approximation; SARSA; Q-Learning; TD(); Actor-Critic Methods; Neural Networks, Backpropagation and Applications to RL; Other topics (e.g., Multi-Agent RL, RL Theory; Deep Reinforcement Learning). This course will emphasize on hands-on experiences, students are expected to become well versed in key ideas and techniques for RL through a combination of lectures, written and coding assignments. Students will advance their understanding and the field of RL through a project. Prerequisites: Calculus and Linear Algebra; Basic Probability and Statistics; Python. 3 credits. Offered in the Spring. Levels: Graduate, Undergraduate

• EECE 570 - System On A Chip Design

  This course will provide an overview of the components of system on a chip (SOC) design from initial technology and architectural choices, to hardware/software design techniques (e.g., data/control flow modeling and implementation, embedded cores and coprocessors, hardware/software communication), as well as SOC implementation issues (e.g., performance, core selection, on-chip communication networks, power management, package constraints, and cost). Also covered are SOC design and implementation processes (e.g., functional integration, simulation, clocking strategies, timing, design for test, and debug strategies). Lecture portion meets with EECE 438. Extra assignments, and/or final project will be required at the graduate level. Prerequisites: EECE 315 and 287. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 573 - Digital Systems Design II

  VLSI design, synthesis and test using hardware description language (HDL) at the register-transfer level (RTL). HDL programming and simulation basics, followed by advanced HDL programming for synthesis and test benching. Principles of RTL synthesis introduced. Design and synthesis of digital systems to meet constraints. Pre-synthesis and post-synthesis verification using simulation. Commercial synthesis tool discussed in detail. Lecture portion meets with EECE 451. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 351 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 574 - CMOS VLSI Circuits & Arch.

  The topics include the principles of MOSFET transistors, characteristics of CMOS digital circuits, layout design and process, performance analysis of CMOS gates, circuit design styles using MOSFET, performance, area and power optimization of CMOS circuits. Commercial design and simulation tools will be used in the class. Laboratory assignments include design, layout, extraction and simulation.Lecture portion meets with EECE 455. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 351 or equivalent. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 575 - VLSI System Design

  Gate level and physical level design of a complex system, such as RISC processor (including the ALU and memory) is discussed. Advanced topics in logic-level design, such as high performance design, dynamic logic, low power design, asynchronous logic, interconnect analysis, cross talk issues, bus architecture, layout floor planning, and placement and routing, will be covered. Students will be asked to use Cadence physical design, analysis, and simulation tools. Prerequisite: EECE 574. Offered every spring semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 578 - Intro to Microfabrication

  Multidisciplinary Introduction to Microfabrication: Introduction to clean room tools, procedures, and theory through the fabrication and characterization of various devices from the fields of electrical engineering, mechanical engineering, physics and chemistry. Fabrication of the devices will cover most clean room tools and techniques, including lithography based patterning methodologies; chemical vapor deposition; sputtering; thermal and e-beam evaporation; thermal oxidation; reactive ion etching; ion implantation; and wet chemical processing. The accompanying lecture will cover the theory of the tools used. Lecture portion meets with EECE 431. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 332 or an equivalent course in basic semiconductor device physics. Offered every spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Graduate, Undergraduate

• EECE 578X - Intro to Microfabrication

  Multidisciplinary Introduction to Microfabrication: Introduction to clean room tools, procedures, and theory through the fabrication and characterization of various devices from the fields of electrical engineering, mechanical engineering, physics and chemistry. Fabrication of the devices will cover most clean room tools and techniques, including lithography based patterning methodologies; chemical vapor deposition; sputtering; thermal and e-beam evaporation; thermal oxidation; reactive ion etching; ion implantation; and wet chemical processing. The accompanying lecture will cover the theory of the tools used. Lecture portion meets with EECE 431. Extra assignments, and/or final project will be required at the graduate level. Prerequisite: EECE 332 or an equivalent course in basic semiconductor device physics. Offered every spring semester. 4 credits. Course fee applies. Refer to the Schedule of Classes. Levels: Graduate, Undergraduate

• EECE 580A - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580B - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580C - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580D - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580E - NeuralNetworks&DeepLearning

  In this course, we focus on the introduction of neural networks as well as their applications in deep learning. Fundamentals of neural networks and deep learning will be discussed, including multilayer perceptron, back-propagation algorithm, regularization theory, convolutional neural networks, recurrent neural networks, and reinforcement learning. After a brief overview of the python programming language and statistical signal processing, neural networks and their applications are followed. Application topics in speech recognition, image and language processing will be described. A final project will let students to work on the applications of deep learning or participate in online deep learning competitions. Prerequisites: EECE301 or equivalent signals and systems knowledge 3 credits, offered in the Fall Levels: Graduate, Undergraduate

• EECE 580F - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580G - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate

• EECE 580H - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580I - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580J - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 580K - Quantum Mech Comp of Mat

  Quantum mechanical description of materials is important to understand structure-property-performance relationships in materials science. Theoretical computation is a power tool and complementary approach to experiments. This course will firstly provide an overview of quantum mechanics and solid-state physics that are important to the understanding of basic concepts underlying the computational methods. We will then discuss practical implementations: students will learn to run the actual first-principles computational code VASP (https://www.vasp.at/) on supercomputers, interpret and present the results. We will illustrate examples to show how computational methods can be used to understand materials structures, defects, and functions for real-world applications, including recent literature in renewable energy, electronics, and quantum information. Credits 3 Levels: Graduate, Undergraduate

• EECE 592 - Tech. Dev. Curriculum I

  This course is a 32-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to a broad review of engineering fundamentals, with emphasis on interdisciplinary topics related to Electronic Systems products and processes, technologies, applications, and problem solving techniques. Coursework includes a team-project and presentation to engineering management. Offered every spring semester. 6 credits. Levels: Graduate, Undergraduate

• EECE 593 - Tech. Dev. Curriculum II

  This course is a 16-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to challenging students with problems very similar to those frequently facing Electronic Systems engineers. Coursework includes a technical project requiring the application of systems, software, and hardware engineering skills. Offered every fall semester. 3 credits. Levels: Graduate, Undergraduate

• EECE 594 - Industrial Internship

  Engineering work experience in industry. Daily logbook, memo progress reports, and formal final report required. Signature of faculty advisor and graduate director required prior to registration. Offered every semester. Variable credit. Levels: Graduate, Undergraduate

• EECE 597 - Independent Study

  Independent study or graduate laboratory exercises supervised by electrical/computer engineering faculty member. Signature of instructor required. Offered every semester. Variable credit. Levels: Graduate, Undergraduate

• EECE 598 - Project/Pre-Thesis

  Hardware and software design and development or other project as defined by a Learning Contract, approved by major professor and project advisor. Seminar presentation required. Formal report submitted to the Project advisor. Signature of advisor required prior to registration. Offered every semester. Variable credit. Levels: Graduate, Undergraduate

• EECE 599 - Thesis

  Mentoring in the methods of research. Theoretical analysis, computer modeling, software and hardware development and experimentation as determined by a Thesis Committee, faculty advisor, second reader or co-advisor and department chair. Oral defense. Preparation of journal article required. Thesis submitted to Graduate School electronically for the University Library. Signature of advisor required prior to registration. Offered every semester. Variable credit. Levels: Graduate, Undergraduate

• EECE 657 - Network Security

  This is a research oriented class that will focus on the state of the art in the network security area. The primary goal of the class is to expose students to cutting edge research works and prepare them for carrying out research independently. Topics include: Sources of Network Vulnerabilities; Overview of Network Security Problems; Standard Internet Security Solutions; Internet Infrastructure Protection; Security in Wireless Sensor Networks; Security Architecture of Grid Computing; Trust, Security and Privacy in P2P/Pervasive Computing Environments; Reconfigurable Hardware Implementation of Security Solutions. Prerequisite: EECE 553 or equivalent. Offered every spring semester. 3 credits. Levels: Graduate

• EECE 658 - Hardware-Based Security

  Information and infrastructure security in cyber space is a critical issue to national interests nowadays. Most research efforts focus on computation. However, eventually security solutions, including algorithm and variant defense schemes, are running on certain hardware. This course exposes students to the state of the art in research into the design, application, and evaluation of hardware techniques to achieve security properties in high-level computation. We will cover the ideas and trends in this active research area from architectural and application perspectives. Prerequisites: EECE 457 or 560; EECE 459 or 553; and EECE 352. Offered every other spring semester. 3 credits. Levels: Graduate

• EECE 680A - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate

• EECE 680B - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate

• EECE 680C - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate

• EECE 680D - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate

• EECE 680E - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 680F - Special Topics

  Topics in electrical/computer engineering that vary from year to year. If applicable, may be run every fall or spring semester. 3 credits. See semester schedule for current offering. Levels: Graduate, Undergraduate

• EECE 697 - Independent Study

  Independent study supervised by electrical/computer engineering faculty member. Student must obtain consent of instructor, who then determines description of program and number of credits. Offered every semester. Variable credit. Levels: Graduate

• EECE 698 - Pre Dissertation Research

  Exploratory research oriented toward PhD dissertation. Offered every semester. Variable credit. Levels: Graduate

• EECE 699 - Dissertation Research

  Research for and preparation of PhD dissertation. Offered every semester. Variable credit. Levels: Graduate

• EECE 700 - Continuous Registration

  Required to maintain matriculation through any spring or fall semester when no other courses are taken. If the minimal one-credit registration is not maintained, student must reapply for admission. Offered every semester. Variable credit. Levels: Graduate

• EECE 701 - Pract/Research &Teaching Asst

  Required for all funded graduate assistants. Research or teaching supervised by faculty advisor. Offered every semester. Variable credit. Levels: Graduate

• ISE 211 - Engineering Economics

  The fundamentals of engineering economy are introduced in this course. Topics covered include time value of money, equivalence, cash flows all at varying interest rates and points of time. The factors commonly used in industry for economic evaluation of alternatives are introduced and applied in this course. The analysis includes both a before tax and after tax evaluation of competing investment alternatives. Prerequisites: Math 227 and EDD 112 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 212 - Engineering Computing

  The objectives of the course are to: demonstrate the ability to design systems for automating processes in manufacturing, demonstrate problem-solving skills in automation, and to demonstrate the skill of using the LabVIEW and MATLAB software packages. Prerequisite: EDD 112 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 231 - Human Factors

  Review of the concepts involved in the application of scientific principles, methods, and history to the development of engineering systems in which people play a significant role. Primary focus is on the man/machine interface and how to design for the human being as part of an overall system. Prerequisite: MATH 226/227 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 261 - Probabilistic Systems I

  This course provides an introduction to probability models and statistical methods most likely to be encountered and used by students in their careers in engineering and the natural sciences. This introduction will emphasize, from the outset, that variation is the source from which all statistical methodology flows. Discussion includes the practical aspects of data collection and descriptive statistics with an introduction to the basic concepts of probability theory and probability distributions, correlation, point estimation, confidence intervals, and test of hypothesis. Prerequisites: Math 227 and EDD 112 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 295 - Seminar Course

  Development of the non-technical skills essential to effective engineering. Focus is on the overview of ISE curriculum and review of technical elective options. Review of internships, resume building, issues relevant to careers in ISE (e.g., typical tasks done by ISEs) are explored. Discussion and exploration of opportunities within program. Prerequisites: Sophomore Standing. Offered in the Fall semester. 1 credit. Levels: Undergraduate

• ISE 311 - Enterprise Systems

  Course introduces the concepts, design and planning of operating systems, with particular emphasis on manufacturing systems. Topics include introduction to lean manufacturing, JIT, Kanban, value stream mapping, standard times, MRP, inventory control, etc. The course includes plant tours to local industries that practice the concepts of the Toyota production system. Prerequisite: ISE 211 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 312 - Manufacturing Systems

  This course has three main areas of focus: production and inventory control, planning and design of manufacturing facilities, and understanding the physical fundamentals of processes and is designed mainly for engineering students intent on following an engineering career in a manufacturing industry. This course covers the models, networking, and systems needed to design and manage a manufacturing enterprise. Topics include facility design and material handling, forecasting techniques, demand management, economic lot size, inventory management, and scheduling methods. This is considered a technical elective. Prerequisite: ISE 311 or consent of department chair. Prerequisite: Junior standing in the Watson School or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Undergraduate

• ISE 314 - Computer Program for Engineers

  This course provides an introduction to computer programming and its applications for industrial and systems engineering (ISE) students emphasizing modern software engineering techniques in the context of industrial systems. Topics will include the fundamental concepts and applications of computer programming, software engineering, database management, computational problem solving, and statistical techniques for data mining. In this course, programming languages such as Python, VB.Net, or Matlab will be taught along with Excel VBA and SQL. Students will learn how to apply computer-programming techniques to solve different ISE problems. Prerequisite: ISE 261 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 315 - Enterprise Resource Planning

  This course introduces the student to the main areas of production, including production planning, control, master scheduling, finance and human resources. It provides a hands on approach to these areas through an Enterprise Resource planning software. Prerequisites: Junior standing in ISE or consent of instructor. Term offered varies. 3 credits Levels: Undergraduate

• ISE 320 - Optimiz & Operation Research I

  Operations research (OR) is devoted to determination of the optimal course of action of a decision problem given resource restrictions. This course primarily covers deterministic optimization and operations research techniques. Following a review of linear algebra, students learn how to mathematically model an engineering problem, how to solve the problem to optimality and how to perform sensitivity analyses on the results. Students learn linear programming (LP), integer programming (IP), branch-and-bound (B & B), and other optimization techniques. Special emphasis on the solution of engineering decision making includes the following areas: transportation models; network models; inventory models; assignment problems; decision making under risk and uncertainty; and game theory. For non-ISE students using this course as an elective for the Sustainability Engineering minor, application of these techniques as applied to decision-making for sustainability are included. Prerequisite: Math 304 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 362 - Probabilistic Systems II & DOE

  Methods of inference involving two independent samples and paired data are presented. The analysis of variance is examined for single-factor and multi-factor experiments. Regression analysis for simple linear models and correlation are discussed followed by non-linear and multiple regression models. A practical, yet fundamental, approach for building quality control charts from statistical concepts, as well as a goodness-of-fit test for testing discrete and continuous underlying distributions, are reviewed. Prerequisites: ISE 261 Probabilistic Systems I or permission of instructor. Offered in the Fall semester. 4 credits Levels: Undergraduate

• ISE 363 - Quality Engineering

  This course is designed to introduce the student to techniques required to maintain and improve quality within manufacturing organizations and the service sector through the use of statistical methodologies. The course covers concepts of quality, quality management and assurance, product quality, design of quality, six sigma, lean (using quality to eliminate waste), control charting, statistical process control, and quality improvement through design by considering concept development and implementation. Traditional and modern quality systems will be discussed including the work of such quality gurus like Taguchi, Deming, Juran, and Shewhart. Prerequisites: ISE 362 or permission of instructor. Offered in the Spring semester. 4 credits. Levels: Undergraduate

• ISE 364 - Eng Project Management

  Effective Engineering Project Management is critical to business and organizational success. Through in-class practice of concepts and processes, learn proven techniques, enhanced skills and best practice principles to lay a solid Engineering Project Management foundation. Through the discipline of initiating, planning, executing, controlling, and closing a project, you will grasp a thorough understanding of how to make your engineering projects successful. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the critical path method with a case study of contemporary engineering project. Prerequisites: ISE 211 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 411 - Production & Schedule Control

  This course covers general concepts of production planning and control but goes more into detail of the concepts and mathematics of scheduling applications and theory. Upon completion of the course, the student will be able to apply mathematical techniques in developing solutions to “production scheduling” problems. The “production scheduling” terminology may seem like these techniques are applicable only to manufacturing, however, they have broad applications across many industries and sectors including, but not limited, to healthcare, service industries, food processing, etc. Prerequisites: ISE 311 and senior-level standing, or permission of the instructor. Offered for the Fall semester. 3 credits. Levels: Undergraduate

• ISE 415 - Operations Mgmt Supply Chains

  Course deals with management of supply chains, in particular, with the operational aspects. A broad overview of supply chains of a company is introduced, together with performance measures and needed critical success factors. The course concentrates on supplies, inventories, manufacturing, and logistics of distribution. Managerial aspects as well as mathematical modeling for better planning and control will be covered. This course is considered a technical elective for undergraduate students. Prerequisite: ISE 311 and ISE 320 or consent of department chair. Crosslisted with SSIE 515. Prerequisite: Senior standing in ISE or consent of instructor. Offered in the spring semester. 3 credits. Levels: Undergraduate

• ISE 416X - Fund. of Energy Sys. Analytics

  This course provides fundamental knowledge of the application of Industrial and Systems Engineering in the Energy field. It focuses on the modeling, design, simulation, and optimization of alternative and conventional energy technologies, the societal and environmental impact of technology developments, and the economic benefits of those developments. Recent literature that covers a wide range of ISE topics related to energy systems such as transportation energy, building energy, manufacturing energy, solar energy, wind energy, bioenergy, greenhouse gas emission, and smart grid operations will be discussed in this course. Students will gain breadth and depth of knowledge, and an interdisciplinary understanding of the contemporary need for energy solutions. 3 Credits. Prerequisites: Senior level standing. Offered in fall or spring. Levels: Undergraduate

• ISE 418 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisite: ISE 314 or permission of instructor. Cross listed with SSIE 523. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 419 - Applied Soft Computing

  Covers relatively new approaches to machine intelligence known collectively as "soft computing". Introduces various types of fuzzy inference systems, neural networks, and genetic algorithms, along with several synergistic approaches for combining them as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction, design, control, databases and data mining. This course is considered a technical elective for undergraduate students. Prerequisites: Basic knowledge of calculus and discrete mathematics and competence in at least one programming language, or permission of instructor. Crosslisted with SSIE 519. Offered in the fall semester. 3 credits. Levels: Undergraduate

• ISE 420 - Optimiz & Operations Res II

  Operations research (OR) is devoted to the determination of the optimal course of action of a decision problem given resource restrictions. This course is intended as a second course in an Optimization and OR sequence and builds upon the material presented in ISE 320. ISE 320 primarily restricts attention to deterministic OR models. In addition to covering additional deterministic techniques (e.g., deterministic dynamic programming and additional inventory problems not covered in ISE 320, among others), ISE 420 covers probabilistic and advanced OR topics such as Monte Carlo simulation, fundamentals of queueing theory, probabilistic dynamic programming, and others. The course also introduces the student to emerging optimization techniques including, but not limited to, tabu search, simulated annealing, and genetic algorithms. Prerequisite: ISE 320 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 421 - Modeling And Simulation

  Model building, nature of simulation and material on the full range of simulation activities, such as input analysis, output analysis, verification and validation, and model animation. Includes random number generation; distribution functions and random variates; applications of discrete event simulation methods to queueing, inventory control and production planning problems; Markov processes, queueing theory and decision analysis. Prerequisites: ISE 362 and ISE 320 or permission of instructor. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 422 - Advanced Decision Modeling

  Course provides a broad foundation in decision models and techniques used in industry and research for technical and managerial problems. Topics include decision theory, risk and uncertainty, value of information, preference measurements, prioritization of alternatives, multiple objectives and hierarchical decisions. Prerequisite: ISE 362 or permission of the instructor. Cross-listed with SSIE 522. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 423 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisite: ISE 314 or permission of instructor. Crosslisted with SSIE 523. Term offered varies. 3 credits. . Levels: Undergraduate

• ISE 426X - Sys Dynamics & Sys Thinking

  In this course, the basic concepts of systems thinking and system dynamics modeling will be introduced and students will understand, evaluate, and improve upon problems that manifest themselves in complex systems, such as diffusion of new technologies; global power consumption; fluctuating sales, production and earnings; environmental challenges such as urban development and poverty; healthcare and public health challenges such as disease spread. System dynamics is a simulation modeling process to quantify problems that unfold through time. In this iterative process, a problem that is pervasive throughout a system is defined, the structure of the system that generates the problem will be studied, and policies or practices that can change the system structure and behavior and therefore alleviate/solve the problem will be studied. Prerequisites: Senior level standing or permission of the instructor. Crosslisted with SSIE 526X. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 430 - Healthcare Policy Analysis

  This course provides an overview of issues and policies related to health care systems, with emphasis on health care systems within the United States. The course will apply various engineering tools and approaches for policy analysis and program planning in this area. Some potential future policy changes will also be discussed, as well as challenges with implementation and change management techniques. Cross-listed with SSIE 530. Prerequisites: Senior standing or permission of the instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 434 - Fundamentals of Health Systems

  One of the growing systems in our society is that of the healthcare delivery system. The purpose of this course is to introduce the concepts behind the healthcare delivery systems and to focus upon the systems improvement or continuous improvement techniques available for complex systems. Topics would include improvement to, and problems with: organizational structure, managing change, the financial structure, the responsibility structure, quality data and implications of quality measures, use of clinical decision support systems and the caregiver's role in the system. There will also be a focus upon suppliers to the healthcare delivery system and the unique requirements placed upon their products and processes. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing or consent of department chair. Cross-listed with SSIE 534. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 437 - Ind & Sys Eng in Healthcare

  Introduction to health systems and healthcare delivery. The application of industrial and systems engineering principles to continuous process improvement in the healthcare domain will be studied. Concepts that will be addressed will include, but not be limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. The course is considered a technical elective for undergraduate students. Prerequisites: ISE 311 or permission of instructor. Crosslisted with SSIE 537. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 438 - Healthcare Financial Eng

  This course is intended to provide an overview of healthcare finance and the current financial environment for the healthcare industry and to learn how to apply engineering economics to healthcare financial management. It will provide information on financial and managerial accounting and how the use of Systems Science and Industrial and Systems Engineering principles can be applied to financial management concepts to allow for health-related organizations to make sound business decisions. Prerequisite: Senior standing or permission of instructor. Crosslisted with SSIE 538. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 439 - Human Factors Eng Healthcare

  This course introduces and emphasizes the role that human factors engineering/ergonomics plays in healthcare systems, with a focus on its applications to help improve quality, safety, efficiency, and effectiveness of patient care. Focused topics include human factors in workflow models; work system design for patient safety; human error analysis/taxonomies to reduce medical errors; task analysis and data collection methods in healthcare environments; clinical staff workload and patient safety; physical ergonomics in healthcare and human performance modeling; and diffusion and adoption of technology in healthcare, with emphasis on the usability and design of medical devices and information systems. Prerequisite: Senior standing or permission of instructor. Crosslisted with SSIE 539. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 440 - Intro to Systems Science

  Includes the following: a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions and historical development of the field; an overview of fundamental systems concepts, principles and laws; and a survey of application areas of systems science and its implications for other fields of study. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing or permission of the department chair. Cross-listed with SSIE 501. Offered in the fall semester. 3 credits. Levels: Undergraduate

• ISE 448 - Healthcare Data Sci&Analytics

  Data analytics plays an increasingly greater role in healthcare organizations. However, many healthcare professionals lack training in this vital area. This course will cover advanced data analytics with applications in healthcare systems engineering. In specific, the course will cover the concepts and methods of the data analytics in three particular areas: (i) descriptive analytics, which includes data description and aggregation; (ii) predictive analytics, which uses machine learning and statistical modeling for prediction and forecasting; and (iii) prescriptive analytics, which makes use of the results of predictive analysis to optimize the decision making process. All introduced methods will be demonstrated along with a variety of data modalities collected from healthcare applications such as pervasive health, disease diagnosis, fraud detection, and pharmaceutical discovery along with medical decision support systems. Through the provided coursework, students will be able to learn a variety of methodologies on data processing and modeling and make the best use of healthcare data to facilitate medical decision making. Prerequisites: Senior level standing, ISE 314 or equivalent, or permission of the instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 456 - Expert Systems In Manufacturin

  Computer-based decision-making tools in which a domain expert's knowledge is embedded are known as knowledge-based expert systems (KBESs). Expert systems allow such expert knowledge to be widely disseminated (e.g., throughout a company or throughout different facilities that a company has). This course introduces the student to all steps of development (e.g., knowledge representations, knowledge acquisition, and expert system development, among others) with special emphasis on manufacturing applications. The course will culminate with the students developing functioning prototype expert systems in a manufacturing area of their choice. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing in ISE or permission of instructor. Crosslisted with SSIE 556. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 462 - Cost Estimating For Engineers

  Changes in our society have resulted in major changes in manufacturing to the point at which labor costs are no longer the controlling cost, or even the major cost. Major costs have shifted to the material and material-related costs, with the overhead and burden costs almost as significant. While innovative design is critical to engineering, being profitable is also critical, and profit starts with determining the proper costs for a product or idea. Topics include costs of labor, equipment, material, overhead or burden, volume/cost relationships, use rates, collection, build-up of costs, costing of manufacturing operations, standard costs and variances. This course is considered a technical elective for undergraduate students. Prerequisite: ISE 211 or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Undergraduate

• ISE 463 - Project Analysis And Control

  Course covers the topics of project planning, economic design making, costing and pricing. The topics include network planning, present worth, annual cost, rate of return, activity based costing, inflation, price change, etc. Prerequisite: ISE 211 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 464 - Elem Of Fuz Log & Fuz Set Thry

  Simple introduction to basic elements of fuzzy logic and fuzzy set theory, including an overview of classical logic and classical set theory. Included are basic concepts and properties of classical sets and fuzzy sets, classical relations and fuzzy relations, classical logic and fuzzy logic, and fuzzy arithmetic. The practical utility of fuzzy logic and fuzzy set theory is illustrated by describing selected applications in various areas of human affairs. This course is considered a technical elective for undergraduate students. Prerequisite: ISE 261 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 465 - Engineering Project Management

  Effective Engineering Project Management is critical to business and organizational success. Through in-class practice of concepts and processes, learn proven techniques, enhanced skills and best practice principles to lay a solid Engineering Project Management foundation. Through the discipline of initiating, planning, executing, controlling, and closing a project, you will grasp a thorough understanding of how to make your engineering projects successful. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the critical path method with a case study of contemporary engineering project. Prerequisites: ISE 261 or permission of instructor. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 468X - Flexible & Hybrid Electronics

  Flexible and hybrid electronics integrate ultra-thin silicon semiconductor technology with non-traditional thin skin-like substrates establishing wearable devices for both human and machine. This course will cover design, materials, fabrication methods and evaluation of printed and thinned silicon-based electronics for applications such as smart energy, human health, and industrial sensing. Emphasis will be placed on fundamental understanding of the hard-soft materials interface, advances in materials printing, and approaches to make these systems robust for use. The course will promote understanding of the engineering requirements that lead to systems design for both manufacturing and reliability. 3 credits. Levels: Undergraduate

• ISE 469 - Materials for Manufacturing

  This course provides an overview of the materials used in manufacturing that are of significant economic, technical and industrial relevance. A special emphasis will be given to polymeric materials currently used in electronics manufacturing as well as those materials emerging for use in additive manufacturing and 3D printing. Selected materials will be classified by composition, properties, manufacturing methods, applications, economics and related supply chains. Prerequisites: Senior standing in engineering or science or permission of the instructor. Crosslisted with SSIE 569. Term offered varies. 3 credits. Levels: Undergraduate

• ISE 470 - Industrl Automation&Ctrl

  Industrial automation is a major field in the application of computer controls and the many advances in computer systems. The objectives of this course are to: demonstrate the ability to design systems for automating processes in manufacturing, demonstrate problem-solving skills in automation, and safely use the machines in the engineering laboratory to complete designed experiments. Lectures and laboratories include exploring the use of sensors, industrial robotics, numerical control, programmable logic controllers, machine vision, electrical circuits and the fundamentals of common electrical devices, fuzzy control, the implementation of online computer control, and the ability to use industrial technical software including Pro-Engineer and AutoCAD. Laboratory work and technical reports are required. Prerequisites: ISE 311 or consent of department chair. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 470X - International STEM Trip

  This course will explore Germany's approach to science, engineering, technology development and manufacturing. We will study, observe and discuss before, during and after organized travel: Germany and European history, the German culture and work ethic, the impact government organizations on economic development. We will learn why Germany is still considered a leader in manufacturing and the economic powerhouse of the European Union. We will answer: what can we learn from Germany and how will it influence our own contributions as scientists and engineers in the United States. Offered in the Summer term. 3 credits Levels: Undergraduate

• ISE 473 - Processes for Electr Mfg.

  The purpose is for the students to gain a broad knowledge and understanding of the basics of printed circuit board manufacturing and assembly. The course offers an introduction to surface mount and insertion mount components, materials and processes as well as to PCB design and manufacturing. Lectures will introduce assembly process flows and component types, PCB construction and defects solder paste printing and equipment, placement processes and equipment, reflow and ovens, flip chip assembly and underfilling, defects and mitigation, reliability optimization and testing. Efforts will be made to include visits to local industrial assembly facilities as well as equipment on campus. The overall goal is to provide the students with a basis for communicating and working with subject matter experts. This course is considered a technical elective for undergraduate students. Prerequisite: Senior standing or permission of instructor. Crosslisted with SSIE 578. Offered in the fall semester. 3 credits. Levels: Undergraduate

• ISE 483X - Evolutionary Sys & Bio-Inspire

  In this course, the fundamental concepts of bio-inspired computing are introduced with a focus on understanding how evolutionary systems are different from other complex systems in nature and society. Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, evolvable, resilient, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course is strongly grounded on the foundations of complex systems science and theoretical biology. It aims to provide an understanding of the distributed architectures of evolving complex systems, and how those can be used to produce artificial intelligence and computational tools, as well as engineering designs, with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, physics, complexity, computer science, informatics, cognitive science, robotics, optimization, control, and cybernetics. Bio-inspired computing at the core of modern artificial intelligence, and thus applied throughout science and engineering, but understanding the mechanisms behind resilience, evolvability, and adaptation are essential to tackle the most vexing biomedical and social complexity problems we currently face. Therefore, the course should interest students in various programs. Students will be introduced to fundamental topics in bio-inspired computing and build up their proficiency in the application of various algorithms in real-world problems developed in lab assignments developed for Python (Jupyter) notebooks. 3 credits Prerequisites: Senior level standing or permission of the instructor / department chair. Basic knowledge of Python. Levels: Graduate, Undergraduate

• ISE 485 - Additive Mfg Processes & Sys

  This course aims to inculcate a comprehensive understanding of additive manufacturing (AM), and will address the following consequential aspects of AM: (1) Materials and Process Mechanics; (2) Machines, Machine Design, and Mechanisms; (3) Quality control, Sensing, and Process Monitoring; and (4) Design for AM and novel trans-disciplinary applications. Prerequisites: ISE 311 or permission of the instructor. Crosslisted with SSIE 585. Offered in the Fall semester. 3 credits. Levels: Undergraduate

• ISE 491 - Systems Design

  Covers the design process from the definition of requirements through the final output. Focus is on the design principles and design methodologies used to ensure a quality outcome. Prerequisite: ISE 311 and 362 or consent of department chair. Course fee applies. Refer to the Schedule of Classes. Offered in the Fall semester. 4 credits. Levels: Undergraduate

• ISE 492 - Systems Design Project

  The capstone project for the undergraduate degree. Students are expected to work in multi-disciplinary teams to provide solutions through design. Prerequisite: ISE 491 or consent of department chair. Course fee applies. Offered in the Spring semester. 4 credits. Levels: Graduate, Undergraduate

• ISE 496 - Industrial Internship

  Special topics in industrial and systems engineering. Semester offered varies. Credits vary. Levels: Undergraduate

• ISE 497 - Independent Study

  Available every semester. Credits vary. Levels: Undergraduate

• ISE 497A - Independent Study

  Available every semester. Credits vary. Levels: Undergraduate

• ME 211 - Intro to Solid Mechanics

  Basic principles of stress and strain of structural members subject to axial, shearing, bending, torsion and combined loads. Mechanical properties of engineering materials. Shear and moment diagrams. Deflection of beams. Must be completed with a grade of C- or better to satisfy ME program requirements. Prerequisite: ME 273 with a grade of C- or better. Offered in the Spring semester and the Summer session. 3 credits Levels: Undergraduate

• ME 212 - ME Programming

  Structured programming for mechanical engineers. Engineering programming with MATLAB. Prerequisite: EDD 112 or a beginning programming course. Offered in the Fall semester, Winter and Summer sessions. 2 credits Levels: Undergraduate

• ME 273 - Statics

  Equilibrium of particles and rigid bodies, equivalent force system, free-body diagrams, centroid of areas, mass moment of inertia, truss analysis, friction. Must be completed with a grade of C- or better to satisfy ME program requirements. Prerequisite: PHYS 131. Offered in the Fall and Spring semesters and the Summer session. 3 credits Levels: Undergraduate

• ME 274 - Dynamics

  Kinematics and kinetics of particles; kinematics and kinetics of rigid bodies (plane motion). Energy and momentum methods. Must be completed with a grade of C- or better to satisfy ME program requirements. Prerequisite: ME 273 with a grade of C- or better. Offered in the Spring semester and the Summer session. 3 credits Levels: Graduate, Undergraduate

• ME 302 - Engineering Analysis

  Methods employed in engineering problem solving. Case studies drawn from engineering disciplines used to apply the mathematical techniques. Prerequisites: MATH 323 and MATH 324. Offered in the Fall semester. 3 credits Levels: Undergraduate

• ME 303 - Engineering Computational Meth

  Application of computational methods to solve engineering and scientific problems. Topics covered include numerical methods (curve fitting, solution of linear and nonlinear equations, integration, ordinary and partial differential equations), graphical visualization and statistical analysis using MATLAB. Prerequisites: ME 212 and ME 302. Offered in the Spring semester and the Summer session. 3 credits Levels: Undergraduate

• ME 331 - Thermodynamics

  Properties of pure substances. Concepts of work and heat, fundamental laws of thermodynamics; closed and open systems. Entropy and entropy production. Basic gas and vapor cycles, basic refrigeration cycles. Prerequisites: PHYS 131. Offered in the Fall semester and in the Summer session. 3 credits Levels: Undergraduate

• ME 351 - Fluid Mechanics

  Hydrostatics, kinematics, pipe flow, momentum and energy relations. Bernoulli equation. Real fluid phenomena, laminar and turbulent motion, boundary layer, lift and drag. Prerequisites: ME 274 with a grade of C- or better and ME 302. Offered in the Spring semester and in the Summer session. 3 credits Levels: Undergraduate

• ME 362 - Science of Engr. Materials

  Introduction to the structures and structure-related properties of engineering materials: metals, ceramics, and polymers. Physical properties of matter. Prerequisites: CHEM 111 and PHYS 132, Prerequisite that can be taken concurrently: ME 363. Offered in the Fall semester. 3 credits Levels: Undergraduate

• ME 363 - Engineering Materials Lab

  Laboratory course to accompany ME 362, Science of Engineering Materials. Prerequisite that can be taken concurrently: ME 362. Offered in the Fall semester. 1 credit Levels: Undergraduate

• ME 381 - Computer Aided Engr

  Fundamentals of computer-aided design, modeling, analysis and optimization. Introduction to finite element method and use of standard packages for design problems. Mechanism simulation. Includes laboratory section. Prerequisite: ME 211 with a grade of C- or better, Prerequisite that can be taken concurrently: ME 212. Offered in the Fall semester. 3 credits Levels: Undergraduate

• ME 391 - Measmnt & Instrumentation

  Modular laboratory course in which the topics of thermodynamics, fluids, heat transfer, vibrations, and solid mechanics are the subjects for the experimental modules. Includes laboratory. Prerequisite: ME 211 with a grade of C- or better, ME 212, and EECE 260. Offered in the Fall and the Spring semesters. 3 credits Levels: Undergraduate

• ME 392 - Mechanical Engineering Design

  Application of fundamental principles of mechanics and strength of materials to mechanical engineering design problems. Topics include fatigue, stress concentrations, and failure theories. Decision making and engineering judgment for open-ended problems are emphasized. Prerequisites: ME 362, and ME 381. Offered in the Spring semester. 3 credits Levels: Undergraduate

• ME 397 - Independent Study

  Independent study supervised by a mechanical engineering faculty member. Student must obtain consent of instructor, who then determines description of program, number of credits (variable), frequency of meeting and location. Appropriate paperwork must be submitted to the department office in order to complete registration. Levels: Undergraduate

• ME 412 - Structural Mechanics

  A bridge between elementary mechanics of deformable bodies and advanced courses in elasticity. Topics covered include fundamental concepts of structural mechanics, analysis of beams, columns, trusses, frames, plates and cables, elastic stability of structures, energy method, principle of virtual work, and other topics from structural and solid mechanics. (Technical elective.) Prerequisite: ME 211. Semester offered varies. 3 credits Levels: Undergraduate

• ME 417 - Intro To Finite Element Method

  The finite element method and its application to solving simple truss, beam, frame and 1-D fluid flow problems are discussed. Fundamentals of using commercial software (such as ANSYS) for the finite element method are covered through simple and intermediate examples. Knowledge of solid and fluid mechanics, basic linear algebra, and a programming language (such as MATLAB) are essential for this course. (Technical elective.) Prerequisites: ME 211, ME 212, and ME 381. Semester offered varies. 3 credits Levels: Undergraduate

• ME 421 - Mechanical Vibrations

  Free vibration of mechanical systems, damping, forced harmonic vibration, support motion, vibration isolation, response due to arbitrary excitation, systems with multiple degrees of freedom, normal modes, free and forced vibrations, vibration absorber, application of matrix methods, numerical techniques, computer applications. Prerequisites: ME 274 with a grade of C- or better and ME 302. Offered in the Spring semester and the summer session. 3 credits Levels: Graduate, Undergraduate

• ME 424 - Control Sys in Mechanical Engr

  Introduction to classical and modern control systems as they relate to mechanical engineering. Modeling, analysis and design of control systems. State space techniques are introduced. Prerequisite: ME 303, Prerequisite that can be taken concurrently: ME 421. Offered in the Fall semester and the summer session. 3 credits Levels: Undergraduate

• ME 425 - Intermediate Dynamics

  Review of the basic principles of kinematics and kinetics of particles and planar rigid bodies. Topics include: 3D kinematics and 3D rotations, Lagranges's approach for both particles and rigid bodies, Euler Angles and Euler Equations. (Technical Elective.) Prerequisite: ME 274 and ME 302. Semester offered varies. 3 credits Levels: Undergraduate

• ME 428 - Robotics

  This interactive course focuses on the design, programming and building of autonomous microcomputer-controlled robots using Lego Mindstorm Robotic kit and electro-mechanical actuators and sensors. Student will find out how to use feedback from sensors, applied mathematics and measurement to program robot to navigate in its environment and accomplish different tasks. Advanced topics like robot motion control and path planning etc. will be introduced. The course comprises many small challenges and two projects. The autonomous robots designed and programmed by each student will compete during the semester. (Technical elective.) Prerequisites: ME 274, ME 303, and ME 392, Prerequisites that can be taken concurrently ME 424. Offered in the Fall and the Spring semesters. 3 credits Levels: Undergraduate

• ME 429 - Intro to Non-linear Dynamics

  Basic principles of nonlinear dynamics and nonlinear oscillations. Topics include: equilibrium solutions, stability concepts, linearization and stability analysis, bifurcation types, periodic solutions, Floquet theory, bifurcations, shooting technique, Poincare section, introduction to quasi-periodic solutions, the method of multiple scales, and Chaos. (Technical Elective.) Prerequisite: ME 302 and ME 303. Semester offered varies. 3 credits Levels: Undergraduate

• ME 435 - Applied Aerodynamics

  Application of basic principles of fluid dynamics and thermodynamics to the aerodynamics of flight. Deals with concepts of lift, drag, aerodynamic moments, dynamics of flow fields about bodies, including theory of airfoils and wings. Analytical techniques for predicting aircraft performance are presented. Fundamental concepts of propulsion including jet propulsion are introduced. (Technical elective.) Prerequisites: ME 331 and ME 351. Semester offered varies. 3 credits Levels: Undergraduate

• ME 437 - Energy Engineering

  Principles of thermodynamics, heat transfer, fluid flow and materials science in describing the operation of energy production and conversion. (Technical elective.) Prerequisite: ME 331. Offered in the Spring semester. 3 credits Levels: Undergraduate

• ME 441 - Heat Transfer

  Introduction to fundamentals of heat transfer. Topics in conduction, forced and free convection, mixed modes (e.g., extended surfaces), heat exchangers, radiation. Development and use of analytic and empirical expressions in terms of dimensionless parameters. Prerequisites: ME 331 and ME 351. Offered in the Fall semester. 3 credits Levels: Undergraduate

• ME 443 - HVAC

  Application of principles of thermodynamics, fluid mechanics and heat transfer to the analysis and design of heating, ventilating and air conditioning (HVAC) systems. Use materials of construction data in conjunction with internal gains to calculate heating and cooling loads. Use of psychometric data with ventilation requirements to select and integrate HVAC components into systems. Students will be required to generate a presentation on a relevant HVAC topic(s). (Technical elective.) Prerequisites: ME 331 and ME 351. Offered in the Fall and Spring semesters. 3 credits Levels: Undergraduate

• ME 451 - Applied Fluids Engineering

  This course aims to provide a broader knowledge base to students who intend to seek a career in a fluids-related field. Topics include inviscid and irrotational flows, open-channel flows, compressible flows, turbulence, turbo machinery, flow networks, and microfluidics. Students will also be introduced to computational fluid dynamics (CFD). (Technical Elective). Prerequisites: ME 303 and ME 351. Semester offered varies. 3 credits. Levels: Undergraduate

• ME 464 - Matls Selection for Mech Desgn

  This course covers the methodology and procedures for selecting materials and processes at various stages of the design process for mechanical engineering applications as well as mechanics and materials knowledge used in solving practical problems. (Technical elective) Prerequisite: ME 211 and ME 362. Semester offered varies. 3 credits Levels: Undergraduate

• ME 480A - Special Topics

  Special Topics. Topics vary by semester. 3 credits Levels: Undergraduate

• ME 480B - Special Topics

  Special Topics. Topics vary by semester. 3 credits Levels: Undergraduate

• ME 482 - Adv Computer-Aided Engineering

  Advanced CAE will cover GD&T, CAM, theory and principles of CAD modeling, and configuration management. Projects and laboratory assignments will include solid modeling and manufacturing file output (CNC). There will be a weekly laboratory. A final project will be a team, concurrent, distributed design project. (Technical Elective). Prerequisites: ME 381. Semester offered varies. 3 credits Levels: Undergraduate

• ME 484 - Design of Mechanical Elements

  This is a second course in mechanical component and systems design. Linkages, cams, fasteners, welds, bearings, gears, brakes and clutches, and power transmission elements will be covered. Design and analysis of components and assemblies will be performed. Proficiency in CAE is required. The software packages that will be used are: Creo (solid modeling and computer-aided design) and ANSYS (finite element analysis). (Technical Elective). Prerequisites: ME 392. Semester offered varies. 3 credits Levels: Undergraduate

• ME 489 - Research Internship

  A student will work in a research lab under the supervision of a professor in the ME Department. It is the responsibility of the student to make arrangements with the professor who will supervise this course. Prior to enrolling in the course a Research Internship Course Proposal (RICP) form must be completed and signed by (1) the student, (2) the professor supervising the research, and (3) the Director of Undergraduate Studies. Prerequisites: Permission of instructor. Offered any semester. Variable credit. Levels: Graduate, Undergraduate

• ME 491 - Mechanical Engineering Lab

  Conduct experiments in the core areas of mechanical engineering. There will be lectures and online modules to provide background and theory to accompany the experiments that cover complex mechanical systems. Topics will include controls, heat transfer, fluid mechanics, thermodynamics and dynamical systems. The course will also involve report writing, oral presentations and team building. Prerequisite: ME 391. Prerequisites that can be taken concurrently: ME 421, ME 424 and ME 441. Offered in the Fall and Spring semesters. 3 credits Levels: Undergraduate

• ME 493 - Senior Project I

  Group project emphasizing definition and planning for solution of an industrial problem. Achievement of prototype or interim design in preparation for final design or product/process realization in ME 494. Course fee applies. Refer to the Schedule of Classes. Prerequisite: ME 392. Prerequisites that can be taken concurrently: ME 421, ME 424 and ME 441. Co-requisite: ME 498. Offered in the Fall semester. 2 credits Levels: Undergraduate

• ME 494 - Senior Project II

  Coordination of group project with unique industrial problem. Analysis, design, experimentation may be brought to bear on solution. Realization of results from final design of product or process with critical evaluation by judging panel. Course fee applies. Refer to the Schedule of Classes. Prerequisite: ME 391 and ME 493. Corequisite: ME 499. Offered in the Spring semester. 2 credits Levels: Undergraduate

• ME 496 - Industrial Internship

  Engineering professional experience. Record of engineering experience required. Student must obtain an industrial advisor and approval of a faculty advisor. Appropriate paperwork must be submitted to the department. Pass/Fail only. Variable credit. Levels: Undergraduate

• ME 497 - Independent Study

  Individual study under direct supervision of a faculty member. Student must obtain permission of instructor, who them determines description of program and number of credits. Appropriate paperwork must be submitted to the department. Variable credit. Levels: Undergraduate

• ME 498 - Senior Project I lab

  Project work with ME 493. Course fee applies. Refer to the Schedule of Classes. Corequisite: ME 493. Offered in the Fall semester. 1 credit Levels: Undergraduate

• ME 499 - Senior Project II lab

  Project work with ME 494. Course fee applies. Refer to the Schedule of Classes. Corequisite: ME 494. Offered in the Spring semester. 1 credit Levels: Undergraduate

• ME 502 - Structural Mechanics

  This course is a bridge between elementary mechanics of deformable bodies and advanced courses in elasticity. Topics covered include fundamental concepts of structural mechanics, analysis of beams, trusses, frames, cables and arches, determinacy and stability of structures, influence lines, energy method, principle of virtual work, force, displacement and stiffness methods, and other topics from structural and solid mechanics. Prerequisites: ME 211 or equivalent course in Strength of Materials. Term varies. 3 credits. Levels: Graduate, Undergraduate

• ME 506 - Vehicle Control & Simulation

  Concepts of modeling and simulation of vehicle dynamics are developed with particular emphasis on real-time simulation. The digital simulation of the continuous system is developed as a discrete dynamic system that may be filtered, tuned, stabilized, controlled, analyzed and synthesized. Also included are coordinate transformation techniques for multi-degree of freedom systems and numerical integration techniques in the context of real-time applications. Term project involves the simulation of the dynamics of a vehicle such as an aircraft or a land vehicle. Prerequisite: BS degree in engineering or physics or consent of instructor. Offered in spring semester.. 3 credits Levels: Graduate, Undergraduate

• ME 511 - Elasticity

  Topics covered include three-dimensional analysis and representation of stress and strain, development of governing equations of elastic media, applications of these equations to two- and three-dimensional problems. Prerequisite: mechanics of materials or consent of instructor. Prerequisite: ME 211 or equivalent. Offered in the Fall. 3 credits Levels: Graduate, Undergraduate

• ME 514 - Plasticity

  Fundamentals of deformation and strength concepts of isotropic materials. Plastic stress-strain relations, criteria for yielding under multiaxial stress and properties of the yield surface under loading and unloading schemes. Hardness tests and forging problems. Elasto-plastic deformation of torsional and flexural members, hollow spheres and thick-walled tubes. Slip-line analysis for indentation problems, and limit analysis for frame structures and plates. Finite element theory with applications and practical programming experience in a convenient FEM code. Dynamic plasticity experimental methods are discussed. Prerequisites: ME 511 or consent of instructor. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 515 - Structural Fatigue Analysis

  New and conventional fatigue design approaches will be presented. Topics include crack initiation, mean stress effects, notch effects, cyclic stress-strain behavior, mean and residual stress effects, cumulative damage, crack propagation, multiaxial fatigue, environmental effects, fatigue of mechanical components, including small scale components. Prerequisite: ME 511. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 517 - Finite Element Analysis I

  An introductory course in the finite element (FE) method dealing with the fundamental principles. Problems solved in the areas of solid mechanics, structures, fluid mechanics and heat transfer. Use of standard FE software such as ANSYS. Prerequisite: mechanics of materials or consent of instructor. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 518 - Applied Mechanics for Design

  Stress and deformation analysis of structural elements such as bars, beams, trusses, and plates that are commonly used in mechanical design. The topics include review of equilibrium, compatibility and constitutive laws, yielding and failure, bending problems, energy methods, plate problems, contact and fracture problems, stability of elastic systems, and inelastic problems. Prerequisites: ME 211 or equivalent. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 520 - Mechanics of Composite Mtls

  Course introduces the concepts and advantages of composite materials to the graduate student and advanced senior students. It covers the nature of composites and mechanics of composites for analytical approaches to model the behavior of material. Prerequisite: ME511 or consent of instructor. Term varies. 3 credits. Levels: Graduate, Undergraduate

• ME 521 - Dynamics Of Mems & Microsys

  Modeling and characterization of MEMS structures. Topics include: static analysis, free undamped vibration, free damped vibration in coupled fields (structural, electrostatic, fluidic, thermoelastic); forced vibration, reduced-order modeling. Introduction to perturbation approaches and nonlinear dynamics. Prerequisites: completion of undergraduate Vibration course or co-requisite with ME421, or permission of instructor. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 522 - Acoustics

  Propagation of sound. Acoustic wave motion. Reflection of sound waves from boundaries. Sound transmission through walls. Sound generation and radiation. Sound propagation in ducts. Acoustic transducers: loudspeakers and microphones. Auditory systems, bioacoustics. Prerequisite: ME535 or ME533, graduate standing in engineering or physics or consent of instructor. Offered in the Spring. 3 credits Levels: Graduate, Undergraduate

• ME 523 - Advanced Rigid-Body Dynamics

  Fundamentals of mechanics for students in engineering practice and students contemplating further in-depth study in mechanics. Topics included are: kinematics of particles and rigid bodies; dynamics of articles and rigid bodies (Newton-Euler equations, impulse momentum and work-energy principles); analytical mechanics (virtual displacements and virtual work, Hamilton's principle, Lagrange's equations). Prerequisite: undergraduate course in dynamics. 3 credits Levels: Graduate, Undergraduate

• ME 524 - Adv. Mech. Vibrations

  Fundamentals of dynamics as applied to mechanically vibrating systems. Equations of motion for systems with multiple degrees of freedom are developed to determine natural modes of vibration of discrete systems. Approximate methods of solution, e.g., Rayleigh-Ritz, Galerkin's method, etc., are discussed. Vibration of continuous systems, e.g., free and forced vibration of strings, bars, beams and plates are considered. Numerical approaches, including the finite element method, are applied to continuous systems. Prerequisite: ME 421 or equivalent and ME535 or ME533 or consent of instructor. Course is offered every spring semester. 3 credits Levels: Graduate, Undergraduate

• ME 527 - Mechatronics

  Methods for analysis and design of electromechanical and piezoelectric systems.  Review of fundamentals in electronics, mechanics, and controls.  Application of Lagrangian and Hamiltonian dynamics to mechanical and electrical systems.  Signal conditioning. Sensors and actuators.  Vibration control.  Prerequisite: graduate standing in electrical or mechanical engineering or physics, or consent of instructor.  3 credits Levels: Graduate, Undergraduate

• ME 528 - Modern Robotics

  This is an entry level graduate course in robotics. This course gives students a full overview of modern robotics, provides the basic knowledge on the foundations of various aspects of robotics, and equips students with inherently interdisciplinary skills in robotics. First, the coordinate frames and transformation for rigid body motion will be discussed. Next, forward and inverse kinematics for manipulator models will be introduced. Manipulator dynamics and path generation will be followed. Robotic applications such as motion planning, motion control, mobile robots, etc. will be discussed. Prerequisites: ME302 Engineering Analysis and ME424 Control Systems in Mechanical Engineering, or equivalent, or with approval of instructor. Offered in the fall. 3 credits Levels: Graduate, Undergraduate

• ME 529X - Intro to Nonlinear Dynamics

  The course aims to introduce the students to the principles of nonlinear dynamics and nonlinear oscillations. The course will describe the basic characteristics and phenomena of nonlinear dynamics and oscillations. It will emphasize analytical and numerical techniques to tackle nonlinear problems. The topics will include equilibrium solutions, stability concepts, linearization and stability analysis, bifurcation types, periodic solutions, Floquet theory, bifurcations, shooting technique, Poincare section, introduction to quasi-periodic solutions, the method of multiple scales, and Chaos. 3 credits Prerequisites: ME 302, ME 303 or equivalent, or permission of instructor Semesters offered varies Levels: Graduate, Undergraduate

• ME 532 - Principles Biomechanical Engin

  Study of the basic mechanical properties of the human body, including the biomechanics of locomotion and measurement of physiological parameters. The applications of statics and dynamics to biomechanics. Studying the elastic, hyperelastic, and viscoelastic material properties of Biological tissues. Image-based multiscale and multiphysics modeling in biomechanics. Projects will be included that will stress mathematical and computational modeling and analysis of the mechanics of tissues, limbs, joints, and bioscaffolds. Prerequisite: ME 211, ME 274, graduate and undergraduate in mechanical engineering. Offered in the Spring. 3 credits Levels: Graduate, Undergraduate

• ME 533 - Applied Mathematical Methods

  The course covers a range of topics involving mathematical and numerical methods for solving engineering problems. The list includes: linear algebra, nonlinear equations, ordinary differential equations, Laplace Transforms, data analysis, statistics and numerical solution to partial differential equations. Both analytical and numerical techniques will be presented and applied to solve problems in mechanical engineering as well as other technical fields. The MATLAB computing environment will be used throughout the course for graphic, numerical computation and symbolic manipulation. Prerequisites: Calculus I-III; Ordinary Differential Equations. Offered in the fall. 3 credits. Levels: Graduate, Undergraduate

• ME 535 - Analytical Methods I

  A survey of important analytical and numerical methods for mathematical modeling of engineering and scientific problems. Solution of partial differential equations, including methods for linear equations, separation of variables and eigenfunction expansions ; review of multi-variable calculus, including vector analysis; selected topics in linear algebra, integral transforms and numerical approximation techniques. The analysis methods are introduced in the context of typical engineering applications. Prerequisites: ordinary differential equations, ME 302. Offered in the Fall. 3 credits Levels: Graduate, Undergraduate

• ME 536 - Fundamentals of Additive Manu

  Description: This course will explore additive manufacturing technologies from a research and industrial perspectives. Topics will include conventional subtractive machining, extrusion based additive manufacturing, metal additive manufacturing, inkjet additive printing, photo-polymerizing technologies, additive nano-manufacturing, and post-processing challenges. Course fee applies. Refer to the Schedule of Classes. Prerequisites: ME-441 (Heat Transfer), ME-362 (Material Science), or equivalent, or permission of the instructor. Normal grading option. Offered in the spring. 3 credits. Levels: Graduate, Undergraduate

• ME 540 - Fundamentals of Energy Transpo

  A foundation for energy transport is developed in terms of the physical modes of heat transfer and the formulation of computational models. Conduction, single-phase forced and natural convection, phase-change convection, and modern energy applications are addressed. Prerequisite: BSME or equivalent or consent of instructor. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 541 - Computational Fluid Dynamics

  Fundamentals of computational fluid dynamics as they relate to compressible and incompressible flows as well as interfacial phenomena. The course involves both MATLAB implementations and the use of commercial software. Prerequisites: fluid mechanics and differential equations, or consent of instructor. Offered in the Spring. 3 credits Levels: Graduate, Undergraduate

• ME 543 - HVAC

  Application of principles of thermodynamics, fluid mechanics and heat transfer to the analysis and design of heating, ventilating and air conditioning (HVAC) systems. Use materials of construction data in conjunction with internal gains to calculate heating and cooling loads. Use of psychrometrics data with ventilation requirements to select and integrate HVAC components into systems. Students will be required to generate a presentation on a relevant HVAC topic(s). (Technical elective.) Prerequisites: Thermodynamics (ME 331 or equivalent), Fluid Mechanics (ME 351 or equivalent) or consent of the instructor. Offered in the Fall and Spring semesters. 3 credits Levels: Graduate, Undergraduate

• ME 550 - Intro To Fluid Dynamics

  A foundation for the analysis of inviscid and viscous incompressible flow is developed. Foundation topics include Eulerian description, material derivative, relative motion (strain-rate tensor), vorticity, Newtonian fluid model. Equations of motion are formulated, leading to Euler and Navier-Stokes equations. Potential flow solutions are discussed. Viscous flow is studied using Stokes, lubrication and boundary layer approximations. Prerequisite: graduate standing or consent of instructor. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 552 - Complex Fluids

  Multiphase flows and complex fluids are treated from a mathematical and physical perspective. Fundamental and applied research topics are covered, including volume-averaged governing equations, particle-fluid and particle-particle interactions, complex fluids and colloidal dispersions, dynamics of droplets and bubbles, interfacial conditions, stability, atomization and sprays, and experimental methods. Prerequisites: ME 550 and MSE560 (or equivalents). Term varies. 3 credits. Levels: Graduate, Undergraduate

• ME 554 - Viscous Flow

  Various topics in viscous incompressible fluid flow. Navier-Stokes equations, classical solutions, asymptotic methods, boundary layers, lubrication approximation, capillarity, thin films with and without free surfaces, interfacial stability. Prerequisites: undergraduate fluid mechanics, ME 535, or consent of instructor. Offered in the Fall. 3 credits Levels: Graduate, Undergraduate

• ME 562 - Mechanical Behav of Engr Mtls

  A study of the response of materials to applied stresses, especially stress-induced failures. Relationship between structure and properties, with emphasis on microstructural changes and failure. Macroscopic and microscopic concepts of fracture mechanics, fatigue, creep and their interactions. Emphasis on design applications and failure analysis. Prerequisites: undergraduate courses in mechanics of materials and materials science, or consent of instructor. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 563 - Thin Film & Coating Technology

  Description: This course discusses processing, structure and properties of thin films and coatings, as well as emerging thin film materials and applications. The goal is to connect fundamental principles of thin film nucleation and growth to various thin film processing techniques including vapor phase deposition, plasma etching, epitaxy, oxidation and solution precursor methods. Structural evolution of thin films and its relation to the properties will be highlighted. Characterization of thin films and surfaces will also be presented. This course will be cross-listed with MSE 563. Prerequisites: ME 362 Science of Engineering Materials, or undergraduate courses in materials science and materials processes, or consent of instructor. Offered in Spring. 3 credits. Levels: Graduate, Undergraduate

• ME 570 - Transmis'n Electron Microscopy

  Basic functions of the various components of a transmission electron microscopy (TEM) instrument; the physics and theory of electron scattering and electron diffraction; the physics of the interactions between fast electrons and specimens; TEM imaging and contrast mechanisms; high resolution TEM; the analytical techniques of energy dispersive spectroscopy (EDS) and electron energy loss spectroscopy (EELS); operational modes and various types of TEM techniques to probe the structure, composition and chemistry of materials. Prerequisite: graduate status, senior standing or consent of instructor. 3 credits Levels: Graduate, Undergraduate

• ME 571 - Manufacturing Processes I

  The course focus is the analysis and applications of traditional and more recently developed manufacturing processes. Also, the basic properties and structure of materials will be reviewed. The range of manufacturing processes to be studied include traditional machining, forming, casting, joining, additive manufacturing and non-traditional machining. The processing of metals, ceramics, polymers, and composites are treated. Prerequisite: ME 362 or equivalent. Offered in the Fall. 3 credits Levels: Graduate, Undergraduate

• ME 575 - Nanoscale Energy Transport

  This course will explore how energy is transported and converted via molecules, electrons, phonons, and photons.  The science of these carriers has critical energy applications, including thermoelectrics, thermal batteries, light emitting diodes, and photovoltaics.  Covered topics include kinetic theory, quantization of energy levels, statistical distributions, Boltzmann transport relations, and nanoscale size effects.  Applications, recent advancement in the field, and experimental techniques will be discussed. ME-441 (Heat Transfer), ME-331 (Thermodynamics), or equivalent. Offered in the spring. 3 credits Levels: Graduate, Undergraduate

• ME 577 - Mechanics in Energy Appl

  In the last a few years, rapid advances have been witnessed in modeling, simulations and characterizations of mechanical behavior of advanced energy materials and systems, and tremendous opportunities arise for further understanding of mechanics in energy materials for superior performance. This project-based graduate elective course will address the importance of mechanics in all aspects of energy conversion, energy storage and energy harvesting. This course is a Community-Engaged Learning course. Prerequisite: None. Offered in the Spring. 3 credits Levels: Graduate, Undergraduate

• ME 580A - Special Topics

  Special Topics. Topics vary by semester. 3 credits Levels: Graduate, Undergraduate

• ME 581 - Computer Aided Engr

  Fundamentals of computer graphics, interactive graphics, introduction to CAD, modeling, analysis and optimization. Introduction of finite element method and use of standard packages for design problems. Dynamic simulation. Cannot be taken in addition to ME 381 or equivalent. Term varies. 3 credits Levels: Graduate, Undergraduate

• ME 582 - Adv Computer-Aided Engineering

  Advanced CAE will cover GD&T, CAM, theory and principles of CAD modeling, and configuration management. Projects and laboratory assignments will include solid modeling and manufacturing file output (CNC). There will be a weekly laboratory. A final project will be a team, concurrent, distributed design project. Prerequisites: ME 381 or ME 581 or equivalent. Offered in the Spring. 3 credits Levels: Graduate, Undergraduate

• ME 584 - Design of Mechanical Elements

  This is a second course in mechanical component and systems design. Design and analysis of fasteners, welds, bearings, gears, and power transmission elements will be covered. Software packages will be used: Creo (solid modeling and computer-aided design), Creo Structure or ANSYS (finite element analysis). Prerequisites: ME 581 and ME 392. Levels: Graduate, Undergraduate

• ME 586 - Reliability Based Mech Desig

  The course provides an introduction to mechanical engineering reliability analysis as well as reliable product design. The goal is to make the student familiar with both the statistical tools as well as the possible failure modes that enable one to model time to failure of products and to use such models during the design phase to ensure reliable product performance. Prerequisite: ME 392 or equivalent course. Offered in the Fall. 3 credits Levels: Graduate, Undergraduate

• ME 590 - Introduction to Nanotechnology

  Basic concepts in nano science and nanotechnology will be introduced. The course will cover nano structured materials and devices, nano fabrication techniques, nano scale materials and devices characterization, applications of nanotechnology in electronics and biomedical applications Levels: Graduate, Undergraduate

• ME 592 - Tech. Dev. Curriculum I

  This course is a 32-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to a broad review of engineering fundamentals, with emphasis on interdisciplinary topics related to Electronic Systems products and processes, technologies, applications, and problem solving techniques. Coursework includes a team-project and presentation to engineering management. Offered in Spring, 6 credits. Levels: Graduate, Undergraduate

• ME 593 - Tech. Dev. Curriculum II

  This course is a 16-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to challenging students with problems very similar to those frequently facing Electronic Systems engineers. Coursework includes a technical project requiring the application of systems, software, and hardware engineering skills. 3 credits Levels: Graduate, Undergraduate

• ME 594 - Industrial Internship

  INDUSTRIAL INTERNSHIP Levels: Graduate, Undergraduate

• ME 597 - Independent Study

  Independent study supervised by a mechanical engineering faculty member. Student must obtain consent of instructor, who then determines description of program, number of credits (variable), frequency of meeting and location. Appropriate paperwork must be submitted to the department office in order to complete registration. 3 credits Levels: Graduate, Undergraduate

• ME 598 - ME Projects

  Literature review, mechanical engineering development or other projects as defined by the project committee. Formal bound report for department library. Levels: Graduate, Undergraduate

• ME 599 - Thesis

  Training in the methods of research. Varied computer modeling, hardware development and experimentation as determined by the MSME thesis committee. Oral examination required. Bound thesis goes in University Libraries. 3 credits Levels: Graduate, Undergraduate

• ME 610 - Fracture Mechanics

  Application of the methods of analytical solid mechanics to calculate the driving forces of crack propagation in materials and those of experimental solid mechanics to characterize the resistance to fracture. Concepts of linear elastic and elastic-plastic fracture mechanics. Prerequisite: ME535. Offered in the Fall. 3 credits Levels: Graduate

• ME 618 - Finite Elem Analysis II

  Second-level course in the understanding of finite element method. Covers variational formulations, non-linear static and dynamic analysis, transient problems and other specialized features of applying the finite element method to solve engineering problems. The FE code ANSYS and/or ABAQUS is used to solve the projects assigned in the course. Prerequisite: ME 517 or equivalent or consent of instructor. Offered in the Spring. 3 credits Levels: Graduate

• ME 622 - Advanced Acoustics

  Physics of sound propagation. Acoustics wave motion. Reflection of sound waves from boundaries. Sound transmission through walls. Sound generation and radiation from vibrating structures. Sound propagation in ducts. Coupled acoustical systems: interaction of sound with structures. Scattering of sound. Acoustics of small-scale systems; viscous effects. Prerequisite: ME522 or consent of instructor. Offered in the Fall. 3 credits Levels: Graduate

• ME 627 - Random Vibrations

  Methods for analyzing the response of vibrating systems with random inputs. Correlation and spectral methods for discrete and continuous vibrating structures. Analysis of non-linear systems using equivalent linearization, Gaussian closure and the Fokker-Plank equation. Applications include flow-induced vibrations, response of distributed systems to spatially random fields, reliability analysis and high-cycle fatigue life predictions. Prerequisites: graduate course in mechanical vibration and a course in ordinary differential equations, or consent of instructor. Offered in the Spring. 3 credits Levels: Graduate

• ME 635 - Analytical Methods II

  Complex variables, complex integration, integral transforms, generalized functions, boundary integral methods, functional analysis. Required course for ME PhD students. Prerequisite: ME 535. Offered in the Spring. 3 credits Levels: Graduate

• ME 641 - Mesoscale Modeling Comp Fluids

  Complex fluids, i.e., polymeric liquids and melts, colloidal suspensions, and micelle solutions, are of great importance in nanotechnology, biomedical engineering, food science, and petroleum industry. The first half of the course will introduce various complex fluids and their physical behavior. The second half of the course will focus on surveying the state-of- the-art computer simulation methods (such as molecular dynamics, dissipative particle dynamics, lattice Boltzmann methods, and others) and their applications to model complex fluids. Prerequisites: ME 535, ME 550, and ME 541 or consent of instructor. 3 credits Levels: Graduate, Undergraduate

• ME 697 - Adv Independent Study

  Independent study supervised by a mechanical engineering faculty member. Student must obtain consent of instructor, who then determines description of program, number of credits (variable), frequency of meeting and location. Appropriate paperwork must be submitted to the department office in order to complete registration. For PhD students. 3 credits Levels: Graduate

• ME 698 - Pre Dissertation Research

  Exploratory research oriented toward PhD dissertation. Levels: Graduate

• ME 699 - Dissertation

  Research for and preparation of PhD dissertation. Levels: Graduate

• ME 700 - Continuous Registration

  Required to maintain matriculation through any spring or fall semester when no other courses are taken. If the minimal one-credit registration is not maintained, student must reapply for admission. Levels: Graduate

• ME 701 - Pract/Teaching &Research Asst

  Required for all funded graduate assistants. Research or teaching supervised by faculty advisor. Levels: Graduate, Undergraduate

• ME 707 - Research Skills

  Development of research skills required within graduate programs. May not be applied toward course credits for any graduate degree. Prerequisite: approval of graduate program director. Various credits Levels: Graduate

• SSIE 500 - Computational Tools

  This course will introduce students to several programming languages and basic programming techniques, with the focus on developing practical code-writing skills for scientific/engineering problem solving. Topics to be covered include: manipulation with numbers, strings, variables, lists, and arrays; creating functions; flow control; data manipulation; imperative, functional, and object-oriented programming; visualization; and presentation. LaTeX will also be introduced for typesetting professional technical documents. This course will also discuss information theory as a sample application area of computational tools. Topics include: information and entropy, mutual information, information coding and compression, Markov information source model, statistical complexity, and computational complexity. Students will write codes in their preferred language to calculate various information theoretic measurements of real-world data. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 501 - Intro to Systems Science

  Includes a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions and historical development of the field; an overview of fundamental systems concepts, principles and laws; and a survey of application areas of systems science and its implications for other fields of study. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 440. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 505 - Applied Probability & Statist.

  Basic concepts in probability and statistics required in the modeling of random processes and uncertainty. Bayes' formula, Bayesian statistics, independent events; random variables and their descriptive statistics; distribution functions; Bernoulli, Binomial, Hypergeometric, Poisson, normal, exponential, gamma, Weibull and multinomial distributions; Chebyshev's theorem; central limit theorem; joint distributions; sampling distributions; point estimation; confidence intervals; student-t, x squared and F distributions; hypothesis testing; contingency tables, goodness of fit, non-parametric statistics, regression and correlation. Prerequisite: one year of calculus. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 506 - Systems Problem Solving

  A comprehensive conceptual framework for systems problem solving is introduced. Discusses methods applicable to broad classes of problems. Prerequisite: SSIE 505 or equivalent or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 510 - Enterprise Systems Engineering

  Global competition is serving as a catalyst for continuous process improvement and the methodical enhancement of system-wide efficiencies. This is true in disciplines ranging from the medical arena and service related systems to manufacturing. The underlying science that contributes to the systematic analysis of complex enterprise-wide systems is the focus of this course. Concepts that can be used in a synergistic manner to enhance an enterprise's efficiency and profitability will be addressed. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 511 - Production & Schedule Control

  This course covers general concepts of production planning and control but goes more into detail of the concepts and mathematics of scheduling applications and theory. Upon completion of the course, the student will be able to apply mathematical techniques in developing solutions to “production scheduling” problems. The “production scheduling” terminology may seem like these techniques are applicable only to manufacturing, however, they have broad applications across many industries and sectors including, but not limited, to healthcare, service industries, food processing, etc. Prerequisites: SSIE 510 or permission of the instructor. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 512 - Integrated Mfg Systems

  Integration of equipment, people and information required in total manufacturing systems. Product/process design, planning and support, procurement support, software/hardware. Prerequisite: graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 514 - Linear Programming for Eng

  Linear programming (LP, also called linear optimization) is an optimization problem seeking to minimize or maximize a linear function subject to a set of linear inequality or equality constraints. There are a wide variety of applications for this approach, including transportation schedules, corporate planning, inventory management, and many others. Students will learn how to build fundamental mathematical models to find the best decisions in real-life decision-making problems in this course. In particular, the students will study how to build abstractions of mathematical models and develop general methods to solve them, rather than a specific algorithm for each problem. From the algorithmic perspective, this course will cover the simplex method, which stems from essential linear algebraic operation while emphasizing mathematical foundations and computational consideration for practical implementations of the students. In addition, the course will discuss theoretical aspects of linear programming such as polyhedral theory, including projections, duality theory, the guarantee of optimality, convexity, degeneracy, and algorithmic convergence. Also, this course will cover sensitivity analysis, network flows, and network simplex as extendable areas and will encourage learning about actual implementation through the use of computer software. SSIE 553-Operations Research or an undergraduate degree in Industrial Engineering (or related discipline) with a background knowledge of operations research and linear algebra (i.e., ISE 320/420-Optimization & Operations Research I/II) will be prerequisites in this course. Levels: Graduate

• SSIE 515 - Operations Mgmt Supply Chains

  This course deals with management of Supply chains, in particular, with the operational aspects. A broad overview of supply chains of a company is introduced, together with performance measures and needed critical success factors. The course concentrates on supplies, inventories, manufacturing, and logistics of distribution. Managerial aspects as well as mathematical modeling for better planning and control will be covered. Enabling the supply chains by enterprise resource planning modules and e-commerce will also be discussed. Prerequisite: SSIE 505 or equivalent. Crosslisted with ISE 415. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 516X - Fund. of Energy Sys. Analytics

  This course provides fundamental knowledge of the application of Industrial and Systems Engineering in the Energy field. It focuses on the modeling, design, simulation, and optimization of alternative and conventional energy technologies, the societal and environmental impact of technology developments, and the economic benefits of those developments. Recent literature that covers a wide range of ISE topics related to energy systems such as transportation energy, building energy, manufacturing energy, solar energy, wind energy, bioenergy, greenhouse gas emission, and smart grid operations will be discussed in this course. Students will gain breadth and depth of knowledge, and an interdisciplinary understanding of the contemporary need for energy solutions. 3 Credits. Prerequisites: Graduate Standing. Offered fall or spring Levels: Graduate, Undergraduate

• SSIE 517 - Fuzzy Set, Uncertainty & Info

  Overview of basic concepts of fuzzy set theory. Difference between probability theory and fuzzy set theory. Foundations of information theory. Uniqueness of information measures, maximum and minimum principles of information, fundamental properties of information. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 519 - Applied Soft Computing

  Covers relatively new approaches to machine intelligence known collectively as soft computing. Introduces various types of fuzzy inference systems, neural networks and genetic algorithms, along with several synergistic approaches for combining them as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction, design, control, databases and data mining. The undergraduate students are not required to do projects on the same level as the graduate students, and are not required to place the degree of emphasis on hybrids. Prerequisites: basic knowledge of calculus and discrete mathematics, and competence in at least one programming language, or consent of the instructor. Crosslisted with ISE 419. Offered in the fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 520 - Modeling And Simulation

  Stochastic processes, review of probability and statistics, covariance, input data selection, random number generators, non-parametric tests for randomness, generation of random variates, output data analysis, terminating and non-terminating simulations, model validation, comparison of alternatives, variance reduction techniques, sensitivity analysis, experimental design and predictive models. Prerequisite: SSIE 505 or equivalent. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 522 - Advanced Decision Modeling

  Course provides a broad foundation in decision models and techniques used in industry and research for technical and managerial problems. Topics include decision theory, risk and uncertainty, value of information, preference measurements, prioritization of alternatives, multiple objectives and hierarchical decisions. Prerequisite: SSIE 505 or equivalent. Crosslisted with ISE 422. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 523 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisites: Graduate standing and basic knowledge of calculus, linear algebra and probability theory, or permission of instructor. Crosslisted with ISE 423. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 524A - Grad Sem in Complex Sys Sci

  This course is a weekly seminar series that serves students as a venue of active discussion on current research topics and interdisciplinary networking. It is formed using the Collective Dynamics of Complex Systems seminar series as a structural basis. Faculty, graduate students and external speakers discuss complex systems related research topics. Students are required to present either literature review or their own research at least once a semester and will be graded based on class attendance, presentation and participating in discussions as well as reflection essays and final papers. Prerequisites: Graduate standing or permission of instructor. Crosslisted with SSIE 524B. Offered fall and spring semesters. 1 credit. Levels: Graduate, Undergraduate

• SSIE 524B - Grad Sem in Complex Sys Sci

  This course is a weekly seminar series that serves students as a venue of active discussion on current research topics and interdisciplinary networking. It is formed using the Collective Dynamics of Complex Systems seminar series as a structural basis. Faculty, graduate students and external speakers discuss complex systems related research topics. Students are required to present either literature review or their own research at least once a semester and will be graded based on class attendance, presentation and participating in discussions as well as reflection essays and final papers. Prerequisites: Graduate standing or permission of instructor. Crosslisted with SSIE 524A. Offered fall and spring semesters. 1 credit. Levels: Graduate, Undergraduate

• SSIE 525 - Princip Of Systems Engineering

  Basic principles of systems engineering applied in transforming client requirements into an operational system. Topics cover the full system life cycle: planning, integrated product/process development, system architecture and design, modeling, requirements analysis, development, integration, test and evaluation. Specialized concepts involved in engineering complex systems are reinforced through case studies and student exercises. Prerequisite: graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 526X - Sys Dynamics & Sys Thinking

  In this course, the basic concepts of systems thinking and system dynamics modeling will be introduced and students will understand, evaluate, and improve upon problems that manifest themselves in complex systems, such as diffusion of new technologies; global power consumption; fluctuating sales, production and earnings; environmental challenges such as urban development and poverty; healthcare and public health challenges such as disease spread. System dynamics is a simulation modeling process to quantify problems that unfold through time. In this iterative process, a problem that is pervasive throughout a system is defined, the structure of the system that generates the problem will be studied, and policies or practices that can change the system structure and behavior and therefore alleviate/solve the problem will be studied. Prerequisites: Graduate standing. Crosslisted with ISE 426X.Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 527 - Systems Design & Human Interac

  The design cycle, requirements specification, prototyping, partition and integration, production and testing. User's perception and use of system, observation, pattern recognition, and measurement using vision, hearing and speech. Presentation of information, analog and digital displays, organization of displays and data, hierarchical information and control levels. Limitations of speed and accuracy of perception and communication, sources of error, distortion and misinterpretation. Fault tolerance, error detection and correction. Prerequisite: Graduate standing or permission of instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 528 - Systems Engineering Project

  Semester-long process-orientated design experience that is a simulation of a real-world project in designing and running a manufacturing floor. Project evolves year to year as students work to include more aspects in the experience. Class is divided into teams, and each team develops its own design for the manufacturing process based on various information provided from the client and machine vendors. Teams use various tools for simulation and analysis in making and modeling their design decisions. Design project does not concentrate so much on the details of the machines and products as on the experience of designing large-scale process projects. Students must draw on systems engineering skills, apply them to a wide range of problems and experience the consequences of their decisions. Each team makes a formal presentation of its design to the faculty and to a board of industrial experts. Instructor(s) determine the final grade with input from the industrial experts. While the students are involved in team-oriented projects, grades are based on an individual's performance. Students who have had an equivalent course at the undergraduate level are not eligible to take this course. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 529 - Computability & Logic

  Studies some of the fundamental theoretical results about logic and about the capacities and limitations of computing devices. Notion of computability is introduced by means of Turing machines, whose halting problem is shown to be unsolvable. Two other notions of computability, abacus-computable functions and recursive functions, are also introduced and their interrelations discussed. Undecidability of first-order logic is also covered. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 530 - Healthcare Policy Analysis

  This course provides an overview of issues and policies related to health care systems, with emphasis on health care systems within the United States. The course will apply various engineering tools and approaches for policy analysis and program planning in this area. Some potential future policy changes will also be discussed, as well as challenges with implementation and change management techniques. Crosslisted with ISE 430. Prerequisites: Graduate standing or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 533 - Human Factors Engin And Design

  Introduction to Human Factors and systems: design for human use; Human Factors research methodologies; information about human performance, abilities, and limitations will be surveyed and applied: physical work and manual handling, applied anthropometry and workplace design, human control of systems, control and data entry devices, and environmental conditions; Human Factors applications including human error, accidents, safety, Human Factors and the automobile, and Human Factors in Systems Design. Prerequisite: basic course in probability and statistics or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 534 - Fundamentals of Health Systems

  One of the growing systems in our society is that of the healthcare delivery system. The purpose of this course is to introduce the concepts behind the healthcare delivery systems and to focus upon the systems improvement or continuous improvement techniques available for complex systems. Topics would include improvement to, and problems with: organizational structure, managing change, the financial structure, the responsibility structure, quality data and implications of quality measures, use of clinical decisions support systems and the care givers role in the system. There will also be a focus upon suppliers to the healthcare delivery system and the unique requirements placed upon their products and processes. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 434. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 535 - Analytical Methods

  Survey and discussion of some of the more important and useful analytical methods for analyzing a wide variety of engineering and scientific problems. Topics include solution of differential equations, including methods for linear equations, power series, eigen-function expansions and separation of variables; topics in multivariable calculus, including vector analysis; and selected topics in linear algebra, integral transforms and functions of a complex variable. Each of the methods is introduced in the context of real, applied problems and then illustrated with typical "real-world" applications. Prerequisite: two semesters of calculus. 3 credits. Levels: Graduate, Undergraduate

• SSIE 535X - Agile Eng & Design Thinking

  In this course, the fundamental and advanced concepts of agile engineering and design thinking will be introduced and students will understand, evaluate, and improve upon complex problems that exist in systems, products, processes and services; and they will learn to solve these problems by way of innovative and new system designs. System design, development and delivery methods are evolving rapidly in business today; with faster time to market and delivering a superior end-user experience as primary goals for any system, product or service. In this iterative process, teams will collaborate on solving complex problems to existing and non-existing systems. The course will use Design Thinking practices to discover and define end-user needs; wherein agile system development, engineering and project management practices will be used to deliver on those needs. System dynamics such as the introduction of new technologies; social and environmental issues; diverse and volatile markets, a fluctuating business environment; will be consider as they influence the complex problems and the underlying systems being studied. A comprehensive and concurrent engineering approach will be used to introduce students to leading practice in systems design, development and delivery that result in signature end-user experiences and superior business outcomes. Complex system problems will be analyzed, and practices that can fundamentally change the system structure and behavior will be applied by students. Prerequisites: Graduate standing. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 536 - Num Modelling Of Phy Phen

  Efficient and effective method for solving differential equations numerically. Single and multi-step methods for initial value problems for ordinary differential equations; matrix and shooting methods for two-point boundary value problems; and shooting methods for eigen-value-eigen-function problems (for resonant frequency and mode shape calculations). Finite difference methods for partial differential equations, including the heat, wave and potential equations. Explicit and implicit methods, method of characteristics, Lax-Wendroff schemes and various methods to accelerate the convergence of the approximate solutions. Considerable emphasis on the interpretation of the numerical solutions in terms of systems they model and the qualitative (as well as quantitative) insight they provide. Prerequisite: Graduate standing or permission of instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 537 - Ind & Sys Eng in Healthcare

  The application of industrial and systems engineering principles to continuous process improvement in the healthcare domain will be studied. Concepts that will be addressed will include, but not be limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. Prerequisite: graduate standing in the department or permission of the instructor. Crosslisted with ISE 437. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 538 - Healthcare Financial Eng

  This course is intended to provide an overview of healthcare finance and the current financial environment for the healthcare industry and to learn how to apply engineering economics to healthcare financial management. It will provide information on financial and managerial accounting and how the use of Systems Science and Industrial and Systems Engineering principles can be applied to financial management concepts to allow for health-related organizations to make sound business decisions. Crosslisted with ISE 438. Prerequisites: SSIE 534 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 539 - Human Factors Eng Healthcare

  This course introduces and emphasizes the role that human factors engineering/ergonomics plays in healthcare systems, with a focus on its applications to help improve quality, safety, efficiency, and effectiveness of patient care. Focused topics include human factors in workflow models; work system design for patient safety; human error analysis/taxonomies to reduce medical errors; task analysis and data collection methods in healthcare environments; clinical staff workload and patient safety; physical ergonomics in healthcare and human performance modeling; and diffusion and adoption of technology in healthcare, with emphasis on the usability and design of medical devices and information systems. Prerequisite: Basic human factors knowledge or permission of the instructor. Crosslisted with ISE 439. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 540 - Relational Data Analysis

  Introduction to fundamentals of analysis of relational data, with emphasis on formal concept analysis. Included are required preliminaries on sets, relations, and related notions. Emphasis is on discovering patterns and dependencies in relational data. Topics include: relations and relational systems; relational data and its representation; concept lattices and formal concept analysis; algorithms for formal concept analysis; uncertainty in relational data; relational factor analysis; attribute dependencies, attribute implications and non-redundant bases; algorithms for extracting attribute dependencies from data; redundancy and redundancy elimination in relational data. Prerequisite: Graduate standing in department or permission of the instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 541 - Analysis of Capital Investment

  Traditional methods are reviewed against changes in manufacturing. Estimating, product cost vs. utilization, risk, sensitivity analysis and decision modeling. Prerequisite: basic course in engineering economics or its equivalent, or approval of department chair. Offered in the Spring semester. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 544 - Heuristic Optimization

  Covers methods of heuristic optimization in the literature. Topics include genetic algorithms, evolution strategies, tabu search and simulated annealing. Major emphasis is on NP complete combinatorial problems found in engineering. Issues such as solution encodings, stochastic convergence, selection methods, local and global search methods are discussed. Prerequisites: SSIE 505 or equivalent and knowledge of at least one programming language, such as C++ or Python. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 545 - Heuristic Problem Solving

  Concept of problem as cognitive dissonance. Methods of problem recognition, definition, solution, implementation, refinement. Intuitive approaches: analysis and syntheses; Meta-problem solving ? development of strategies appropriate to problem type; inventive thinking: deferred judgment, metaphorical and visual thinking, finding order in chaos. Prerequisites: curiosity of creative process and genuine desire to innovate. 3 credits. Levels: Graduate, Undergraduate

• SSIE 546 - The Psychology Of Problem Solv

  Current topics in cognitive science, including perception, learning, pattern recognition, creativity, artificial intelligence, neural networks, brain theory, evolution of function. 3 credits. Levels: Graduate, Undergraduate

• SSIE 547 - Modern Complexity Theory

  Complexity theory is a new name for studies based largely on computational investigations of nonlinear dynamics and artificial evolutionary systems. The conceptual foundations go back to earlier thinking about self-organization systems and the origin of life. The focus of the first few lectures will be on the complexity limits of the currently popular formal and computational models, including Prigogine's dissipative structures, Kauffman's NK Boolean networks, Bak's self-organized criticality, and the broad area of discrete and continuous nonlinear dynamics, including chaos and cellular automata. The following lectures will elaborate von Neumann's earlier ideas of open-ended evolution of complexity that requires semiotic control of dynamics, and will illustrate how these ideas relate to current controversies in evolution theory over the relative importance of self-organization and natural selection. Prerequisite: graduate standing in science or philosophy or permission of instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 548 - Healthcare Data Sci&Analytics

  Data analytics plays an increasingly greater role in healthcare organizations. However, many healthcare professionals lack training in this vital area. This course will cover advanced data analytics with applications in healthcare systems engineering. In specific, the course will cover the concepts and methods of the data analytics in three particular areas: (i) descriptive analytics, which includes data description and aggregation; (ii) predictive analytics, which uses machine learning and statistical modeling for prediction and forecasting; and (iii) prescriptive analytics, which makes use of the results of predictive analysis to optimize the decision making process. All introduced methods will be demonstrated along with a variety of data modalities collected from healthcare applications such as pervasive health, disease diagnosis, fraud detection, and pharmaceutical discovery along with medical decision support systems. Through the provided coursework, students will be able to learn a variety of methodologies on data processing and modeling and make the best use of healthcare data to facilitate medical decision making. Prerequisites: ISE 437/SSIE 537 or consent of the instructor. Crosslisted with ISE 448. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 550 - Intro To Optimization

  Basic course in engineering optimization with emphasis on algorithms and applications. Topics: single-variable optimization, multivariable optimization, linear programming (formulation, simplex method, interior point methods, sensitivity analysis, applications), constrained optimization (LaGrange multipliers, Kuhn-Tucker conditions, direct search methods) and, time permitting, quadratic programming and linearization methods. Students gain enough proficiency to build optimization models of practical problems and solve them using tools learned in class. Use of available optimization computer codes. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 553 - Operations Research

  Operations research (OR) is devoted to the determination of the best course of action of a decision problem, given resource restrictions. Course provides the engineer with a firm grounding in the use of OR (mathematical) techniques devoted to the modeling and analysis of decision problems. Techniques include the following: decision modeling; linear, integer and dynamic programming; emerging optimization techniques (e.g., genetic algorithms, simulated annealing, etc.); game theory; and queueing theory. Problem areas include the following: transportation models; project/production scheduling; inventory models; assignment problems. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 556 - Expert Systems In Elect. Pkg.

  Role of artificial intelligence-based expert systems in manufacturing as related to electronics packaging domain. Expert systems design and development as applied to electronics packaging. Knowledge acquisition and representation techniques solution spaces and search techniques, inference and deduction mechanism, and design and development of prototype systems. Prerequisite: graduate standing or approval of department chair. Crosslisted with ISE 456. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 561 - Quality Assurance For Engineer

  Statistical quality control, designing for quality, process control, vendor and customer quality issues, quality costs and production. Prerequisites: SSIE 505 or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 562 - Reliability

  Reliability networks, failure mode and effect analysis, apportionment, fault trees and human reliability. Prerequisites: SSIE 561 and probability and statistics, or permission of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 564X - Engineering Project Management

  Effective Engineering Project Management is critical to business and organizational success. This course will teach students both the technical and the sociocultural dimensions of Engineering Project Management. Students will be thoroughly trained in the following topics: Project Selection, Organization Structure and Culture, Project Definition, Project Time and Cost Estimation, Project Networks, Project Risk Management, Resource Scheduling, Project Crashing, Leadership, Team Management, Outsourcing, Progress Monitoring and Project Evaluation, and Project Closure. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the Critical Path Method with case studies of contemporary engineering projects. Students will learn how to better control financial, physical, and human resources, improve stakeholder relations, shorten development times, lower costs, and increase profit. Prerequisite: Graduate standing or permission of instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 565 - Engineering Project Management

  Effective Engineering Project Management is critical to business and organizational success. This course will teach students both the technical and the sociocultural dimensions of Engineering Project Management. Students will be thoroughly trained in the following topics: Project Selection, Organization Structure and Culture, Project Definition, Project Time and Cost Estimation, Project Networks, Project Risk Management, Resource Scheduling, Project Crashing, Leadership, Team Management, Outsourcing, Progress Monitoring and Project Evaluation, and Project Closure. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the Critical Path Method with case studies of contemporary engineering projects. Students will learn how to better control financial, physical, and human resources, improve stakeholder relations, shorten development times, lower costs, and increase profit. Offered Fall, Spring, or Summer as needed. 3 credits. Levels: Graduate, Undergraduate

• SSIE 566 - Designing With Experiments

  Basics of applying statistical design, and the design function, statistical experimental design, control of experimental setting, Taguchi methods and analysis of results. Prerequisites: SSIE 505 or equivalents, or permission of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 568X - Flexible & Hybrid Electronics

  Flexible and hybrid electronics integrate ultra-thin silicon semiconductor technology with non-traditional thin skin-like substrates establishing wearable devices for both human and machine. This course will cover design, materials, fabrication methods and evaluation of printed and thinned silicon-based electronics for applications such as smart energy, human health, and industrial sensing. Emphasis will be placed on fundamental understanding of the hard-soft materials interface, advances in materials printing, and approaches to make these systems robust for use. The course will promote understanding of the engineering requirements that lead to systems design for both manufacturing and reliability. Prerequisites: undergraduate degree in the sciences or engineering. 3 credits. Levels: Graduate, Undergraduate

• SSIE 569 - Materials for Manufacturing

  This course provides an overview of the materials used in manufacturing that are of significant economic, technical and industrial relevance. A special emphasis will be given to polymeric materials currently used in electronics manufacturing as well as those materials emerging for use in additive manufacturing and 3D printing. Selected materials will be classified by composition, properties, manufacturing methods, applications, economics and related supply chains. Prerequisites: undergraduate engineering or science background that includes the equivalent of freshman chemistry and physics or permission of the instructor. Crosslisted with ISE 469. 3 credits. Levels: Graduate, Undergraduate

• SSIE 575 - Systems Design

  Systems approach to design process. Complex, poorly defined interdisciplinary problems. Design activities depend on class composition-professionals from health field, legal profession, municipal administration particularly welcome, since design project could then be chosen from such applications. Design viewed as problem solving and opportunity development activity. Problem solving involves problem recognition-sensitivity to design misfits; definition developing design specifications and goals; solution for actual design process from concept development to "construction" of object of design; implementation of placing object into action; review and refinement analysis of design in its environment for purposes of discovering mistakes and refining object. Opportunity development involves process and invention. Topics include user identification and satisfaction, adaptive design, design by attribute, design consciousness, models of design process, planning and scheduling, idea sketching and visual thinking, model construction. Prerequisite: Graduate standing or permission of the instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 578 - Processes for Electr Mfg.

  The purpose is for the students to gain a broad knowledge and understanding of the basics of printed circuit board manufacturing and assembly. The course offers an introduction to surface mount and insertion mount components, materials and processes as well as to PCB design and manufacturing. Lectures will introduce to assembly process flows and component types, PCB construction and defects, solder paste printing and equipment, placement processes and equipment, reflow and ovens, flip chip assembly and underfilling, defects and mitigation, reliability optimization and testing. Efforts will be made to include visits to local industrial assembly facilities as well as equipment on campus. The overall goal is to provide the students with a basis for communicating and working with subject matter experts. Prerequisite: Graduate standing or consent of department chair. Crosslisted with ISE 473. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 579 - Processes For Area Array Devic

  The Construction of area array devices (including BGA, CSP, flip chip, and wafer level) and their packaging. Assembly of these packages onto substrates at the first and second level of packaging. Process, yield, reliability, and rework issues. Cost impact of area array packaging. Packaging for optoelectronics and MEMS and related assembly issues (will include an overview of Optoelectronics and MEMS). Board level issues that pertain to the assembly of advanced packages including routing and microvias. Prerequisite: SSIE 578 or permission of instructor or department chair. 3 credits. Levels: Graduate, Undergraduate

• SSIE 583X - Evolutionary Sys & Bio-Inspire

  In this course, the fundamental and advanced concepts of bio-inspired computing are introduced with a focus on understanding how evolutionary systems are different from other complex systems in nature and society. Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, evolvable, resilient, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course is strongly grounded on the foundations of complex systems science and theoretical biology. It aims to provide an understanding of the distributed architectures of evolving complex systems, and how those can be used to produce artificial intelligence and computational tools, as well as engineering designs, with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, physics, complexity, computer science, informatics, cognitive science, robotics, optimization, control, and cybernetics. Bio-inspired computing at the core of modern artificial intelligence, and thus applied throughout science and engineering, but understanding the mechanisms behind resilience, evolvability, and adaptation are essential to tackle the most vexing biomedical and social complexity problems we currently face. Therefore, the course should interest students in various programs. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 585 - Additive Mfg Processes & Sys

  This course aims to inculcate a comprehensive understanding of additive manufacturing (AM), and will address the following consequential aspects of AM: (1) Materials and Process Mechanics; (2) Machines, Machine Design, and Mechanisms; (3) Quality control, Sensing, and Process Monitoring; and (4) Design for AM and novel trans-disciplinary applications. Prerequisites: Graduate standing or permission of the instructor. 3 credits. Levels: Graduate, Undergraduate

• SSIE 592 - Tech, Dev, Curriculum I

  This course is a 32-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to a broad review of engineering fundamentals, with emphasis on interdisciplinary topics related to Electronic Systems products and processes, technologies, applications, and problem solving techniques. Coursework includes a team-project and presentation to engineering management. Term offered varies. Variable credit. Levels: Graduate, Undergraduate

• SSIE 593 - Tech Dev Curriculum

  This course is a 16-week in-house course taught at BAE Systems for students enrolled in the BAE ELDP program only and devoted to challenging students with problems very similar to those frequently facing Electronic Systems engineers. Coursework includes a technical project requiring the application of systems, software, and hardware engineering skills. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 594 - Industrial Internship

  Industrial engineering, systems science and other professional experience. Daily log book, memo progress reports and a formal final report required. Prerequisite: consent of department chair. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 597 - Independent Study

  Supervised by department faculty member. Student must obtain consent of instructor, who then determines description of program, number of credits, frequency of meeting and location. Appropriate paperwork must be submitted to complete registration. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 598 - MS Termination Project

  In depth study and analysis of a selected topic in health or other service system, or manufacturing system asapproved by the project advisor. Course requires a formal report, defense, and presentation. Term offered varies. 1-6 credits Levels: Graduate, Undergraduate

• SSIE 599 - Thesis

  Training in the methods of research. Oral examination required. Minimum of six credits total. Bound thesis goes in University Libraries and department library. Term offered varies. Credits vary. Levels: Graduate, Undergraduate

• SSIE 605 - Appl'd Multivar. Data Analysis

  Course introduces different multivariate data analysis and modeling tools, which can be used for simultaneously analyzing data with multiple dependent variables. It is designed to emphasize applied methodologies and applications in multivariate data analysis, especially in engineering fields. Topics to be covered include: multivariate regression, logistic regression, multivariate analysis of variance (MANOVA), principal components analysis, cluster analysis, canonical correlation, factor analysis, and discriminant analysis. The effective use of advanced data analysis software, such as SAS, for solving real-world engineering problems will also be addressed. Prerequisite: SSIE 505 or its equivalent. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 606 - Sys Problem Solving Workshop

  Continuation of SSIE 506; project oriented. Depending on background and interests, student may choose projects from any of the following categories: theoretical or practical work in developing some aspect of examples demonstrating applications of methods learned in SSIE 506 in special discipline; survey of current literature devoted to certain aspects of systems problem solving. Specific projects selected on basis both of the interests of individual students and composition of group. Prerequisite: SSIE 506. 2 credits. Levels: Graduate

• SSIE 612 - Adv. Topics In Integrated Mfg.

  The continual need to improve quality and productivity and remain competitive in a global market requires the comprehensive integration of people, equipment, computers and information within a manufacturing systems engineering framework. Course studies manufacturing integration issues with a special focus on integrating elements such as process planning, group technology, concurrent engineering, product quality, cost analysis, flexible manufacturing, inventory control, information flow and management, and global computer-integrated manufacturing (CIM) concept. Prerequisite: SSIE 512 or equivalent or permission of instructor. Term offered varies. 3 credits. Levels: Graduate

• SSIE 613 - Adv Stats Method for Dat Analy

  This course will cover advanced data analysis methods with emphasis of their applications in industrial and systems engineering. There are three major components to the course. The first component is a brief review on basic univariate and multivariate analysis. The second component is an introduction to advanced statistical analysis methodologies such as time series modeling and forecasting, confirmatory factor analysis, multi-group discriminant analysis, multivariate analysis of variance, and structural equation modeling. The third component of the course focuses on real applications of these methods in research and data-driven decision making. Prerequisite: SSIE 505 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 615 - Advanced Supply Chains

  Course deals with modeling of supply chains, concentrating on production and operations. Quantitative models will be developed and analyzed to study the benefits of information sharing, joint planning, and coordination among the various components of a supply chain. Strategic uses of information and various strategies of supply chains, like appropriate contacts, component commonality, and postponement will be covered. The material will also include service industry, and designing and managing globally dispersed entities. A major activity would be students working in teams, and identifying relevant problems and developing models to study them. Prerequisite: SSIE 515, and/or SSIE 520 or permission of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 616 - Adv Topics Applied Soft Compu

  Course is designed to follow a currently offered course, SSIE 519: Applied Soft Computing. Both courses are designed to cover relatively new approaches to machine intelligence and systems analysis known collectively as soft computing. The 519 course already introduces various types of fuzzy inference systems, neural networks, and genetic algorithms, along with several synergistic approaches for combining them, including "neuro and fuzzy" techniques, neuro-fuzzy models, the use of neural models in fuzzy systems design, genetic auto-tuning techniques, genetic training of neural nets, fuzzified neural nets, and neural genetic fuzzy models. Naturally, with so many new approaches developing in this field, it is possible in an entry-level graduate course only to cover the main topics in depth and to offer only a general overview on the more advanced hybrid approaches. The purpose of SSIE 616 is to allow students to pursue these advanced approaches to a much greater depth. The emphasis will be on applications, including modeling, prediction, design, control, databases, and data mining, just as is already the case in the 519 course. Prerequisite: SSIE 519. Offered in the Spring semester. 3 credits. Levels: Graduate

• SSIE 616X - Energy Systems Analytics

  Description: Data analytics is the science of drawing insights from raw data for human consumption. It aims to reveal trends and metrics that would otherwise be lost in the mass of information. The gained insights can then be used to optimize systems or processes to increase their overall efficiency. This course provides fundamental knowledge of data analytics applications in the energy systems field. The motivation and potential advantages of implementing advanced data analytics for typical energy systems will be introduced. Basic concepts and the procedures of the typical descriptive, diagnostic, predictive, prescriptive analytics for general energy system problems will be discussed. Recent literature that covers a wide range of topics related to energy systems data analytics such as peer-to-peer energy trading and its blockchain implementation, power system cybersecurity and data injection, electrical load forecasting, building energy management, renewable generation modeling, and penetration of electric vehicles. By dealing with huge amount of data from energy information systems, many benefits can be brought to the existing infrastructure and improve customer service as well as the social welfare in the era of big data. Python, Pandas, Scikit Learn, Keras/Tenserflow will be introduced as computational tools via hands-on exercises. This is a research-oriented course that allows students to engage in research activities that leads to academic publications. 3 Credits. Prerequisites: SSIE 505 Applied Probability and Statistics; or Equivalent. Offered in fall or spring Levels: Graduate

• SSIE 617 - Fuz. Sets, Fuz.Logic & Fuz.Sys

  Course consists of two parts. The first part covers fundamentals of fuzzy set theory and the associated fuzzy logic. The second part is devoted to applications of the theory. Topics of the theoretical part include basic concepts of fuzzy set theory and fuzzy logic; representations of fuzzy sets; extension principle that facilitates fuzzifications of classical mathematical concepts; aggregation operations on fuzzy sets; the concept of a fuzzy number and arithmetic operations on fuzzy numbers; fuzzy relations; fuzzy relation equations; basic ideas of fuzzy logic; possibility theory based on fuzzy sets; and information aspects of fuzzy sets. In the application part, methods of constructing fuzzy sets in various application contexts are overviewed and representative applications of fuzzy sets and fuzzy logic are examined. The application areas covered include systems science; approximate reasoning in expert systems; database and information retrieval systems; pattern recognition and image processing; decision making; medicine; economics; psychology; and various areas of engineering. Prerequisites: SSIE 505 or equivalent and calculus and discrete mathematics, or permission of instructor. 3 credits Levels: Graduate, Undergraduate

• SSIE 619 - Generalized Information Theory

  Primary concept in generalized information theory is uncertainty, and information is defined in terms of uncertainty reduction. After a brief overview of classical information theories, a broad framework for their various generalizations is examined in detail. The main focus of the course is on the capability of measuring uncertainty and the associated information within this broad framework, and to utilize this capability for dealing with problems involving uncertainty and uncertainty-based information. Prerequisites: calculus and basic probability theory (e.g., SSIE 505). 3 credits. Levels: Graduate

• SSIE 621 - Advanced Simulation

  This course is intended as an advanced course in simulation modeling for complex systems. This is a project- and research-oriented course designed to give graduate students a foundation from which to explore areas of their own interest. Focused topics include techniques in simulation model design, advanced techniques for output data analysis, comparing alternative system designs, variance reduction techniques, design of experiments for simulation modeling, simulation-based optimization, agent-based and distributed simulation, and continuous process simulation. Prerequisites: SSIE 520 or equivalent, and knowledge of at least one programming language. 3 credits. Levels: Graduate

• SSIE 630 - Neural Network &Genetic Models

  The use of autonomous self-organizing models in deducing complex systems properties, behavior and relations; intelligence, learning, adaptation and emergence in artificial systems; perceptions and threshold logic units, discriminate functions, general non-parametric training; committee, piecewise linear, layered and parametric machines; evolutionary programming, genetic algorithms and satisfying vs. optimizing search strategies. 3 credits. Levels: Graduate

• SSIE 631 - Foundations Of Neural Networks

  Covers theory and practical applications of artificial neural networks. Neural networks are a broad class of computing mechanisms with active research in many disciplines, including all types of engineering, physics, psychology, biology, mathematics, business, medicine and computer science. Emphasizes the practical use of neural networks for industrial problems such as pattern recognition, predictive models, pattern classification, optimization and clustering. Topics include learning rules, paradigms and validation. Prerequisites: SSIE 505 or equivalent and SSIE 520, and knowledge of at least one programming language. Term offered varies. 3 credits. Levels: Graduate

• SSIE 633 - Advanced Human Factors

  This course is intended as an advanced course in human factors/ergonomics engineering. The course is project/research oriented in nature to provide the graduate students a foundation from which they can explore areas of their own interests. Focused topics include fundamentals and dynamics of human performance modeling, particularly using digital humans; advanced human factors research and development, including ethics, methods, and analysis tools; human-machine systems modeling and design; human reliability analysis; adaptive hybrid systems; and control theory for humans. Application areas will include quality/process control in manufacturing, healthcare, transportation, aviation, and military systems. Prerequisite: SSIE 533 (or equivalent) or permission of the instructor. Offered in the Spring semester. 3 credits. Levels: Graduate

• SSIE 637 - Advanced Topics in Health Syst

  This course is intended as an advanced course in health systems and health care delivery. This course is oriented to provide the graduate students with an in-depth study of the application of industrial and system engineering principles for continuous process improvement in the health care. Concepts that are addressed and studied include, but are not limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. SSIE 537 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate

• SSIE 640 - Knowledge Discovery & Decision

  Introduces selected advanced topics of knowledge discovery and decision making. Particular focus is on logical and logico-statistical approaches to knowledge discovery and decision making. Emphasis is on detailed understanding of particular methods including the purpose, mathematical and computational foundations, pitfalls, typical applications, and software tools for every method. Topics include: the process of knowledge discovery and decision making. Elements of measurement theory, information measurement, uncertainty in data and its management. GUHA method and logico-statistical calculi of knowledge discovery: logic of induction and observational calculi, logic of suggestion, GUHA software, LISP-miner. Logical analysis of data (LAD), general methodology, approximation of subspaces by Boolean functions, LAD for binary and real-valued attributes, decision making and classification with LAD. Decision making with analytical hierarchy process (AHP). Formal concept analysis (FCA), basic approach and extensions. 3 credits Levels: Graduate

• SSIE 641 - Adv Topics in Network Science

  This course provides concepts, models, methods and tools developed in the rapidly advancing field of Network Science. Instructions will be largely based on primary literature published recently. Topics to be discussed will include: Complex network topologies, methods for network analysis, visualization and simulation, models of dynamical/adaptive networks, techniques for mathematical analysis, network stability and robustness, and applications to social, biological and engineering systems. Prerequisites: SSIE 523 or permission of the instructor. Students taking this course should have solid knowledge of linear algebra, probability and statistics, and differential equations. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 643 - Advanced Engineering Analytics

  The explosion of wireless sensing, smart devices as well as advanced communication techniques have generated the vast amount of big data. While data are the foundation of the Digital Age, Data Analytics is imperative to organize, analyze and interpret the valuable information underlying the massive data. This course provides an overview of advanced analytics algorithms, with applications across various engineering domains, including manufacturing, healthcare, and smart energy. The major topics include model assessment and selection, advanced regression (ridge/Lasso, mixed effects, nonparametric), support vector machine and kernel methods, ensemble learning (stochastic gradient boosting), dimension reduction (subspace learning, non-negative matrix factorization, functional PCA), anomaly detection (unsupervised, high-dimension data), data visualization (multi-dimensional scaling and manifold learning). Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 644 - Found Of Adaptive Optimization

  This course is a survey of the newer, most common adaptive search methods. This is a project- and research-oriented course designed to give graduate students a foundation from which to explore areas of their own interest. Focused topics include simulated annealing, genetic algorithms, evolution strategies, tabu search, ant colony methods, and particle swarm optimization. Other search methods such as genetic programming, evolutionary programming and random search methods will be briefly covered. Major emphasis is on NP complete combinatorial problems found in engineering. Issues such as solution encodings, stochastic convergence, selection methods, local and global search methods are discussed. Prerequisite: SSIE 505 or equivalent, SSIE 520, and knowledge of at least one programming language. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 650 - Systems Optimization

  Broad spectrum of models and methods for systems optimization. Motivating examples; classical constrained and unconstrained methods; search techniques; linear programming; network and transportation systems; introduction to integer programming. Prerequisite: SSIE 550, SSIE 553 or consent of instructor. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 660 - Stochastic Systems

  Advanced topics in conditional probability, discrete-state Markov chains, exponential and Poisson processes, Erlang processes, basic queueing models and several important variations, congestion problems in banks, airline reservation systems, waiting lines in machine job shops, problems encountered on design and analysis of multiprogramming and timesharing computer systems, renewal theory, continuous transition Markov chains, introduction to system reliability. Modeling and methodology. Emphasis on application. Prerequisite: SSIE 505 or equivalent. 3 credits Levels: Graduate

• SSIE 661 - Advanced Issues In Quality

  The topic of quality has taken more and more of a critical nature for manufacturing systems. Course has two components. The first is a practical application of the concepts of quality, including the design and execution of experiments in a real setting. The second is the analysis and study of future issues in the field of quality, such as the development of loss equations, cost of high quality, and people and high quality. Prerequisite: SSIE 561 or a general design of experiments course. Offered in the Fall semester. 3 credits. Levels: Graduate

• SSIE 664 - Advanced Eng Management

  This course is specifically designed for engineering management students who are interested in advanced concepts related to leadership and management in highly technical and complex teams, departments, and organizations. Students will gain an advanced understanding of the engineering and management skills needed to excel in their future positions, create new opportunities, and grow as respected, competent engineering leaders, strategic planners, policy makers and engineers in today's complex systems and global economy. Also underlined are discussions of advanced engineering leadership attributes, steps to acquire these attributes, and strategies to develop core competencies. The course is organized into four major sections: Introduction to Management Challenges for Engineers, Functions of Engineering Management, Business Essentials for Engineering Managers, and Engineering Management in the New Millennium. Course contents are designed to achieve the T-shaped competencies with both broadbased perspectives and in-depth analytical skills. Such a background is viewed as essential for advanced engineering professionals and managers to exert a strong leadership role in the dynamic and challenging marketplace. This course also covers the latest research and development in the engineering management literature. A significant component of this course requires students to conduct and complete comprehensive literature reviews and detailed project case studies. Prerequisites: SSIE 564X Engineering Project Management. Term offered varies. 3 credits. . Levels: Graduate, Undergraduate

• SSIE 665X - Adv Topics Energy Syst Opt

  The main objective of this course is to introduce various optimization problems and solution approaches for optimal planning, operation, and control of energy systems. Specifically, in this course, fundamental theory and knowledge of mathematical optimization models will be provided, and students will learn how to formulate and solve the optimization problems relating to energy systems, such as unit commitment, economic dispatch, power flow, power procurement, energy storage operation, demand response, etc. From the theoretical point of view, this study focuses on stochastic optimization techniques, e.g., two-stage stochastic programming, risk-averse stochastic programming, and chance-constrained programming, that are currently increasing in popularity while being applied to a wide range of optimization problems in modern energy systems that require making decisions in presence of uncertain data, e.g., renewable generations. This course will also be designed as a research-oriented course that allows students to engage in research work by providing an opportunity to formulate and solve a variety of practical problems using optimization software. Because in general, solving energy systems optimization problems are computationally very challenging, the implementation of decomposition and sampling algorithms and performing computational experiments using programming language will also be covered. Prerequisites: Either SSIE 553: Operations Research or SSIE 514X: Linear Programming for Engineers or permission from the instructor. Also, programming skills, e.g., Python, C/C++, Java, etc. are highly recommended. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 671 - Integer Programming

  This course aims to provide a unifying exposition of mathematical theory and algorithms to solve integer linear programs, application of which includes scheduling, production planning, network optimization, transportation and assignment problems, etc. In particular, Integer Programming (IP) is a requisite when tackling problems involving a number of interrelated “yes-or-no” questions, which is of great importance in decision making optimization. The course will cover various topics such as 1) Introduction to Integer Programming, 2) Simplex and Beale Tableaus, 3) Cutting Plane Techniques, 4) Dual Fractional MILP Cut, 5) Dual All–Integer Integer Programming, 6) Primal All–Integer Integer Programming, and 7) Branch and Bound Enumeration. Prerequisites: SSIE 553 (Operations Research), or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 673 - Adv Iss In Proc For Electr Pkg

  Addresses advanced topics within the processes associated with the manufacture and assembly of printed circuit boards. Addresses advanced issues in bare-board manufacture, including printed circuit board structures and processes, interconnection technology-related considerations, materials and processes used in bare-board manufacture, and reliability issues. The impact of board-pad metallurgies and surface coating on the solder joint is addressed. In the assembly area, the focus is on advanced topics in the surface mount printed circuit board assembly area. Ultrafine pitch assembly and area array assembly are addressed. Topics include solder paste deposition, component and materials issues, solderability, placement, reflow, process yield related issues, the impact of moisture, encapsulation and product reliability. Concepts such as concurrent engineering and design for manufacturing are also addressed. On completion, students have an acceptable understanding of the advanced scientific and/or engineering concepts that relate to the manufacture and assembly of surface mount printed circuit boards. Assists engineers in scientific problem solving and process development for the printed circuit board assembly domain. Prerequisite: SSIE 578 or permission of instructor. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 685X - Advanced Additive Mfg

  This course will cover advanced additive manufacturing processes, systems, materials, designs, and applications. The course topics will include innovative fields and templates assisted additive manufacturing processes and systems, 4D printing for shape-morphing systems using stimulus-responsive materials, micrometer and nanometer scale additive manufacturing, and advanced applications by additive manufacturing. The application topics will include biomedical, biomimetics and bioinspired devices; electronic and robotic devices; energy harvest, storage, and conversion devices; and other areas. Prerequisites: ISE485/SSIE585 Additive Manufacturing Processes and Systems or permission of the instructor. Offered in the Spring semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 697 - Independent Study

  Supervised by department faculty member. Student must obtain consent of instructor, who then determines description of program, number of credits, frequency of meeting and location. Appropriate paperwork must be submitted to complete registration. Term offered varies. Credits vary. Levels: Graduate

• SSIE 698 - Predissertation Research

  Exploratory research oriented toward PhD dissertation. Term offered varies. Credits vary. Levels: Graduate

• SSIE 699 - Dissertation

  Research for and preparation of PhD dissertation. Term offered varies. Credits vary. Levels: Graduate

• SSIE 700 - Continuous Registration

  Required to maintain matriculation through any spring or fall semester when no other courses are taken. If minimal one-credit registration is not maintained, student must reapply for admission. Term offered varies. Typically 1 credit. Levels: Graduate

• SSIE 701 - Pract/Teaching &Research Asst

  Required for all funded graduate assistants. Research or teaching supervised by faculty advisor. Term offered varies. Credits vary Levels: Graduate

• SSIE 707 - Research Skills

  Development of research skills required within graduate program. May not be applied toward course credits for any graduate degree. Term offered varies. Credits vary. Levels: Graduate

Graduate courses

• SSIE 515 - Operations Mgmt Supply Chains

  This course deals with management of Supply chains, in particular, with the operational aspects. A broad overview of supply chains of a company is introduced, together with performance measures and needed critical success factors. The course concentrates on supplies, inventories, manufacturing, and logistics of distribution. Managerial aspects as well as mathematical modeling for better planning and control will be covered. Enabling the supply chains by enterprise resource planning modules and e-commerce will also be discussed. Prerequisite: SSIE 505 or equivalent. Crosslisted with ISE 415. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 516X - Fund. of Energy Sys. Analytics

  This course provides fundamental knowledge of the application of Industrial and Systems Engineering in the Energy field. It focuses on the modeling, design, simulation, and optimization of alternative and conventional energy technologies, the societal and environmental impact of technology developments, and the economic benefits of those developments. Recent literature that covers a wide range of ISE topics related to energy systems such as transportation energy, building energy, manufacturing energy, solar energy, wind energy, bioenergy, greenhouse gas emission, and smart grid operations will be discussed in this course. Students will gain breadth and depth of knowledge, and an interdisciplinary understanding of the contemporary need for energy solutions. 3 Credits. Prerequisites: Graduate Standing. Offered fall or spring Levels: Graduate, Undergraduate

• SSIE 523 - Collective Dyn of Complex Syst

  Introduces students to the study of collective dynamics demonstrated by various natural, social and artificial complex systems, i.e., systems made of a massive amount of lower-level components interacting with each other in a nonlinear way. Discusses several computational modeling frameworks, including agent-based models (particle models, ecological and evolutionary models, game-theoretic models), complex network models (small-world and scale-free networks, dynamical networks, adaptive networks), and spatial models (cellular automata, partial differential equations). Also discusses mathematical concepts and tools to analyze and understand their behavior, e.g., mean-field approximation, linear stability analysis, scaling, renormalization, bifurcation, chaos, pattern formation, and phase transition. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not strictly required. Prerequisites: Graduate standing and basic knowledge of calculus, linear algebra and probability theory, or permission of instructor. Crosslisted with ISE 423. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 519 - Applied Soft Computing

  Covers relatively new approaches to machine intelligence known collectively as soft computing. Introduces various types of fuzzy inference systems, neural networks and genetic algorithms, along with several synergistic approaches for combining them as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction, design, control, databases and data mining. The undergraduate students are not required to do projects on the same level as the graduate students, and are not required to place the degree of emphasis on hybrids. Prerequisites: basic knowledge of calculus and discrete mathematics, and competence in at least one programming language, or consent of the instructor. Crosslisted with ISE 419. Offered in the fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 522 - Advanced Decision Modeling

  Course provides a broad foundation in decision models and techniques used in industry and research for technical and managerial problems. Topics include decision theory, risk and uncertainty, value of information, preference measurements, prioritization of alternatives, multiple objectives and hierarchical decisions. Prerequisite: SSIE 505 or equivalent. Crosslisted with ISE 422. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 530 - Healthcare Policy Analysis

  This course provides an overview of issues and policies related to health care systems, with emphasis on health care systems within the United States. The course will apply various engineering tools and approaches for policy analysis and program planning in this area. Some potential future policy changes will also be discussed, as well as challenges with implementation and change management techniques. Crosslisted with ISE 430. Prerequisites: Graduate standing or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 534 - Fundamentals of Health Systems

  One of the growing systems in our society is that of the healthcare delivery system. The purpose of this course is to introduce the concepts behind the healthcare delivery systems and to focus upon the systems improvement or continuous improvement techniques available for complex systems. Topics would include improvement to, and problems with: organizational structure, managing change, the financial structure, the responsibility structure, quality data and implications of quality measures, use of clinical decisions support systems and the care givers role in the system. There will also be a focus upon suppliers to the healthcare delivery system and the unique requirements placed upon their products and processes. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 434. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 537 - Ind & Sys Eng in Healthcare

  The application of industrial and systems engineering principles to continuous process improvement in the healthcare domain will be studied. Concepts that will be addressed will include, but not be limited to, process mapping, optimization, scheduling, lean and flexible systems, quality enhancement, simulation, supply chain management, inventory control, and information management. Prerequisite: graduate standing in the department or permission of the instructor. Crosslisted with ISE 437. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 538 - Healthcare Financial Eng

  This course is intended to provide an overview of healthcare finance and the current financial environment for the healthcare industry and to learn how to apply engineering economics to healthcare financial management. It will provide information on financial and managerial accounting and how the use of Systems Science and Industrial and Systems Engineering principles can be applied to financial management concepts to allow for health-related organizations to make sound business decisions. Crosslisted with ISE 438. Prerequisites: SSIE 534 or permission of the instructor. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 539 - Human Factors Eng Healthcare

  This course introduces and emphasizes the role that human factors engineering/ergonomics plays in healthcare systems, with a focus on its applications to help improve quality, safety, efficiency, and effectiveness of patient care. Focused topics include human factors in workflow models; work system design for patient safety; human error analysis/taxonomies to reduce medical errors; task analysis and data collection methods in healthcare environments; clinical staff workload and patient safety; physical ergonomics in healthcare and human performance modeling; and diffusion and adoption of technology in healthcare, with emphasis on the usability and design of medical devices and information systems. Prerequisite: Basic human factors knowledge or permission of the instructor. Crosslisted with ISE 439. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 501 - Intro to Systems Science

  Includes a general characterization of systems science as a field of study; intellectual roots, philosophical assumptions and historical development of the field; an overview of fundamental systems concepts, principles and laws; and a survey of application areas of systems science and its implications for other fields of study. Prerequisite: Graduate standing or permission of instructor. Crosslisted with ISE 440. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 548 - Healthcare Data Sci&Analytics

  Data analytics plays an increasingly greater role in healthcare organizations. However, many healthcare professionals lack training in this vital area. This course will cover advanced data analytics with applications in healthcare systems engineering. In specific, the course will cover the concepts and methods of the data analytics in three particular areas: (i) descriptive analytics, which includes data description and aggregation; (ii) predictive analytics, which uses machine learning and statistical modeling for prediction and forecasting; and (iii) prescriptive analytics, which makes use of the results of predictive analysis to optimize the decision making process. All introduced methods will be demonstrated along with a variety of data modalities collected from healthcare applications such as pervasive health, disease diagnosis, fraud detection, and pharmaceutical discovery along with medical decision support systems. Through the provided coursework, students will be able to learn a variety of methodologies on data processing and modeling and make the best use of healthcare data to facilitate medical decision making. Prerequisites: ISE 437/SSIE 537 or consent of the instructor. Crosslisted with ISE 448. Term offered varies. 3 credits. Levels: Graduate, Undergraduate

• SSIE 565 - Engineering Project Management

  Effective Engineering Project Management is critical to business and organizational success. This course will teach students both the technical and the sociocultural dimensions of Engineering Project Management. Students will be thoroughly trained in the following topics: Project Selection, Organization Structure and Culture, Project Definition, Project Time and Cost Estimation, Project Networks, Project Risk Management, Resource Scheduling, Project Crashing, Leadership, Team Management, Outsourcing, Progress Monitoring and Project Evaluation, and Project Closure. Microsoft Project software will be used to demonstrate activities, duration, task relationships, Gantt Charts, network diagrams and the Critical Path Method with case studies of contemporary engineering projects. Students will learn how to better control financial, physical, and human resources, improve stakeholder relations, shorten development times, lower costs, and increase profit. Offered Fall, Spring, or Summer as needed. 3 credits. Levels: Graduate, Undergraduate

• SSIE 568X - Flexible & Hybrid Electronics

  Flexible and hybrid electronics integrate ultra-thin silicon semiconductor technology with non-traditional thin skin-like substrates establishing wearable devices for both human and machine. This course will cover design, materials, fabrication methods and evaluation of printed and thinned silicon-based electronics for applications such as smart energy, human health, and industrial sensing. Emphasis will be placed on fundamental understanding of the hard-soft materials interface, advances in materials printing, and approaches to make these systems robust for use. The course will promote understanding of the engineering requirements that lead to systems design for both manufacturing and reliability. Prerequisites: undergraduate degree in the sciences or engineering. 3 credits. Levels: Graduate, Undergraduate

• SSIE 569 - Materials for Manufacturing

  This course provides an overview of the materials used in manufacturing that are of significant economic, technical and industrial relevance. A special emphasis will be given to polymeric materials currently used in electronics manufacturing as well as those materials emerging for use in additive manufacturing and 3D printing. Selected materials will be classified by composition, properties, manufacturing methods, applications, economics and related supply chains. Prerequisites: undergraduate engineering or science background that includes the equivalent of freshman chemistry and physics or permission of the instructor. Crosslisted with ISE 469. 3 credits. Levels: Graduate, Undergraduate

• SSIE 578 - Processes for Electr Mfg.

  The purpose is for the students to gain a broad knowledge and understanding of the basics of printed circuit board manufacturing and assembly. The course offers an introduction to surface mount and insertion mount components, materials and processes as well as to PCB design and manufacturing. Lectures will introduce to assembly process flows and component types, PCB construction and defects, solder paste printing and equipment, placement processes and equipment, reflow and ovens, flip chip assembly and underfilling, defects and mitigation, reliability optimization and testing. Efforts will be made to include visits to local industrial assembly facilities as well as equipment on campus. The overall goal is to provide the students with a basis for communicating and working with subject matter experts. Prerequisite: Graduate standing or consent of department chair. Crosslisted with ISE 473. Offered in the Fall semester. 3 credits. Levels: Graduate, Undergraduate

• SSIE 585 - Additive Mfg Processes & Sys

  This course aims to inculcate a comprehensive understanding of additive manufacturing (AM), and will address the following consequential aspects of AM: (1) Materials and Process Mechanics; (2) Machines, Machine Design, and Mechanisms; (3) Quality control, Sensing, and Process Monitoring; and (4) Design for AM and novel trans-disciplinary applications. Prerequisites: Graduate standing or permission of the instructor. 3 credits. Levels: Graduate, Undergraduate