The Linear programming problem. The simplex method. The transportation and assignment models. Branch and bound and cutting planes algorithms for Integer programming. Steepest descent, Introduction to unconstrained and constrained nonlinear problem. Dynamic Programming. Introduction to Stochastic processes. Introduction to single server queuing systems. Applications of the above models are emphasized through formulation exercise Case studies, and term projects. Prerequisite: Graduate Standing (Not open to Credit for SE Majors)

Industrial instrumentation: measurement techniques in industrial processes. Computer data acquisition. NC and CNC machine tools. Computer process interfacing and control. Feedback control systems. Group technology. Flexible manufacturing systems. Automated assembly. Industrial robots. Computer-aided inspection and testing. Automated factories. Case studies. Prerequisites: Graduate Standing, SE 401

Review of linear programming, revised simplex method, product form of the inverse, duality, dual simplex method, primal dual simplex method, sensitivity analysis, parametric programming, bounded variable linear programs, decomposition principle, classical networks, shortest path problem, maximal flow problem, multicommodity networks. Additional topics may be selected from complementarity, fractional programming and computational efficiency of linear programming algorithms. Case studies. Prerequisite: SE 303, Math 280, or equivalent

The course explores in detail the interrelationships between the architecture and systems software of a modern minicomputer: configuration; real-time operating systems; memory management; interactive editor, program scheduling; priority levels; swapping; input/output control; resource management. Real time programming languages. Prerequisite: Graduate Standing

An integrated treatment of continuous linear systems and control theory, Both input/output and state space methods are discussed with more emphasis on state space methods. Topics include: input/output and state space representations of dynamic systems. Canonical forms, transformation, and equivalent systems. Stability/stabilizability, controllability/reachability, and observability/ detectability. State feedback controllers. Full and reduced order observer. Output feedback controllers. Prerequisite: Graduate Standing. (Crosslisted with EE 550)

Analysis of production and inventory systems, forecasting, single and multi-period deterministic inventory models, stochastic inventory models, deterministic and stochastic production planning, Multistage and dynamic production planning models, MRP systems, Pull, Push and Just-in-Time Systems. Prerequisites: SE 402 or Consent of the Instructor

Characteristic of large scale systems. Analysis and design procedures. Model aggregation. Model perturbation. Time and frequency domain techniques. System de-composition and multilevel optimization techniques. The maximum principle and Hamilton-Jacobi theory. Linear regulator problem. Singular control. Open loop and closed loop hierarchical control of continuous-time systems. Hierarchical control of discrete-time linear and nonlinear systems. Prerequisite: SE 416 or equivalent

Geometric modeling. Engineering Analysis. Design Review and evaluation. Automated drafting. Hardware in CAD. Computer graphics software. Functions of a graphics package. Data base structure and content for CAD/CAM integration. Applications such as (N/C, electronics design, piping, mechanical design, control system). Prerequisite: Graduate Standing

Microprocessor architecture. Memory. I/O interface components and their characteristics. Designing Interface circuits. Interfacing to standard buses and peripherals. Interface software design and implementation. Applications. Prerequisite: SE 417 or equivalent

Fundamentals of stochastic processes; review of modeling from the first principle (energy/mass balance, momentum preservation etc.); process identification from step response, first, second and higher order processes; frequency response identification; correlation methods; least squares identification; determining model orders; model validation; recursive least squares identification; AR, MA modeling of system, linear prediction; application and case studies. Prerequisite: Graduate Standing

Performance measures for dynamic optimal control problems. Variational approach and the maximum principle. Dynamic programming and Hamilton-Jacobi theory. Singular control. Optimal control systems, e.g. minimum time, regulator, servo mechanisms, minimum energy etc. Inter-active numerical techniques for finding optimal trajectories. Case Studies. Prerequisite: SE Graduate Standing. (Crosslisted with EE 552)

Distributed control systems configuration. Communications networks. Operator Interface Stations. Control algorithms in distributed control systems. Economic justification of distributed control. Evaluation of distributed computer control systems. Microcomputer control networks. Future trends in distributed computer control. Prerequisite: SE 401 or equivalent

Principles of intelligent measurement devices. Special purpose sensors; installation; maintenance. Analytical instrumentation: gas chromatography; mass spectroscopy; infrared spectroscopy. Calibration. Industrial measurements such as online analysis of process streams; weight; pH meters, engine monitoring and tuning; machine alignment; noise and vibration. Inferential measurement. Estimation of efficiency, wear, fouling, creep. Prerequisite: SE 312 or Consent of the Instructor

Introduction to nonlinear systems. Phase plane techniques. describing function approach. Liapunov method. Popov criterion. Hilbert spaces and nonlinear operators. Input/output feedback theory. Passivity and positivity of nonlinear operators. Circle criterion. Multipliers and the small gain theorem. Robustness of feedback systems. Unbounded operators. Applications. Prerequisite: SE 416 or equivalent

Mathematical models and deterministic modeling generalities, model building methodology for differential and difference equations (lumped processes); partial differential equations (distributed processes). Methodology for model information storage and integration. Support languages for simulation. Hardware trends and their impact on simulation. Case studies. Prerequisite: SE 301 or equivalent

Application of mathematical programming to the facility location, and layout. Point and area location and layout problems in continuous discrete space are examined. Prerequisite: SE 422 or equivalent

Formulation of engineering problems as nonlinear programs; Optimality conditions for nonlinear programs; Algorithms for unconstrained optimization; algorithms for constrained non-linear program; methods of feasible directions (Sequential unconstrained minimization techniques), comparison of algorithms for nonlinear programs. Case Studies. Prerequisite: SE 305 or (MATH 280 and Advanced Calculus)

Fundamental concepts of mathematical and simulation models; efficient generation of random variates, construction of discrete event simulation models, discussion of available computer languages, variance reduction techniques, Jacknifying and classical methods, output analysis. Prerequisite: SE 405 or equivalent

The course covers the nature, scope, and importance of forecasting, with techniques for forecasting and time series analysis. Topics include regression, moving averages, exponential smoothing, correlation and least square technique, analysis of forecast errors, Box-Jenkins models and Bayesian methods in forecasting. The design of forecasting systems will be emphasized with application oriented examples. Prerequisite: Graduate Standing

Review of 1-D time-and frequency-domain representation of signals and systems, including sampling and reconstruction, convolution and correlation, DFT and FFT, z-transforms and random signals. Transformation representation of LTI systems. Digital filter (FIR and IIR) Design and structures. Analysis of finitelength effects in Digital filters. Spectral Analysis, Introduction to multirate DSP. DSP applications and hardware. Prerequisite: SE 432, equivalent, or Consent of Instructor. (Crosslisted with EE 563)

Modeling with graphs and networks, data structures for network and graphs, shortest path algorithms, properties of the matrix, label setting and label correcting algorithms, spanning tree algorithms, maximum flow algorithms, maximum flow minimum cut theorem. algorithms for the assignment, semi-assignment and the transportation problems, minimum-cost flow algorithms, the simplex method on a graph, out-of-kilter algorithm, embedded networks, constrained network and generalized network, multi-commodity network. Modeling with network includes cases from production, facility location, distribution and inventory and human resource planning. Prerequisite: SE 501 or equivalent

Structuring decision problems: single criterion versus multiple criteria, certainty versus risk and uncertainty versus conflict, criteria and attributes, payoffs and losses. Utility for decision making. Decision making with single and multiple criteria under certainty: selected discrete MCDM models. Decision making under risk: decision trees, single and multiple stages. Value of information. Decision making under uncertainty. Decision making under conflict: game theory. Decision support systems. Case studies. Prerequisite: SE 205, Consent of the Instructor

Design aspects of maintenance systems, maintenance strategies, maintenance control systems, maintenance planning and scheduling, models of preventive maintenance and condition monitoring, models of the effect of maintenance on production systems, new trends in maintenance strategies and modeling. Prerequisite: SE 429 or Consent of the Instructor

Numerical control, Computer control in NC machine tool. Group technology. Computer aided planning, computer integrated production management. Shop floor control and computer process monitoring systems. Computer integrated manufacturing systems. CAD/CAM implementation. Prerequisite: SE 502 or Consent of the Instructor

Maintainability, fault trees and failure mode analysis. Combinatorial reliability; series, parallel and r-out-of-n configuration; general computation techniques. Catastrophic failure models: hazard rate models. System reliability: approximation methods and reliability bounds. Repairable systems: methods based on renewal theory, system availability. Reliability models identification and parameter estimation. Design for maintainability. Prerequisite: Graduate Standing

Basic concepts in robotics. Architecture of an industrial robot. Robot drives and sensors. Computer control of industrial robots. Programming of industrial robots. Intelligent robots. Applications of industrial robots. Prerequisite: SE 502

Design of industrial operations with emphasis on the effective uses of the human body. An examination of the problems of establishing time standards and proposed solutions. Learning curves, fatigue allowances, variations of the MTM system, computerized work measurement systems, staffing problems. Term project on industrial methods design. Prerequisites: SE 323, Graduate Standing

Statistical methods in the design and analysis of quality control systems: sampling inspection, attributes and variables; comparison of sampling plans; control charts; adaptive quality control; total quality control. Machine and process capability studies; organizing for quality; machine case studies/projects with local industries. Prerequisites: SE 320, Graduate Standing

A scientific and engineering approach to experimentation and analysis of data. Single-factor experiments; Latin squares etc., factorial experiments. Missing data analysis; nested factorial design; multifactor design; fractional replications. Case studies. Prerequisite: SE 325 or equivalent

Design of man-machine systems utilizing results from various disciplines including anthropometric data and engineering research. Emphasis is placed on making optimal use of human capabilities. Includes consideration of research techniques in human factors engineering. Prerequisite: Graduate Standing

General approach to controller design; Adaptive control methods; Model reference Adaptive systems, parametric optimization methods, Liapunov function method, hyperstability and positivity concepts; self-tuning controllers, minimum variance selftuner, explicit and implicit algorithms, pole assignment regulators; variable structure systems, sliding motion, choice of control function, control of phase canonic models. Applications. Prerequisites: SE 416, Graduate Standing. (Crosslisted with EE 651)

A basic methodology course in Occupational Safety and Health. Topics cover a spectrum of contemporary safety and risk management problems drawn from process as well as manufacturing industries. Problems will be handled using methods of Operations Research and Simulation. A project is a part of the course. Prerequisites: Graduate Standing, Consent of the Instructor

Queuing Systems: some important random processes, birth-death queuing systems in equilibrium; markovian queues in equilibrium. Prerequisite: SE 205, STAT 315, or equivalent

Introduction to stochastic process, stationarity, ergodicity, Poisson process, linear models, Markov chains, renewal theory, Markov renewal processes, semi- Markov processes and Applications in queuing and other areas. Prerequisite: Graduate Standing

Variety of sequencing and scheduling problems in O.R., job shop and flow shop scheduling, discussion of performance measures, dynamic programming, integer programming, computational complexity and NP-completeness results, discussion of well solved problems, branch and bound methods, variety of heuristic approaches for intractable practical problems, guaranteed accuracy heuristics. Prerequisites: Graduate Standing, Consent of the Instructor

An evaluation of various factors affecting human physical performance in industrial environment. Topics include anthropometry, bio-mechanics, energy expenditure, heat stress fatigue. Prerequisite: Graduate Standing

User characteristics, Design of keyboards, Controls, and VDT's; Human factors in personal computers, Computer aided design, Computer-aided manufacturing and Control rooms; Human error in computer systems. Prerequisite: Graduate Standing

Examples of optimization problems in engineering design: flexural systems, stressed systems, mechanical systems, digital filters. Optimality conditions. Single and multivariable unconstrained optimization. Constrained optimization. Survey of global optimization: exact and non-exact methods. Each student is expected to solve an optimal design problem related to his background. Prerequisites: Graduate Standing, a background in vector calculus. (Not open to credit for SE majors)

Examples of combinatorial optimization problems in engineering. Simulated annealing, genetic algorithms, tabu search, evolutionary methods and neural networks. Hybrid methods. Application to large engineering optimization problems. Term project. Prerequisite: Graduate Standing (Cannot be taken for credit with EE 556)

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Graduate students working towards either M.S. or Ph.D. degrees, are required to attend the seminars given by faculty, visiting scholars, and fellow graduate students. Additionally each student must present at least one seminar on a timely research topic. Among other things, this course is designed to give the student an overview of research in the department, and a familiarity with the research methodology, journals and professional societies in his discipline. Graded on a Pass or Fail basis.

Large scale LP, decomposition principle, computational complexity of the simplex method, the ellipsoid method, review of penalty methods in nonlinear programming, numerical solution of large scale positive definite linear system of equation, interior point methods for linear programming and their efficient implementation for large scale LP, computer project. Prerequisite: SE 503

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Advanced forecasting models including Box and Jenkins approach. Advanced aggregate production planning models includes linear, quadratic and nonlinear programming models. Desegregation schemes. Lot sizing techniques for material requirement planning. Nervousness and freezing just-in-time manufacturing philosophy. Group technology. Algorithms for part family formation. Flexible manufacturing systems. World-class manufacturing. Effects of maintenance and quality on production. Research papers from various journals in the field are covered. Term projects. Prerequisite: SE 508

Prerequisite: SE 599

Fundamentals of stochastic processes; review of least squares identification; properties of least squares estimators; prediction error and instrumental variable methods; recursive estimation; maximum likelihood estimator; Cramer-Rao inequality; model structure determination; identification of closed loop systems; model validation; extension to MIMO and nonlinear plants; applications and case studies. Prerequisites: SE 507, SE 513

Synthesis and implementation of digital control systems for complex systems; control configurations; process modeling and identification; dynamic matrix control and internal model control; adaptive control systems; Supervisory and optimizing control; applications and case studies for distillation, combustion, heat exchangers, and flow reactors; recent developments in computer process control. Prerequisite: SE 515 or equivalent

Elements of Convex analysis, optimality conditions for smooth optimization problems, formulation of quadratic programs as linear complementarity problems (LCP), successive linear programming or quadratic programming methods for NLP, convergence of nonlinear programming algorithms, complementary pivot method for LCP, complementary pivot methods for fixed point computing and their application to NLP, survey of other methods for constrained NLP (Frank- Wolte method, methods of feasible directions, reduced gradient methods, penalty and barrier methods, gradient projection methods, active set methods and others), case studies. Prerequisite: SE 521 or MATH 412

Interval arithmetic. Functions of intervals. Systems of interval linear and nonlinear equations and inequalities. Unconstrained global optimization. Inequality and equality constraints global optimization problems. Prerequisite: SE 501 or equivalent.

2-D time-and frequency-domain representation of signals and systems, discrete random process. Linear prediction. Least squares (LS) and Recursive Least (RLS) Techniques with applications to Filter Design, System Modeling and array signal processing. Power spectrum Estimation. Cepstral Analysis, Selective Coverage of latest tools used in signal processing such as Neural nets, Higher-Order Statistics and Wavelets. Applications. Prerequisite: SE 524 or Consent of the Instructor

Extension to the classical network problem formulation including constrained, multi-commodity and nonlinear networks. Uni-modularity property, assignment and matching, Lagrangian relaxation and network optimization. The decomposition approach for solving constrained and multi-commodity network. Traveling salesman problem, routing models, branch and bound and heuristics for routing problems. Polynomial time scaling algorithms, strongly polynomial algorithm for network problems. Algorithms for nonlinear networks. Complexity of network algorithms. Prerequisite: SE 503 or SE 525

Different formulations of the stochastic programming problem. Chance constrained problems, the recourse problem, linear programming under uncertainty. Decision rules in chance constrained programming, deterministic equivalence in stochastic programming, multi-stage stochastic programming, Duality and Computational issues in stochastic programming, Problems of existence of solution and optimality conditions in stochastic programming, stability of solutions in stochastic programming. Prerequisites: SE 503 or equivalent; Consent of the Instructor

Structuring decision problems with multiple criteria. Fundamentals and recent advances in multiple criteria decision making (MCDM) models. Selected approaches for discrete MCDM. Multiple criteria optimization: schemes for generating efficient solutions selected approaches: Goal programming, interactive approaches, surrogate worth tradeoff. Group decision making and negotiation. MCDM support systems. Case studies. Prerequisites: SE 503 or Equivalent; Consent of the Instructor

Dynamic and Kinematic analysis of robot manipulators; sensors (position, velocity, force, vision, tactile) actuators and power transmission; direct drive and indirect drive; point to point control; straight and curved path following; industrial practice in servo control; application of optimal linear quadratic control; nonlinear control and compliance control; collision avoidance; modeling and control of robots in the manufacture environment. Prerequisite: SE 532 or equivalent

Advanced concepts in the identification, design, analysis, development and implementation of human operated systems; existing and emerging systems identified from industry. Case examples of theories of communication, decision and control. Prerequisite: SE 443 or equivalent

The queue G/M/m, the method of collective marks, the queue G/G/1. Bounds, inequalities and approximation, priority queues. Application in computers. Prerequisite: SE 541

Characterization and Specification of stochastic processes, stationarity and ergodicity, correlation function and power spectra, wiener, Poisson, Markov and Gaussian processes; Martingales; orthogonality principle and mean square estimation; stochastic integrals. Introduction to stochastic differential equations and stochastic calculus. Prerequisite: SE 543

Argument principle; Rouche's Theorem; chordal metric; Concepts of uncertainty and robustness in control systems design; unstructured uncertainty; structured uncertainty; real parameter uncertainty; necessary and sufficient conditions for robust stability; structured singular value (?, time varying uncertainty, etc.). Prerequisite: SE 416 or equivalent

Formulation examples, computational complexity of algorithms and problems, P, NP-complete and NP-hard classes of problems, cutting plane theory, branch and bound, knapsack problem, Bender decomposition, partial enumeration and implicit enumeration methods, Lagrangian relaxation, local search and other heuristic approaches, simulated annealing, computer project. Prerequisite: SE 421 or equivalent

Metric spaces, Banach and Hilbert spaces, introduction to operator theory; systems as operators; small gain theorem; linear systems; stability and instability invertibility and causality; passivity properties of feedback systems. Prerequisite: SE 416 or equivalent

Multi-Stage problems and recursive algorithms, application in a variety of areas, Markov renewal programming and discrete dynamic programming, applications to optimal control. Prerequisite: SE 421 or equivalent

Introduction to Hilbert Spaces; Banach Spaces; and Hardy Spaces; Laurent, Hankel, and Toeplitz Operators; parameterization of all stabilizing controllers (Youla's parameterization); factorization theory; model matching problem; Nehari's Theorem; Wiener-Hopf optimal controllers; Hoo optimization problem; model reduction; l1-optimal control and other state of the art control system synthesis methods. Prerequisites: SE 514; SE 652 or equivalent

Speech production models; acoustical properties of vocal tract; classification of speech sounds, application to Arabic speech; time and frequency domain models for speech production; linear prediction methods; pitch detection algorithms; formant frequency trajectories; homomorphic speech processing; acoustic properties of Arabic sounds; allophone and diphone techniques for speech synthesis; speech coding techniques; speech VOCODERS; vector quantization; CELP vocoders; speech recognition; distance measures; dynamic programming for template matching; hidden markov model HMM techniques, application to phonetics based Arabic speech recognition. Prerequisite: SE 624 or Consent of the Instructor. (Crosslisted with EE 613).

Stochastic state space model; properties of Wiener process; stochastic differential equation; linear optimal filtering and prediction; Kalman filter and Wiener-Hopf filter, fixed lag smoothing and fixed point smoothing; filtering and prediction using stochastic ARMA model; extended Kalman filter; parameter estimation for stochastic dynamic systems; adaptive filtering and prediction. Prerequisites: SE 416, SE 463, SE 514

Analysis of production and inventory systems, deterministic inventory models, stochastic inventory models, deterministic and stochastic production planning, process selection, multistage and dynamic production planning models, modern materials management techniques like Just-in-Time, Kanban etc., single and multiple source models. Prerequisite: SE 402 or equivalent

Basic problem and methods; pattern classification; feature extraction and learning methods; heuristic search techniques; goal directed and ordered search; representation techniques; production systems; semantic networks and frames; input/output systems; problem solving and expert systems; expert systems in automation systems, CAD/CAM, material handling, scheduling, and process control. Prerequisite: Graduate Standing

Analysis of costs of manufacture and discussion of the economics of low, medium, and high volume manufacture with emphasis on the factors of production. Economics of replacement. Prerequisite: SE 508

Computer processing and recognition of pictorial data; mathematical description of images and human perception picture digitization and encoding; image processing hardware; unitary transforms and image compression; image enhancement, restoration, and segmentation; shape description and pattern recognition; application to motion estimation. Robot automatic guidance, image tracking systems, feature extraction similarity measures, clustering techniques, syntactic methods in pattern recognition and applications. Prerequisite: SE 656

Systematic presentation of conceptual and pragmatic metrologies, tools, and techniques for productivity measurement, evaluation, planning, and improvement. Focus is on productivity engineering and management as ongoing, consistent process through a formalized, rational, and unified treatment of the productivity four-phases cycle. Prerequisite: SE 323 or equivalent

Quantitative study of the non-traditional material removal and forming processes. Economic aspects as well as theory and industrial applications. Electrochemical machining, electrical discharge machining, high energy forming, and laser and electron beam machining. Prerequisite: SE 322. (Crosslisted with ME 572)

Remote control systems architecture; introduction to network layers structure; transmission media, infrared, transmission lines, ultrasonic, laser, radio propagation. Signal modulation and coding, communication protocols, radio transmitter/receivers, microcomputer based systems, data acquisition and telemetry,servomechanisms, manipulators, image feedback systems; advanced, communication,command, and control systems; unmanned aircraft and space vehicles control systems. Prerequisites: SE 401; SE 416 or equivalent

Dynamic equations of rigid bodies; missile dynamic equations; introduction to missile aerodynamics; linearization of the equations of motion; gain scheduling techniques; longitudinal equations of motion, longitudinal autopilot; missiles lateral dynamics; lateral autopilot; inertia cross coupling; advanced control systems; measurement of missile motion, gyros, laser gyros; guidance systems techniques and design. Prerequisite: SE 416 or equivalent. (Crosslisted with ME 552)

The objective of this course is to select a specific area in Systems & Control and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council.

The objective of this course is to select a specific area in Operations & Research and study cases and research papers to enable the student to conduct research at the frontier of this area. The specific contents of the special topics will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council.

The objective of this course is to select a specific area in Robotics and Intelligent System, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council.

The objective of this course is to select a specific area in Production Systems and Quality Control, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council.

The objective of this course is to select a specific area in Distributed Computer Control & Control Applications, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council.

The objective of this course is to select a specific area in Man-Machine Systems, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council.

PhD students are required to attend departmental seminars delivered by faculty, visiting scholars and graduate students. Additionally, each PhD student should present at least one seminar on a timely research topic. PhD students should pass the comprehensive examination as a part of this course. This course is pre-requisite to registering the PhD dissertation XXX X710. The course is graded as pass or fail.

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Prerequisite: SE 699