Electrical & Computer Engineering - Systems

Courses

ECES 510 Analytical Methods in Systems 3.0 Credits

This course is intended to provide graduate student in the field of signal and image processing with the necessary mathematical foundation, which is prevalent in contemporary signal and image processing research and practice.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 511 Fundamentals of Systems I 3.0 Credits

Core course. Covers linear operators, including forms and properties (differential equations, transfer function, state space, causality, linearity, and time invariance); impulse response, including convolution, transition matrices, fundamental matrix, and linear dynamical system; definition, including properties and classification; representation, including block diagrams, signal flow, and analog and digital; properties, including controllability and observability; and eigenstructure, including eigenvalues and eigenvector and similarity transformations.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 512 Fundamentals of Systems II 3.0 Credits

Core course. Covers realization and identification, including minimal realization, reducibility and equivalence of models, and identification of systems; stability, including bounded input-bounded output, polynomial roots, and Lyapunov; and feedback compensation and design, including observers and controllers and multi-input/multi-output systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 511 [Min Grade: C]

ECES 513 Fundamentals of Systems III 3.0 Credits

Core course. Covers multivariable systems, numerical aspects of system analysis and design, design of compensators, elements of robustness, and robust stabilization.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 521 Probability & Random Variables 3.0 Credits

Probability concepts. Single and multiple random variables. Functions of random variables. Moments and characteristic functions. Random number and hypothesis testing. Maximum likelihood estimation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 522 Random Process & Spectral Analysis 3.0 Credits

Random Process. Poisson Process, Shot Noise. Gaussian Process. Matched Filters. Kalman Filters. Power Spectral Density. Autocorrelation and cross correlation. PSD estimation. Entropy. Markov Processes. Queuing Theory.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C]

ECES 523 Detection & Estimation Theory 3.0 Credits

Detection of signals in noise. Bayes criterion. NP criterion. Binary and M_ary hypotheses. Estimation of signal parameters. MLE and MAP estimation. 1D and 2D signals. ROC Analyses. Decision fusion.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C] and ECES 522 [Min Grade: C]

ECES 558 Digital Signal Processing for Sound & Hearing 3.0 Credits

Introduction to the computational modeling of sound and the human auditory system. Signal processing issues, such as sampling, aliasing, and quantization, are examined from an audio perspective. Covers applications including audio data compression (mp3), sound synthesis, and audio watermarking.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 559 Processing of the Human Voice 3.0 Credits

Introduction to the computational modeling of the human voice for analysis, synthesis, and recognition. Topics covered include vocal physiology, voice analysis-synthesis, voice data coding (for digital communications, VoIP), speaker identification, speech synthesis, and automatic speech recognition.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C] and ECES 558 [Min Grade: C]

ECES 561 Medical Robotics I 3.0 Credits

This course will introduce the emerging, multidisciplinary field of medical robotics. Topics include: introduction to robot architecture, kinematics, dynamics and control; automation aspects of medical procedures; safety, performance limitations; regulatory and economics and future developments.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 562 Medical Robotics II 3.0 Credits

This course will continue the introduction to the emerging, multidisciplinary field of medical robotics. Topics include: medical procedure automation; robot testing and simulation techniques; This is a project based course that will afford students the opportunity to work with existing medical robotic systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 561 [Min Grade: C]

ECES 604 Optimal Estimation & Stochastic Control 3.0 Credits

Introduction to control system problems with stochastic disturbances; linear state space filtering, Kalman Filtering, Non-linear systems; extended Kalman Filtering. Robust and H-infinity methods.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C] and ECES 521 [Min Grade: C]

ECES 607 Estimation Theory 3.0 Credits

General characteristics of estimators. Estimators: least squares, mean square, minimum variance, maximum a posteriori, maximum likelihood. Numerical solution. Sequential estimators.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 614 Passive Network Synthesis 3.0 Credits

An introduction to approximation theory; driving point functions; realizability by lumped-parameter circuits; positive real functions; properties of two and three element driving point functions and their synthesis; transfer function synthesis; all-pass networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 615 Analysis & Design of Linear Active Networks 3.0 Credits

DC and AC models of bipolar transistors and FETs; design of differential operational amplifiers; optimal design of broad-band IC amplifiers; design of tuned amplifiers; design for optimal power gain, distortion, and efficiency; noise in transistor circuits.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 621 Communications I 3.0 Credits

Covers modulation techniques: baseband PAM, passband PAM, QAM, and PSK; orthogonal signaling: FSK; symbol/vector detection: matched filter and correlation detector; sequence detection: ISI; equalization: adaptive and blind; carrier synchronization; and timing recovery.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 622 Communications II 3.0 Credits

Covers shot noise, noise in detectors, analog fiberoptic systems, carrier and subcarrier modulation, digital systems bit error rates for NRZ and RZ formats, coherent optical communication systems-heterodyne and homodyne systems, wavelength division multiplexing, system design concepts, power budgets, rise time budgets, and optical switching networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 623 Communications III 3.0 Credits

Covers fundamentals of information theory: information measure, entropy, and channel capacity; source encoding and decoding; rate distortion theory; linear codes; block codes; convolutional codes, Viterbi algorithm; encryption and decryption; and spread spectrum communications.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 631 Fundamentals of Deterministic Digital Signal Processing 3.0 Credits

Fundamentals of Deterministic Digital Signal Processing. This course introduces the fundamentals of deterministic signal processing.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 632 Fundamentals of Statistical Digital Signal Processing 3.0 Credits

Fundamentals of Statistical Deterministic Digital Signal Processing. The course covers topics on statistical signal processing related to data modeling, forecasting and system identification.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 640 Genomic Signal Processing 3.0 Credits

This course focuses on signal processing applied to analysis and design of biological systems. This is a growing area of interest with many topics ranging from DNA sequence analysis, to gene prediction, sequence alignment, and bio-inspired signal processing for robust system design.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 642 Optimal Control 3.0 Credits

Introduces the concept of optimal control first by static optimization for state space formulated systems. The concept is expanded as the linear quadratic regulator problem for dynamic systems allowing solution of the optimal control and suboptimal control problems for both discrete and continuous time. Additional topics include the Riccati equation, the tracking problem, the minimum time problem, dynamic programming, differential games and reinforcement learning. The course focuses on deriving, understanding, and implementation of the algorithms.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 643 Digital Control Systems Analysis & Design 3.0 Credits

Covers analysis and design of sampled-data control system using Z-transform and state-variable formulation, sampling, data reconstruction and error analysis, stability of linear and non-linear discrete time systems by classical and Lyapunov's second method, compensator design using classical methods (e.g., rootlocus) and computer-aided techniques for online digital controls, optimal control, discrete-time maximum principle, sensitivity analysis, and multirate sampled-data systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 513 [Min Grade: C]

ECES 644 Computer Control Systems 3.0 Credits

Introduction to the fundamentals of real-time controlling electromechanical dynamic systems, including modeling, analysis, simulation, stabilization and controller design. Control design approaches include: pole placement, quadratic and robust control performances.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 651 Intelligent Control 3.0 Credits

Concepts of Intelligence in Engineering Systems, Learning Automation, Principles of Knowledge Representation. Levels of Resolution and Nestedness. Organization of Planning: Axioms and Self-Evident Principles.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 660 Machine Listening and Music IR 3.0 Credits

This course introduces methods for the computational analysis, recognition, and understanding of sound and music from the acoustic signal. Covered applications include sound detection and recognition, sound source separation, artist and song identification, music similarity determination, and automatic transcription.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 670 Seminar in Systems I 2.0 Credits

Involves presentations focused on recent publications and research in systems, including communications, controls, signal processing, robotics, and networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 671 Seminar in Systems II 2.0 Credits

Continues ECES 670.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 672 Seminar in Systems III 2.0 Credits

Continues ECES 671.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 681 Fundamentals of Computer Vision 3.0 Credits

Develops the theoretical and algorithmic tool that enables a machine (computer) to analyze, to make inferences about a "scene" from a scene's "manifestations", which are acquired through sensory data (image, or image sequence), and to perform tasks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 682 Fundamentals of Image Processing 3.0 Credits

The course introduces the foundation of image processing with hands-on settings. Taught in conjunction with an imaging laboratory.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 684 Imaging Modalities 3.0 Credits

This course is intended to produce students and image processing with a background on image formation in modalities for non-invasive 3D imaging. The goal is to develop models that lead to qualitative measures of image quality and the dependence of quality imaging system parameters.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 685 Image Reconstruction Algorithms 3.0 Credits

This course is intended to provide graduate students in signal and image processing with an exposure to the design and evaluation of algorithms for tomographic imaging.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 684 [Min Grade: C] and BMES 621 [Min Grade: C]

ECES 690 Special Topics in Systems Engineering 9.0 Credits

Covers special topics of interest to students and faculty.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 697 Research In Systems Engineering 1.0-12.0 Credit

Research in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 699 Supervised Study in Systems Engineering 9.0 Credits

Supervised study in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 801 Advanced Topics in Systems I 3.0 Credits

Familiarizes students with current research results in their field of interest, specifically in works reported in such journals as The IEEE Transactions.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 802 Advanced Topics in Systems II 3.0 Credits

Continues ECES 801.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 803 Advanced Topics in Systems III 3.0 Credits

Continues ECES 802.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 811 Optimization Methods for Engineering Design 3.0 Credits

Applications of mathematical programming and optimization methods in engineering design problems such as networks, control, communication, and power systems optimization. Optimization problem definition in terms of objective function, design variables, and design constraints. Single variable and multivariable search methods for unconstrained and constrained minimization using Fibonacci, gradient, conjugate gradient, Fletcher-Powell methods and penalty function approach. Classical optimization--Lagrange multiplier, Kuhn-Tucker conditions. Emphasis is on developing efficient digital computer algorithms for design.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 812 Mathematical Program Engineering Design 3.0 Credits

Approximation theory projection theorem. Solution of nonlinear equations by Newton-Raphson method. Resource allocation, linear programming, and network flow problems. Integer programming for digital filter design and power system expansion problems. Gamory's algorithm.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 813 Computer-Aided Network Design 3.0 Credits

Simulation and circuit analysis programs: PCAP, MIMIC, GASP. Data structures. Algorithms. Languages. Interactive I/O. Integration of subroutines for simulation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 817 Non-Linear Control Systems 3.0 Credits

Covers key topics of feedback linearization, sliding mode control, model reference adaptive control, self-tuning controllers and on-line parameter estimation. In addition additional no n-linear topics such as Barbalat’s Lemma, Kalman-Yakubovich Lemma, passivity, absolute stability, and establishing boundedness of signals are presented. The focus of the course is the understanding each of these algorithms in detail through derivation and their implementation through coding in Matlab and Simulink.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 513 [Min Grade: C]

ECES 818 Machine Learning & Adaptive Control 3.0 Credits

System identification and parameter estimation, gradient search, least squares and Neural Networks methods. Closed loop implementation of system learning and self-organizing controllers. Random searching learning systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 821 Reliable Communications & Coding I 3.0 Credits

Covers fundamentals of information theory, including measures of communication, channel capacity, coding for discrete sources, converse of coding system, noisy-channel coding, rate distortion theory for memoryless sources and for sources with memory, and universal coding.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C] and ECES 522 [Min Grade: C]

ECES 822 Reliable Communications & Coding II 3.0 Credits

Introduces algebra of coding, including groups, rings, fields, and vector fields. Covers finite fields, decoding circuitry, techniques for coding and decoding, linear codes, error-correction capabilities of linear codes, dual codes and weight distribution, important linear block codes, perfect codes, and Plotkin's and Varshamov's bounds.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 821 [Min Grade: C]

ECES 823 Reliable Communications & Coding III 3.0 Credits

Continues techniques for coding and decoding. Covers convolutional codes; Viterbi algorithm; BCH, cyclic, burst-error-correcting, Reed-Solomon, and Reed-Muller codes; and elements of cryptography.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 822 [Min Grade: C]

ECES 890 Advanced Special Topics in Systems Engineering 1.0-9.0 Credit

Covers advanced special topics of interest to students and faculty.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 898 Master's Thesis in Systems Engineering 1.0-12.0 Credit

Master's thesis in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 921 Reliable Communications & Coding I 3.0 Credits

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 997 Dissertation Research in Systems Engineering 1.0-12.0 Credit

Graded Ph.D. dissertation in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 998 Ph.D. Dissertation in Systems Engineering 1.0-12.0 Credit

Ph.D. dissertation in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

  • Schedule of Classes
  • All Course Descriptions
  • Co-op
  • Academic Advising
  • Admissions
  • Tuition & Fees
LEARN MORE