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.
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.
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.
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.
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.
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.
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C]
ECES 523 Detection & Estimation Theory 3.0 Credits
This course introduces the field of detection and estimation and provides tools for classifying and learning about patterns in the face of total, partial or incomplete prior knowledge. Topics covered include Bayes classifier; Parametric estimation and supervised learning (MLE and Bayes Learning); Hypothesis testing; Decision Fusion; Unsupervised learning; and Non parametric testing.
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [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.
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.
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.
Repeat Status: Not repeatable for credit
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.
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.
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.
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.
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.
Repeat Status: Not repeatable for credit
ECES 620 Multimedia Forensics and Security 3.0 Credits
This course introduces students to fundamental concepts in multimedia forensics and security. Topics covered include signal processing and machine learning techniques to detect forgeries, identify editing or manipulation, and determine the source of an image or video through direct signal analysis.
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C]
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.
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.
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.
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.
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.
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.
Repeat Status: Not repeatable for credit
ECES 641 Bioinformatics 3.0 Credits
This course will focus on developing the computational, algorithmic, and database navigational skills required to analyze genomic data that have become available with the development of high throughput genomic technologies. We will also illustrate statistical signal processing concepts such as dynamic programming, hidden markov models, information theoretic measures, and assessing statistical significance. The goals will be achieved through lecture and lab exercises that focus on genomic databases, genome annotation via hidden markov models, sequence alignment through dynamic programming, metagenomic analyses, and phylogenetics with maximum likelihood approaches.
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.
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.
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.
Repeat Status: Not repeatable for credit
ECES 650 Statistical Analysis of Genomics 3.0 Credits
This course focuses on the computational and statistical methods required to analyze metagenomic data. Students learn R and QIIME for conducting analyses. Students learn how to classify DNA sequences, distance and diversity metrics, ordination (ordering) techniques, and comparative statistical methods such as ANOVA and variations.
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.
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.
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.
Repeat Status: Not repeatable for credit
ECES 671 Seminar in Systems II 2.0 Credits
Continues ECES 670.
Repeat Status: Not repeatable for credit
ECES 672 Seminar in Systems III 2.0 Credits
Continues ECES 671.
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.
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.
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.
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.
Repeat Status: Not repeatable for credit
Prerequisites: ECES 684 [Min Grade: C] and BMES 621 [Min Grade: C]
ECES 697 Research In Systems Engineering 1.0-12.0 Credit
Research in systems engineering.
Repeat Status: Can be repeated multiple times for credit
ECES 699 Supervised Study in Systems Engineering 0.0-9.0 Credits
Supervised study in systems 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.
Repeat Status: Can be repeated multiple times for credit
ECES 802 Advanced Topics in Systems II 3.0 Credits
Continues ECES 801.
Repeat Status: Can be repeated multiple times for credit
ECES 803 Advanced Topics in Systems III 3.0 Credits
Continues ECES 802.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Repeat Status: Can be repeated multiple times for credit
ECES I599 Independent Study in ECES 0.0-12.0 Credits
Self-directed within the area of study requiring intermittent consultation with a designated instructor.
Repeat Status: Can be repeated multiple times for credit
ECES I699 Independent Study in ECES 0.0-12.0 Credits
Self-directed within the area of study requiring intermittent consultation with a designated instructor.
Repeat Status: Can be repeated multiple times for credit
ECES I799 Independent Study in ECES 0.0-12.0 Credits
Self-directed within the area of study requiring intermittent consultation with a designated instructor.
Repeat Status: Can be repeated multiple times for credit
ECES I899 Independent Study in ECES 12.0 Credits
Self-directed within the area of study requiring intermittent consultation with a designated instructor.
Repeat Status: Can be repeated multiple times for credit
ECES I999 Independent Study in ECES 12.0 Credits
Self-directed within the area of study requiring intermittent consultation with a designated instructor.
Repeat Status: Can be repeated multiple times for credit
ECES T580 Special Topics in ECES 0.0-12.0 Credits
Topics decided upon by faculty will vary within the area of study.
Repeat Status: Can be repeated multiple times for credit
ECES T680 Special Topics in ECES 0.0-12.0 Credits
Topics decided upon by faculty will vary within the area of study.
Repeat Status: Can be repeated multiple times for credit
ECES T780 Special Topics in ECES 0.0-12.0 Credits
Topics decided upon by faculty will vary within the area of study.
Repeat Status: Can be repeated multiple times for credit
ECES T880 Special Topics in ECES 0.0-12.0 Credits
Topics decided upon by faculty will vary within the area of study.
Repeat Status: Can be repeated multiple times for credit
ECES T980 Special Topics in ECES 0.0-12.0 Credits
Topics decided upon by faculty will vary within the area of study.
Repeat Status: Can be repeated multiple times for credit
