Electrical Engineering/Telecommunications Engineering
About the Program
Master of Science in Electrical/Telecommunications Engineering (MSEET): 45.0 - 48.0 quarter credits
Doctor of Philosophy: 90.0 quarter credits
Fueled by the rapid spread of technologies such as electronic mail, cellular and mobile phone systems, interactive cable television, and the information superhighway, Drexel's program in Telecommunications Engineering responds to the growing demand for engineers with telecommunications expertise. The program combines a strong foundation in telecommunications engineering with training in other important issues such as global concerns, business, and information transfer and processing.
Drexel University's program in Telecommunications Engineering combines the expertise of its faculty in electrical and computer engineering, business, information systems, and humanities. Through its interdisciplinary approach, Drexel's Telecommunications Engineering program trains and nurtures the complete telecommunications engineer.
The MS in Electrical Engineering/Telecommunications Engineering degree is awarded to students who demonstrate in-depth knowledge of the field. The average time required to complete the master's degree is two year of full-time or three years of part-time study.
For more information, visit the Department of Electrical and Computer Engineering’s web site.
Admission Requirements
Applicants must meet the general requirements for graduate admission, which include at least a 3.0 GPA for the last two years of undergraduate study and for any graduate level study undertaken, and are required to hold a bachelor of science degree in electrical engineering or a related field. Applicants whose undergraduate degrees are not in the field of electrical engineering may be required to take a number of undergraduate courses. The GRE General Test is required of applicants for full-time MS and PhD programs. Applicants whose native language is not English and who do not have a previous degree from a U.S. institution are required to take the Test of English as a Foreign Language (TOEFL).
MS in Electrical and Telecommunications Engineering
The Master of Science in Electrical and Telecommunications Engineering curriculum encompasses 45.0 or 48.0 (with the Graduate Co-Op) approved credit hours, chosen in accordance with the following requirements and a plan of study arranged with the departmental graduate advisor in consultation with the student's research advisor (if applicable). Before the end of the first quarter in the Department of Electrical and Computer Engineering, for a full-time student, or by the end of the first year for a part-time student, this lan of study must be filed and approved with the departmental graduate advisor.
Degree Requirements
A total of at least 30.0 credit hours must be taken from among the graduate course offerings of the Department of Electrical and Computer Engineering. These credits must be taken at Drexel University. No transfer credit may be used to fulfill these requirements, regardless of content equivalency.
| Electrical and Computer Engineering Courses | 30.0 | |
| Select ten of the following: | ||
| Principles of Computer Networking | ||
| Performance Analysis of Computer Networks | ||
| Advanced Topics in Computer Networking | ||
| Fundamentals of Systems I | ||
| Fundamentals of Systems II | ||
| Fundamentals of Systems III | ||
| Probability & Random Variables | ||
| Random Process & Spectral Analysis | ||
| Detection & Estimation Theory | ||
| Fundamentals of Deterministic Digital Signal Processing | ||
| Fundamentals of Statistical Digital Signal Processing | ||
| Fundamentals of Image Processing | ||
| Fundamentals of Communications Engineering | ||
| Physical Foundations of Telecommunications Networks | ||
| Wireless Systems | ||
| Total Credits | 30.0 | |
With the remaining required 15.0 credit hours, students may take graduate coursework, subject to the approval of the departmental graduate advisor, in electrical and computer engineering, mathematics, physics or other engineering disciplines.
In addition, students pursuing an MS in Electrical and Telecommunications Engineering are allowed and strongly encouraged to take the following course as part of their required 15.0 credit hours:
| COM 650 | Telecommunications Policy in the Information Age | 3.0 |
Although not required, students are encouraged to complete a Master’s Thesis as part of the MS studies. Those students who choose the thesis option may count up to 9.0 research/thesis credits as part of their required credit hour requirements.
Graduate Co-Op Program
Students may choose to participate in the Graduate Co-Op Program, where 6.0 credit hours can be earned for a six month co-operative education experience in industry, working on curriculum related projects. The total number of required credit hours is increased to 48 for those students who choose to pursue the Graduate Co-Op option. This change represents an increase in non-departmental required credit hours to a total of 18.0 credit hours, 6.0 of which are earned from the co-operative education experience.
Please note that ECEC 500 (Fundamentals of Computer Hardware) and ECEC 600 (Fundamentals of Computer Networks) do not count toward the credit requirements to complete the MS in Electrical Engineering degree program.
For more information on curricular requirements, visit the Department of Electrical and Computer Engineering’s web site.
PhD in Electrical Engineering
General Requirements
The following general requirements must be satisfied in order to complete the PhD in Electrical Engineering:
- 90.0 credit hours total
- candidacy examination
- research proposal
- dissertation defense
Students entering with a master’s degree in electrical or computer engineering or a related field will be considered a post-masters PhD student and will only be required to complete a total of 45.0 credit hours, in accordance with University policy.
Curriculum
Appropriate coursework is chosen in consultation with the student’s research advisor. A plan of study must be developed by the student to encompass the total number of required credit hours. Both the departmental graduate advisor and the student’s research advisor must approve this plan.
Candidacy Examination
The candidacy examination explores the depth of understanding of the student in his/her specialty area. The student is expected to be familiar with, and be able to use, the contemporary tools and techniques of the field and to demonstrate familiarity with the principal results and key findings.
The student, in consultation with his/her research advisor, will declare a principal technical area for the examination. The examination includes the following three parts:
- A self-study of three papers from the archival literature in the student’s stated technical area, chosen by the committee in consultation with the student.
- A written report (15 pages or less) on the papers, describing their objectives, key questions and hypotheses, methodology, main results and conclusions. Moreover, the student must show in an appendix independent work he/she has done on at least one of the papers – such as providing a full derivation of a result or showing meaningful examples, simulations or applications.
- An oral examination which takes the following format:
- A short description of the student’s principal area of interest (5 minutes, by student).
- A review of the self-study papers and report appendix (25-30 minutes, by students).
- Questions and answers on the report, the appendix and directly related background (40-100 minutes, student and committee).
In most cases, the work produced during the candidacy examination will be a principal reference for the student’s PhD dissertation; however, this is not a requirement.
Research Proposal
Each student, after having attained the status of PhD Candidate, must present a research proposal to a committee of faculty and industry members, chosen with his/her research advisor, who are knowledgeable in the specific area of research. This proposal should outline the specific intended subject of study, i.e. , it should present a problem statement, pertinent background, methods of study to be employed, expected difficulties and uncertainties and the anticipated form, substance and significance of the results.
The purpose of this presentation is to verify suitability of the dissertation topic and the candidate's approach, and to obtain the advice and guidance of oversight of mature, experienced investigators. It is not to be construed as an examination, though approval by the committee is required before extensive work is undertaken. The thesis proposal presentation must be open to all; announcements regarding the proposal presentation must be made in advance.
The thesis advisory committee will have the sole responsibility of making any recommendations regarding the research proposal. It is strongly recommended that the proposal presentation be given as soon as possible after the successful completion of the candidacy examination. The student must be a PhD candidate for at least one year before he/she can defend his/her doctoral thesis.
Dissertation Defense
Dissertation Defense procedures are described in the Office of Graduate Studies policies regarding Doctor of Philosophy Program Requirements. The student must be a PhD candidate for at least one year before he/she can defend his/her doctoral thesis.
Dual Degree
The ECE Department offers outstanding students the opportunity to receive two diplomas (BS and MS) at the same time. The program requires five (5) years to complete. Participants, who are chosen from the best undergraduates students, work with a faculty member on a research project and follow a study plan that includes selected graduate classes. This program prepares individuals for careers in research and development; many of its past graduates continued their studies toward a PhD.
For more information on eligibility, academic requirements, and tuition policy visit the Engineering Combined BS/MS page.
Facilities
Drexel University and the Electrical and Computer Engineering Department are nationally recognized for a strong history of developing innovative research. Research programs in the ECE Department prepare students for careers in research and development, and aim to endow graduates with the ability to identify, analyze, and address new technical and scientific challenges. The ECE Department is well equipped with state-of-the-art facilities in each of the following ECE Research laboratories:
Research Laboratories at the ECE Department
- Adaptive Signal Processing and Information Theory Research Group
- Applied Communications and Information Networking Center
- Applied Networking Research Laboratory
- Biochemical Signal Processing Laboratory
- Cleanroom Microfabrication Facility
- Data Fusion Laboratory
- Drexel Network Modeling Laboratory
- Drexel Wireless Systems Laboratory
- Electric Power Engineering Center
- Electronic Design Automation (EDA) Facility
- Microwave Photonics Center
- Microwave-Photonics Device Laboratories
- Music and Entertainment Technology Laboratory
- NanoPhotonics Laboratory
- Opto-Electro-Mechanical Laboratory
- Plasma and Magnetics Laboratory
- Power Electronics Research Laboratory
- Supervisory Control Laboratory
- Testbed for Power/Performance Management of Enterprise Computing Systems
Elec & Comp Engr-Computers Courses
ECEC 500 Fundamentals Of Computer Hardware 3.0 Credits
Covers computer organization and architecture; elements of computer hardware, processors, control units, and memories; hardware for basic mathematical operations; tradeoffs between speed and complexity; examples of embedded systems; microcontrollers; systems modeling.
Repeat Status: Not repeatable for credit
ECEC 501 Computational Principles of Representation and Reasoning 3.0 Credits
This course presents fundamentals of discrete mathematics as applied within the computer engineering and manufacturing environment. Students are given the theoretical background in representation and reasoning for a broad variety of engineering problems solving situations. Entity-relational techniques of representation are demonstrated to evolve into the object-oriented approach. Various search techniques are applied in the cases of representing engineering systems by using theory of automata techniques.
Repeat Status: Not repeatable for credit
ECEC 502 Principles of Data Analysis 3.0 Credits
This course presents theoretical methods and techniques of model development applicable within the computer engineering design and manufacturing environment. Students are given the theoretical background in data analysis (including "data mining"). Emphasis is on hybrid systems and discrete events systems. Various methods of recognizing regularities in data will be presented. Elements of the theory of clustering and classification will be dealt with for the paradigm of software and hardware problems.
Repeat Status: Not repeatable for credit
ECEC 503 Principles of Decision Making 3.0 Credits
This course presents theoretical fundamentals and engineering techniques of decision making and problem solving applicable within the computer engineering design and manufacturing environment. Students are given the theoretical background in optimization methods for a broad variety of situation. Elements of the theory of planning and on-line control of systems are presented within the scope of software and hardware computer design and control.
Repeat Status: Not repeatable for credit
ECEC 511 Combinational Circuit Design 3.0 Credits
Representing arithmetic. Logic and syntax data for machine processing. Switching algebra: Boolean and multiple values. Identification and classification of functions. Realizing completely specified and incompletely specified Boolean functions. Issues in designing large communication/control Boolean functions. Fault and testing of Boolean function.
Repeat Status: Not repeatable for credit
ECEC 512 Sequential Circuit Design 3.0 Credits
Finite automata and their realization by sequential machines, capabilities, transformation, and minimization of finite automata, linear finite automata. Clocked pulsed and level mode sequential circuits. Malfunctions in sequential circuits: hazards, races, lockouts, metastability. Issues of state assignment. Evolution of memory elements design: ROM vs. RAM vs. associative memory.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 511 [Min Grade: C]
ECEC 513 Design for Testability 3.0 Credits
Economics vs. Complexity vs. Strategy of Testing; Fault Models; Test Generation; Testability Analysis & Designing Testable Circuits; Testing Microprocessors, Memories and Computer Components; Test Data Compression; Fault Tolerant Hardware; Reliably vs. Availability; Redundancy and Error Correcting Codes.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 511 [Min Grade: C] and ECEC 512 [Min Grade: C]
ECEC 520 Dependable Computing 3.0 Credits
Fundamental design issues involved in building reliable, safety-critical, and highly available systems. Topics include testing and fault-tolerant design of VLSI circuits, hardware and software fault tolerance, information redundancy, and fault-tolerant distributed systems.
Repeat Status: Not repeatable for credit
ECEC 541 Robotic Computer Interface Controls I 3.0 Credits
Covers sensors, actuators, mechanical components of robots, kinematics, inverse kinematics, dynamics, and equations of motion.
Repeat Status: Not repeatable for credit
ECEC 542 Robotic Computer Interface Controls II 3.0 Credits
Covers the robot control problem, including PD, PID, position, force and hybrid controllers, resolved rate and acceleration control, and multiprocessor architecture.
Repeat Status: Not repeatable for credit
Prerequisites: ECES 641 [Min Grade: C] and ECES 643 [Min Grade: C] and ECEC 541 [Min Grade: C]
ECEC 543 Robotic Computer Interface Controls III 3.0 Credits
Covers non-linear control techniques, FLDT, and advanced topics.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 542 [Min Grade: C]
ECEC 600 Fundamentals of Computer Networks 3.0 Credits
Fundamentals design principles of ATM, Internet and local area networks; protocol layers and the Internet Architecture; medium access protocols; application protocols and TCP/IP utilities; basic principles and virtual circuit switching; naming and addressing; flow and congestion control protocols; routing algorithms; Quality-of-Service in computer networks; security issues in networks.
Repeat Status: Not repeatable for credit
ECEC 621 High Performance Computer Architecture 3.0 Credits
Maximizing single processor performance. Concepts and techniques for design of computer systems. Processor design, instruction set architecture design and implementation, memory hierarchy, pipelines processors, bus bandwidth, processor/memory interconnections, cache memory, virtual memory, advanced I/O systems, performance evaluation.
Repeat Status: Not repeatable for credit
ECEC 622 Parallel Computer Architecture 3.0 Credits
Advanced techniques of computer design. Use of parallel processing to achieve high performance levels. Fine and coarse grained parallelism. Multiple CPU parallelism, through multiprocessors, array and vector processors. Dataflow architectures and special purpose processors. Design implications of memory latency and bandwidth limitations. Speedup problems.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 621 [Min Grade: C]
ECEC 623 Advanced Parallel Computer Architecture 3.0 Credits
Modern research topics and methods in parallel computer architectures. Parallel algorithms, interconnection networks, SIMD/MIMD machines, processor synchronization, data coherence, dataflow machines, special purpose processors. Select topics in parallel computing.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 622 [Min Grade: C]
ECEC 631 Principles of Computer Networking 3.0 Credits
Principles of circuit switching, packet switching and virtual circuits; protocol layering; application layer protocols for e-mail and web applications; naming and addressing; flow control and congestion avoidance with TCP; Internet Protocol (IP); routing algorithms; router architectures; multicast protocols; local area network technologies and protocols; issues in multimedia transmissions; scheduling and policing; Quality-of-Service and emerging Internet service architectures; principles of cryptography.
Repeat Status: Not repeatable for credit
ECEC 632 Performance Analysis of Computer Networks 3.0 Credits
Covers probability theory and its applications to networks, random variable and random processes; Markov chains, multi-dimensional Markov chains; M/M/1, M/M/m, M/M/m/m, M/G/1 and G/G/1 queueing systems and their applications in computer networks; analysis of networks of queues: Kleinrock Independence Approximation; Time-reversibility and Burke's theorem; Jackson's theorem; the phenomenon of long-range dependence and its implications in network design and traffic engineering.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 631 [Min Grade: C]
ECEC 633 Advanced Topics in Computer Networking 3.0 Credits
perspectives in the areas of switch/router architectures, scheduling for best-effort and guaranteed services, QoS mechanisms and architectures, web protocols and applications, network interface design, optical networking, and network economics. The course also includes a research project in computer networking involving literature survey, critical analysis, and finally, an original and novel research contribution.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 631 [Min Grade: C] and ECEC 632 [Min Grade: C]
ECEC 654 Knowledge Engineering I 3.0 Credits
Covers conceptual modeling, including an overview of knowledge representation. Includes semantic networks, reduced semantic networks, logic of incomplete knowledge bases, extensional semantic networks, and applications of conceptual models.
Repeat Status: Not repeatable for credit
ECEC 655 Knowledge Engineering II 3.0 Credits
Covers expert systems, including language and tools of knowledge engineering. Includes reasoning about reasoning, design and evaluation, heuristics in expert systems, expert systems for decision support, and expert systems in conceptual design.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 654 [Min Grade: C]
ECEC 656 Knowledge Engineering III 3.0 Credits
Covers information-intensive systems, including information representation in autonomous systems. Includes clauses and their validation; clustering in linguistic structures; linguistic and pictorial knowledge bases; discovery in mathematics, including am; and methods of new knowledge generation.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 655 [Min Grade: C]
ECEC 661 VLSI Design 3.0 Credits
Covers CMOS design styles, techniques, and performance; VLSI computer hardware, arithmetic units, and signal processing systems; and cat tools for layout design and simulation.
Repeat Status: Not repeatable for credit
ECEC 662 VLSI Array Processors I 3.0 Credits
Covers VLSI testing, including design for testability and parallel computer architectures; signal and image processing algorithms and mapping algorithms onto array structures; and systolic array processors.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 661 [Min Grade: C]
ECEC 663 VLSI Array Processors II 3.0 Credits
Covers wavefront array processors; matching hardware to arrays; hardware design, systems design, and fault-tolerant design; and implementations and VLSI design projects.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 662 [Min Grade: C]
ECEC 671 Electronic Design Automation for VLSI Circuits I 3.0 Credits
This course focuses on the electronic design automation problems in the design process of VLSI integrated circuits. In this first quarter of the course, algorithms, techniques and heuristics structuring the foundations of contemporary VLSI CAD tools are presented. Boolean algebra, graph theory, logic minimization and satisfiability topics are presented.
Repeat Status: Not repeatable for credit
ECEC 672 Electronic Design Automation for VLSI Circuits II 3.0 Credits
This course focuses on the electronic design automation problems in the design process of VLSI integrated circuits. In this second quarter of the course, physical VLSI design steps of technology mapping, floor planning, placement, routing and timing and presented individual and team-based small-to-medium scale programming projects are assigned.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 671 [Min Grade: C]
ECEC 673 Deep Sub-Micron Integrated Circuit Design 3.0 Credits
This course focuses on the design challenges of digital VLSI integrated circuits in deep sub-micron manufacturing technologies. Automation challenges and high-performance circuit design techniques such as low-power and variation-aware design are presented. The course material is delivered in a lecture format structured on recent presentations, articles, and tutorials.
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 671 [Min Grade: C]
ECEC 690 Special Topics Computer Engineering 9.0 Credits
Covers special topics of interest to students and faculty.
Repeat Status: Can be repeated multiple times for credit
ECEC 697 Research in Computer Engineering 9.0 Credits
Research in computer engineering.
Repeat Status: Can be repeated multiple times for credit
ECEC 699 Supervised Study in Computer Engineering 9.0 Credits
Supervised study in computer engineering.
Repeat Status: Can be repeated multiple times for credit
ECEC 890 Advanced Special Topics in Computer 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
ECEC 891 Advanced Topics in Computer Engineering 0.5-9.0 Credits
Advanced topics in computer engineering.
Repeat Status: Can be repeated multiple times for credit
ECEC 898 Master's Thesis in Computer Engineering 9.0 Credits
Master's thesis in computer engineering.
Repeat Status: Can be repeated multiple times for credit
ECEC 997 Dissertation Research in Computer Engineering 1.0-12.0 Credit
Graded Ph.D. dissertation in computer engineering.
Repeat Status: Can be repeated multiple times for credit
ECEC 998 PhD Dissertation in Computer Engineering 1.0-12.0 Credit
Ph.D. dissertation in computer engineering.
Repeat Status: Can be repeated multiple times for credit
Elec & Computer Engr-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
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.
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.
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 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 522 [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 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 642 Optimal Control 3.0 Credits
This course introduces the Modern Control concepts: linear quadratic performance and practical designs for engineering applications. Topics include: calculus of variations, differential games and H-infinity methods.
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 641 [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 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] and ECES 558 [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 690 Special Topics in Systems Engineering 9.0 Credits
Covers special topics of interest to students and faculty.
Repeat Status: Can be repeated multiple times for credit
ECES 697 Research In Systems Engineering 9.0 Credits
Research in systems 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.
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
Non-Linear Systems: analysis, stability and control, Lyapunov stability, singular perturbation methods. Feedback system: Describing Functions, Circle and Popov criteria.
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C] and ECES 642 [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 9.0 Credits
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 PhD Dissertation in Systems Engineering 1.0-12.0 Credit
Ph.D. dissertation in systems engineering.
Repeat Status: Can be repeated multiple times for credit
