Electrical Engineering
About the Program
Master of Science in Electrical Engineering (MSEE): 45.0 - 48.0 quarter credits
Doctor of Philosophy: 90.0 quarter credits
The program in electrical engineering prepares students for careers in research and development, and aims to endow graduates with the ability to identify, analyze and address new technical and scientific challenges. At present, the department offers graduate coursework in six general areas: (1) computer engineering; (2) control, robotics and intelligent systems; (3) electrophysics; (4) image and signal processing and interpretation; (5) power engineering and energy; and (6) telecommunications and networking.
The Master of Science in Electrical Engineering degree requires a minimum of 45.0 approved credits chosen in accordance with a plan of study arranged with the permission of a student’s advisor and the departmental graduate advisor. Students who complete a six-month period of internship through Drexel’s Graduate Co-op Program (GCP) must complete 48.0 credits including 6.0 GCP credits.
The plan must contain a selection of core courses from the department's offerings and may include appropriate graduate courses from other engineering departments or from physics or mathematics. Further information can be obtained from the department office or from the graduate advisor.
All students also are encouraged to engage in thesis research. The combined thesis and research cannot exceed 9 credits.The program is organized so that a student may complete the degree requirements in two years of full-time study or three years of part-time study.
For more information about the programs, including information about teaching and research assistantships, visit the Department's Electrical Engineering web site.
Admission Requirements
Applicants must satisfy general requirements for graduate admission, including a minimum 3.0 GPA (on a 4.0 scale) for the last two years of undergraduate studies, as well as for any subsequent graduate work, and hold a bachelor's degree or the equivalent in electrical engineering, computer engineering, or the equivalent from an accredited college or university. A degree in science (physics, mathematics, computer science, etc. ) is also acceptable. Applicants with degrees in sciences may be required to take a number of undergraduate engineering courses. An undergraduate degree earned abroad must be deemed equivalent to a U.S. bachelor's.
Applicants for full-time MS and PhD programs must take the GRE general test. Students whose native language is not English and who do not hold a degree from a U.S.institution must take the TOEFL within two years before application.
Master of Science in Electrical Engineering
The Master of Science in Electrical Engineering curriculum encompasses 45.0 or 48.0 (with the Graduate Co-op option) 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, said plan of study must be filed and approved with the departmental graduate advisor.
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.
| Electrophysics Concentration | ||
| Required Courses | ||
| Six Electrophysics Courses (ECEE courses) | 18.0 | |
| Four General Electrical and Computer Engineering (ECEC, ECEE, ECEP, ECES, ECET) courses | 12.0 | |
| Total Credits | 30.0 | |
| Controls, Robotics, Intelligent Systems Concentration | ||
| Required Courses | ||
| ECES 511 | Fundamentals of Systems I | 3.0 |
| ECES 512 | Fundamentals of Systems II | 3.0 |
| ECES 521 | Probability & Random Variables | 3.0 |
| ECES 522 | Random Process & Spectral Analysis | 3.0 |
| Three General Electrical and Computer Engineering (ECEC, ECEE, ECEP, ECES, ECET) Courses | 9.0 | |
| Select three of the following: | 9.0 | |
| Optimal Estimation & Stochastic Control | ||
| Optimal Control | ||
| Computer Control Systems | ||
| Intelligent Control | ||
| Non-Linear Control Systems | ||
| Machine Learning & Adaptive Control | ||
| Total Credits | 30.0 | |
| Power Engineering | ||
| Required Courses | ||
| ECEP 501 | Power System Analysis | 3.0 |
| ECEP 502 | Computer Analysis of Power Systems | 3.0 |
| ECEP 503 | Synchronous Machine Modeling | 3.0 |
| Five General Electrical and Computer Engineering (ECEC, ECEE, ECEP, ECES, ECET) Courses | 15.0 | |
| Select one of the following sequences: | 6.0 | |
| Fundamentals of Systems I | ||
or ECES 512 | Fundamentals of Systems II | |
| Probability & Random Variables | ||
or ECES 522 | Random Process & Spectral Analysis | |
| Total Credits | 30.0 | |
| Signal/Image Processing | ||
| Required Courses | ||
| ECES 521 | Probability & Random Variables | 3.0 |
| ECES 522 | Random Process & Spectral Analysis | 3.0 |
| ECES 523 | Detection & Estimation Theory | 3.0 |
| ECES 631 | Fundamentals of Deterministic Digital Signal Processing | 3.0 |
| ECES 682 | Fundamentals of Image Processing | 3.0 |
| Five General Electrical and Computer Engineering (ECEC, ECEE, ECEP, ECES, ECET) Courses | 15.0 | |
| Total Credits | 30.0 | |
| Non-designated Specialization | ||
| Required Courses | ||
| Three 3-course Departmental Sequences ** | 27.0 | |
| General Electrical and Computer Engineering (ECEC, ECEE, ECEP, ECES, ECET) Courses | 3.0 | |
| Total Credits | 30.0 | |
| ** | Students should check with the departmental graduate advisor for more information about these sequences. |
Options for Degree Fulfillment
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.
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.
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 48.0 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
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
