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Computer Science
The following conventions are used for numbering graduate courses in different areas of computer science:
(x stands for a digit in the range 09)
Credits: 3
Introduction to the fundamental concepts and techniques of discrete mathematics that are essential for the study of computer science. The main goal of this course is to develop mathematical skills and sophistication for proving theorems, solving problems, and counting and approximating values. Topics include sets; elementary logic; numbers; functions and relations;summations; generating functions; elementary number theory; elementary probability, statistics, and combinatorics (e.g. Burnside's Lemma); introduction to algebraic systems, including groups; and applications of these topics in computer science.
Prerequisites: MATH 152, MATH 221, and at least one math course beyond linear algebra.
CMSC 608 Graduate Seminar Credits: 0
This course exposes the graduate student in CMSC to current research in areas of interest to the department's faculty and students. The speakers are usually researchers outside the department and university. On occasion, speakers may be faculty members or advanced students. There are no credits
for this course, which meets once a week, but all graduate students are required to attend for two semesters.
CMSC 611 Advanced Computer Architecture Credits: 3
Memory system design, pipeline structures, vector computers, scientific array processors, multiprocessor architecture. Within each topic, the emphasis is on fundamental limitations: memory bandwidth, interprocessor
communication, processing bandwidth and synchronization.
Prerequisite: CMSC 411 or permission of instructor.
CMSC 621 Advanced Operating Systems Credits: 3
A detailed study of advanced topics in operating systems including: synchronization mechanisms,
virtual memory, deadlocks, distributed resource sharing, computer security, and modeling of operating systems.
Prerequisite: CMSC 421 or permission of instructor.
CMSC 625 Modeling and Simulation of Computer Systems Credits: 3
Performance evaluation methods, Markovian queuing models, open networks of queues, closed product form queuing networks, simulation and measurement of computer systems, benchmarking, and workload characterization.
Prerequisite: CMSC 411 or CMSC 421, or permission of instructor.
CMSC 631 Principles of Programming Languages Credits: 3
A comparison of three types of modern programming languages: assertive, functional, and logic based. Fundamental semantic methods, including operational, axiomatic, and denotational semantics and corresponding techniques for program verification, including Hoare's logic,
Dijkstra s predicate transformers, and denotational methods.
Prerequisite: CMSC 331 or permission of instructor.
CMSC 635 Advanced Computer Graphics Credits: 3
A study of advanced topics in computer graphics emphasizing algorithms for display of 3D objects including: wire frame representation, polygon mesh models, shading algorithms, parametric representation of curves, hidden surface elimination, fractals, and ray tracing. Other topics include: advanced topics from the computer graphics literature, page description languages, CORE, GKS, PHIGS, CGI, the X window system, X window intrinsics, Motif and widget programming.
Prerequisite: CMSC 435 or permission of instructor.
CMSC 641 Design and Analysis of Algorithms Credits: 3
Fundamental algorithms, mathematical tools for analyzing algorithms, and strategies for designing algorithms. Topics include graph algorithms (including network flow), parallel algorithms, and algorithms for selected combinatorial tasks. Tools include asymptotic notations, recurrences, amortized analysis, and probabilistic analysis. Strategies include divide and conquer, greedy, dynamic programming, time space tradeoff, and randomization. Introduction to NP completeness.
Prerequisite: CMSC 441 or permission of instructor.
CMSC 645 Advanced Software Engineering Credits: 3
Modern approaches to software development: requirements analysis, system design techniques,
formal description techniques, implementation, testing, debugging, metrics, human factors, quality assurance, cost estimation, maintenance, and tools.
Prerequisite: CMSC 445 or permission of instructor.
CMSC 651 Automata Theory and Formal Languages Credits: 3
Formal languages and their corresponding classes of automata: regular languages and finite automata, context free languages and pushdown automata, context sensitive languages and linear bounded automata, recursively innumerable sets, and Turing machines. Also, pumping lemmas, closure properties, and decision problems for various classes of languages. Other sorts of automata may be studied, including multi headed automata, probabilistic automata, and Petri nets.
Prerequisite: CMSC 451 or permission of instructor.
CMSC 652 Cryptography and Data Security Credits: 3
Conventional and public key cryptography. Selected crypt systems, including DES and RSA. Digital signatures, pseudo-random number generation, cryptographic protocols, and cryptanalytic techniques. Applications of cryptography to electronic commerce.
Prerequisites: CMSC 441 and MATH 221 or permission of instructor.
CMSC 653 Coding Theory and Applications Credits: 3
An introduction to the theory of error correcting codes with an emphasis on applications and implementations. Shannon's theorems, bounds on code weight distributions, linear codes, cyclic codes, Hamming and BCH codes, linear sequential circuits, encoding/decoding algorithms. Other topics may be drawn from Goppa, ReedSolomon, QR codes, nonlinear codes, and convolutional codes.
Prerequisite: CMSC 203 or MATH 221, or permission of instructor.
CMSC 655 Numerical Computations
Credits: 3
Numerical algorithms and computations in a parallel processing environment. The architecture of supercomputers, vectorizing compilers and numerical algorithms for parallel computers.
Prerequisite: CMSC 411 and Math 221, or permission of instructor.
CMSC 656 Symbolic and Algebraic Processing Credits: 3
Applications and Foundations of Symbolic Algebra. Applications and examples are studied using at least one large symbolic algebra package. Symbolic algebra combines elements of AI, analysis of algorithms, and abstract algebra. Foundations include problems of representation, canonical and normal forms, polynomial simplification, Buchberger s algorithm, g.c.d. in one and several variables, panic methods, and formal methods for integration.
Prerequisites: CMSC 203 and CMSC 341 or permission of instructor.
CMSC 657 Networks and Combinatorial Optimizations Credits: 3
Graph theoretic concepts, unimodular matrices, transportation problems, minimum cost network flows, maximal flows in networks, shortest path algorithms,spanning three problems, multi-commodity flows and decomposition algorithms, assignment and matching problems, computational complexity of algorithms, and other special topics such as matroid theory and nonlinear network minimization.
Prerequisite: CMSC 641 or permission of instructor.
CMSC 661 Principles of Data Base Systems Credits: 3
Advanced topics in the area of data base management systems: data models and their underlying mathematical foundations, data base manipulation and query languages, functional dependencies, physical data organization and indexing methods, concurrency control, crash recovery, data base security, and distributed data bases.
Prerequisite: CMSC 461 or permission of instructor.
CMSC 665 Network Information Retrieval Credits: 3
CMSC 671 Principles of Artificial Intelligence Credits: 3
A study of topics central to artificial intelligence, including logic for problem-solving, intelligent search techniques, knowledge representation,
inference mechanisms, expert systems, and Prolog programming.
Prerequisite: CMSC 471 or permission of instructor.
CMSC 681 Advanced Computer Networks Credits: 3
Topics central to the design and development of advanced computer communication networks, including distributed and failsafe routing in large and dynamic networks, gateways and interconnection of heterogeneous networks, flow control and congestion avoidance techniques, network architectures, computer and communication security, communication protocol standards, formal specification and verification of protocols, implementation and conformance testing of protocol standards, network partitioning and intelligent reconfiguration of networks.
Prerequisite: CMSC 481 or CMSC 621, or permission of instructor.
CMSC 691 Special Topics in Computer Science Credits: 13
CMSC 692 Communication Skills for Computer Scientists Credits: 1
The student will fulfill this requirement by participating in a seminar course. Students will gain experience in public speaking, learn basic teaching techniques, and present papers on a topic chosen by the instructor.
CMSC 693 Research and Writing Project for Computer Scientists Credits: 12
The student will complete a research project on a topic approved by the student's advisor, write a technical report, and defend this work in an oral presentation.
CMSC 699 Independent Study in Computer Science Credits: 13
CMSC 711 VLSI Systems Credits: 3
A study of structured system design methodology in the VLSI environment. The topics include VLSI implementation of logic, system controllers, system timing, abstractions of VLSI circuits, algorithms for VLSI processor arrays, highly concurrent VLSI systems, and VLSI design tools.
Prerequisite: CMSC 611 or permission of instructor.
CMSC 721 Theory of Processes Credits: 3 Formal approaches to the theory of communicating systems of processes, and logical systems for reasoning about them. Specific systems may include Milner's calculus of communicating systems (CCS), Hoare's communicating sequential processes (CSP), and Kahn's applications of fixpoint theory to communicating processes.
Prerequisite: CMSC 621 or CMSC 631 or CMSC 681, or permission of instructor.
CMSC 731 Semantics of Programming Languages Credits: 3
The fundamentals of axiomatic and denotational semantics, together with their corresponding techniques for program specification and verification. Axiomatic methods include Hoare s logic and Dijkstra s predicate transformers. Denotational methods include fixpoint theory and an introduction to the lambda calculus. Denotational methods are used to prove the soundness of selected axiomatic proof rules.
Prerequisite: CMSC 631 or permission of instructor.
CMSC 741 Theory of NP Completeness
Credits: 3
An in-depth study of the classes P and NP, along with the concepts of reducibility and completeness. NP complete problems are
surveyed, and reduction techniques are examined in greater detail. An important goal is to develop skill at proving problems NP complete.
Prerequisite: CMSC 641 or permission of instructor.
Models of parallel computation and methods for the representation of parallel algorithms are presented. Measures of parallel complexity, and techniques for analyzing algorithms with respect to these new measures, and parallel complexity classes, such as NC, are studied.
Prerequisite: CMSC 641 or permission of instructor.
CMSC 751 Theory of Computation Credits: 3 Formal models of computation, such as Turing machines, RAM models, and loop languages are all shown to compute the class of partial recursive functions, leading to the Church Turing thesis. Basic recursive function theory, including universal functions, undecidable problems, and properties of recursive and r.e. sets. Basic concepts of first-order logic and their relationship to recursion theory. Topics in advanced recursion theory may include abstract complexity theory, oracles, the arithmetic hierarchy, and priority methods.
Prerequisite: CMSC 651 or permission of instructor.
CMSC 761 Theory of Relational Data Bases Credits: 3
An in-depth study of relational data base theory. Topics include first-order logic, relational calculus and algebra, query languages, query optimization, functional and multi-valued dependencies, normal forms, and concurrency control.
Prerequisite: CMSC 661 or permission of instructor.
CMSC 771 Heuristics and Knowledge Representation Credits: 3
An in-depth study of two topics central to artificial intelligence: heuristics and knowledge representation. Topics in heuristics will include the use of heuristics in problem solving, heuristic search techniques, the admissibility of heuristic search algorithms, performance analysis of heuristic methods, and heuristics for game playing. Topics in knowledge representation will include predicate calculus, frame representations, semantic nets, and inheritance.
Prerequisite: CMSC 671 or permission of instructor.
CMSC 781 Distributed Computing Credits: 3
Topics central to the design of distributed computing systems including distributed synchronization and resource sharing, concurrency control in distributed data bases, distributed simulations, languages for distributed computing, proof techniques for distributed systems, and distributed operating systems.
Prerequisites: CMSC 621 and CMSC 681, or permission of instructor.
CMSC 791 Graduate Seminar Credits: 3
CMSC 799 Master's Thesis Research
Credits: 1-6
This course is for students in the CMSC Master's Program engaged in master's thesis research; may be taken for repeated credits, but only a maximum of 6 credit hours applied toward M.S. thesis option requirements.
Prerequisite: Open only to CMSC thesis option students.
CMSC 899 Doctoral Dissertation Research Credits: 1-6
ENEE 608 Graduate Seminar Credits: 0
This course exposes the graduate student in EE to the current research in areas of interest to the departments faculty and students. The speakers are usually researchers outside the department and university. On occasion, speakers may be faculty members or advanced students. There are no credits for this course, which meets once a week, but all graduate students are required to attend for two semesters.
ENEE 610 Digital Signal Processing Credits:3
Fundamentals of digital processing of signals: discrete signals and systems, the Z transform, Fourier analysis of discrete time signals and systems, the discrete time Fourier transform and discrete Fourier transform, direct and computer aided design of recursive and non-recursive digital filters, finite length register effects in digital filter implementations, DSP chip development systems, and introduction to recent issues in digital signal processing.
Prerequisite: Basic signal and systems theory, or consent of instructor.
ENEE 611 Adaptive Signal Processing
Credits: 3
Fundamentals of adaptive filters and associated algorithms: Wiener filters, linear prediction (forward and backward), steepest descent methods, least squares methods, stochastic gradient-based algorithms, Kalman filters and standard recursive structures, recursive least squares (RLS) estimation, recursive least squares lattice filters, QR decomposition techniques, fast transversal filters and fast recursive algorithms, algorithm performance, and discussion of selective applications.
Prerequisites: ENEE 610 and 620, or consent of instructor.
ENEE 612 Digital Image Processing Credits: 3
Principles of two dimensional processing of image data: fundamentals of 2D signal processing, image transforms, image enhancement, image filtering and restoration, image analysis and understanding, image coding, and applications image processing.
Prerequisites: ENEE 610 and 620, or consent of instructor.
ENEE 614 Biomedical Signal Processing Credits: 3
Principles and techniques of analysis and processing of signals originating from living biological substances and systems: review of signal theory and processing techniques; bioelectric signals associated with nerve cells, muscle cells, and volume conductors; characteristics of dynamic biomedical signals (bioelectric, impedance, acoustic, mechanical, biomagnetic, biochemical, and multidimensional); and discussion and demonstration of some current applications and instrumentation.
Prerequisites: ENEE 610 and 611 or 612, or consent of instructor.
ENEE 620 Probability and Random Processes Credits: 3
Fundamentals of probability theory and random processes for electrical engineering applications and research: set and measure theory and probability
spaces; discrete and continuous random variables
and random vectors; probability density and distribution functions, and probability measures; expectation, moments, and characteristic functions; conditional expectation and conditional random variables, limit theorems and convergence concepts; random processes (stationary/non-stationary, ergodic, point processes, Gaussian, Markov, and second-order); applications to communications and signal processing.
Prerequisite: Undergraduate probability or consent of instructor.
ENEE 621 Detection and Estimation Theory I Credits: 3
Fundamentals of detection and estimation theory for statistical signal processing applications: theory of hypothesis testing (binary, multiple, and composite hypotheses, and Bayesian, Neyman Pearson, and minimax approaches); theory of signal detection (discrete and continuous time signals; deterministic and random signals; white Gaussian noise, general independent noise, and special classes of dependent noise, e.g., colored Gaussian noise; signal design and representations); theory of signal parameter estimation (Bayesian techniques for standard performance criteria, e.g., MMSE and MAP; nonrandom parameter techniques, e.g., ML; and estimator properties and bounds, e.g., unbiased/biased, minimum variance, sufficient statistics, and CramerRao bound); and theory of signal waveform estimation (linear/nonlinear estimation, discrete/continuous signals, and Wiener and Kalman filtering).
Prerequisite: ENEE 620 or consent of instructor.
ENEE 622 Information Theory Credits: 3
The mathematical theory of communication: noiseless source coding concepts (discrete memoryless sources, information measures, entropy, relative entropy, Shannon's noiseless source coding theorem, and prefix, Huffman, and Shannon codes); Channel coding concepts (discrete memoryless channels, channel capacity, Shannon's channel coding theorem, and Arimoto Blahhut's channel capacity computational algorithm); Ratedistortion theory (source distortion measures,
rate distortion functions, Shannon's source coding
theorem subject to a fidelity criterion, and
Blahut's rate distortion computational algorithm); Gaussian sources and channels; and advanced topics.
Prerequisite: ENEE 620 or consent of instructor.
ENEE 623 Communication Theory I Credits: 3
Fundamentals of analog and digital communications: amplitude modulation, single sideband modulation, frequency and phase modulation; sampling and quantization; representations of signals and systems; band limited signals and systems. Digital modulation/demodulation techniques, error performance, coherent and non-coherent modulation and receiver structures, timing and synchronization issues, and simulation tools.
Prerequisites: ENEE 620 and 621, or consent of instructor.
ENEE 624 Error Correcting Codes Credits: 3
Fundamentals of coding theory: linear block and trellis codes, decoder structures, random and burst error detection and correction techniques, encoding/decoding performance and bounds, concatenated codes, and interleaving structures.
Prerequisites: ENEE 620 and 622. Concurrent: ENEE 623, or consent of instructor.
ENEE 625 Data Compression Credits: 3
Principles and techniques of data compression: review of source coding theory; lossless data compression techniques such as Huffman, arithmetic, and L2W coding; and lossy data compression techniques such as transform coding, scalar quantitation, vector quantitation, predictive coding, and subband coding.
Prerequisites: ENEE 620 and 622, or consent of instructor.
ENEE 630 Solidstate Electronics Credits: 3
Fundamentals of solid state physics for the micro-electronics field: review of quantum mechanics, rigid space lattices, reciprocal lattices, dynamics of lattices, statistical mechanics, classical
concepts of electron transport, quantum theory of
electrons in crystals, semiconductors, and excess carriers in semiconductors.
Prerequisite: Consent of instructor.
ENEE 631 Semiconductor Devices Credits: 3
Principles of semiconductor device operation: review of semiconductor physics, pn junction
diodes, bipolar transistors, metal semiconductor contacts, JFETs and MESFETs, and MIL and MOSFET structures.
Prerequisite: ENEE 630, or consent of instructor.
ENEE 632 Integrated Circuits Credits: 3
Fundamentals of bipolar and MOS analog and digital integrated circuit techniques: basic IC structure and fabrication, passive components, bipolar transistors and diodes, characteristics matching, temperature compensation, output stages, OpAmps, voltage regulators, multipliers, PLLs, large signal analysis of bipolar devices, bipolar and MOS/CMOS logic circuits, MOS circuit techniques, memories, MOS analog circuits, A/D converters, and thin film and microwave integrated circuits.
Prerequisites: ENEE 630 and 631, or consent of instructor.
ENEE 680 Electro magnetic Theory I Credits: 3
Fundamentals of electro magnetic theory: theoretical analysis of Maxwells equations, boundary value problems of electrostatics and magneto statics, and engineering applications.
Prerequisite: Consent of instructor.
ENEE 681 Electro magnetic Theory II
Credits: 3
Continuation of ENEE 680: the homogeneous wave equation, plane wave propagation, interactive potential, simple radiating systems, relativistic covariance of Maxwell's equations, and engineering applications.
Prerequisite: ENEE 680, or consent of instructor.
ENEE 682 Quantum and Wave Phenomena Credits: 3
Introduction of quantum and wave phenomena from the electrical engineering perspective: Schrodinger's equation; operator algebra, one dimensional potentials, harmonic oscillators, raising and lowering operators, one dimensional semiconductor model, hydrogen atom, spin and quantum statistics, and time independent and time dependent perturbation theory.
Prerequisite: Consent of instructor.
ENEE 683 Lasers Credits: 3
Introduction to basic theory of lasers: interaction of radiation and matter, stimulated and spontaneous emissions, rate equations, laser amplification and oscillation, Gaussian beam optics, optical resonators, Qswitching, mode locking, light modulation, and some specific laser systems.
Prerequisites: ENEE 680, 681, and 682, or consent of instructor.
ENEE 684 Introduction to Photonics Credits: 3
Fundamentals of photonics and photonic devices: review of lasers, the basics of optical beams, and acoustooptic and electro-optic effects, semiconductor photonic devices, fiber optics; nonlinear optical effects, harmonic generation, optical parametric amplification and oscillation; Kerr effects, stimulated Raman scattering, and phase conjugation.
Prerequisites: ENEE 680, 681, 682, and 683, or consent of instructor.
ENEE 698 Project in Electrical Engineering Credits: 13
Individual projects on topics in electrical engineering. May be taken for repeated credit up to a maximum of three credits. Required of non-thesis M.S. students.
Prerequisite: Completion of core courses, or consent of instructor.
ENEE 699 Independent Study Credits: 13
Independent study of topics in electrical engineering.
Prerequisite: Consent of instructor.
ENEE 710 Digital Speech Processing Credits: 3
Fundamentals and techniques for the digital processing of speech: digital signal processing concepts review, speech production models, characteristics of the speech signal, time domain speech analysis, linear predictive coding (LPC), homomorphic speech processing, speech enhancement, speech recognition, speech coding, and speech synthesis.
Prerequisites: ENEE 610 and 611, or consent of instructor.
ENEE 711 Neural Networks in Signal Processing Credits: 3
Fundamentals and characteristics of artificial neural network paradigms and their capacities for association, learning, generalization, self organization, and problem solving: introduction and survey of various neural network models and paradigms, including back propagation, Hopfield networks, and competitive learning techniques; case studies of recent neural network applications to pattern recognition, control systems, tracking systems, data compression, and decision support; and comparisons with linear adaptive signal processing theory and techniques.
Prerequisites: ENEE 610, 611, and 612, or consent of instructor.
ENEE 712 Pattern Recognition Credits: 3
Principles of statistical pattern recognition: hypothesis testing and decision theory review, maximum likelihood recognition strategies, parameter estimation and density approximation, linear discriminant functions, training set selection, feature extraction and nonlinear approaches,
pattern recognition via linear system techniques, spatial filtering techniques, and expert system and artificial intelligence concepts.
Prerequisites: ENEE 610, 612, 620, and 621, or consent of instructor.
ENEE 714 Biomedical Imaging Credits: 3
Application of digital image processing techniques to biomedical imaging: digital image processing review (basic concepts of reconstruction algorithms, transform methods, back projection, series expansion methods, 3D reconstruction and display) with emphasis on biomedical imaging problems; introduction to computerized tomography (CT) and its physical and mathematical problems; and
applications in biomedical research and clinical medicine.
Prerequisites: ENEE 612 and 614, or consent of instructor.
ENEE 718 Topics in Signal Processing
Credits: 3
ENEE 718 comprises advanced topic courses in signal processing that reflect the research interests of the faculty and their Ph.D. students. A specific offering under this title, designated by a letter appended to this course number, is generally not offered every year.
Prerequisites: (Depends on offering). Open to students who have passed the qualifying exam, or consent of instructor.
ENEE 721 Detection and Estimation Theory II Credits: 3
Advanced concepts of signal detection and estimation theory with applications: sequential detection; non parametric and robust detection concepts; small signal and small sample size concepts and performance; estimation techniques for smoothing, filtering, and prediction; recursive, interactive, and extended Kalman filter and other state estimation techniques and their performance; robust estimation concepts; general nonlinear filtering and approximately optimal simplified filters; and discussion of current applications in communications and statistical signal processing.
Prerequisites: ENEE 620 and 621, or consent of instructor.
ENEE 723 Communication Theory II
Credits: 3
Digital signaling over bandwidth constrained channels and channels with distortion: digital communications over fading multi path channels, inter symbol interference and its effects, adaptive equalization, combined coding and modulation
techniques (e.g., trellis coded modulation), and spread spectrum techniques. Discussion of selective applications.
Prerequisites: ENEE 620, 621, and 623, or consent of instructor.
ENEE 728 Topics in Communications
Credits: 3
ENEE 728 comprises advanced topic courses in communications that reflect the research interests
of the faculty and their Ph.D. students. A specific offering under this title, designated by a letter appended to this course number, is generally not offered every year.
Prerequisite: (Depends on offering). Open to students who have passed the Ph.D. qualifying exam, or have consent of instructor.
ENEE 737 Semiconductor Device Processing Techniques Credits: 3
Introduction to basic semiconductor device processing techniques: etching, photolithography, metalization, and device characterization. Laboratory exercises will complement the lectures and demonstrate the principles.
Prerequisites: ENEE 630 and 631, or consent of instructor.
ENEE 738 Characteristics of Semiconductor Optoelectronics Credits: 3
Introduction to current semiconductor optoelectronic devices and survey of new research results: review of semiconductor physics and device characteristics; optical receiver concepts such as photo conductors, metal-semiconductor concepts, MSM, pin, receiver design, and APD; waveguide concepts such as waveguide devices, waveguide modes, waveguide couplers, EO effects and modulation, periodic waveguides, polarization devices, waveguide filters, and BPM; and LED amplifier and laser concepts such as edge/surface emitting, optical gain, traveling wave amplifiers, FP, DFB, DBR, QW lasers, active filters, small
signal modulation, modelocking, line width, and noise.
Prerequisites: ENEE 630, 631, 681, 682, and 683, or consent of instructor.
ENEE 788 Topics in Photonics Credits: 3
ENEE 788 comprises advanced topic courses in photonics that reflect the research interests of the faculty and their Ph.D. students. A specific offering under this title, designated by a letter appended to
this course number, is generally not offered every year.
Prerequisite: (Depends on offering.) Open to students who have passed the Ph.D. qualifying exam, or have consent of instructor.
ENEE 799 Master's Thesis Research
Credits: 1-6
This course is for MSEE students engaged in master's thesis research; may be taken for repeated credits, but only a maximum of 6 credit hours applied toward M.S. thesisoption requirements.
Prerequisite: Open only to MSEE thesis option students.
ENEE 800 Graduate Research
Credits: 1-6
This course is for Ph.D. students engaged in graduate research, but not yet admitted to Ph.D. candidacy.
Prerequisite: Open only to EE students who have passed the Ph.D. qualifying exam.
ENEE 899 Doctoral Dissertation Research
Credits: 1-6
This is the dissertation research course for Ph.D. students who have been admitted to Ph.D. candidacy; may be taken for repeated credits (2 semesters required), but only a maximum of 12 credit hours applied towards Ph.D. requirements.
Prerequisite: Open only to EE students admitted to Ph.D. candidacy
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