Electrical Engineering
Departmental Websites: Engineering, Electrical Engineering
Department Head: Professor Robert Magnusson
Professors: Anwar, Bansal, Bar-Shalom, Enderle, Fox, Jain, Javidi, Luh, Pattipati,
Taylor, and Willett
Professor-in-Residence: DeMaria
Research Professor: Boggs
Associate Professors: Ayers, Donkor, and Zhu
Assistant Professors: Chandy, Escabi, Fei, B. Wang, L. Wang, and Zhou
Several areas of study and research leading to M.S. and Ph.D. degrees are offered: Electronics and Photonics, Biomedical Engineering, and Information, Communication, and Decision Systems. Students may also choose to pursue an M.S. degree in Electrical Engineering without a concentration.
The significant involvement of the Department of Electrical and Computer Engineering in interdisciplinary programs is indicative of the broad scope of its basic interests and activities. Admission to one of the programs does not require an undergraduate degree in electrical engineering. It is quite common for graduate students with undergraduate degrees in other fields of engineering or in biology, mathematics, physics, psychology, or statistics to hold fellowships, assistantships, and part-time instructorships in the Department of Electrical and Computer Engineering. This mixing of faculty and graduate students with a variety of backgrounds integrates diverse ideas into departmental research projects.
Research and education in information, communication, and decision systems include people-machine systems, societal and transportation systems, multivariable system theory, digital control systems, digital and optical signal processing, optical computing, image analysis and processing, optoelectronic neural networks computer-aided design, estimation theory, adaptive control, stochastic communication and control, and coding theory. Activities in electronics and photonics include research in diffractive optics, optoelectronics, sensor technology, electro-optics, quantum electronics, semiconductor lasers, semiconductor heterojunctions with application to integrated circuits, electronic materials, antenna design, microwave technology, and high voltage engineering. Separate listings should be consulted for information concerning biomedical engineering as well as for collaborative fields such as computer science and engineering, and materials science.
Special Requirements for the Ph.D. Program. Admitted students must submit evidence of capacity for independent study in the form of a master’s thesis or comparable achievement.
For information regarding fellowships, assistantships, and part-time instructorships, the applicant should address the chairperson of the Biomedical Engineering Graduate Admissions Committee, Information, Communication, and Decision Systems Graduate Admissions Committee, or the Electronics and Photonics Graduate Admissions Committee, depending upon the major interest of the applicant. The address in every case is 371 Fairfield Road, Unit 2157, Room 450, Storrs, Connecticut 06269-2157
Special Facilities. Departmental facilities include the following research laboratories: Biomedical Instrumentation Laboratory, Cyber Laboratory, Electrical Insulation Research Laboratory, Central Laboratory for Imaging Research, Micro/Opto-electronics Research Laboratory, Nanophotonics Laboratory, Optical Signal Processing/Computing Laboratory, Manufacturing Systems Laboratory, and the Photonics Research Laboratory. These laboratories contain a variety of computers and workstations, interface facilities, a clean room with semiconductor growth and characterization facilities, MBE and MOVPE facilities, and other specialized equipment. Fellowships, assistantships, and part-time instructorships are available. For more information, visit <www.engr.uconn.edu/ece/>.
COURSES OF STUDY
Courses designated by the dagger symbol (†) are approved
for Satisfactory (S) / Unsatisfactory (U) grading.
Registration restrictions: In addition to the listed prerequisites, approval of the Department head and instructor is required for non-degree students for registration in all courses.
ECE 300. Special Topics in Electrical and Systems Engineering
1-3 credits. Lecture.
Classroom and/or laboratory courses in special topics as announced in advance for each semester.
ECE 301. Introduction to System Theory
3 credits. Lecture. Recommended preparation: ECE 202.
Modeling and analysis of linear systems. Introduction to functions of a complex variable. Linear algebra with emphasis on matrices, linear transformations on a vector space, and matrix formulation of linear differential and difference equations. State variable analysis of linear systems. Transform methods using complex variable theory, and time-domain methods including numerical algorithms.
ECE 302. Linear Multivariable System Design
3 credits. Lecture. Prerequisite: ECE 301.
Observability and controllability. Application of canonic forms in system design. Methods of pole placement. Observer design. Noninteracting multivariable systems.
ECE 307. Dielectric and Magnetic Materials Science
3 credits. Lecture.
The macroscopic and microscopic views of dielectric and magnetic materials. Theories of spontaneous polarization and magnetization. Applications of anisotropic materials. Non-linear dielectrics at radio and optical frequencies. Superconductivity and superconducting magnets.
†ECE 311. Seminar
1 credit. Seminar.
Presentation and discussion of advanced electrical engineering problems.
ECE 313. Applied Probability and Stochastic Processes
3 credits. Lecture.
Statistical methods for describing and analyzing random signals and noise. Random variables, conditioning and expectation. Stochastic processes, correlation, and stationarity. Response of linear systems to stochastic inputs. Applications.
ECE 314. Information Theory
3 credits. Lecture. Prerequisite: ECE 313.
Basic concepts: entropy, mutual information, transmission rate and channel capacity. Coding for noiseless and noisy transmission. Universal and robust codes. Information-theoretic aspects of multiple-access communication systems. Source encoding, rate distortion approach.
ECE 316. Digital Signal Processing
3 credits. Lecture.
Discrete-time signals and systems. The z-transform. The Discrete Fourier Transform (DFT). Convolution and sectioned convolution of sequences. IIR and FIR digital filter design and realization. Computation of the DFT: The Fast Fourier Transform (FFT), algorithms. Decimation and interpolation. Parametric and nonparametric spectral estimation. Adaptive filtering. Finite word length effects.
ECE 317. Advanced Signal Processing
3 credits. Lecture. Prerequisites: ECE 313 and ECE 316.
Wiener filter theory. Linear prediction. Adaptive linear filters: LMS and RLS algorithms, variants, lattice structures and extra-fast implementation. Convergence properties. High resolution spectral estimation. Hidden Markov models, Monte-Carlo methods for signal processing. Multiresolution decomposition and wavelets. Blind methods.
ECE 318. Neural Networks for Classification and Optimization
3 credits. Lecture.
This course provides students with an understanding of the mathematical underpinnings of classification techniques as applied to optimization and engineering decision-making, as well as their implementation and testing in software. Particular attention is paid to neural networks and related architectures. The topics include: Statistical Interference and Probabilty Density Estimation, Single and Multi-layer Perceptions, Radial Basis Functions, Unsupervised Learning, Preprocessing and Feature Extraction, Learning and Generalization, Decision Trees and Instance-based Classifiers, Graphical Models for Machine Learning, Neuro-Dynamic Programming.
ECE 320. Independent Study in Electrical Engineering
1-6 credits. Independent Study.
Individual exploration of special topics as arranged by the student with an instructor of his or her choice.
ECE 322. Modern Manufacturing System Engineering
3 credits. Lecture.
Issues and methods in modern manufacturing systems. Integrated product and process development. Design for quality, on-line quality control and improvement, reliability during product development, and design for testability. Computer-aided production management, production planning and scheduling, and optimization-based planning and coordination of design and manufacturing activities. Targeted toward students, professional engineers, and managers who want to have an impact on the state-of-the-art and practice of manufacturing engineering, and to improve manufacturing productivity
ECE 327. Fuzzy and Neural Approaches to Engineering
3 credits. Lecture. Prerequisite: ECE 301.
Fuzzy sets, applications to fuzzy logic and fuzzy control, and concepts and methodologies for fuzzy optimization. Fundamental models of neural networks, learning rules, and basic recurrent networks for optimization. The integration of fuzzy systems with neural networks. Examples from engineering applications.
ECE 329. Computational Methods for Optimization
3 credits. Lecture. Prerequisite: ECE 301.
Computational methods for optimization in static and dynamic problems. Ordinary function minimization, linear programming, gradient methods and conjugate direction search, nonlinear problems with constraints. Extension of search methods to optimization of dynamic systems, dynamic programming.
ECE 330. Optimal Control Systems
3 credits. Lecture. Prerequisite: ECE 301.
Optimization techniques for linear and nonlinear systems. Calculus of variations, dynamic programming, and the Pontryagin maximum principle. Computational methods in optimal control.
ECE 331. Nonlinear System Theory
3 credits. Lecture. Prerequisite: ECE 301.
Stability of time-varying nonlinear systems. Liapunov’s direct method. Describing functions. Popov’s stability criterion. Adaptive control.
ECE 332. Information, Control, and Games
3 credits. Lecture. Prerequisites: ECE 301 and ECE 313.
Problems of static and dynamic optimization where more than one decision maker is involved, each having own payoff and access to different information. Review of elementary decision and control theory, non-cooperative games, cooperative games, bargaining models, differential games, team decision theory, Nash games, Stackelberg games (leader-follower problems). Introduction to large-scale systems and hierarchical control.
ECE 333. Man-Machine Systems Analysis
3 credits. Lecture. Prerequisites: ECE 301 and ECE 313.
Role of the human as a decision and control element in a feedback loop. Mathematical models of human control characteristics and instrument monitoring behavior. Effects of human limitations upon overall task performance. Parallel discussion of measurement and experimental techniques. Validation of theoretical results by comparisons with existing human response data.
ECE 334. Experimental Investigation of Control Systems
3 credits. Lecture. Prerequisites: ECE313 and ECE331.
A study of experimental techniques and advanced design of control systems.
ECE 335. Advanced Computer Networks and Distributed Processing Systems
3 credits. Lecture. This course and CSE 330 may not both be taken for credit.
Design and evaluation of distributed computer communication and processing systems. Case studies, development of suitable queuing and other models to describe and evaluate design problems such as capacity assignment, concentration and buffering, network topology design, routing, access techniques, and line control procedures.
ECE 336. Stochastic Models for the Analysis of Computer Systems and Communication Networks
3 credits. Lecture. Prerequisite: ECE 313.
Continuous and discrete-time Markov chains and their applications in computer and communication network performance and reliability evaluation. Little’s theorem and applications; review of stochastic processes; simple Markovian queues; open, closed, and mixed product-form networks; computational algorithms for closed and mixed product form networks; flow-equivalence and aggregation; M/G/1 queue with vacations and applications to time-division and frequency-division multiplexing; reservations and polling; multi-access communication; reliability and performability models of computer systems.
ECE 337. VLSI Fabrication Principles
3 credits. Lecture.
Semiconductor materials and processing, emphasizing compound semiconductors, optoelec-tronic materials, shallow devices, and fine-line structures. Semiconductor material properties; phase diagrams; crystal growth and doping; diffusion; epitaxy; ion implantation; oxide, metal, and silicide films; etching and cleaning; and lithographic processes.
ECE 338. Semiconductor Devices and Models
3 credits. Lecture.
Band theory, conduction in semiconductors, carrier statistics, deep levels, impurities with multiple charge states, heavy doping effects, non-uniform doping. Non-equilibrium processes, carrier scattering mechanisms, the continuity equation, avalanche multiplication, carrier generation, recombination, and lifetime. P-n junctions, non-abrupt junctions, various injection regimes, and device models. Metal semiconductor junctions, current transport mechanisms, and models. BJT, JFET, MESFET, and MOSFET, and device models.
ECE 339. Fundamentals of Opto-Electronic Devices
3 credits. Lecture.
Absorption and emission mechanisms in direct and indirect semiconductors. Semiconductor optoelectronic devices such as light-emitting diodes, injection lasers, photocathodes, solar cells, and integrated optics.
ECE 340. Electronic Materials
3 credits. Lecture. Prerequisite: ECE 245 or MMAT 313.
Physical and electronic properties, and device applications of disordered materials including amorphous semiconductors, liquid crystals, bubble-memory magnetic materials. Applications of amorphous semiconductors including xerography and solar cells.
ECE 341 MOS Device and VLSI Fundamentals
4 credits. Lecture.
Physics of MOS capacitors and transistors, derivation of V-1 relation expressing subthreshold, threshold, and saturation region behavior; short-channel effects in scaled-down transistors; scaling laws; VLSI fabrication technologies; design and layout gates and gate arrays; physics, device layout and design of semiconductor memories including static and dynamic RAMs. Laboratory emphasizes introduction to nonvolatile RAMs; computer aids in VLSI design; schematic capture, SPICE simulation, layout of custom IC’s, and VHDL.
ECE 342. Electronic Theory of Semiconductors
3 credits. Lecture.
Topics include crystallography, energy bands in crystals, effective mass theorem, virtual energies and miniband formation in finite and infinite superlattice, electronics and holes in electric and magnetic fields, crystal vibrations (phonons), and theory of conduction in semiconductors.
ECE 345. Nanotechnology
3 credits. Lecture.
Nanoelectronic and optoelectronic devices: Quantum confinement in 1D, 2D and 3D (quantum wells, wires, and dots) structures; density of states and carrier density in low-dimensional structures; fabrication methodology for quantum wire transistors and lasers; single-electron transistors/tunneling devices; growth and characterization of nanostructured materials with grain sizes in the range of 10-50 nm. Organic monolayers: Langmuir-Blodgett monolayers, Self-Assembled monolayers, Multi-layer structures, technological applications of organic thin films.
ECE 346. Microwave Techniques
3 credits. Lecture.
A theoretical analysis of microwave components, systems, and measuring techniques. Scattering matrix analysis is applied to microwave devices having two or more ports.
ECE 348. Electromagnetic Wave Propagation
3 credits. Lecture. Prerequisite: ECE 207 or PHYS 306.
Engineering application of Maxwell’s field theory to electromagnetic wave propagation in various media. Reflection, refraction, diffraction, dispersion, and attenuation. Propagation in sea water and in the ionosphere.
ECE 349. Antenna Theory and Applications
3 credits. Lecture.
Analysis and synthesis of antenna systems including electric- and magnetic-dipole, cylindrical, helical, reflector, lens, and traveling-wave antennas. Theory of arrays including patterns, self and mutual impedances.
ECE 350. Advanced Optoelectronics
3 credits. Lecture. Prerequisite: ECE 339.
Review of optoelectronic devices and integrated circuit (IC) technologies (analog and digital); logic gates; self-electro-optic devices (SEEDs), microlasers, Fabry-Perot (F-P) etalons and optoelectronic IC (OEICs); modulators: F-P modulators (absorptive and refractive), spatial light modulators (SLMs) and their applications; bistable devices; bistable laser amplifiers, resonant tunneling transistor lasers, and polarization bistability; optical interconnects; architectural issues and optical processors based on S-SEED, optical neural networks, and other devices.
ECE 351. Advanced Semiconductor Devices
3 credits. Lecture.
Fundamental properties of heterostructures, strained-layer superlattices, NIPI structures, multiple quantum well, quantum wire, and quantum dot structures. Operation, modelling of the electrical characteristics, design, and applications of HBJT, HEMT, and resonant tunneling devices. Second-order effects in submicron MOSFETs and MESFETs.
ECE 352. Transport in Semiconductors
3 credits. Lecture. Prerequisite: ECE342 or PHYS 322.
Topics include theory of energy bands in crystals; carrier scattering; the Boltzman equation and its approximations; low field transport; high field effects; transport in heterojunctions; quantum effects; and Monte Carlo simulation.
ECE 353. Fundamentals of Photonics
3 credits. Lecture.
Principles of optics including rays, waves, beams, electromagnetics, polarization and statistics. Basic postulates, simple optical components, graded index and matrix optics, monochromatic waves, interference, polychromatic light, Gaussian beams and propagation, diffraction, Fourier transforms, holography, dispersion and pulse propagation, polarizing devices and applications. Concepts of coherence and partial coherence as applied to various light sources in optical experiments and systems.
ECE 354. Optical Systems Engineering
3 credits. Lecture.
Design and analysis of paraxial optical systems, including stable and unstable laser resonators, and the propagation of geometric beams, Gaussian beams, and plane waves through complex optical systems. Topics include ray optics; ray matrices; polarization of light; diffraction theory; the connection between geometrical optics and diffraction; and performance analysis.
ECE 355 Optical Waveguides
3 credits. Lecture.
Propagation of electromagnetic waves in dielectric slab and fiber waveguides as described by geometrical ray optics and normal mode analysis. Integrated optic guides, step and graded index fiber guides. Single mode vs. multimode transmission, coupling, and other system considerations.
ECE 356. Optoelectronic Devices
3 credits. Lecture.
Optoelectronic devices as applied to fiber optic communications, optical switching and interconnects. Semiconductor laser devices, including dc, ac smallsignal, ac large signal, and noise with emphasis upon analytical models. Vertical cavity devices and technology. Semiconductor optical amplifiers, waveguide and vertical cavity modulators, photodetectors, optical switches, receivers and transmitters. Techniques for OE integration and the relevance of bipolar and field-effect devices for monolithic integration. Technologies for optoelectronic integration for telecom and datacom optical interconnect. WDM techniques for optical networks.
ECE 357. Advanced Numerical Methods in Scientific Computation
3 credits. Lecture. Prerequisite: ECE 301.
Development, application and implementation of numerically stable, efficient and reliable algorithms for solving matrix equations that arise in modern systems engineering. Computation of matrix exponential, generalized inverse, matrix factorizations, recursive least squares, eigenvalues and eigenvectors, Lyapunov and Riccati equations.
ECE 358. Nonlinear Optical Devices
3 credits. Lecture. Prerequisite: ECE 353.
Wave propagation in nonlinear media, generation of harmonics in optical materials, optical parametric processes, stimulated emission and scattering processes. Device modeling and application of fiber and semiconductor lasers, optical amplifiers and modulators. Electro-optic, acousto-optic, and magneto-optic devices. Soliton generation and propagation.
ECE 359. Advanced VSLI Design
3 credits. Lecture. Recommended preparation: ECE 249 and ECE 252 (or equivalent).
Advanced concepts of circuit design for digital VLSI components in state of the art MOS technologies. Emphasis is on the circuit design, optimization, RTL design, synthesis, and layout of either very high speed, high density or low power circuits and systems for use in applications such as micro-processors, signal and multimedia processors, memory and periphery. Other topics include challenges facing digital circuit designers today and in the coming decade, such as the impact of scaling, deep submicron effects, interconnect, signal integrity, power distribution and consumption, and timing.
ECE 361. Communication Theory
3 credits. Lecture. Prerequisite: ECE 313.
Design and analysis of digital communication systems for noisy environments. Vector representation of continuous-time signals; the optimal receiver and matched filter. Elements of information theory. Quantization, companding, and delta-modulation. Performance and implementation of common coherent and non-coherent keying schemes. Fading; intersymbol interference; synchronization; the Viterbi algorithm; adaptive equalization. Elements of coding.
ECE 362. Estimation Theory and Computational Algorithms
3 credits. Lecture. Prerequisites: ECE 301 and ECE 313.
Estimation of the state and parameters of noisy dynamic systems with application to communications and control. Bayesian estimation, maximum-likelihood and linear estimation. Computational algorithms for continuous and discrete processes, the Kalman filter, smoothing and prediction. Nonlinear estimation, multiple model estimation, and estimator Kalman, multiple model estimation, and estimator design for practical problems.
ECE 363. Stochastic Control
3 credits. Lecture. Prerequisite: ECE 301 or ECE313.
Methods of decision-making and control in a stochastic environment. Elements of utility theory. Principle of optimality and deterministic dynamic programming. Stochastic dynamic programming. Control of dynamic systems with imperfect state information. Certainty equivalence and the control’s dual effect. Sequential hypothesis testing. Passive and active stochastic adaptive control algorithms. Decentralized control methods.
ECE 364. Linear Program and Network Flows
3 credits. Lecture. Prerequisite: ECE 301.
Computational methods for linear programming with special emphasis on sequential and parallel algorithms for Network Flow Problems. Standard and canonical forms of linear programming, revised Simplex methods, basis updates, decomposition methods, duality, shortest paths, minimal spanning trees, maximum flows, assignment problems, minimum cost network flows, and transportation problems.
ECE 365. Advanced Signal Detection
3 credits. Lecture.
Focus on discrete-time detection of signals in noise which is not necessarily Gaussian. Topics include: classical Neyman-Pearson and Bayes theory, efficacy and asymptotic relative efficiency; some canonical noise models; quantized detection; narrowband signal detection; distance measures and Chernoff bounds; sequential detection; robustness; non-parametric detection; continuous-time detection and the Karhunen-Loève expansion.
ECE 366. Optical Information Processing
3 credits. Lecture.
Two-dimensional signal processing using optical techniques. Topics include: review of two-dimensional linear system theory; scalar diffraction theory, Fresnel and Fraunhofer diffraction; Fourier transforming and imaging properties of lenses; image formation; frequency analysis of optical imaging systems; modulation transfer function; two-dimensional spatial filtering; coherent optical information processing; frequency-domain spatial filter synthesis; holography, Fourier and nonlinear holograms.
ECE 368. Wireless Communication
3 credits. Lecture. Prerequisites: ECE 316 and ECE 361.
Introduces basic concepts in wireless communication and networks with emphasis on techniques used in the physical layer of current and future wireless communication systems. Covers channel modeling, modulation, spread spectrum techniques, multiuser communication theory, wireless network protocols, and current cellular and PCS systems. Special topics in equalization and array signal processing are included.
ECE 369. Pattern Recognition and Neural Networks
3 credits. Lecture.
Review of probability and stochastic processes. Statistical pattern recognition. Nonlinear signal processing and feature extraction. Correlation filters. Metrics for pattern recognition. Baysian classifiers. Minimum probability of error processors. Supervised and unsupervised learning. Perception learning methods. Multilayer neural networks. Applications to security and encryption.
ECE 370. Biomedical Instrumentation I
3 credits. Lecture. Prerequisite: ECE 313.
Origins of bioelectric signals; analysis and design of electrodes and low-noise preamplifiers used in their measurement. Statistical techniques applied to the detection and processing of biological signals in noise, including the treatment of nerve impulse sequences as stochastic point processes. Methods of identifying the dynamic properties of biosystems.
ECE 372. Communication and Control in Physiological Systems
3 credits. Lecture.
Processing, transmission, and storage of information in nerve systems. Mechanisms of neuro-sensory reception, coding and signal-to-noise ratio enhancement. Analysis of invertebrate and vertebrate visual systems. Neural spatio-temporal filters in feature extraction and pattern recognition. Analysis of control systems and regulators associated with vision: e.g., gaze control, accommodation, pupil area, and intra-ocular pressure.
ECE 373. Biomedical Instrumentation Laboratory
3 credits. Laboratory.
Experimental investigation of electrodes, transducers, electronic circuits, and instrumentation systems used in biomedical research and in clinical medicine.
ECE 374. Digital Image Processing
3 credits. Lecture. Prerequisite: ECE 241 or ECE 247.
Problems and applications in digital image processing, two-dimensional linear systems, shift invariance, 2-D Fourier transform analysis, matrix Theory, random images and fields, 2-D mean square estimation, optical imaging systems, image sampling and quantization, image transforms, DFT, FFT, image enhancement, two-dimensional spatial filtering, image restoration, image recognition, correlation, and statistical filters for image detection, nonlinear image processing, and feature extraction.
ECE 377. Engineering Problems in the Hospital
3 credits. Lecture.
Given in collaboration with staff from the University’s School of Medicine and from hospitals in Hartford. Aim is to familiarize the student with engineering problems in a modern hospital. Role of the small computer in the hospital; implanted pace-makers; heart catheterization. Students are expected to investigate and solve an engineering problem associated with clinical medicine as a semester project.
ECE 378. Biomedical Imaging
3 credits. Lecture.
Fundamentals of detection, processing and display associated with imaging in medicine and biology. Topics include conventional and Fourier optics, optical and acoustic holography, optical and digital image enhancement, ultrasonography, thermography, isotope scans, and radiology. Laboratory demonstrations will include holography and optical image processing.
ECE 379. Advanced Ultrasonic Imaging Technique
3 credits. Lecture. Prerequisite: EE 378.
Introduction to advanced techniques of ultrasonic image formation for biomedical applications. Introduction to acoustic wave propagation. A,B,C,M and Doppler ultrasonic imaging modes. Interaction of ultrasound with biological tissues. Acoustical holography. Ultrasonic transducer design and calibration. Transducer arrays. Ultrasound detection modes. Laboratory demonstrations will include Schlieren visualization of ultrasound fields and transducer calibration techniques.
ECE 380. Medical Imaging Systems
3 credits. Lecture. Also offered as BME 360.
Medical imaging principles and systems of x-ray, ultrasound, optical tomography, magnetic resonance imaging, positron emission tomography. The students are required to have the courses of instrumentation, signal analysis using Fourier Transform and Laplace transform. Students are also required to have advanced mathematics on differential equations and matrix calculations.
†GRAD 395. Master’s Thesis Research
1 - 9 credits.
†GRAD 396. Full-Time Master’s Research
3 credits.
†GRAD 397. Full-Time Directed Studies (Master’s Level)
3 credits.
GRAD 398. Special Readings (Master’s)
Non-credit.
GRAD 399. Thesis Preparation
Non-credit.
†GRAD 495. Doctoral Dissertation Research
1 - 9 credits.
†GRAD 496. Full-Time Doctoral Research
3 credits.
†GRAD 497. Full-Time Directed Studies (Doctoral Level)
3 credits.
GRAD 498. Special Readings (Doctoral)
Non-credit.
GRAD 499. Dissertation Preparation
Non-credit.