Program tanımları
Program Description
The Ph.D. program in Computer Engineering aims to provide advanced education and a cutting edge research experience in computer engineering or in electrical and computer engineering crossing the boundary of the two disciplines. The focus of this program is excellence in research. Graduates of the program can join industry or continue to work in academia.
Degree Requirements
Students can apply to the Ph.D. programs with a B.S. or M.S. degree. The Ph.D. degree requires successful completion of 14 courses beyond the B.S. degree or 7 courses beyond the M.S. degree. All students must pass the Ph.D. Qualifying Examination in the first year after they have been admitted to the Ph.D. program. Students are encouraged to begin research early. Students who have passed the Ph.D. qualifying examination are assisted in matters pertaining to their thesis research by a faculty thesis advisory committee. The research advisor serves as the chair of this committee. The committee meets with the student at least once each semester. Ph.D. students must submit a satisfactory written Ph.D. thesis proposal in their second year of study. At the completion of the Ph.D. research, the students must submit a written Thesis and pass an oral defense to complete the degree requirements.
In addition to the two courses listed below, students in this program can take any of the courses listed under the "MS in Electrical and Computer Engineering" program or under other graduate programs subject to the approval of their advisors in accordance with their research specialty.
ECOE 582 - Selected Topics in Computer Engineering
ECOE 695 - Ph.D. Thesis
COURSE DESCRIPTIONS
ECOE 501
Random Processes
Discrete random variables, continuous random variables, functions of random variables, multiple random variables, vector random variables, independence of random variables, functions of multiple random variables, Central Limit Theorem. Discrete-time random processes, continuous-time random processes, stationary random processes, ergodicity, auto and cross correlation functions, power spectral density; spectral estimation, white noise processes, Markov chains.
Prerequisite: ENGR 200 or consent of the instructor.
ECOE 504
Digital Speech and Audio Processing
(Also ELEC 404)
Segmental descriptions of speech, the vocal mechanism, digital models for speech production, digital waveform coding, time-domain analysis methods, differential, predictive, and adaptive quantization, short-time spectrum analysis, linear prediction analysis (LPC) methods, pitch detection and vocoders, analysis-by-synthesis systems, modern coding techniques and standards. Audio and wide-band speech coding techniques and standards. Speech enhancement, quality assestment. Fundamentals of speech recognition, dynamic time warping, and Hidden Markov Models (HMM).
Prerequisite: ELEC 303 or consent of the instructor.
ECOE 505
Linear Systems and Estimation Theory
Linear functions and linear dynamical systems, Multiple Input Multiple Output (MIMO) Systems, State Space Descriptions, Quadratic Forms, Maximum Likelihood and Maximum Aposteriori Estimation, SVD and Its Applications, Deterministic and Stochastic Least Squares, Wiener and Kalman Filtering, Spectral Factorization.
Prerequisite: Linear Algebra, Elementary Course on Signals and Systems.
Corequisite: ECOE 501 or consent of the instructor.
ECOE 506
Digital Image and Video Processing
(Also Elec 406)
Review of multi-dimensional sampling theory, aliasing, and quantization, fundamentals of color, human visual system, 2-D Block transforms, DFT, DCT and wavelets, image filtering, edge detection, enhancement, and restoration. Basic video file formats, resolutions, and bit rates for various digital video applications. Motion analysis and estimation using 2D and 3D models. Motion-compensated filtering methods for noise removal, de-interlacing, and resolution enhancement. Digital image and video compression methods and standards, including JPEG/JPEG2000 and MPEG-1/2 and 4. Content-based image and video indexing and MPEG-7.
Prerequisite: ELEC 303 or consent of the instructor.
ECOE 508
Computer Vision and Pattern Recognition
(Also ELEC 408)
Study of computational models of visual perception and their implementation in computer systems. Topics include: image formation; edge, corner and boundary extraction, segmentation, matching, pattern recognition and classification techniques; 3-D Vision: projection geometry, camera calibration, shape from stereo / silhouette / shading, model-based 3D object recognition; color texture, radiometry and BDRF; motion analysis.
Prerequisite: ELEC 201 or consent of the instructor.
ECOE 510
Computer Graphics
(Also ELEC 410)
Theory and practice of 3D computer graphics. Topics covered include 3D display techniques, representations and transformations; illumination and color models; 3D passive and active reconstruction techniques; animation and rendering; scientific visualization; surface simplification; multiresolution and progressive object modeling; mesh compression and subdivision surfaces, Web3D/VRML.
Prerequisite: COMP 120 or consent of the instructor.
ECOE 511
Digital Communications
(Also ELEC 411)
Characterization of communication signals & systems, digital modulation schemes, optimum reception for the additive white Gaussian noise (AWGN) channel, signal design for band-limited channels, Nyquist criterion, intersymbol interference (ISI), optimum reception for channels with ISI and AWGN, linear equalization, decision feedback equalization, adaptive equalization, channel capacity & coding, linear block codes, convolutional codes, multichannel and multicarrier systems, spread spectrum signals for digital communications, multiuser communications. Design oriented exercises using computer aids.
Prerequisite: ELEC 316 or consent of the instructor.
ECOE 512
Advanced Digital Signal Processing
Adaptive Filtering, LMS, RLS and Fast Algorithms, Array Signal Processing, Blind Algorithms & Subspace Methods for Channel Identification and Equalization, Convex Optimization and Its Applications, Multirate Signal Processing and Filter Banks.
Prerequisite: ECOE 505 or consent of the instructor.
ECOE 513
Information Theory
Entropy, Relative Entropy and Mutual Information; Asymptotic Equipartition Theory; Entropy Rates of a Stochastic Process; Data Compression; Kolmogorov Complexity; Channel Capacity; Differential Entropy; The Gaussian Channel; Maximum Entropy and Spectral Estimation; Rate Distortion Theory, Network Information Theory.
Prerequisite: ECOE 501 or consent of the Instructor
ECOE 514
Wireless Communications
(Also ELEC 414)
The cellular concept, channel assignment strategies, frequency reuse, handoff strategies, interference sources, mobile radio propagation, large-scale path loss, small-scale fading and multipath, modulation techniques for mobile radio, diversity combining, transmit and receive antennas for wireless communication systems, multiple access techniques in wireless, wireless system design for delay intolerant services, wireless system design for delay tolerant services, error correction coding and ARQ schemes, wireless networking, wireless systems & standards: GSM, IS-95, cdma2000, W-CDMA, 3GPP2 1xEV-DO, 3GPP2 1xEV-DV, fourth generation wireless system proposals. Design oriented exercises using computer aids.
Prerequisite: ECOE 511 or consent of the instructor.
ECOE 515
Distributed Computing Systems
(Also COMP 415)
Introduction to distributed computing, overview of operating systems, process synchronization and deadlocks, threads and thread synchronization, communication protocols, synchronization in distributed systems, management of time, causality, logical clocks, consistent global states, distributed mutual exclusion, distributed deadlock detection, election algorithms, agreement protocols, consensus, multicast communication, distributed transactions, replication, shared memory model, scheduling, distributed file systems, fault tolerance in distributed systems, distributed real-time systems.
Prerequisite: Comp 304 or consent of the instructor.
ECOE 517
VLSI and Digital Design
(Also ELEC 417)
Issues in digital integrated circuit design. The devices. CMOS Inverter. Combinational logic gates in CMOS. Designing sequential logic circuits. Designing arithmetic building blocks. Timing issues in digital circuits. Memories and array structures. Design verification and testing. Design projects using computer aided design tools: SPICE, MAGIC, IRSIUM, OCTTOOLS. Project design requirements include architectural design, logic and timing verification, layout design, and test pattern generation. The resulting chips may be fabricated.
Prerequisite: ELEC 311 or consent of the instructor.
ECOE 518
Numerical Analysis of Circuits and Systems
Introduction to mathematical formulations and computational techniques for the analysis and numerical simulation of circuits and systems. Applications are drawn from the time-frequency domain and noise analysis of electronic circuits at the transistor level; electromagnetic analysis for interconnect in VLSI circuits; analysis of wave propagation in integrated optics and optical fibers; simulation of communication systems; circuit and system macro-modeling. Topics include sparse direct and iterative matrix-implicit solution techniques for linear systems of equations, solution of eigenvalue problems, Newton methods for nonlinear problems, numerical methods for the solution of ordinary and partial differential equations, reduced-order modeling.
Prerequisite: Consent of the instructor.
ECOE 519
Introduction to Artificial Intelligence
A graduate-level introduction to artificial intelligence with the goals of understanding human intelligence from a computational point of view and building applied systems that can reason, learn, and adapt. Review of seminal work on language, vision, robotics, game playing with an emphasis on machine learning techniques.
Prerequisite: Consent of the instructor.
ECOE 520
Advanced Computer Architecture
(Also ELEC 420)
Performance of computer architectures, instruction set design, pipelining, instruction-level parallelism, superscalar computer architectures and their performance, memory systems, cache design and analysis, storage systems, interconnection networks, multiprocessor architectures, and embedded systems.
Prerequisite: COMP 303 or consent of the instructor.
ECOE 521
Photonics and Lasers
(Also ELEC 421)
Review of electromagnetism; electromagnetic nature of light, radiation, geometrical optics, Gaussian beams, transformation of Gaussian beams; electromagnetic modes of an optical resonator, interaction of light with matter, classical theory of absorption and dispersion, broadening processes, Rayleigh scattering, quantum theory of spontaneous and stimulated emission, optical amplification, theory of laser oscillation, examples of laser systems, Q switching and mode locking of lasers.
Prerequisite: ELEC 206 or consent of the instructor.
ECOE 522
Micro-opto-electro-mechanical Systems
(Also ELEC 422)
Introduction to microsystems and micro-electro-mechanical-systems (MEMS) and their integration with optics; microfabrication and process integration; MEMS modeling and design; actuator and sensor design; mechanical structure design; optical system design basics; packaging; optical MEMS application case studies; scanning systems (Retinal Scanning Displays, Barcode scanners); projection display systems (DMD and GLV); infrared imaging cameras; optical switching for telecommunications.
Prerequisite: ELEC 321 or consent of the instructor.
ECOE 523
Optical Information Processing
(Also ELEC 423)
Review of 2-D linear system theory and 2-D Fourier transforms. Integral transforms used in optical signal processing; fundamentals of physical optics and diffraction theory; Fourier and imaging properties of optical systems; coherent and incoherent optical image processing; electro-optical and acousto-optical devices; fundamental architectures for correlation and spectrum analysis; interferometry; selected applications in machine vision, pattern recognition, radar signal processing; discrete analog optical processors; holography.
Prerequisite: ELEC 321 or consent of the instructor.
ECOE 524
Optical Fiber Communications
(Also ELEC 424)
Introduction to optical fiber communication systems. Transmission properties of optical fibers. Optical amplifiers. Lasers and photo-detectors. Anolog and digital modulation schemes. Modulator, transmitter and receiver design. Dense and ultra-dense wavelength division multiplexing. Transmission impairments, noise, nonlinearities, dispersion compensation and management, modeling and simulation. Optical fiber communication networks, optical interconnect for highspeed VLSI.
Prerequisites: ELEC 206, ELEC 316 or consent of the instructor.
ECOE 525
Photonic Materials and Devices
(Also ELEC 425)
Survey of the properties and applications of photonic materials and devices; semiconductors; photon detectors, light emitting diodes, noise in light detection systems; light propagation in anisotropic media, Pockels and Kerr effects, light modulators, electromagnetic wave propagation in dielectric waveguides, waveguide dispersion; nonlinear optical materials, second harmonic generation, Raman converters.
Prerequisite: ELEC 206 or consent of the instructor.
ECOE 527
Antennas and Propagation
(Also ELEC 427)
Applications of Maxwell’s equations. Electrostatic versus electrodynamic phenomena, and concept of electromagnetic radiation; radiation from a moving point charge; definitions of some radiation parameters like the input impedance, gain and radiation patterns of antennas; radiation from Thin-Wire Antennas and their electrical characteristics; concept of arrays and their applications; microstrip antennas and their roles in emerging telecommunication systems; Propagation for wireless communications systems; cellular network design based on propagation studies.
Prerequisite: Elec 401 or consent of the instructor.
ECOE 529
Parallel Computing
Overview of parallel architectures: interconnection networks, memory hierarchy. Parallel programming models and languages: shared address space, message passing, data driven, and data parallel models. Performance modeling and scalability analysis, sources of parallel overhead. Design of parallel algorithms and programs: partitioning, fundamental communication operations, mapping, load balancing. Study of parallel matrix, graph, and search algorithms.
Prerequisite: COMP 202 or consent of instructor.
ECOE 532
Internet Services
Overview of networking technologies, standardization, foundations of the Internet, architecture and basic protocols; IP addressing issues; autonomous systems, intra and inter domain routing; Internet multicasting, mobile IP, private networks, auto configuration, and domain name system; World Wide Web, HTTP; Internet multimedia communications protocols and supporting algorithms; real-time service considerations; the Internet multimedia architecture, ; IP telephony, and Internet video systems; streaming audio and video, media-on-demand, live broadcasting and interactive communications, multiparty conferencing; packet loss and delay; network aware application design; Quality of Service; network programming assignments.
Prerequisite: ELEC 416 or consent of the instructor.
ECOE 554
Machine Learning
An introduction to the fields of machine learning and data mining from a statistical perspective. Machine learning is the study of computer algorithms that improve automatically through experience. Vast amounts of data generated in many fields from biology to finance to linguistics makes a good understanding of the tools and techniques of machine learning indispensable. Topics covered include regression, classification, kernel methods, model assessment and selection, boosting, neural networks, support vector machines, nearest neighbors, and supervised learning.
Prerequisite: Consent of the instructor.
ECOE 556
Algorithms and Computational Complexity
(Also COMP 456)
Advanced topics in data structures, algorithms, and their computational complexity. Asymptotic complexity measures. Graph representations, topological order and algorithms. Forests and trees. Minimum spanning trees. Bipartite matching. Union-find data structure. Heaps. Hashing. Amortized complexity analysis. Randomized algorithms. Introduction to NP-completeness and approximation algorithms. The shortest path methods. Network flow problems.
Prerequisites: Comp 202 or consent of the instructor.
ECOE 560
Design Methodologies and Tools for Software/Hardware Systems
Methodologies, tools, and algorithms for designing and implementing integrated systems with hardware and software components. Complexity management through top-down design, refinement and abstraction. Synthesis and optimization for performance, cost, testability and reliability. Design space exploration and trade-offs. Fundamental data structures and algorithms used in design automation. Heuristic solutions to optimization problems that arise in design automation. Analysis, testing, validation, and formal verification of software and embedded electronic systems. Design and implementation verification. Reconfigurable and reprogrammable systems.
Prerequisite: Consent of the instructor.
ECOE 570
Bioinformatics and Algorithms in Computational Biology
Algorithms, models, representations, and databases for collecting and analyzing biological data to draw inferences. Overview of available molecular biological databases. Sequence analysis, alignment, database similarity searches. Phylogenetic trees. Discovering patterns in protein sequences and structures. Protein 3D structure prediction: homology modeling, protein folding, representation for macromolecules, simulation methods. Protein-protein interaction networks, regulatory networks, models and databases for signaling networks, data mining for signaling networks.
ECOE 590
Seminar
A series of lectures given by faculty or outside speakers. Participating students must also make presentations during the semester.
ECOE 695
Ph.D. Thesis
Independent research towards Ph.D. degree.
TEAC 500
Teaching Experience
Provides hands-on teaching experience to graduate students in undergraduate courses. Reinforces students' understanding of basic concepts and allows them to communicate and apply their knowledge of the subject matter.
ENGL 500
Graduate Writing
This is a writing course specifically designed to improve academic writing skills as well as critical reading and thinking. The course objectives will be met through extensive reading, writing and discussion both in and out of class. Student performance will be assessed and graded by Satisfactory/Unsatisfactory
