PhD in Computer Engineering

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.

Core Courses (four credits each) (Select eight of these.)

CPE: 801: Random Processes 4

Thesis

Registrations

COURSE DESCRIPTION

CPE: 801: 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.

CPE 811: Computer Vision and Pattern Recognition:

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.

CPE 822: Computer Graphics:

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.

CPE 834: 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.

CPE 835: Image Processing:

Mathematical model of an image. Frequency spectrum of an image. Sampling of an image, aliasing and conditions on sampling frequency. Separability in 2-D signals. Periodicity concept in an image. Expansion of an image into Fourier series. Construction of an image from its harmonics. The 2-D Fourier transform. The Fourier transform of separable images. The z-transform and transfer function. The linear operations applied to an image: convolution, mask and impulse response. 2-D FIR filters and IIR filters. Methods of image enhancement. Image segmentation. Image restoration and compression. Cellular neural networks and their applications in 2-D filtering. Recognition and interpretation.

CPE 842: Computer Speech Processing:

Man-machine communication. Speech models and representations. Speech synthesis. Speech coding. Speech recognition. Dynamic time warping and hidden Markow models. Neural networks for speech processing. Speech enhancement

CPE 843: Parallel Algorithms for Scientific Applications:

Algorithm design for high-performance computing. Parallel algorithms for multidimensional tree data structures, space-filling curves, random number generation, graph partitioning and load balancing. Applications to grid and particle-based methods and computational biology.

CPE 844: Information Assurance Capstone Design:

Capstone design course which integrates the security design process. Design of a security policy. Creation of a security plan. Implementation of the security plan. The students will attach each other's secure environments in an effort to defeat the security systems. Students evaluate the security plans and the performance of the plans. Social, political and ethics issues. Student self-evaluation, journaling, final written report, and an oral report.

CPE 845: Advanced Topics in Computer Architecture:

Current topics in computer architecture design and implementation. Advanced pipelining, cache and memory design techniques. Interaction of algorithms with architecture models and implementations. Tradeoffs in architecture models and implementations.

CPE 850: Engineering Internship:

One semester and one summer maximum per academic year professional work period. Offered on a satisfactory-fail basis only.

CPE 852: Programming Languages Syntax and Semantic:

Syntax and semantics of imperative, functional and logic languages. Definition of abstract machines for each paradigm. Inheritance in object-oriented languages. Abstract interpretation based on detonational and operational semantics. Compiler design for functional, logic and object-oriented programming language.

CPE 853: Data Mining:

Overview of data mining classification; regression, time series. Measuring predictive performance. Data preparation, data reduction. Mathematical solutions, statistical methods, distance solutions, decision trees, decision rules. Text mining. Case studies.

CPE 854: Advanced Network Programming:

Overview of network layers. Network programming issues. Socket programming. RPC programming. TLI programming. Web programming. CGI, PERL, PHP and Java programming. Programming projects.

CPE 855: Advanced Computer Architectures:

Non Von Neumann machines. High level language machines. Direct execution architecture. Data flow machines. Reconfigurable systems. Super computers. VLSI impact on computer architecture

CPE 856: Advanced Computer Graphics:

Overview of graphics systems. Image models, sampling, and quantization. Image acquisition hardware, stereo imaging and 3D model formation. Modeling techniques for curves, surfaces, and solids. Rendering techniques: ray tracing, volume rendering, procedural texture, radiosity. Light and illumination models. Texture and environment mapping.

CPE 857: Computer Vision:

Image formation. Early processing: low-level vision and feature extraction. Boundary detection. Region growing. Texture. Motion. Two-dimensional and three-dimensional representation. High-level vision: learning and matching.

CPE 858: Advanced Topics in Parallel and Distributed Computing:

Parallel methods for computations in the areas of bioinformatics, computational physics, and other disciplines. Parallel algorithms. Topics in distributed computing. Parallel and distributed computing architectures.

CPE 859: Computer Performance Evaluation:

Introduction to computer performance measurement and evaluation. Job processing models. Queuing Theory. Simulation techniques. Systems analysis techniques. Estimating CPU performance. Programmed measurement techniques.

CPE 872: Advanced Natural Language Processing:

Levels of natural language processing: morphological, syntactic and semantic analysis. Transformational grammars. Affix grammars and 2-level representation and processing. Meaning and interpretation. Applications: intelligent interfaces, text processing aids, machine translation, natural language understanding. Transition networks and ATN parsing.

CPE 881: Special Studies in Computer Engineering:

Study of current research topics in computer engineering by Ph.D. students under the guidance of a faculty member and presentation of the chosen topic.

CPE 896: Directed Research:

The student pursues research under the direction of a faculty member. To register, students contact their advisor with the name of the faculty member under whose direction they would like to work and a brief explanation of the research area to be explored.

CSC 897: Thesis I:

The student develops a framework within which doctoral research will be conducted and offers evidence of qualifications to pursue the research. Prerequisite: Satisfactory completion of all course work.

CSC 898: Thesis II:

Concepts and theories underlying the student’s doctoral research are articulated; the problem is clearly stated; specific, measurable goals are specified; a thorough literature review is presented; the methods of conducting the research are delineated; and a strategy to achieve the goal is given. Prerequisite: Dissertation I.

CSC 899: Continuing Thesis:

Students who have not completed the dissertation by the end of Thesis II must register for continuing thesis each term in order to receive faculty and administrative advice and support related to the thesis.