ELE 509. ANALYSIS OF LINEAR SYSTEMS: Signals, systems, orthogonal decomposition, Fourier analysis, Laplace transforms, Z-transforms, state variable, and their application to the analysis of linear systems. 3 sem. hrs.
ELE 561. DIGITAL SIGNAL PROCESSING I: A study of one-dimensional digital signal processing, including a review of continuous-system and analysis and sampling. Topics include z-transform techniques, digital filter design and analysis, and Fast Fourier Transform processing techniques. Prerequisite: ELE 509. 3 sem. hrs.
ELE 562. DIGITAL SIGNAL PROCESSING II: A study of the architectural requirements for one-dimensional digital signal processing. This includes the techniques for the design of both hardware and software elements needed for implementations of digital signal processors as well as discussions of applications of those processors. Prerequisite: ELE 561. 3 sem. hrs.
ELE 563. IMAGE PROCESSING: An introduction to image processing including the human visual system, image formats, two-dimensional transforms, and image reconstruction. Prerequisite: ELE 561. 3 sem. hrs.
ELE 572. LINEAR SYSTEMS AND FOURIER OPTICS: Mathematical techniques pertaining to linear systems theory; Fresnel and Fraunhoffer diffraction; Fourier transform properties of lenses; frequency analysis of optical systems, spatial filtering, applications such as optical information processing and holography. Prerequisite: Acceptance into the EE graduate program or permission of the department chairperson. 3 sem. hrs.
ELE 661. STATISTICAL SIGNAL PROCESSING: This course studies discrete methods of linear estimation theory. Topics include random vectors, linear transformations, linear estimation, optimal filtering, linear prediction, and spectrum estimation. Prerequisite: ELE 561. 3 sem. hrs.
ELE 662. ADAPTIVE SIGNAL PROCESSING: An overview of the theory, design, and implementation of adaptive signal processors. This includes discussions of various gradient research techniques, filter structures, and applications. An introduction to neural networks is also included. Prerequisite: ELE 661. 3 sem. hrs.
ELE 663. STATISTICAL PATTERN RECOGNITION: This course provides a comprehensive treatment of the statistical pattern recognition problem. The mathematical models describing these problems and the mathematical tools necessary for solving them are covered in detail. Prerequisite: ELE 661. 3 sem. hrs.
See full list of Graduate courses.
Maintained by John Loomis, last updated Aug 28, 1997