Communication and Signal Processing Group

Undergraduate Courses

E E 224. Signals and Systems I. (3-3) Cr. 4. F.S. Prereq: 201, Math 267, Phys 222. Mathematical preliminaries. Introduction to signals and systems. Signal manipulations. System properties. LTI systems, impulse response and convolution. Fourier Series representation and properties. Continuous and discrete-time Fourier Transforms and properties. Sampling and reconstruction. Modulation and demodulation. Applications and demonstrations using Matlab.

E E 322. Probabilistic Methods for Electrical Engineers. (Cross-listed with Stat). (3-0) Cr. 3. F.S. Prereq: E E 224. Introduction to probability with applications to electrical engineers. Sets and events, probability space, conditional probability, total probability and Bayes' rule. Discrete and continuous random variables, cumulative distribution function, probability mass and density functions, expectation, moments, moment generating function, multiple random variables, functions of random variables. Elements of statistics, hypothesis testing, confidence intervals, least squares. Introduction to random processes.

E E 324. Signals and Systems II. (3-3) Cr. 4. F.S. Prereq: 224. Laplace and z-Transforms, properties and inverses. Applications to LTI systems and analog/digital filters. Feedback systems and stability. State-space representation and analysis. Nonmajor graduate credit.

E E 421. Communication Systems I. (3-0) Cr. 3. F. Prereq: 224, credit or registration in 322. Frequency domain analysis, spectral filtering, bandwidth. Linear modulation sytems. Angle modulation systems. Phase locked loop, super-heterodyne receiver. Sampling and pulse code modulation. Digital data transmission, line coding, pulse shaping, multiplexing. Nonmajor graduate credit.

E E 422. Communication Systems II. (3-0) Cr. 3. Prereq: 421 and enrollment in 423. Introduction to probability and random processes; Performance of analog systems with noise; Performance of digital communication with noise; optimum receivers, transmission impairments, and error rates; Introduction to information theory and coding: source coding, channel coding, channel capacity. Nonmajor graduate credit.

E E 423. Communication Systems Laboratory. (0-3) Cr. 1. Prereq: 421, enrollment in 422. Construction and evaluation of modulators, demodulators, modems, and other components for analog and digital communications. Design and evaluate wireless communication systems and their key components. Noise measurement. Design and construction of a communication system. Nonmajor graduate credit.

E E 424. Introduction to Digital Signal Processing. (3-3) Cr. 4. Prereq: 324. Discrete Fourier Transform (DTF). Signal processing using the DFT. Fast Fourier algorithms. Design of IIR and FIR filters. Multi-rate signal processing. Spectral Analysis. Simulation and real-time laboratory experiments illustrating practical DSP implementations and applications. Nonmajor graduate credit.

Graduate Courses

E E 520. Selected Topics in Communications and Signal Processing. (3-0) Cr. 3. Repeatable. Space-time processing. Multiuser communications, Wireless Communications, Statistical signal processing. Pattern recognition. Coding theory. Multirate communications and signal processing. Signal processing and communications applications.

E E 521. Advanced Communications. (3-0) Cr. 3. F. Prereq: 422, Coreq 523. Digital communication systems overview. Characterization of communication channels. Digital modulation and demodulation design and performance analysis. Channel capacity and error-control coding concepts. Waveform design for band-limited channels. Equalization. Wireless fading channels and performance.

E E 523. Random Processes for Communications and Signal Processing. (3-0) Cr. 3. Prereq: 322, Math 317. Axioms of probability; Repeated trials; Functions of a random variable and multiple random variables: covariance matrix, conditional distribution, joint distribution, moments, and joint moment generating function; Mean square estimation; stochastic convergence; Some important stochastic processes: Random walk, Poisson, Wiener, and shot noise; Markov chaines; Power spectral analysis; Selected applications.

E E 524. Digital Signal Processing. (3-0) Cr. 3. F. Prereq: 322, 424, Math 317. Signal modeling. Introduction to filter banks and multi-rate signal processing. Spectral estimation (classical and high resolution). Optimal and adaptive filtering. Introduction to adaptive arrays. Applications.

E E 527. Detection and Estimation Theory. (3-0) Cr. 3. S. Prereq: 422. Classical statistical decision theory, decision criteria, binary and composite hypothesis tests. Error probability and Chernoff bound. Statistical estimation theory and performance measures. Maximum likelihood estimation and sufficiency, Cramer-Rao bound, Bayesian estimation, optimum demodulation, signal design. Applications.

E E 528. Digital Image Processing. (3-0) Cr. 3. S. Prereq: 322, 424. Image representation, sampling, and formats. Edge models, histograms, intensity enhancement, and image statistics. Image transforms and multi-resolution signal processing. Image restoration. Compression and coding techniques. Mathematical morphology. Object recognition and computer vision concepts. Current applications.

E E 545. Artificial Neural Networks. (3-0) Cr. 3. F. Prereq: 324. Introduction to the fundamentals of artificial neural networks (ANNs). Theory and practical implementation of networks. ANNs for pattern recognition, function approximation, prediction. Activation functions, neural net architectures, supervised and unsupervised learning. Various neural network methods and architectures.

E E 547. Pattern Recognition. (3-0) Cr. 3. F. Prereq: 324. Mathematical formulation of pattern recognition problems and decision functions. Statistical approaches: Bayes classifier, probability density function estimation and expectation minimization. Clustering (supervised and unsupervised), learning, and neural network algorithms. Fuzzy recognition systems. Feature selection systems. Classifier comparison. Current applications.

E E 590. Special Topics. Cr. 1-6. Repeatable. Formulation and solution of theoretical or practical problems in electrical engineering.

E E 597. Seminar in Communications and Signal Processing. Cr. 1. Repeatable. Satisfactory-fail only.

E E 621. Coding Theory. (3-0) Cr. 3. Prereq: 521. Fundamentals of error-control coding techniques: coding gain, linear block codes. Galois fields. Cyclic codes: BCH, Reed-Solomon. Convolutional codes and the Viterbi algorithm. Trellis-coded modulation. Iterative decoding. Recent developments in coding theory.

E E 622. Information Theory. (3-0) Cr. 3. Prereq: 521, 523. Information system overview. Entropy and mutual information. Data Compression and source encoding. Discrete memoryless channel capacity. Noisy channel coding theorem. Rate distortion theory. Waveform channels. Advanced topics in information theory.