Graduate Course Descriptions

ECEn 541: Active and Passive Filter Design (3).

Prerequisites: ECEn 313, 380.

Design methods for electronic filters based on passive components, active components, and integrated circuit components.

1. Approximations to ideal responses: Butterworth. Chebychev, elliptic, and Bessel functions.
2. Transfer functions derived from approximate functions.
3. Synthesis of driving point functions.
4. Synthesis of passive transfer functions.
5. Synthesis of doubly-terminated ladder networks.
6. Active synthesis using amplifiers.
7. Biquad synthesis.
8. Limitations on integrated circuit filters.
9. Contemporary IC filter synthesis.

ECEn 543: CMOS Amplifier Design (3).

Prerequisite: ECEn 443 or 445

Factors affecting performance of MOS devices in analog applications. Design of MOS amplifiers, buffers, and comparators.

1. Mathematical tools used to analyze complex circuits: dominant poles, signal flow graphs.
2. First-order and second-order effects in an MOS device.
3. MOS models and SPICE modeling parameters.
4. Single-stage amplifiers.
5. Noise considerations.
6. CMOS op amp design and compensation.
7. Advanced current mirrors and op amps.
8. Comparators: static and dynamic.
9. Contemporary amplifier designs.

ECEn 562: Optical Communication Components and Systems (3).

Prerequisite: ECEn 460.

Fiber-optic communication system components and their operating and performance characteristics.

1. Optical communications systems hierarchies
2. Signal sources
3. Detectors
4. Propagation in optical fibers
5. Photonic switching
6. Modulation and gain in optical fibers
7. Distributed feedback
8. Mode coupling

ECEn 563: Applied Computational Electromagnetics (3).

Prerequisite: ECEn 460.

Current theory and practice in numerically solving Maxwell's equations for antenna and circuit design and radar scattering prediction.

1. Finite-Difference solutions
2. Finite-Difference Time-Domain method
3. Variational calculus and functional analysis
4. Integral equation solutions
5. Method of Moments
6. Green's function computation
7. Finite element formulation
8. Monte Carlo simulations
9. Microwave imaging and inverse scattering

ECEn 564: Radar Systems Performance (3).

Prerequisites: ECEn 460, 485.

Performance and evaluation of various radar systems. Range equations, signal detection, ambiguity function, system configurations, and components.

1. Fundamentals of radar
2. Radar equation
3. SNR
4. System components
5. Detection theory and matched filtering
6. Probability of detection and false alarm
7. Target scattering models
8. Clutter
9. Moving target indicator radars
10. Range and frequency resolution
11. Radar ambiguity function
12. Pulse compression
13. Angle measurement
14. Tracking versus detection radars

ECEn 568: Microwave Remote Sensing (3).

Prerequisite: Graduate standing or intructor's consent.

Emphasis on space-borne remote sensing of earth's atmosphere, land, and oceans. Primary methods and applications for both active (radar) and passive (radiometry).

1. Introduction to Microwave Remote Sensing
2. Antenna systems concepts
3. Radiative Transfer
4. Atmospheric sensing
5. Radiometer systems
6. System temperature
7. Basics of radar
8. Radar scattering
9. Radar resolution
10. Real and synthetic aperture radars

ECEn 620: Advanced Digital Systems (3).

Prerequisite: ECEn 451; proficiency in C or C++.

Advanced synchronous systems design, HDL's, introduction to systolic arrays, high-speed low-power digital circuit architectures.

1. Clocking and high performance systems (clock distribution, clock skew, timing analysis).
2. Systems Architectures (systolic arrays, pipelining, bit- and digit-serial design, asynchronous state machines).
3. Systems Design (VHDL modeling and simulatin, VHDL synthesis).
4. Electronic Design Automation (EDA) tools.

ECEn 621: Computer Arithmetic (3).

Prerequisite: ECEn 324.

Fundamental principles and development of algorithms for performing arithmetic on digital computers and application-specific processors.

1. Numbering systems
2. Sequential algorithms for arithmetic
3. Integer arithmetic
4. Floating point arithmetic
5. Fast addition
6. High-speed multiplication
7. High-speed division and square root
8. Evaluation of elementary functions
9. Most significant digit first arithmetic

ECEn 628: Advanced Computer Architecture (3).

Prerequisite: ECEn 324.

Lab experience with hardware and software techniques for exploiting instruction-level parallelism.

1. Advanced pipelining (implementing precise interrupts, hardware and software branch prediction)
2. Dynamic scheduling algorithms
3. Hardware support for multiple-issue execution
4. Software support for multiple-issue execution (data dependence analysis, compiler optimizations)
5. Design and coding architectural simulators with accurate cycle counts

ECEn 629: Reconfigurable Computing Systems (3).

Prerequisite: ECEn 524.

Introduction to FPGA devices, lab experience developing FPGA-based configurable systems.

1. FPGA device architecture.
2. Configurable system architecture
3. Physical design issues
4. Configuration strategies
5. Configurable system synthesis
6. CAD tools for configurable systems
7. Alternative devices

ECEn 670: Stochastic Processes (3).

Prerequisites: ECEn 380, Stat 421, and either graduate standing or consent of instructor.

Review of elemetary probability and introduction to random processes: definitions, properties, covariance, spectral density, time average, stationarity, ergodicity, linear system relations, mean square estimation, Markov processes.

1. Review of probability theory (probability spaces, random variables, derived probability spaces, independence, conditioning, etc.)
2. Introduction to stochastic processes (definitions, Kolmogorov's extension theorem)
3. Continuous time and discrete time processes
4. Examples (random walk, Markov processes)
5. Mean functions, correlation functions, covariance functions
6. Strict- and wide-sense stationarity
7. Stochastic mean-square calculus (conditions for mean-square continuity, mean-square differentiability, and mean-square integrability)
8. Power spectrum: definitions and interpretations
9. Linear system relationships
10. Empirical means and autocorrelations; Ergodic theorems (laws of large numbers) for the mean and correlation function
11. Brownian motion and white noise
12. Wiener filters (continuous and discrete-time)
13. Important stochastic processes (ARMA processes, counting processes, etc.)

ECEn 671: Mathematics of Signals and Systems (3).

Prerequisites: ECEn 380, Math 343, graduate standing instructor's consent.

Introduction to mathematics of signal processing, communication, and control theory: linear spaces, Eignevalue and singular value decompositions, quadratic forms, linear operators, adjoints, dual spaces.

1. Linear Vector Spaces: Properties, Examples (finite & infinite dimensional), Basis, transformations, linear independence, Subspaces, range & null spaces.
2. Normed Vector Spaces: Norms (Lp, Frobenius, induced norms), Inner-products, Hilbert spaces, Orthogonality, orthonormality, Gram-Schmidt, Projections, approximation via projections, Dual spaces.
3. Linear Operators on Vector Spaces: Finite dimensional matrix operations, Infinite dimensional operators, Solving systems of equations, overdetermined, underdetermined, min-norm & least-squares solutions, Pseudo- & generalized inverses, Adjoint operators, Linearization of non-linear operators, generalized Taylor's series approximations.
4. Finite Dimensional Matrix Theory: Eigen-decomposition, left & right eigenvectors, characteristic polynomials, etc., Singular value decomposition (interpretation), Other decompositions (square root, cholesky, QR, ULV, LDU, Schur), Quadratic forms, positive & negative definiteness, Matrix inequalities, Special operators (Toeplitz, Hankel, Sylvester, Vandermonde, circulant, etc.), Kronecker & Hadamard products, Matrix calculus (optimization of scalar-valued functions of matrices).

ECEn 672: Detection and Estimation Theory (3).

Prerequisite: ECEn 670; Stat 421 or equivalent; graduate standing or instructor's consent.

Sufficiency, completeness; Neyman-Pearson and Bayes detector; maximum likelihood, Bayes, minimum mean square, and linear estimation; Kalman filters and selected topics.

1. The formalism of statistical decision theory (game theory, mathematical structures of decision theory).
2. Introductory concepts (sufficiency, completeness, exponential families, minimum variance unbiased estimators).
3. Neyman-Pearson theory (Likelihood ratios, receiver operating characteristics, simple and composite hypotheses).
4. Bayes decision theory (Bayes risk, Bayes envelope function, randomized rules, minimax rules).
5. Maximum likelihood estimation (Maximum likelihood principle, Cramer Rao bounds, asymptotic properties of maximum likelihood estimators).
6. Bayes estimation theory (MAP estimation, conjugate priors, improper priors, sequential Bayes estimators).
7. Linear estimation theory (Minimum mean-square estimation, geometric interpretations, Gram-Schmidt, Innovations, matrix factorizations, white noise interpretations).
8. Estimation of State Space Systems (Innovations with state space models, the discrete-time Kalman filter, the Kalman gain, smoothing, the extended Kalman filter).

ECEn 678: Digital Image Processing (3).

Prerequisites: ECEn 487, ECEn 670; graduate standing or instructor's consent.

Digital processing theory and techniques for two-dimensional image analysis, enhancement, restoration, data compression, and reconstruction from projections.

1. Image perception, monochrome vision model, color perception.
2. 2-D linear systems: special functions, convolution.
3. Fourier transforms, z-transforms, OTF, MTF.
4. Matrix theory for image processing.
5. Random fields.
6. Image transforms.
7. Image enhancement, histogram operations, median filtering, edge enhancement.
8. Image restoration: Inverse filter, Wiener filter.
9. Model based, statistical, and iterative restoration, Markov random fields, MAP restoration.
10. Image reconstruction from projections.
11. Image data compression, JPEG, MPEG.
12. Review of current research trends.

ECEn 773: Linear System Theory (3).

Prerequisites: ECEn 483, ECEn 671.

Mathematical introduction to time-varying linear systems: state space descriptions, controllability, observability, Lyapunov stability, observer-based control. Design of linear quadratic regulators and infinite horizon Kalman filters.

1. State space formulations of linear systems.
2. State transformations.
3. Internal vs. external stability.
4. Controllability and observability.
5. State feedback.
6. Introduction to linear-quadratic regulators.
7. Observers, observer-based feedback.
8. Linearization off nonlinear systems.
9. Time-varying systems.
10. Lyapunov theory.

ECEn 775: Error Control Coding (3).

Prerequisite: Graduate standing or instructor's consent.

Theory and implementation of block and convolutional codes for error control in digital communications and computer applications. Cyclic codes, CRC's, BCH, Reed-Solomon, Viterbi algorithm.

1. Block codes (generator and parity check matrices, standard array table decoding, syndrome decoding).
2. Galois fields.
3. Binary cyclic codes (encoding and syndrome computation).
4. BCH and Reed-Solomon codes (encoding and decoding).
5. Convolutional codes.
6. Maximum-likelihood decoding and the Viterbi algorithm.
7. Applications.

ECEn 777: Digital Signal Processing (3).

Prerequisites: ECEn 487, ECEn 670, ECEn 671; graduate standing or instructor's consent.

Advanced theory and applications of digital signal processing including optimal statistical processing, adaptive processing, and array processing methods.

1. Multirate signal processing.
2. Linear Prediction.
3. Least squares system modeling.
4. Adaptive Filtering.
5. Optimal array processing, beamforming.
6. Model-based spectral and direction of arrival estimation.
7. Statistical signal processing.
8. Signal invariance processors.
9. Wavelet transforms.
10. Time-frequency distributions, recent developments
11. High order spectral estimation (cumulants)
12. Sonar/Radar processing and detection theory.
13. Current research review.