Electrical & Computer Engineering

• ENG EC 701: Optimal and Robust Control
  Undergraduate Prerequisites: (ENGEC501 OR ENGME501 OR ENGSE501) - This course is aimed at an introduction (with rigorous treatment) to the fundamentals of optimal and robust control. It will be divided roughly into two parts. The first will cover aspects of robust control including model reduction, H_2 and H_ infinity control, and feedback control of uncertain systems. The second will delve into optimal control including topics such as the linear quadratic regulator, the calculus of variations, the maximum principle, and the Hamilton-Jacobi-Bellman equation. Same as ENG ME 701 and ENG SE 701. Students may not receive credits for both.
• ENG EC 702: Recursive Estimation and Optimal Filtering
  Undergraduate Prerequisites: (ENGEC505) - State-space theory of dynamic estimation in discrete and continuous time. Linear state-space models driven by white noise, Kalman filtering and its properties, optimal smoothing, non-linear filtering, extended and second-order Kalman filters, and sequential detection. Applications to radar, sonar, and optimal multitarget tracking, parameter identification.
• ENG EC 707: Radar Remote Sensing
  Undergraduate Prerequisites: Experience in electromagnetic waves, analog and discrete signal proces sing, or consent of the instructor. - Principles of radar systems and radar signal analysis with emphasis on environmental remote sensing. Topics include antenna fundamentals, wave propagation/scattering in various media, the radar equation, radar cross-section, target characteristics, ambiguity function, radar system components, pulse compression techniques, and aperture synthesis. Highlighted systems include ground-penetrating radars, synthetic aperture radar (SAR), weather radars, and incoherent scatter radars, and LIDAR.
• ENG EC 708: Advanced Process Control
  Undergraduate Prerequisites: (ENGEC402 OR ENGEC501 OR ENGME507) or equivalent with permission of instructor. - Graduate Prerequisites: . - Integrated study of process control and modern control theory. Includes process modeling and simulation, analysis of linear and non-linear dynamics, evaluation and selection of actuators and measurements, control structure design for single and multiple variable systems, and control algorithm design. Examples drawn from a variety of process control applications.
• ENG EC 710: Dynamic Programming and Stochastic Control
  Undergraduate Prerequisites: (ENGEK500) ENGEK381 and ENG EC402 or ENG EC501. - Introduction to sequential decision making via dynamic programming. The principle of optimality as a unified approach to optimal control of dynamic systems and Markovian decision problems. Applications from control theory and operation research include linear-quadratic problems, the discrete Kalman Filter, inventory control, network, investment, and resource allocation models. Adaptive control and numerical solutions through successive approximation and policy iteration, suboptimal control, and neural network applications involving functional approximations and learning. Same as ENG ME 710 and ENG SE 710. Students may not receive credits for both.
• ENG EC 713: Advanced Computer Systems & Architecture
  Undergraduate Prerequisites: (ENGEC513 OR ENGEC535 OR ENGEC527) - This class is designed to enable students to follow the latest developments in computer systems and architecture. The lectures cover a broad array of recent subjects, such as memory management in multi-core systems, hardware multi- threading, heterogenous systems, modern operating systems, large-scale system architectures, virtualization, data center management, energy awareness in computing systems, system reliability, and emerging areas, such as quantum computing, and neuromorphic computing. The concepts are reinforced with research paper readings and hands-on assignments that involve computer system design and analysis.
• ENG EC 716: Advanced Digital Signal Processing
  Undergraduate Prerequisites: (ENGEC516) - Selected topics from time-frequency distributions, parametric signal modeling, high-resolution spectral estimation, multi-rate signal processing, multidimensional signal processing, adaptive signal processing, alternative algorithms for DFT computation, symbolic and knowledge based signal processing. Application examples chosen from speech, image, communication, and biomedical applications.
• ENG EC 717: Image Reconstruction and Restoration
  Undergraduate Prerequisites: (ENGEC516 & ENGEC505) - Principles and methods of reconstructing images and estimating multidimensional fields from indirect and noisy data; general deterministic (variational) and stochastic (Bayesian) techniques of regularizing ill-posed inverse problems; relationship of problem structure (data and models) to computational efficiency; impact of typically large image processing problems on viability of solution methods; problems in imaging and computational vision including tomography and surface reconstruction. Computer assignments.
• ENG EC 719: Statistical Learning Theory
  Undergraduate Prerequisites: (ENGEC414 OR ENGEC503) ; Undergraduate Corequisites: (ENGEC505) - Classical and contemporary theories of machine learning. Topics/emphasis may change based on instructor preference in different years. A project involving computer implementation of a learning or inference algorithm accompanied by or in support of theoretical analysis is required
• ENG EC 720: Digital Video Processing
  Undergraduate Prerequisites: (ENGEC516 OR ENGEC505 OR ENGEC520) or equivalent - Review of sampling/filtering in multiple dimensions, human visual system, fundamentals of information theory. Motion analysis: detection, estimation, segmentation, tracking. Image sequence segmentation. Spectral analysis of image sequences. Video enhancement: noise reduction, super-resolution. Video compression: transformation, quantization, entropy coding, error resilience. Video compression standards (H.26X and MPEG families). Future trends in image sequence compression and analysis. Homework and project will require MATLAB programming.
• ENG EC 724: Advanced Optimization Theory and Methods
  Undergraduate Prerequisites: (ENGEC524) consent of instructor - Introduces advanced optimization techniques. Emphasis on nonlinear optimization and recent developments in the field. Topics include: unconstrained optimization methods such as gradient, conjugate direction, Newton and quasi-Newton methods; constrained optimization methods such as gradient projection, feasible directions, barrier and interior point methods; duality theory and methods; convex duality; and introduction to other advanced topics such as semi-definite programming, incremental gradient methods and stochastic approximation algorithms. Applications drawn from control, production and capacity planning, resource allocation, communication and neural network problems. Same as ENG ME 724 and ENG SE 724. Students may not receive credits for both.
• ENG EC 725: Queueing Systems
  Undergraduate Prerequisites: (ENGEK500 OR ENGEC505) consent of instructor - Performance modeling using queueing networks analysis of product form and nonproduct form networks, numerical methods for performance evaluation, approximate models of queueing systems, optimal design and control of queueing networks. Applications from manufacturing systems, computer systems, and communication networks. Same as ENG ME 725 and ENG SE 725. Students may not receive credits for both.
• ENG EC 730: Information-Theoretical Design of Algorithms
  Recently developed information-theoretical approach to the analysis and design of computer algorithms. Previous knowledge of information theory or the theory of algorithms is not required, though desirable. Main topics include the complexity of algorithms; P, E, NP, and NP?hard problems; basic concepts of information theory, optimal coding; information-theoretical approach to sorting, order statistics, binary search, decision trees, hashing, minimization of Boolean functions, test, and similar problems; and design of efficient computer algorithms.
• ENG EC 732: Combinatorial Optimization and Graph Algorithms
  Undergraduate Prerequisites: (ENGME411 OR CASCS330) or equivalent course on optimization or algorithms. - of algorithms for the solution of optimization problems with discrete decision spaces. Review of linear programming and duality. Discussion of advanced network optimization algorithms and matroid optimization. Approximate algorithms for NP-Hard optimization problems. Submodular optimization. Same as ENG SE 732. Students may not receive credits for both.
• ENG EC 733: Discrete Event and Hybrid Systems
  Undergraduate Prerequisites: (ENGEK500) or equivalent; consent of instructor - Review of system theory fundamentals distinguishing between time-driven and event-driven dynamics. Modeling of Discrete Event and Hybrid Systems: Automata, Hybrid Automata, Petri Nets, basic queueing models, and stochastic flow models. Monte Carlo computer simulation: basic structure and output analysis. Analysis, control and optimization techniques based on Markov Decision Process theory with applications to scheduling, resource allocation and games of chance. Perturbation Analysis and Rapid Learning methods with applications to communication networks, manufacturing systems, and command-control. Same as ENG ME 733 and ENG SE 733. Students may not receive credits for both.
• ENG EC 734: Hybrid Systems
  Undergraduate Prerequisites: (ENGSE501 OR ENGEC501 OR ENGME501) or consent of instructor. - The course offers a detailed introduction to hybrid systems, which are dynamical systems combining continuous dynamics (modeled by differential equations) with discrete dynamics (modeled by automata). The covered topics include modeling, simulation, stability analysis, verification, and control of such systems. The course contains several applications from both natural and manmade environments, ranging from gene networks in biology, to networked embedded systems in avionics and automotive controls, and to motion planning and control in robotics. Same as ENG ME 734 and ENG SE 734. Students may not receive credits for both.
• ENG EC 745: Nanomedicine: Principles and Applications
  The use of nanoscience and technology for biomedical problems has spawned a field of applications ranging from nanoparticles for imaging and therapeutics, to biosensors for disease diagnostics. Nanomedicine is a rapidly growing field that exploits the novel properties of nanoscale materials and techniques to rapidly advance our understanding of human biology and the practice of medicine. This course focuses on the fundamental properties, synthesis and characterization of nanomaterials, coupled with their applications in nanomedicine, including: micro- and nano-particles for drug delivery and imaging, microfluidics for in vitro diagnostics, nanomaterials and platforms for biological applications. The biomedical applications include cancer, cardiovascular disease, and infectious diseases. Same as ENG BE 745. Students may not receive credit for both.
• ENG EC 754: Computer-Aided Verification and Synthesis
  Undergraduate Prerequisites: (ENGEC330) Familiarities of propositional logic, basic probability theory and bas ic graphic graph algorithms, and experience with one programming langu age (e.g., C++, Python) are assumed. An undergraduate course - This course will introduce the fundamental theory in computer-aided verification and synthesis for building provably dependable computer systems. The topics covered include logic specifications, modeling formalisms, verification techniques, and inductive synthesis strategies. A special focus of the course is on interplay between deductive reasoning (logical inference and constraint solving) and inductive inference (learning from data). We will also survey applications of these techniques to a wide range of problems in hardware, software, cyber-physical systems, robotics, and biology.
• ENG EC 762: Quantum Optics
  Undergraduate Prerequisites: (ENGEC560) or equivalent, or consent of instructor. - Review of the postulates of quantum mechanics. Quantization of the electromagnetic field. Coherent, thermal, squeezed, and entangled states, and their associated photon statistics. Interaction of light with matter. Spontaneous and stimulated transitions. Theory of optical detection. Quantum theory of the laser. Interaction of light with two-level atoms, including photon echo and self-induced transparency. Quantum theory of parametric interactions.
• ENG EC 763: Nonlinear and Ultrafast Optics
  Undergraduate Prerequisites: (ENGEC560) - Tensor theory of linear anisotropic optical media. Second- and third-order nonlinear optics. Three-wave mixing and parametric interaction devices, including second-harmonic generation and parametric amplifiers and oscillators. Four-wave mixing and phase conjugation optics. Electro-optics and photo-refractive optics. Generation, compression, and detection of ultra short optical pulses. Femtosecond optics. Pulse propagation in dispersive linear media. Optical solitons.