Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• ENG EC 710: Dynamic Programming and Stochastic Control
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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.
• ENG EC 765: Biomedical Optics and Biophotonics
  This course surveys the applications of optical science and engineering to a variety of biomedical problems, with emphasis on optical and photonics technologies that enable real, minimally-invasive clinical applications. The course teaches only those aspects of biology itself that are necessary to understand the purpose of the application. The first weeks introduce the optical properties of tissue, and following lectures cover a range of topics in three general areas: 1) Optical spectroscopy applied to diagnosis of cancer and other tissue diseases; 2) Photon migration and optical imaging of subsurface structures in tissue; and 3) Laser-tissue interactions and other applications of light for therapeutic purposes. In addition to formal lectures, recent publications from the literature will be selected as illustrative of various topical areas, and for each publication one student will be assigned to prepare an informal presentation (with overhead slides or PowerPoint) reviewing for the class the underlying principles of that paper and outlining the research results. Same as ENGBE765; students may not receive credit for both.
• ENG EC 770: Guided-wave Optoelectronics
  Discussion of physics and engineering aspects of integrated optics and optoelectronic devices. Semiconductor waveguides, lasers, and photodetectors. Layered semiconductor structures, quantum wells, and superlattices. QW detectors, emitters, and modulators. OEICs. Photonic switching.
• ENG EC 771: Physics of Compound Semiconductor Devices
  Physics of present-day compound devices, and emerging devices based on quantum mechanical phenomena. MESFETs, Transferred Electron Devices, avalanche diodes, photodetectors, and light emitters. Quantum mechanical devices based on low dimensionality confinement through the formation of heterojunctions, quantum wells, and superlattices. High electron mobility transistors, resonant tunneling diodes, quantum detectors, and lasers. Materials growth and characterization are integral to the course.
• ENG EC 772: VLSI Graduate Design Project
  EC772 is a project-oriented course that demonstrates the use of high-level design techniques. There are lectures, milestone presentations, and a final presentation. The lectures, interleaved with tutorials showing the utilization of Verilog, the Cadence RTL compiler, and Silicon Encounter, define the general design flow. Additional design issues are also elaborated in the form of classroom lectures, which take up a fraction of the course class time. Student groups of 2-5 define their own projects, which are scrutinized by the entire class as to difficulty and possibility of success. Milestones entail both oral (presented in class times) and written components. Typically, by the time of the final presentation, the milestone documents can be simply, with test results (not necessarily simple), are combined to demonstrate the veracity of the final chip design. Pay special attention to prerequisites. Verilog is at the heart of almost everything. EC311 and EC413 or equivalent courses can provide the minimal Verilog proficiency for LEAP students. These courses do not qualify for grad student credit, so EC551 (Verilog: may be co-req) or equivalent Verilog skill is necessary. EC571 VLSI Design or strong equivalent proficiency in digital circuits at the transistor level is also essential.

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