Courses

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

• ENG EC 580: Analog VLSI Circuit Design
  Undergraduate Prerequisites: (ENGEC412) - Anatomy of an operational amplifier using chip design techniques. Applications of op amps in wave-shaping circuits, active filters including capacitive switching. Analog multiplexing and data acquisition circuits, A/D, D/A, S/H are examined. Frequency selective circuits and interface circuits such as optocouplers are analyzed.
• ENG EC 583: Power Electronics for Energy systems
  Undergraduate Prerequisites: (ENGEC410) - Introduction to power electronics with emphasis on conversion circuits for energy systems. DC to DC conversion using buck, boost, and buck-boost converters. DC to AC inverters. Connection to power grid. Properties of MOS transistors used for high power conversion applications. Properties of magnetic elements and interactions with power circuits. Applications of power electronic circuits to energy systems, including solar cell installations, wave and wind power, and electric vehicles. High frequency inductors and transformers.
• ENG EC 585: Quantum Engineering & Technology (QET)
  Prerequisites: CAS MA 225 (Multivariate Calculus), Linear Algebra, ENG EK 125 (Introduction to Programming for Engineers), ENG EK103 (Computational Linear Algebra), CAS PY 313 / 314 (Waves and Modern Physics). Background knowledge in classical electrodynamics, semiconductors physics, and elementary quantum mechanics. This course introduces graduate students to Quantum Engineering and Technology (QET) by providing a comprehensive and rigorous discussion of the basic principles and engineering design concepts of quantum coherent structures and devices for communications, computation, simulation, metrology, and sensing. The course will provide the mathematical foundation, selected design methods, and in-depth discussions of engineering device implementations of coherent quantum systems, with emphasis on quantum optical ones. The course provides a broad yet rigorous training in quantum mechanics for engineering students interested in understanding both the technical applications and the broader societal impact of quantum principles in technology.
• ENG EC 591: Photonics Lab I
  Undergraduate Prerequisites: (CASPY313) ; Undergraduate Corequisites: (ENGEC562) - Introduction to optical measurements. Laser safety issues. Laboratory experiments: introduction to lasers and optical alignment; interference; diffraction and Fourier optics; polarization components; fiber optics; optical communications; beam optics; longitudinal laser modes. Optical simulation software tools.
• ENG EC 601: Product Design in Electrical and Computer Engineering
  Undergraduate Prerequisites: Graduate Standing or permission of instructor. - Engineers influence their community, society and the world. Engineers build products and services that can enhance people's lives. The product starts with an idea and is delivered through research (technical and societal), design, implementation, testing and support. During this class, students will experience all of this. The course provides design and practical insights into building products that involve WEB and mobile app development, data simulation, analysis and modeling, cloud computing, signal processing and/or computer vision. In the class, we work on how to take an idea and concept and translate it into product requirements. Afterwards, we translate the product requirements into system and engineering requirements. We also discuss solution selection techniques. We then work on implementing our ideas into systems and verify that they address the product requirements and fulfill the concept we started with. During the class, we go over how to choose solutions to build our products. We also discuss real product realization, implementations and tradeoffs. The class is taught via an example product and the class sessions are interactive. Students are divided into groups where they work in parallel on their projects during class sessions and hackathons. Teams define their target audience, product mission, requirements and features. The class adopts agile software development based on a two-week sprint. Students present their sprint results to the class.
• ENG EC 602: Design by Software
  Undergraduate Prerequisites: Graduate standing or permission of instructor. - Software plays a central role in all aspects of electrical and computer engineering. This course will provide the foundation for effectively using software as a key part of a career as a professional electrical or computer engineer. Fundamentals of software development systems: system languages, high-level object-oriented languages, and computational languages. Data structures and algorithms in problem analysis and design. Strategies for designing software and designing with software. Software design and development: methodologies, principles and practice. Formalizing software: management, requirements, specifications, testing. Survey of software applications in ECE, including real-time systems, the web, networked systems, audio, graphics, and video systems, research and engineering analysis, consumer electronics and computing, instrumentation and measurement, design, modeling, prototyping, simulation, optimization and information analysis. Students can choose projects and assignments with application to/inspired by/drawn from a broad array of ECE fields including the traditional areas of electro-physics/photonics, computer engineering, and information and data science. Open to graduate students only.
• ENG EC 605: Computer Engineering Fundamentals
  Undergraduate Prerequisites: Graduate standing or permission of the instructor. - This is an introductory course to computer engineering, focusing on the hardware/software interface, and presenting a bottom-up view of a computer system. Topics include logic design: binary arithmetic, combinational and sequential logic. Computer organization: assembly language programming, CPU design, and memory systems. Introduction to compilers, operating systems, and computer networks. Open to graduate students only.
• ENG EC 674: Optimization Theory 2
  This course is an introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies and the underlying mathematical structures. We will cover the classical theory as well as the state of the art. The major topics we will cover are: 1. Theory and algorithms for linear programming. 2. Introduction to combinatorial problems and methods for handling intractable problems. 3. Introduction to nonlinear programming. 4. Introduction to network optimization. Optimization techniques have many applications in science and engineering. To name a few: * Optimal routing in communication networks. * Transmission scheduling and resource allocation in sensor networks. * Production planning and scheduling in manufacturing systems. * Fleet management. * Air traffic flow management by airlines. * Optimal resource allocation in manufacturing and communication systems. * Optimal portfolio selection. * Analysis and optimization of fluxes in metabolic networks. * Protein docking. Prerequisites: Working knowledge of Linear Algebra and some degree of mathematical maturity. Same as ENG EC 674, ENG SE 524, ENG EC 674. Students may not receive credits for both.
• ENG EC 700: Advanced Topics in Electrical and Computer Engineering
  Undergraduate Prerequisites: Graduate standing or consent of instructor. - Advanced topics of current interest in electrical and 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 710: Dynamic Programming and Reinforcement Learning
  Undergraduate Prerequisites: (ENGEK500) ENGEK381 and ENG EC402 or ENG EC501. - This course covers the fundamentals of sequential decision making in both known and unknown environments through dynamic programming and reinforcement learning. The first part of the course delves into selecting optimal permissible actions upon observing a system state with established system evolution rules. This section examines finite and infinite horizon stochastic dynamic systems, introducing methods like value iteration, policy iteration, and linear programming solution approaches. Subsequently the course shifts to strategies for optimal action selection under uncertain stochastic system dynamics, covering techniques such as temporal differences, Q-learning, policy gradient, actor-critic, neural network/deep-learning-based reinforcement learning, and federated learning. This course is cross-listed as ENG ME 710 and ENG SE 710. Note: Credit is granted for only one of these courses.
• 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 721: Advances in Cyber and IoT Security
  Prerequisites: ENGEK, 500 or 505 or 521 and ENGEC541 or instructor consent. - This course covers new developments in cybersecurity, with an emphasis on networking and communications aspects and the Internet of Things (IoT). Selected topics may include threat modeling, game theory for cybersecurity, blockchains, side-channel analysis, network infrastructure security, and security for connected vehicles. The course blends theory and practice and culminates with a research project, building on recent results from the literature.
• 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.