Offers an integrated lecture/lab course that covers the basics of the Unix operating system, high-level programming concepts, introductory digital design, wireless networking, and Simulink design. Includes hands-on exercises and class exercises around challenges defined by the instructor or by guest lecturers. EECE 7237. Studies machine learning (the study and design of algorithms that enable computers/machines to learn from experience/data). 4 Hours. 4 Hours. 4 Hours. Introduces basics of radio propagation and studies their effect on communication signals. ECE 187. EECE 7242. Focuses on the various building blocks of analog circuits, their operation, and the underlying principles and techniques, with analysis supplemented by CAD simulation. Also addresses compensator design based on root-locus and frequency response, and modern control system design using state-variable feedback. ECE Depth Courses as listed in the catalog Academic Advising For support with academic questions, students should reach out to their undergraduate academic advisor in their home college as well as the Graduate School by emailing gradstudentservices@coe.neu.edu for more information about the PlusOne pathway of interest within COE. Covers fundamentals of probability and stochastic processes with applications to estimation and queuing theory. This opportunity provides undergraduate students with practical research experience, knowledge of modern research practices, and advanced technical skills. Also discusses the applications of algorithm analysis and complexity theory to analyzing and solving problems. Find A-Z • ECE … Topics include the characteristics of discrete signals and systems, sampling, and A/D conversion; the Z-transform, the Fourier transform, and the discrete Fourier transform; fast Fourier transform algorithms; design techniques for IIR and FIR digital filters; and quantization effects in digital signal processing. Special Topics in Electrical and Computer Engineering. Methodology includes lectures, textbooks, and emphasis on readings from relevant literature. Covers the structure and implementation of a modular compiler. Data Visualization. Covers the practical aspects of control systems design through lab experiments. After completing this course, the successful student should be able to design, characterize, choose, or specify power-management circuits or ICs for a system. 0 Hours. Early Childhood Education (MA or MEd) Graduate Master's College of Education. For course descriptions not found in the UC San Diego General Catalog 2019–20, please contact the department for more information. EECE 7296. Examines important electrodynamic applications by the use of advanced mathematical techniques. Computer vision is an exciting but disorganized field that builds on very diverse disciplines such as image processing, statistics, pattern recognition, control theory, system identification, physics, geometry, computer graphics, and learning theory. Starts with the formulation of typical forward problems in electromagnetic and acoustic wave field propagation and scattering, emphasizing biomedical and nondestructive testing applications, and continues with a survey of imaging methodologies including the so-called qualitative imaging methods. 4 Hours. Fundamentals of Computer Engineering. 4 Hours. Noah Lichtenstein, BS, computer engineering and computer science, says the integrated way of learning and experience with co-op will allow him to jump in to a job when he graduates. By reducing circuit complexity, critical path delay, and power dissipation at the expense of introducing processing errors in computation, inexact computing is one of the leading emerging paradigms in nanoscale computing. EECE 7228. Uses finite element and higher-order finite difference methods to solve problems in electrostatics and wave propagation. Considers the efficient generation of optimal solutions, the development and evaluation of heuristics, and the computation of tight upper and lower bounds. Starts with a detailed description of three-phase modeling of basic power system elements such as transmission lines, transformers, and generators. 2020-2021 Undergraduate Course Catalog Courses Print-Friendly Page (opens a new window) Facebook this Page (opens … Please email nucopycenter@northeastern.edu for details. Introduction to Software Security. Introduces fundamental techniques in computer engineering used throughout the graduate curriculum. Special Topics in Power Electronics. Sofia Vanessa Benitez Quevedo, BS, electrical engineering, completed three co-ops near and far including iRobot as a systems engineer, Flex in California working on emerging tech on the internet of things, and Starry, a start up in Boston using cutting-edge technology for the next generation. Presents many of the issues involved in the design and analysis of new and evolving computer architectures. Course projects emphasize model-based design for control of assistive robots in smart environments. Wireless Communication Systems. Topics include magnetics units, magnetic materials classification, origin of ferromagnetism and ferrimagnetism, magnetic anisotropies, magnetostriction, magnetic domain theory, ferromagnetic/ferrimagnetic resonance, soft magnetic materials, hard magnetic materials, applications of magnetic materials, information storage, and leading-edge research. Covers the fundamentals of parallel machine-learning algorithms, tailored specifically to learning tasks involving large data sets. EECE 7203. Covers basic RF design concepts including linearity, noise figure, sensitivity, impedance matching, and imperfections of integrated passive components (parasitics, quality factors). Northeastern ECE faculty and graduate students conduct world-class research at the forefront of electrical and computer engineering. Lectures are supported by illustrative examples, hands-on exercises, and numerical implementations grounded in real-world examples. EECE 7311. Reviews basic protective relaying and relay settings using typical distribution system examples. Seeks to prepare students to do research in machine learning. Touch base with your advisor to discuss which classes you should take in the next semester. High-Performance Computing. Begins with basic concepts such as CMOS device models, DC and small-signal analysis techniques for single- and multistage amplifiers, biasing configurations, and reference generation circuits. Advanced Computer Vision. Covers the fundamental elements of solid-state device physics and the application of these principles. Covers accelerating scientific and other applications on computer clusters, many-core processors, and graphical processing units (GPUs). Dissertation Term 1. This option results in an increase in total hours beyond that required for the master's degree only. Presents an abundance of examples, models, tables, and guidelines to clearly examine new aspects of state estimation, the testing of network observability, and methods to assure computational efficiency. Examines how mobility affects networks, systems, and applications. Covers fundamentals of probabilistic system modeling, building toward techniques that allow complex., as well designing high-performance computing and data analysis through concepts of computer engineering ECE is home to in... Addresses a set of analytical and practical problems, with emphasis on image.! Design through lab experiments, molecular, and system performance such circuits and.: ECE 183 with a particular focus on the development and evaluation of power converters ) of the including. Design, focusing on the use of transient simulation programs for design and analysis of power electronics studied... And algorithmic concepts behind numerical optimization theory for objective functions with finite of... 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