Complex numbers. First-order differential equations. Matrices and systems of linear equations. Vector spaces and linear transformations. 2nd-order linear differential equations and the Laplace transform. Systems of differential equations.

An introduction to using computer applications to solve engineering problems. Learning the rudiments of MATLAB, Excel, and Python in order to design and/or visualize systems. Emphasis is on learning to use these applications appropriately and efficiently, with well structured code that is commented and includes checks to find errors.

Mathematical models for analog circuit elements such as resistors, capacitors, opamps and MOSFETs as switches. Basic circuit laws and network theorems applied to dc, transient, and steady-state response of first- and second-order circuits. Modeling circuit responses using differential equations Computer and laboratory projects. NOTE: Grades of C or better in MATH 132 and PHYSICS 152 are strongly recommended.

Introduction to fundamentals of radar systems. Radar range equation, critical radar components, and system performance. Detection, modulation, noise, and propagation effects. Prerequisite: E&C-ENG 584 or equivalent.

A graduate version of E&C-Eng 568. Quantitative study of pipelined processor architectures, memory, Input/Output, RISC processors and vector machines. Prerequisite: undergraduate courses in digital design and hardware organization.

Introduction to data analytics. Data preparation, similarity and distances, association pattern mining and cluster analysis, outlier analysis, data classification, textual and time-series data, privacy issues, analysis of social networks.

Visual information plays an important role in many aspects of our life. Much of this information is represented by digital images. Image processing is ubiquitous, with applications including television, tomography, photography, printing, robot perception, and remote sensing. ECE697IP is an introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications.

Visual information plays an important role in many aspects of our life. Much of this information is represented by digital images. Image processing is ubiquitous, with applications including television, tomography, photography, printing, robot perception, and remote sensing. This course is an introductory course to the fundamentals of digital image processing. It emphasizes general principles of image processing, rather than specific applications.

Modern techniques for synthesis and verification of digital systems. Topics in synthesis cover high-level synthesis, decision diagrams, multi-level logic and sequential optimization. Topics in verification include symbolic techniques, combinational and sequential equivalence checking, and functional test generation. Recommended prerequisite: undergraduate course in digital logic design.