Course descriptions

Advanced programming techniques using data structures. Topics include recursion, lists, stacks, queues, trees, graphs, and hash tables.

Introductory course in digital logic and its specification and simulation. Number systems. Binary arithmetic. Boolean algebra. Combinational logic design. Sequential logic design (controllers). Data Path components (e.g., Registers, adders, register files, etc.). Memory components (e.g., RAM, ROM). Register transfer level (RTL) design.

Introduction to digital design techniques, logic gates, Medium Scale Integration (MSI) parts and flip-flops, Timing analysis, Programming and use of Programmable Logic Devices (PLD).

This course introduces discrete structures. Topics include basic logic, sets, relations, functions, proof techniques, counting methods, recursion, trees, and graphs.

Analysis methods for discrete-time signals and systems in the time and frequency-domains including signal models and Fourier techniques. Continuous-time concepts are included as introductory material.

DSP programming assignments in MATLAB and/or TMS DSP processors.

Computer architecture and organization. Topics include performance evaluation, instruction, set architecture, data path, control unit, pipelining, memory hierarchy, storage, and other I/O topics.

Operating Systems

Computer Engineering Internship

Techniques for software validation and verification. Topics include: concepts of software quality (metrics, assurance, planning & implementation, process standards, validation, verification, reviews, walkthroughs and inspections). Software testing process and techniques at different levels. Validating test data.

Computer network lab experiments with Packet Tracer, Wireshark, and other tools.

Introduction to software maintenance and re-engineering. Topics include evolution and maintenance models, re-engineering, legacy information systems, impact analysis, refactoring, program comprehension, principles of reuse and reverse engineering, approaches, and tools used to extract information from existing software systems.

Survey of artificial intelligence techniques. Topics include search, logic, knowledge representation, reasoning with uncertainty, learning, and other AI techniques.

Introduction to digital image processing. Topics include Digital image fundamentals, intensity transformations, and spatial filtering, filtering in frequency domain, Image restoration and reconstruction, Color image processing, Image compression, and Image segmentation.

Design and analysis of fault-tolerant digital systems. fault models, hardware redundancy, information redundancy, evaluation techniques, system design procedures.

Introduction to computer security and privacy. Topics include security in programs, operating systems, networks and databases.

Introduction to machine learning with images and natural text. A coverage of machine learning and data mining techniques such as decision trees, random forests, neural networks, kNN, Adaboost, stochastic gradient descent, regression, and transformers.

Robot sensors and actuators, motors, robot path planning, simultaneous localization and mapping, and robot vision.

A coverage of deep neural networks including Alexnet, convolutional neural networks, long short term memory networks, recurrent neural networks, and Resnet.

Image information, image filtering, template matching, histogram transformations, edge detection, boundary detection, region growing and pattern recognition. Complementary laboratory exercises are required.

Image and video processing lab experiments including pattern recognition, filtering, and edge detection.

Introduction to software engineering. Topics include software development life cycle, software processes, requirements engineering, software architecture and design, software testing, software evolution, and software modeling using UML

Magnetics and magnetically coupled circuits, electromechanical energy conversion, rotating magnetic fields, stepper motors, DC machines, induction machines, synchronous machines and brushless DC machines.

Examination of fields of engineering and career opportunities in engineering. Professional expectations in the workplace. Introduction to campus resources for assisting student success.

Introduction to a systematic approach to engineering design (problem clarification, concept generation, concept selection, prototyping methods, engineering ethics) and fundamental design communication techniques. Computer aided design tools are introduced to assist in design analysis.

Engineering project analysis from an engineering economics perspective. Topics include interest, equivalent worth, comparing alternatives, rate of return methods, depreciation and taxes, inflation and price changes, benefit-cost analysis, and risk analysis.