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.

Advanced digital design techniques, Micro controller based design, hardware, and software co design.

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

Circuit elements, signals, Kirchhoff’s laws, network transforms, mesh and node analysis, transient and complete response of RL, RC, and RLC circuits.

Safety, basic measurements and meters, oscilloscopes, resistor networks, measurement of capacitors and inductors, RLC circuit response.

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

Introduction to computer science and programming. Topics include information and data representation, hardware, programming methodology, algorithm design, abstract data types, programming language, operating systems, applications and communications.

Introduction to application development. Topics include: basics of problem solving techniques, game programming, mobile applications.

An introductory course to problem solving and computational thinking using computer programming. Topics include: problem solving fundamentals, programming basics, variables and expressions, input/output, control structures, functions, strings and files.

Web design practices using modern technologies. Topics include: Website basic elements, Web page design, creating site content, and launching a web site.

Programming fundamentals from an object oriented design perspective. Topics include: basic procedural concepts, objects, classes, interface, methods, encapsulation, information hiding, and inheritance.

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

Object oriented analysis, design, and advanced programming concepts. Topics include: polymorphism, abstract classes, interfaces, exception handling, graphical user interface, and modelling notations.

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.

Practicing advanced problem solving techniques: arrays, stacks, queues, strings, sorting, arithmetic, algebra, combinatorics, number theory, backtracking, geometry, graphs algorithms and traversals. Introduction to dynamic programming, preparing students majoring in computer science for international and regional ACM programming contests.

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

Introduction to systems programming in Unix/Linux environment. Topics include scripting, libraries, utilities, development tools, piping, binary files, exception handling, command-line arguments, and symbolic debugging.

Exploration of the Unix operating system, including its tools and utilities for program development, such as makefile, piping and redirection, shell scripts, regular expressions, and symbolic debuggers. In addition, this course explores advanced features of the C programming language, including various file processing, command-line and variable arguments, exception handling, and generic interfacing.

Introduction to Data Science. Topics include data acquisition, data management, data preparation and integration, data analysis, model development and deployment, and visualization.

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

Introduction to web development. Topics include client/server side scripting, dynamic websites, web services, database connectivity, security, and authentication.

Design & analysis of recursive and non-recursive algorithms. Topics include asymptotic analysis of algorithm efficiency, algorithm design techniques (e.g. divide-and-conquer, dynamic programming, greedy approaches) and their applications to computational problems, and introduction to parallel algorithms.

Principles of programming language design. Specification of syntax and semantics. Underlying implementation of block structured languages. Dynamic memory allocation for strings, lists and arrays. Imperative versus applicative programming. First order logic and logic programming. Lambda calculus and functional programming languages. Modern programming languages.

In-depth study of object-oriented design patterns. Topics include: creational patterns, structural patterns, and behavioral patterns. Pattern's intent, motivation, applicability, participants, collaborations, consequences, and implementations.

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.

Introduction to modern business analysis. Topics include: planning and monitoring, elicitation, requirements, analysis, solution assessment and validation, requirements management and communication, business problems identification, business cases development and plans for achieving effective solutions.

Fundamentals of Database Management Systems. Modern database models. Basic database management concepts. Query languages. Relational algebra and relational calculus. Database design through normalization. Development experience in a team environment.

Introduction to mobile application development. Topics include creating and deploying mobile applications, development approaches, programming languages, frameworks, application architecture, event-driven programming, model-view-controller, user interface and usability.

Introduction to visual representation methods and techniques for increasing the understanding of complex data. Topics include: human visual perception, visualization design principles, interactive visualization techniques, and application of visualization in analyzing complex data from various fields.

Introduction to machine learning principles, algorithms and applications. Topics include regression, classification, clustering, artificial neural networks and model evaluation.