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.

Micro controller-based digital systems design methodology and techniques. Basic machine organization. Interface design. C and assembly language programming for real-time embedded systems.

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.

Design of computer networks with emphasis on network architecture, protocols and standards, performance considerations, and network technologies. Topics include LAN, MAN, WAN, congestion/flow/error control, routing, addressing, broadcasting, multi-casting, switching, and inter-networking. A modeling tool is used for network design and simulation.

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

A complete design cycle. Working in small teams, students will design, document, analyze, implement, and test a product. Topics include Iteration in design, prototyping, group dynamics, design reviews, making effective presentations, concurrent design, designing for test, ethics, and standards, testing and evaluation.

A continuation of COEN 491.

Materials and device structures for applications in analog and digital electronics. Topics include characteristics and basic circuits for diode, FET transistors, BJT transistors, and operational amplifiers.

Laboratory tools and measurement techniques for basic electronic circuits using diodes, field-effect transistors, bipolar junction transistors, and operational amplifiers. Topics include DC biasing and applications in analog and digital electronics.

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.

Analysis of steady-state AC circuits, phasor notation, polyphase circuits, complex frequency, and frequency response, magnetically coupled circuits.

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

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

Power system components and transmission lines, three-phase balanced power system theory, analysis and design including economic and reliability considerations, and fault analysis.

Power system components and transmission lines, three-phase balanced power system theory, analysis and design including economic and reliability considerations, and fault analysis.

Laplace transforms, Formulation of the control problem, system equations and models, time and frequency domain analysis, stability, and design of linear control systems.

Software tools for control systems analysis.

Provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software.

Weekly laboratories provide experience with servo drives, real-time control, and embedded software. Students will design and fabricate working robotic systems in a group-based term project.

Introduction to programmable automation, programmable logic controller (PLC) hardware, programming languages and techniques, closed-loop strategies using PLC’s, sensors, transducers. Case studies. Laboratory experiments.

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.

Signals and systems for digital communications. Topics include signals and their spectra, source formatting, digital baseband data communication, and digital pass-band modulation and demodulation.

Laboratory and software tools for the analysis of linear and non-linear systems. Topics include spectral analysis, transforms, and applications.

Diode and transistor circuits, small signal analysis, amplifier design, differential, and operational amplifiers, flipflop circuits and wave shaping.

Experiments in design with diodes, transistors, differential and operational amplifiers, and logic components.

Static electric and magnetic fields using vector analysis and time-varying electromagnetic fields using Maxwell’s equations. Topics include Coulomb’s law, Gauss’s law, Ampere’s law, dielectric and magnetic materials, plane waves, and transmission lines.

Electrical Engineering Internship

This course explores strategic processes and partnership required for the management of sustainable energy infrastructures and innovation in energy systems. Topics relate to renewable energy, island-grids, (smart) micro-grids, technologies for the conversion of renewable energies, renewable energy potential studies, modeling, simulation, and control of power systems.

Project tasks will be conducted individually or in teams of 2-3 students. The main part of the project is the execution of technical experiments in the lab or the computer-aided development of solutions.

Advanced programmable logic controller (PLC) programming, function block, structured text, function chart, sequencer. Factory communications, system simulation, human-machine interface (HMI) programming. Advanced PID control. Network security and reliability. Class-wide project.

Random signals and their characterization; noise performance of amplitude, angle and pulse modulation systems; digital data transmission; use of coding for error control.

Power semiconductor devices in switching mode converter and control circuits, phase-controlled rectifiers, synchronous inverters, AC regulators, cyclo-converters; self-commuted inverters; and frequency changers; thermal analysis and protection. Applications to industry and HVDC.

Network analysis applied to power systems; the load flow concept; economic operation of power systems; synchronous machine reactances and transient stability; symmetrical components and asymmetrical faults; protective relaying.

Introduction to microwave systems and antennas. Radiation mechanisms and design aspects. Antenna arrays and applications. Passive and active microwave devices. Microwave design principles.

Lab is focused on conformal antennas, broadband metamaterials, conformal broadband pixelated reconfigurable antennas, conformal reconfigurable antennas, low-cost phased arrays for portables/wearables, wireless power transfer and wireless sensing.

A complete design cycle – working in small teams, students will design, document, analyze, implement, and test a product. Topics include Iteration in design, prototyping, group dynamics, design reviews, making effective presentations, concurrent design, designing for test, ethics, and standards, testing, and evaluation.

A continuation of ELEN 491.

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.

Internship in computer science provides students with an opportunity to integrate theory and practice in a work setting. The course helps students to grow professionally and intellectually. It also sharpens their communication and teamwork skills. Enrolled students are required to complete a minimum of 150 working hours at approved organizations.

Introduction to eCommerce. Topics include electronic data interchange, digital currency, electronic catalogs, telecommunications infrastructure, data warehousing, software agents, storage retrieval, cryptographic techniques, and different levels of security.

Introduction to user interface design principles and techniques. Topics include user and task analysis, interaction design, interaction elements and styles, design patterns, visual communication, prototyping, and evaluation of different user interfaces.

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.

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.

Principles and techniques for designing and implementing a compiler. Topics include lexical analysis, parsing techniques, syntax-directed translation, intermediate languages, and code generation.

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.

The senior capstone course provides computer science students the opportunity to employ knowledge gained from courses throughout the curriculum to develop a software solution to a real-world problem from conception to completion. Students work in teams to develop software applications. Advanced methods related to requirements analysis, software design, project management, documentation and testing, programming techniques, and database designs will be applied on an incremental basis since the emphasis is on semester-long capstone team projects.

This course is an in-depth study of software development processes in the context of an actual project. Discussion includes object-oriented processes, such as Rational Unified Process, as well as process management issues such as scheduling, risk-assessment, various metrics, and the selection of appropriate development methodology and tools.

Introduction to computer communications networks. Topics include circuit-switching, packet switching, TCP/IP model layers, and wireless networks.

Design & implementation of operating systems. Topics include basic operating system structure, process and thread management, deadlocks, memory management, file management, input / output management, and distributed systems.

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

Topics in Computer Science

Introduction to Academic Research in Computer Science. Topics include: Research techniques and approaches, Computer Science research areas and metrics, literature review, writing a research paper, referencing and citations, and developing an effective workflow.

Practicum in Computer Science prepares students for successful computing related careers. The course aims to train and certify the students with market demanding fields such as computer networks, database systems, computer system/ applications, business and leadership etc.

Introduction to the design, development, and implementation of innovative solutions across various cutting-edge technologies. This course empowers students to tackle real-world problems by utilizing the latest technological tools and services. Students collaborate to create impactful projects, guided by both industry professionals and academic mentors, to address challenges in business and community settings.

Introduction to ethics theory and ethical decision making within the computing profession context. Topics include local and global impact of computing solutions, intellectual property, privacy, network security, electronic crimes and computer reliability.