80 courses with the subject EECE, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
EECE 201Fundamentals of Electrical System Analysis3
This course is an introduction to basic concepts of Electrical Networks, including Kirchoff's Laws, fundamental analysis of resistive networks using nodal and loop analysis, Superposition, Thevenin and Norton Theorems. Introduction to operational amplifiers as well as capacitive and inductive networks. Transient analysis of first-order systems. PSPICE, other various computer tools and linear algebra matrix techniques will be employed for the analysis of electrical networks. Three hours of lecture per week and three-four lab sessions during the semester. Prerequisite: MATH 185.
EECE 203Fundamentals of Electrical System Analysis II3
Building on the concepts in EECE 201, this course is an introduction to the transient behavior of 1st and 2nd order systems; AC steady state analysis in the frequency domain; power considerations in single and polyphase circuits; and transformers and magnetically coupled networks. PSpice, computer tools and linear algebra matrix techniques will be employed for the analysis of electrical networks. Three lecture hours per week and three-four lab sessions during the semester. Prerequisite: EECE 201 Minimum Grade is C.
This course introduces hardware-software design applications and computer software development. Students will work with hands-on projects where they will gain an understanding of the relationship between the software they write and the hardware it is controlling. The software portion of the course covers the fundamentals of programming and is divided into three modules that introduce students to the C, C++, and Python programming languages.
This course introduces the fundamental principles of the design of digital systems. The material includes number representations, switching algebra, and logic systems for the analysis and synthesis of combinational and sequential circuits. Basic design concepts and implementation technology, and the use of HDL and computer-based design tools are also covered. The course will include a course-embedded laboratory component. Prerequisite: MATH 185 -Minimum Grade is C.
This course presents register transfer systems and datapaths, microprocessors, microprocessor architecture and operation, instruction formats, assembly language programming, procedures and parameter passing, system bus timing, interfacing memory and I/O ports, serial and parallel data transfer, and interrupts. C-language programming for hardware device interfacing is introduced. A course-embedded laboratory will be included. Prerequisite: EECE 229.
EECE 300Fundamentals of Electrical & Computer Engineering for Non-Engineering Programs4
This course is an introduction to basic concepts of Electrical Networks, including Kirchoff's Laws, fundamental analysis of resistive networks using nodal and loop analysis, Superposition, Thevenin and Norton Theorems. Introduction to operational amplifiers as well as capacitive and inductive networks. Transient analysis of RL, RC and RLC circuits; AC steady state analysis in the frequency domain; power considerations in single phase circuit; ideal transformers. Biasing of Transistor and Diode circuits. Digital Logic and Boolean Systems; Microprocessors and Microcontrollers. Signals and Filtering. For NON-ENGINEERING Students.
Modeling and analysis of continuous-time systems. Convolution of signals and representation of linear time invariant systems. Fourier series. The Fourier Transform and its applications. The Laplace Transform and its applications to continuous-time systems. Stability of continuous time systems. State-space analysis using linear algebra techniques. Prerequisite: EECE 203.
The Sampling Theorem. The Z-Transform and discrete-time systems analysis. Stability of discrete-time systems. Design of analog and digital filters. The Discrete Fourier Transform and its applications. The Fast Fourier Transform. State-space analysis using linear algebra techniques. Prerequisite: EECE 303.
Terminal characteristics of solid-state devices. Power supply design. Transistor circuit biasing. Graphical analysis of transistor circuits. Small signal transistor circuit models and gain analysis. Selected lab sessions during the semester. Prerequisite: EECE 201- Minimum Grade is C.
Multistage transistor circuit analysis and design. Frequency response of electronic circuits. Operational amplifiers. Power amplifiers. Digital electronic circuits. Selected lab sessions during the semester. Prerequisite: EECE 305.
EECE 307Mathematical Methods for Electrical & Computer Engineering4
Application of the basic principles of Vector Calculus and Linear Algebra to representative areas of Electrical and Computer Engineering. Subject matter includes review of vector and matrix methods and techniques with subsequent utilization in areas of Circuit Analysis, Linear and Control Systems, Power Systems, and Electromagnetics, with specific consideration given to the role of vector operators in Maxwell’s Equations Prerequisites: MATH 285; Minimum grade is C.
EECE 308Mathematical Methods for Electrical and Computer Engineering3
Application of the basic principles of Vector Calculus and Linear Algebra to representative areas of Electrical and Computer Engineering. Subject matter includes review of vector and matrix methods and techniques with subsequent utilization in areas of Circuit Analysis, Linear and Control Systems, Power Systems, and Electromagnetics, with specific consideration given to the role of vector operators in Maxwell's Equations. Prerequisite: MATH 285 with a minimum grade of C.
An introduction to the principles of Electromagnetics with particular emphasis on waves and their applications. Topics will be chosen from: nature of electromagnetism; fields; transmission lines (lumped parameter models, lossless lines, open- and short-circuit models, standing wave ratios, transient responses, impedance matching); radiation; fiber optics; telecommunication systems. Prerequisites: EECE 307 and MATH 286.
The Sampling Theorem. The Z-Transform and discrete-time systems analysis. Stability of discrete-time systems. Design of analog and digital filters. The Discrete Fourier Transform and its applications. The Fast Fourier Transform. State-space analysis using linear algebra techniques. Prerequisite: EECE 303.
EECE 315Probability and Statistics for Electrical & Computer Engineering4
Application of the basic principles of Probability and Statistics to areas of Electrical and Computer Engineering involving uncertainty and randomness in Signals and Systems. Principles include discrete and continuous random variables and their distributions, moments, and characteristic functions; empirical distribution functions; parameter estimation; confidence limits; linear regression; hypothesis testing; and statistical approaches to engineering decisions. Topics for consideration are taken from Communications, Power Systems, Signal Processing, Image Processing, and Control Systems and are used to both develop and illustrate principles and their application. Prerequisites: MATH 285; Minimum grade is C.
EECE 316Probability and Statistics for Electrical and Computer Engineering3
Application of the basic principles of Probability and Statistics to areas of Electrical and Computer Engineering involving uncertainty and randomness in Signals and Systems. Principles include discrete and continuous random variables and their distributions, moments, and characteristic functions; empirical distribution functions; parameter estimation; confidence limits; linear regression; hypothesis testing; and statistical approaches to engineering decisions. Topics for consideration are taken from Communications, Power Systems, Signal Processing, Image Processing, and Control Systems and are used to both develop and illustrate principles and their application. Prerequisites: MATH 285 with a minimum grade of C.
This course gives an introduction to the concepts of object-oriented software development, and hardware integration. The course covers the basics of object-oriented Java programming, interaction of JAVA with hardware. It also introduces the student to the Integrated Development Environments, Agile Software Development, and UML (Unified Modeling Language). Pre-Requisite EECE 210 and EECE 229.
Principles of embedded computer systems design, including hardware and software integration, system architecture, interfacing methods, communication techniques, and design tradeoffs necessary to implement embedded systems and interface them with the physical world. Prerequisites: EECE 210 and EECE 232. Cross-listed with ECEG 721.
Detection, acquisition, and analysis of information from the environment. Topics will include: sensors and measurement methods, instrumentation and transducers for the measurement of signals, information conditioning, computer control of data acquisition, and interpretation of results. Pre-requisites: EECE 303, EECE 305.
EECE 329Modeling Techniques in Electrical & Computer Engineering3
The application of computing techniques for the simulation and modeling of complex electrical, electromechanical and computer hardware systems. Models will be developed that will allow simulation to replace the cost of expensive experimental work on the actual engineering systems. The course will focus on efficient mathematical modeling of real-world engineering systems.
Hands-on experiments and demonstrations in industrial electric drives, requirements placed by mechanical systems on electric drives, and their role in various applications such as flexible production systems, energy conservation, renewable energy and transportation. Power electronics in drives using switch-mode converters and pulse width modulation to synthesize the voltages in dc and ac motor drives. Design of a controller using Matlab /Simulink. Prerequisites: EECE 303, EECE 305. Cross-listed with ECEG 700.
EECE 403Trustworthy AI Applications in Electrical & Computer Engineering3
Recently ethical, legal, and privacy consequences on humanity and environment of Artificial Intelligence (AI) have received increased attention. This course examines the trustworthiness of AI foundations related to data preparation, algorithm design, systems development, and deployment in electrical and computer engineering applications. Legal frames are investigated on how AI’s processes of development and deployment could be adapted for safety goals. As case studies, AI electrical and computer engineering applications will be examined for ethical aspects, fairness, privacy, and liability. Prerequisites: EECE 210 and EECE 321 for EECE and CMPE students Prerequisites: EECE 300 for non-EECE students. Cross-listed with ECEG 703.
Design principles of biomedical devices, bioelectronics, medical nanodevices, transducers, sensors, interface electronics, microcontrollers, and engineering programming. Signal modalities, bioelectrical signal monitoring, acquisition, analysis, and processing. Case studies and platform-based designs of medical devices, and instrumentation. Prerequisites: (EECE 304 or EECE 312) and EECE 306. Cross-listed with ECEG 704. Prerequisite: EECE 300 for non EECE students.
EECE 407Electrical Power Distribution Engineering3
This course covers the fundamentals of electric power distribution systems. With increased deployment of distributed generation, controllable loads, and metering devices, it has become more and more important for researchers and power industry professionals to better understand power distribution systems. Distribution System Planning, Reliability of Electrical Distribution Systems, Quality of Electrical Power Systems, Integration of Renewable Energy into Power Systems, Energy Storage Systems, The Smart Grid, SCADA (Supervisory Control & Data Acquisition), Microgrids. Class project/research paper required. This course is cross-listed with ECEG 707 Three lectures. Three credits.
This course provides students with essential skills in performing penetration testing, vulnerability identification, and risk mitigation. Students will utilize advanced tools to detect and exploit vulnerabilities in target network environments. Prerequisites: EECE 210 and EECE 321.
This course is the first semester of a year-long effort in which senior ECE students, working in teams or individually, complete a project under the direction of a faculty coordinator and mentor. The project must address a question of importance related to electrical and/or computer engineering. In this first semester, students will: identify the problem to be investigated; research the associated topics including relevant literature; develop the engineering tools (e.g., application software, HLLs) as needed or appropriate; develop a comprehensive plan for completion of the project; and complete any necessary preliminary testing or feasibility studies. The plan must reflect those normally produced by professional engineers in similar assignments. The team members will meet frequently with the faculty mentor to discuss and evaluate progress. The faculty mentor will lecture on those topics common to such projects as well as any technical material that is necessary. Prerequisite: EECE 304 & EECE 306 & EECE 315 & (EECE 320 or EECE 326).
Students will complete the engineering design undertaken in EECE 410. The outcomes to be achieved are consistent with those specified in the ABET general engineering criteria. In particular, when completed, students will have: understood modeling associated with a design; demonstrated skills in using a computer in the course of an engineering design; exhibited critical thinking; have solved an open-ended problem; successfully functioned on an interdisciplinary team; completed a successful engineering design; shown that they can communicate effectively; have understood ethical implications of their efforts; and understood how continued learning is important in refinement of the enterprise. To meet these outcomes, students will be required to make a presentation before the faculty of the department. In addition, students or teams must submit a final report that will be evaluated by members of the department or invited reviewers. Prerequisite: EECE 410.
North American Electric Reliability Corporation (NERC) standards and related compliance concerns in relationship to operational principles of the power systems. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 737.
The proliferation of smart mobile systems gives rise to new areas of security vulnerability. This course explores the security considerations associated with smart consumer mobile devices, smartphones, mobile telecommunication systems, and sensor networks. Topics include smartphone security, mobile location privacy, and wireless sensor security. Prerequisites: EECE 210 and EECE 321. Cross-listed with ECEG 717.
Topics of importance in Power Electronics including techniques for the design of Electric Vehicles, highly efficient power supplies, power factor correction and motor control systems . High voltage DC to AC power conversion methods . Vehicle battery design and charging issues. Laboratory experience with semiconductor electronic switching devices and different motor types. Prerequisite: EECE 304 and EECE 306. Cross-listed with ECEG 718.
Independent investigation, under the guidance of an approved advisor and the sponsorship of an electrical engineering faculty member, terminating in a final report, and when feasible, a tested design. Prerequisites: EECE 304 and EECE 306.
Independent investigation, under the guidance of an approved advisor and the sponsorship of an electrical engineering faculty member, terminating in a final report, and when feasible, a tested design. Prerequisites: EECE 304 and EECE 306.
This course addresses the foundations of inverse problems in robotics and IoT systems. The list of topics includes image sensors; different types of noise; Gaussian and Poisson distributions; estimation techniques; denoising; total variation regularizations; weak signals and photon limit; variance stabilizing transforms; motion estimation under noise; noise estimation. Foundations covered in this course are emphasized with a class project. Prerequisites: EECE 304 and EECE 306.
EECE 424Hardware/Software Design Trade off Techniques3
This course is designed to promote the skills of computer engineering students in the areas of software and hardware integration and related ECE applications. This course focuses on the use of .NET Gadgeteer to program and configure hardware. Microsoft Foundation Class (MFC) library and Windows programming will be highlighted. Several topics will be covered, including Windows architecture, message-driven programming, dialog-based application development, SDI and MDI applications, Device Contexts, and database access. At the end of the course, a comprehensive project covering key concepts in hardware programming is assigned. Prerequisites: EECE 210 and EECE 321.
Principles of linear feedback control systems. System modeling. Transient response and steady-state error analysis. Stability and analysis of systems from Routh-Hurwitz, Nyquist, and Root Locus viewpoints. Controller design and compensation techniques. Prerequisite: EECE 303.
The design of modern digital signal processing software and hardware using actual DSP devices, analog interfacing to DSP hardware. A review of Signal processing concepts, design of FIR & IIR filters, design of algorithms for computing the FFT and Inverse FFT, analog interfacing hardware on the DSK board, the use of the MatLab Signal Processing package as a part of the overall DSP system design process. Prerequisites: EECE 304.
Introduction to Optical Engineering. Principles of reflection and refraction of light. Geometrical Optics: lenses and optical instruments. Elements of Lasers, Light Modulators and Detectors. Optics from a systems perspective, Diffraction and Interference of light waves. Coherent optical signal processing. Prerequisites: EECE 304 and EECE 306.
Operation of the bulk electric power system in North America. Basic types of high voltage equipment and station configurations. Methods and equipment to control power flow and voltage levels on the power system. Prerequisite: EECE 203 Minimum Grade is C. Cross-listed with ECEG 734.
Basic concepts of computer graphics systems include display devices, graphics software and the display of solid objects. Point plotting procedures, line drawing algorithms and circle generators. Displays and controllers, storage and refresh devices. Two dimensional transformations; clipping and windowing. Graphics software; windowing functions, display files; geometric models. Interactive raster graphics. Three-dimensional graphics including surface display, perspective and hidden surface removal. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 765.
EECE 437Introduction To Quantum Concepts and Computing3
Classical and quantum bits (Qubits). Quantum states as Hilbert space vectors and their matrix representations. Operators, Eigenvalues and Eigenvectors. Bloch sphere representation of a qubit. Quantum postulates and elements of quantum dynamics. Evolution of a two state system. Quantum gates and elements of system architecture. Criteria for successful quantum computation. Some current problems in system realization. Prerequisite: EECE 307.
Introduction to multimedia, PC architecture and assembly language basics. Color TV and video concepts. PC audio standards, the MIDI music standard, and audio signal processing. Multimedia presentation and authoring techniques. HTML authoring and the fundamentals of the World Wide Web. Prerequisites: EECE 304 and EECE 306.
Analysis of faulted power systems, symmetrical and asymmetrical systems, transient stability, emergency control and system protection. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 738.
Introduction to the operation of industrial manipulators. Robotic theory includes homogeneous coordinate transformations, kinematics and dynamics of articulated manipulator arms, and elements of feedback control theory. The design of hardware and software used for motion control. Introduction to computer vision and artificial intelligence. Prerequisites: EECE 304 and EECE 306.
Detection, image formation, and engineering design of vision and imaging sensors and systems. Unmanned aerial and underwater imaging systems, biomedical image recognition, medical image understanding, inspection, and robotics applications. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 742.
Engineering and physical principles of biomedical modalities, as applied to clinical diagnostics and pharmaceutics, gene arrays and Omics imaging technologies central to the detection process, system design, data analysis and classification. Clinical examples. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 743.
Engineering design of miniaturized medical devices, operating on electrical, and quantum principles, with reduced form factor and weight, while reducing power consumption and boosting performance. Integration trends, functionality, scalability, reconfigurability. Case studies and platform-based designs of miniaturized medical devices, such as medical implantable devices, heart monitors, pacemakers, video cameras. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 745.
Digital image processing for manipulation and enhancement of images, development of advanced techniques for object recognition, object classification, image reconstruction, image compression, and feature extraction. Computational analytic and interpretive approaches to optimize extraction and use of imaging data. Selected machine learning and deep learning techniques to automate tasks such as object detection, classification, segmentation, and content-based retrieval. .Prerequisites: (EECE 304 or EECE 312) and EECE 306 or [for non-EECE students: EECE300]. Cross-listed with ECEG 747.
EECE 448Applied Machine Learning for Electrical & Computer Engineering3
This course introduces machine learning principles and techniques, from the basics through modern neural network architectures such as generative pre-trained transformers, explore supervised, unsupervised, semi supervised and reinforcement learning architectures, and learn about small-scale and large-scale hardware architectures for machine, energy cost. Hardware topics related to power consumption, IoT, autonomous navigation and robotics, edge computing, smart cities, and vehicular network. Prerequisites: (EECE 304 or EECE 312) and EECE 306 or for non-EECE students, EECE 300. Cross-listed with ECEG 748.
History of the UAV, basics of mechatronic design, common sensor payloads, high-definition cameras, sonars, lidars, vision and imaging design parameters. Major design challenges, laws and regulations, operations and safety. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 749.
Bioinformatics principles applied to microscopic and biomedical image acquisition methods and applications, methods and applications of image analysis and related machine learning, pattern recognition and data mining techniques, image oriented multidimensional. Methods and applications for the analysis of post-translational modifications, proteomic, mass spectroscopic, and chemoinformatic data. Prerequisite: EECE 315. Cross-listed with ECEG 753.
Nanoparticles for optical bioimaging, optical diagnostics and light guided and activated therapy. Use of nanoparticles platforms for intracellular diagnostics and targeted drug delivery, PEBBLE nanosensors. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 755.
Instrumentation, devices, and techniques to characterize the physiochemical, optical properties, and in vitro immunological, biological, and stability characteristics of drugs delivery, proteins, and nanomaterials. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 756.
The underlying philosophy of this course is to introduce new evolutionary cognitive vision systems that use artificial neurons to mimic the functions and characteristics of the human brain and drive improvements in costs, efficiency, and processing. Students taking this course will acquire integrative knowledge of bioinspired vision systems and artificial intelligence and explore ultra-low power brain-inspired computing architectures and algorithms, namely neuromorphic computing and sensing frameworks. As result, students will develop engineering skills towards the conception, and design of novel bio-inspired vision robotic applications, systems, and techniques for different segments of industry, autonomous robotic systems, edge computing, healthcare, defense, and consumer electronics. Prerequisites: (EECE 304 or EECE 312) and EECE 306 or For non-EECE students: EECE 300. Cross-listed with ECEG 757.
Cybersecurity as it relates to systems and then on the engineering principles for secure systems. The course focuses on the differences between threats and vulnerabilities, examples of cybersecurity attacks and events, frameworks, requirements and principles for securing systems. Prerequisites: EECE 210 and EECE 321. Cross-listed with ECEG 758.
Methods that seeks to solve the problem of how to securely send cryptographic keys between two parties by encoding them within light particles, or photons. Quantum cryptography and key distribution technique. Prerequisites: EECE 304 and EECE 306.
EECE 460Big Data, & Deep Learning for Electrical & Computer Engineering3
This class will focus of how to extract actionable, non-trivial knowledge from unstructured, heterogenous, massive number of data sets using machine learning and deep learning techniques. On the tool's side, we will cover the basic systems and techniques to store large volumes of data and modern systems for cluster computing based on MapReduce patterns such as Hadoop MapReduce, Apache Spark, and Flink. FPGAs, GPUs, and neuromorphic processors with emphasis on edge, fog, and cloud computing architectures, industry, manufacturing communications, autonomous navigation systems, IoT, systems, remote sensing. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 767.
Theoretical and practical aspects of network security. Security of TCP/IP applications; firewalls; wireless LAN security; denial-of-service defense. Prerequisites: EECE 210 and EECE 321. Cross-listed with ECEG 761.
Explore principles, technologies, tools, and trends in data and application security within software and hardware systems. Topics include data security fundamentals, access control models and policies, secure applications development practices, secure software and hardware architectures, trusted computing principles. Prerequisite: EECE210 and EECE321. Cross-listed with ECEG 760.
Software and hardware design problems for DBMS; an overview of database systems, data manipulation languages, normal forms, machine architectures. This course will focus on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. Case studies on open-source and commercial database systems are used to illustrate these techniques and trade-offs. More topics can be added by the instructor. Prerequisites: EECE 304 and EECE 306.
This course provides a theoretical and practical treatment of quantum computing. Topics covered include brief quantum mechanics history, and the postulates of quantum theory. Dirac notation, quantum operators, composition, entanglement, and measurements. Quantum Computing via quantum circuit model: Description of qubit and universal set of gates. Simple quantum protocols: teleportation, superdense coding. The Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm. Grover’s algorithm for searching. Entanglement and Bell’s theorem. Quantum communications and quantum error correction. Applications in cybersecurity, cryptography, financial modeling, drug development and artificial intelligence; Senior Status Cross-listed with ECEG 777.
This course presents basic information on Energy outlook, interconnection issues of distributed alternate energy resources, efficiency of power production, electric energy conversion and storage (fossil fuel, nuclear, hydro, solar, fuel cells, wind, and batteries). This course also explores the different energy link integration methodologies using Matlab/Simulink Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 768.
Exploring the operation of electrical and electronic devices, focusing on the internal physical laws that determine their utility and limitations. Thermal, optical, electrical, magnetic and quantum properties; energy audit, waves. Transducers, heat sinks, diodes, solar cell, LED, TEDs, FET, memories, nanostructure. Three lectures. Prerequisite: PHYS 101 and PHYS 102 with Minimum Grade is C.
This course is intended to provide an introduction to remote sensing of objects with applications in defense and environment. The course covers the basic principles of image sensors and techniques, image interpretation, remote sensing theory, and digital image analysis in relation to optical, thermal and microwave remote sensing systems. Examples of remote sensing applications will be presented along with methods for obtaining quantitative information from remote sensing imagery. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 769.
This course is intended to provide the fundamental principles of space systems, in terms of electro-optical sensing, robotic vision, and imaging. Critical space missions such as monitoring of the integrity of spacecraft structures, detection of debris, object recognition and classification will be presented and discussed. Defense and commercial applications will be introduced and discussed. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 770.
EECE 471Artificial Intelligence Applications in Electrical & Computer Engineering3
This course introduces methods for designing computer visualization, robotics, and IoT systems utilizing artificial intelligence, and machine vision. The following topics specifically related to the area of electrical and computer engineering will be covered: classification algorithms, information transference human/machines, single-agents and multi-agent Systems (MAS), expert systems, engineering knowledge presentation, automated planning, uncertain knowledge, reasoning in engineering design, simple and complex decision making, and time varying systems. Prerequisites: EECE 210 and EECE 321 or for non-EECE students: EECE 300. Cross-listed with ECEG 729.
The course describes and investigates Local and Wide Area Networks. Description of topologies and protocols for ETHERNET and TOKEN RING. The OSI model and applicability to LANs. IPX/SPX and TCP/IP protocols. Protocols stacks for PC'S. Server based and peer to peer networks. Network operating systems including NETWARE and NT Server Connectivity devices, hubs, bridges, switches, and routers. The Internet and Internet access. WANs including ATM, SONET, ISDN, and other high speed networks. Prerequisites: EECE 304 and EECE 306.
EECE 473Operating Systems for Computer Engineering3
A study of the modular design of operating systems and device drivers. Demand paging and virtual memory; scheduling algorithms, race conditions between processes; file systems, real time operating systems analytic tools for the evaluation of operating systems. Computer engineering applications. Prerequisite: EECE-232 or equivalent. Lecture with embedded lab. Cross-listed with ECEG 728.
Digital and analog wireless and wired communications systems, including satellite communications and personal mobile communication systems. Techniques used in modern communication systems such as source coding, channel coding, multiplexing, multiple access, spread spectrum, cellular concepts. Passband digital transmission, and basics of cognitive and software radio. Lecture +Labs. Prerequisites: EECE 303 and EECE 315.
This course focuses on providing the skills and knowledge necessary to install, operate, and troubleshoot a small branch office Enterprise network, including configuring a switch, a router, and connecting to a WAN and implementing network security. A Student should be able to complete configuration and implementation of a small branch office network. Finally, this course will link the contents to the modern networking elements such as Network Function Virtualization and the Software Defined Networks. Prerequisites: EECE 210 and EECE 321. Cross-listed with ECEG 727.
Modern power system/energy conversion operation. Models for interconnected power grids, transmission lines, transformers, and power flow analysis. Development of basic power flow digital simulation programs and run power labs. Prerequisites: EECE 303 and EECE 305. Cross-listed with ECEG 736.
This course will provide students with an understanding of fundamental data mining methodologies and with the ability to formulate and solve problems with them. Special emphasis attention will be paid to practical, efficient and statistically sound techniques. Hands-on experience with data mining software, primarily R, to allow development of basic execution skills. Prerequisites: (EECE 304 or EECE 312) and EECE 306 or non-EECE students EECE 300. Cross-listed with ECEG 705.
The objective of this course is to familiarize students with various essential aspects in harnessing wind energy and its conversion and delivery as electricity. A broad understanding of essential elements in wind-electric systems: turbines, wind- plant development and their integration into the utility grid, environmental impacts, wind forecasting and more. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 782.
Artificial intelligence is advancing rapidly, performing well-defined tasks and integrating into nearly every industry. This course explores whether artificial intelligence can achieve human-like general intelligence by exploring core principles of human behavior and cognition. Students will study key topics include vision, audition, language, learning, emotion and social cognition, creativity, and consciousness, applying their knowledge in a final group research project. Crosslisted with ECEG 783.
Cyber-Physical Systems (CPS) integrate physical components and computational capabilities, connected through networks, to interact and collaborate with each other and with the physical world. Security architectures for CPS, secure communication protocols, mitigation strategies, intrusion detection and prevention, and case studies on CPS security incidents. Prerequisites: EECE 210 and EECE 321. Cross-listed with ECEG 788.
This course provides a broad introduction and understanding of fundamental principles, concepts and techniques in cybersecurity to develop secure systems and protect sensitive information. It covers various topics, including cryptography, access control, network security, system security in both software and hardware aspects. Prerequisites: EECE 210 and EECE 321.
EECE 491Special Topics in Electrical and/or Computer Engineering3
Topics of current interest to senior electrical engineering students. Subject matter will be announced in advance of semester offering. Written permission of the chair is required. Prerequisites: EECE 304 and EECE 306.
Topics of current interest to senior electrical engineering students focusing on power systems. Subject matter will be announced in advance of semester offering. Prerequisites: EECE 304 and EECE 306.
This course offers an in-depth exploration of emerging and specialized areas within the field of cybersecurity. Topics of current interest to senior electrical engineering and computer engineering students. Subject matter will be announced in advance of semester offering. Prerequisites: EECE 210 and EECE 321.
EECE 494Special Topics in Artificial Intelligence (AI) in Electrical and Computer Engineering3
On a variety of levels, the course explores Artificial Intelligence (AI): systems and tool chains for AI engineers in depth. Topics of current interest to senior electrical engineering and computer engineering students. The subject matter of the course will be announced in advance of the semester. Prerequisites: (EECE 304 or EECE 312) and EECE 306 or for non-EECE students: EECE 300.
