Electrical and Computer Engineering Courses
Introduction to electrical and computer engineering disciplines, specializations, the engineering design process, mathematics required for these disciplines, computer-based modeling and simulation tools, and professional responsibilities.
Introduction to circuit analysis, methods, resistive circuits, AC circuits, first-order transients, AC power, operational amplifiers and machines. Not open to electrical engineering majors or to students who have earned credit for ECE 225.
Response of circuits to transient signals, both deterministic and random. Laplace transform solution techniques for circuits and differential equations. Relationship between Laplace and Fourier transforms. Frequency response and representation of circuits and systems. Modeling of uncertainty in circuit elements.
Semiconductor materials and properties, fundamentals of p-n junctions, diodes, diode circuits and operation, signal generators, rectifier and wave-shaping circuits, bipolar and field effect transistors, MOSFET, transistor DC circuit analysis and basic transistor amplifiers. Writing proficiency within this discipline is required for a passing grade in this course.
Operational amplifiers, BJTs, MOSFETs, integrated current biasing and active loads, differential and multistage amplifiers, frequency response, feedback and stability, power amplifiers, and introduction to digital circuits. The lab deals with experiments illustrating concepts in electronics. Writing proficiency within this discipline is required for a passing grade in this course.
Time domain and frequency domain analysis of continuous and discrete signals and systems; Fourier integral, Fourier series, Z-transform. Numerical implementation using MatLab. Computing proficiency is required for a passing grade in this course.
The ECE Department offers the opportunity for select undergraduate students to become actively engaged in research and development programs lead by our faculty and graduate students. This opportunity provides undergraduate students with practical research experience, knowledge of modern research practices, and advanced technical skills. Students are evaluated on a pass/fail basis.
Basic architecture and applications of wireless sensor networks (WSN). Hardware components of WSN, WSN operating systems, transport layer, routing layer, MAC layer and data link layer of WSN.
Solid state physics for semiconductor devices, p-n junction, metal-semiconductor junction, JFET/MESFET, MOSFET, BJT and non-ideal behaviors of solid state devices. Organic thin film devices including organic solar cells, thin film transistors, light emitting diodes and their application for flexible displays.
Digital design issues in the context of VLSI systems. Introduction to CMOS digital design methodology, layout techniques, behavior models, circuit simulation and testing of complex systems.
Design and testing issues in the context of mixed-signal embedded systems. Introduction to CMOS mixed-signal design methodology, layout techniques, analog to digital converters, digital to analog converters, circuit simulation, and testing and packaging of complex mixed-signal systems.
Crystal structure and defects, film nucleation and growth models, growth of polycrystalline and epitaxial films, vacuum science technology, physical and chemical vapor deposition, solution based methods and thin film characterization techniques.
Mathematics and physics of the radiation, propagation and scattering of electromagnetic waves. Boundary value problems involving finite and infinite structures, waveguides, antennas and media.
Basic power systems concepts and per unit quantities; transmissions line, transformer and rotating machine modeling; power flow; symmetrical component of power systems; faulted power system analysis.
Energy levels and wave functions of semiconductor microstructures; envelope function approximation; quantum wells, superlattices; excitons; optical and electrical properties; selection rules; quantum confined Stark Effect; Wannier-Stark localization; field-effect transistors, tunneling devices, quantum well lasers, electro-optic modulators and quantum-well intersubband photodetectors.
Elemental and compound semiconductors; fundamentals of semiconductor physical properties; solid state physics; optical recombination and absorption; light emitting diodes; quantum well lasers; quantum dot lasers; blue lasers; semiconductor modulators; photodetectors; semiconductor solar cells; semiconductor nanostructure devices.
Diamagnetism and Paramagnetism, Ferromagnetism, Antiferromagnetism, Ferrimagnetism, magnetic anisotropy, domains and the magnetization process, fine particles and thin films and magnetization dynamics.
Nanofabrication with electron beam lithography, focused ion beam, lithography, and nanoimprint; microscopies for nanostructures, including SEM, EDX, TEM, AFM, STM; nanoscale devices based on nanostructured materials (carbon nanotubes and metal oxide nanomaterials).
Classical and modern feedback control system methods; stability; Bode, root locus, state variables and computer analysis.
Digital systems design with hardware description languages, programmable implementation technologies, electronic design automation design flows, design considerations and constraints, design for test, system-on-a-chip designs, IP cores, reconfigurable computing and digital system design examples and applications.
Machine learning studies methods that allow computers to learn from the data and act without being explicitly programmed. This course provides an introduction to machine learning and covers various supervised and unsupervised learning techniques, methods of dimensionality reduction and assessment of learning algorithms.
Basic computer organization, computer arithmetic, assembly language, machine language, simple and pipelined central-processor organization, memory system hierarchy, and measuring computer performance.
Programmable Logic Controllers, fundamentals of ladder logic programming and PLC systems, advanced PLC operation, and related topics, including networking, control applications and human-machine interface design.
Computational Intelligence is a discipline that relies on biologically inspired computation to solve real-world problems that otherwise are infeasible or impossible to solve using classical engineering approaches. The course will cover the fundamental techniques of computational intelligence and study practical applications in real-world engineering problems.
Investigation of a problem or problems, usually involving research with a faculty member. Credit is based on the individual assignment.
First of a two-course sequence to provide design experience through capstone design, a team-based two-semester-long design project. Also, the first-semester course will include instruction in design methodology, engineering ethics, societal impacts, project economics and management tools.
Special courses in all areas of electrical or computer engineering, offered as the need arises. Credit is based on the course requirements.
Second of a two-course sequence to provide design experience through capstone design, a team-based two-semester-long design project.