Spring 2019

Information representations, embedded programming, digital and serial I/O, analog-to-digital conversion, and interrupt mechanisms. Elementary analog, logic, and state-machine design.

Introduction to solving engineering problems using computational methods. Survey of various programming languages and their strengths and weaknesses. Program structure, input/output, conditionals, loops, functions, arrays.

Lumped circuit elements; network equations; zero-input and zero-state responses; sinusoidal steady-state analysis; impedance;resonance; network functions; power concepts; transformers; Laplace transforms.

Introduction to hardware, operating systems, signal processing and control, network protocols, mobile applications, databases, cloud computing, machine learning, and security for the Internet of Things.

Representation and analysis of linear time-invariant systems primarily for the continuous time case. Convolution, Fourier series and transform, Laplace transform, controls and communications applications.

Fundamentals of sound, acoustics and digital audio signal processing.

Network analysis and theorems; transient analysis; transformers; semiconductor physics and circuits; power amplifiers, modulation and demodulation, and pulse, digital, and switching circuits. Introduction to instrumentation. Not available for credit to electrical engineering majors.

Basic static and dynamic electromagnetic field theory and applications; electrostatics, magnetostatics, Maxwell's equations, energy flow, plane waves incident on planar boundaries, transmission lines.

Semiconductor device characteristics and applications. Physical models of electronic conduction in solids, p-n junctions, bipolar and field effect transistors and other solid-state devices.

Basic analog and digital circuit design using Bipolar Junction Transistors, Field Effect Transistors, and integrated circuits.

Digital system design and implementation; synchronous design of datapath and control; schematic/Verilog-based design, simulation, and implementation in Field Programmable Gate Arrays; timing analysis; semester-end project. Prerequisite: EE 101 or EE 154L. (Duplicates credit in EE 254 and in former EE 201L)

Object-oriented programming techniques, basic data structures, and elementary complexity analysis for the modeling, simulation, and solution of engineering problems.

Introduction to concepts of randomness and uncertainty: probability, random variables, statistics. Applications to digital communications, signal processing, automatic control, computer engineering and computer science.

Experiments and design project in digital signal processing (e.g. real-time DSP, acoustics, video) including: system specification, preliminary analysis, trade-off studies, implementation, presentation. Recommended preparation: EE 469. Capstone design experience.

Crystal structures, x-ray diffraction, thermal properties of solids, diamagnetism and paramagnetism, free-electron model of metals, semiconductors, ferromagnetism, super-conductivity, imperfections in crystals.

Applications and electrical evaluation of selected processes used in electronic microfabrication.

Components of power systems; analysis techniques in electrical power generation transmission and utilization; and environmental and economic considerations in system operations and planning.

Designing, building and programming mobile robots; sensors, effectors, basic control theory, control architectures, some advanced topics, illustrations of state-of-the-art. Teamwork; final project tested in a robot contest. Junior standing or higher. Prerequisites: CSCI 103.

Application of solid-state electronic devices to the design of linear and mixed-signal systems. Laboratory experiments and projects involving the design of electronic hardware. Capstone design experience.

Analysis, design, and experimental evaluation of transistor-level communication circuits and micro-systems. Transmission lines, impedance matching, noise, distortion, tuned amplifiers, mixers, oscillators, phase-locked loops.

Network architectures; layered protocols, network service interface; local networks; long-haul networks; internal protocols; link protocols; addressing; routing; flow control; higher level protocols.

Introduction to parallel programming techniques, models and optimization strategies; Application mapping to multi-core, accelerator, GPU and cloud platforms; High Performance Computing and Data Science applications.

Intensive introduction to programming principles, discrete mathematics for computing, software design and software engineering concepts. Not available for credit to computer sciencemajors, graduate or undergraduate.

Register Transfer level machine organization; performance; arithmetic; pipelined processors; exceptions, out-of-order and speculative execution, cache, virtual memory, multi-core multi-threaded processors, cache coherence.

Specification, design, implementation, testing and documentation of a digital system project using embedded processors, programmable logic, analog I/O interfaces and application specific hardware. Capstone design experience.

Analog and digital communication systems. Modulation (AM, FM) Analog and digital communication systems. (De)modulation and (de)multiplexing of AM/FM/PM, noise, digital data formats, error rates, and spectral analysis. Review of wireless, networking, and optical systems.

Electric dipole transitions; traveling wave and resonant amplifiers; laser pumping and rate equations; threshold, frequency, and power output of lasers; holography; laser communication systems.

Chemical and physical properties of solid materials used by chemical engineers, including polymers, metals, and ceramics. Materials design for industrial applications.

Analysis and design of digital MOS VLSI circuits including area, delay and power minimization. Laboratory assignments including design, layout, extraction, simulation and automatic synthesis.

Vibration measurement and analysis; simulation, design, and experimental verification of mechanical control systems; system identification; implementation of controllers; performance evaluation via experimentation and stimulation.

Fundamentals of Digital Signal Processing covering: discrete timelinear systems, quantization, sampling, Z-transforms, Fouriertransforms, FFTs and filter design.

Individual research and readings. Not available for graduate credit.

For the undergraduate degree. Credit on acceptance of thesis. IP (EE 494a) or letter grade (EE 494b).

For the undergraduate degree. Credit on acceptance of thesis. IP (EE 494a) or letter grade (EE 494b).

Atomic structure, bonding in covalent, ionic and Van der Waals crystals, Brillouin zones, lattices, diffraction, electronic states, lattice vibrations, specific heat, electrical conductivity, and magnetism. Prerequisite: EE 471 or EE 539.

Rigorous coverage of probability, discrete and continuous random variables, functions of multiple random variables, covariance, correlation, random sequences, Markov chains, estimation, and introduction to statistics. Duplicates credit in former EE 464 and EE 465.

Laboratory oriented with lectures keyed to practical procedures and processes. Solid-state fabrication and analysis fundamentals; basic device construction techniques. Prerequisite: BSEE

Physical basis of technologies for the fabrication of micro- and nano-scale devices. Thin-film deposition, etching, and material modification processes; patterntransfer methods.

Introduction to linear algebra and matrix theory and their underlying concepts; applications to engineering problems; mathematically rigorous and foundational to other classes in communication, control, and signal processing.

Project-oriented investigation of simulation methods used for the analysis and design of complex stochastic systems whose operation and performance are affected by random events. Corequisite: EE 503. Recommended preparation: MatLab programming experience.

Probability theory and stochastic processes, including renewal theory, Markov chains, Brownian motion, martingales, and stochastic calculus. Applications in communication networks, queuing theory, and financial systems.

Statistics and data analysis emphasizing computation and problem solving: confidence intervals, hypothesis tests, bootstrap and Monte Carlo estimation, regression, Bayesian and statistical learning techniques.

Students will build a mathematical background for studying Financial Engineering. Emphasis is on analysis, proofs and examples. Mathworks Financial toolbox will be introduced.

Speech production, acoustics, perception, synthesis, compression, recognition, transmission. Coding for speech, music, and CD-quality. Feature extraction. Echo cancellation. Audio, visual synchronization. Multimedia, Internet use.

Power system planning, studies, and design; time-domain modeling and analysis of power-system networks; power flow, stability, fault, and economic dispatch analysis; symmetrical components.

Fundamentals of digital audio signal processing, room acoustics, and psychoacoustics. Algorithms for real-time implementation of immersive audio systems for integrated media applications.

Advanced scientific and engineering principles of biomedical imaging including magnetic resonance, X-ray computed tomography, ultrasound, and single photon and positron emission tomography. Prerequisites: BME 513 or EE 483. Open only to master and doctoral students.

Overvoltages during faults, voltage recovery, arcing faults, restrikes, theory of switching surges. Systems grounding, traveling waves, lightning and surge protection, insulation coordination.

Renewable energy sources and their integration in electrical networks. Power-flow control from highly variable resources. Cost analysis and planning.

Control and stability of large-scale systems such as the electric power grid. Integration with information networks. Corequisites: EE 521. Recommended preparation: EE 482.

Understanding of network processor architecture, applications, and other relevant issues. Program network processor and test under realistic network environment. Design and deploy custom network processor. Prerequisite: EE 457. Recommended preparation: EE 450.

The mobile communication channel; techniques used to combat the channel; cellular communications; multiple-access techniques; example mobile communication systems. Prerequisite: EE 503.

Non-linear integrated circuits, data-converter architectures and implementations, comprehensive design project.

Fundamentals of light amplification; laser amplifiers and oscillators; atomic pumping; maser and laser systems; definitions of coherence; measurements in quantum electronics.

Design, analysis and implementation of digital control systems using microcomputers; Z-transform methods; frequency domain and state space approach; computational aspects; sampling and quantization.

Elements of radio frequency communication systems: modulation/demodulation strategies, transmission-channel impairments, performance criteria, hardware (low-noise amplifiers, mixers, oscillators), digital back-end, contemporary case studies.

Nanotechnology for advanced nanomaterials, nanoelectronics devices, and circuits, including the rigorous treatment of electronic band structures of carbon nanotubes, graphene, and two-dimensional materials.

Asynchronous channels and architectures; implementation design styles; controller synthesis; hazards, and races; Petri-nets; performance analysis, and optimization; globally asynchronous locally synchronous design. Open only to graduate students.

Broadband network architectures and services, technologies for high-speed access and core networks, optical infrastructure for layered network architectures, high performance switch and router architectures. Prerequisites: EE 450 and EE-503.

Computer architecture from a design perspective: Pipelined processors, speculative execution, VLIW, vector processors, GPU/GPGPU, memory technology and systems, interconnection networks, shared-memory and message-passing multiprocessors, chip multiprocessors.

Distribution free classification, discriminant functions, training algorithms; statistical classification, parametric and nonparametric techniques; artificial neural networks.

Foundations of symbolic intelligent systems, search, logic, knowledge representation, planning, learning.

Random vectors, sequences, and functions. Linear transformations, second moment theory, spectral densities, narrowband processes, Gaussian processes, correlation detection, linear minimum mean square error estimation.

Parameter estimation and state estimation technique including: least squares, BLUE, maximum-likelihood, maximum a posteriori, Kalman-prediction, Kalman-filtering and Kalman smoothing and extended Kalman filtering.

Digital modulations. Optimal reception. Performance analysis. Classical and modern codes. Viterbi, forward-backward, and iterative decoding. Practical designs for channels with memory or nonlinearities. Example systems.

Entropy and mutual information. Variable and fixed-length, lossless and lossy compression. Universal compression. Text and multimedia compression. Channel capacity. Error-correcting codes. Erasure and Gaussian channels. Prerequisite: EE 503. Duplicates credit in former EE 565a.

Coherent and incoherent optical transforming, imaging and two-dimensional information processing systems; optical image processing, spatial frequency response and filtering; optical and digital holography.

Image sampling, 2-D image transform, image enhancement, geometric image modification, morphologic processing, edge detection, texture analysis, image filtering and restoration.

Classical electromagnetic field theory and its usage in understanding and analytically modelling advanced applied electrical engineering devices.

a: Microwave network theory for transmission lines and waveguides, discontinuities, impedance transformers, resonators, multi-junction networks, periodic structures, non-reciprocal and active devices. Prerequisite: 470. b: Parameter matrices, approximate design procedures for distributed networks from lumped networks, coupled lines, equivalent coupled-line circuits, Kuroda's identities, and capacitance matrix transformations. Prerequisite: 470.

Physical operation and implementation of modern solid-state memory structures. Emerging advanced memory technologies.

Integrated circuit fabrication; circuit simulation; basic device physics; simple device layout: structured chip design; timing; project chip; MOS logic; system design; silicon compilers.

VLSI design project; chip level design issues: power and clock distribution, packaging, I/O; design techniques; testability; chip fabrication and test.

Design and simulation of CMOS and nano electronic circuits modeling brain cells, including neurons and glial cells; low-power design; simulation laboratory.

Real-time adaptive signal processing design projects using special purpose DSP processors. Suitable project areas include acoustics, speech, arrays, image compression and biomedical signal processing. Permission of instructor required.

Research leading to the master's degree. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Principles of magnetic resonance imaging. Spin physics, Fourier-based acquisition and reconstruction, generation of tissue contrast, fast imaging, artifact correction, advanced image reconstruction. Prerequisite: EE 483. Recommended preparation: EE-441 and EE-503. Familiarity with MATLAB is required.

Credit on acceptance of Thesis. Graded CR/NC.

For the Master's Degree. Credit on acceptance of Thesis. Graded CR/NC.

For the Master's Degree. Credit on acceptance of Thesis. Graded CR/NC.

Multirate signal processing, wavelets and filter banks. Overcomplete and sparse signal representations. Graph signal processing. Applications to compression, sensing, time-frequency analysis, and machine learning.

Introduction to wireless networking technologies; fundamental architectural and design principles used at all protocol layers; optimization and performance evaluation using mathematical analysis and simulations. Prerequisites: EE-450 and EE-503. Recommended preparation: EE 467, familiarity with Matlab and C programming.

Introduction to research in electrical engineering. Topics vary by semester. May be repeated for up to one unit of credit for MS students, two units of credit for PhD students. Open only to Master’s and Doctoral Students.

The course content will be selected each semester to reflect current trends and developments in the field of electrical engineering.

Fundamentals and limitations of nanoscale electronic and photonic semiconductor components. Engineering classical, mesoscale, and quantum electron and photon dynamics in advanced devices.

Methods for building linear and nonlinear dynamical models from input and output time series data; theoretical models, algorithms, and toolbox implementation.

State-of-the-art computing devices; current trends in device technologies; future directions.

First order design of optical systems; origin of aberrations and their effects on wave propagation and imaging based on geometrical and physical optics.

Laboratory study of specific problems by candidates for the degree Engineer in Electrical Engineering. Graded CR/NC.

Research leading to the doctorate. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of Dissertation. Graded CR/NC.

Credit on acceptance of Dissertation. Graded CR/NC.

Credit on acceptance of Dissertation. Graded CR/NC.

Credit on acceptance of Dissertation. Graded CR/NC.

Credit on acceptance of Dissertation. Graded CR/NC.