INTRO-ELECTRICAL ENGINEERING
Basic concepts of electrical engineering. Exploration of selected topics and their application. Electrical variables, circuit laws, nonlinear and linear elements, ideal and real sources, transducers, operational amplifiers in simple circuits, external behavior of diodes and transistors, first order RC and RL circuits. Digital representation of a signal, digital logic gates, flipflops. A lab is an integral part of the course. Required of electrical engineering and computer engineering majors.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 3.5 points
DIGITAL SYSTEMS LABORATORY
Companion lab course for CSEE W3827. Experiments cover such topics as logic gates; flip-flops; shift registers; counters; combinational logic circuits; sequential logic circuits; programmable logic devices. The lab generally meets on alternate weeks.
- M 4:10 p.m.-6:40 p.m.
- T 4:10 p.m.-6:40 p.m.
- F 1:10 p.m.-3:40 p.m.
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Instructor:
Kenneth Shepard
CULPA: 2 Wikipedia h-index: 64 Info - 1 points
ELECTRONIC CIRCUITS LABORATORY
Companion lab course for ELEN E3331. Experiments cover such topics as macromodeling of nonidealities of opamps using SPICE; Schmitt triggers and astable multivibrations using op-amps and diodes; logic inverters and amplifiers using bipolar junction transistors; logic inverters and ring oscillators using MOSFETs; filter design using opamps. The lab generally meets on alternate weeks.
- M 4:10 p.m.-6:40 p.m.
- T 4:10 p.m.-6:40 p.m.
- F 1:10 p.m.-3:40 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 1 points
ELECTRONIC CIRCUITS
Operational amplifier circuits. Diodes and diode circuits. MOS and bipolar junction transistors. Biasing techniques. Small-signal models. Single-stage transistor amplifiers. Analysis and design of CMOS logic gates. A/D and D/A converters.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 3 points
EE SENIOR DESIGN PROJECT
Students work in teams to specify, design, implement and test an engineering prototype. Involves technical as well as non-technical considerations, such as manufacturability, impact on the environment, economics, adherence to engineering standards, and other real-world constraints. Projects are presented publicly by each design team in a school-wide expo.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 3 points
ELECTROMAGNETICS
Basic field concepts. Interaction of time-varying electromagnetic fields. Field calculation of lumped circuit parameters. Transition from electrostatic to quasistatic and electromagnetic regimes. Transmission lines. Energy transfer, dissipation, and storage. Waveguides. Radiation.
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Keren Bergman
CULPA: 1 Wikipedia h-index: 69 Info - 4 points
INTRO TO COMMUNICATION SYSTEMS
A basic course in communication theory, stressing modern digital communication systems. Nyquist sampling, PAM and PCM/DPCM systems, time division multipliexing, high frequency digital (ASK, OOK, FSK, PSK) systems, and AM and FM systems. An introduction to noise processes, detecting signals in the presence of noise, Shannons theorem on channel capacity, and elements of coding theory.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Irving Kalet
CULPA: 3
h-index: 12
Info
- 3 points
Computing with Brain Circuits of Model O
Building the functional map of the fruit fly brain. Molecular transduction and spatio-temporal encoding in the early visual system. Predictive coding in the Drosophila retina. Canonical circuits in motion detection. Canonical navigation circuits in the central complex. Molecular transduction and combinatorial encoding in the early olfactory system. Predictive coding in the antennal lobe. The functional role of the mushroom body and the lateral horn. Canonical circuits for associative learning and innate memory. Projects in Python.
- M 7 p.m.-9:30 p.m.
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Instructor:
Aurel A Lazar
h-index: 53 Info - 3 points
COMPUTER HARDWARE DESIGN
Practical aspects of computer hardware design through the implementation, simulation, and prototyping of a PDP-8 processor. High-level and assembly languages, I/O, interrupts, datapath and control design, pipelining, busses, memory architecture. Programmable logic and hardware prototyping with FPGAs. Fundamentals of VHDL for register-transfer level design. Testing and validation of hardware. Hands-on use of industry CAD tools for simulation and synthesis.
- MW 8:40 a.m.-9:55 a.m.
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Instructor:
Tanvir Ahmed Khan
Info - 3 points
DIGITAL COMMUNICATIONS
Digital communications for both point-to-point and switched applications is further developed. Optimum receiver structures and transmitter signal shaping for both binary and M-ary signal transmission. An introduction to block codes and convolutional codes, with application to space communications.
- W 7 p.m.-9:30 p.m.
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Instructor:
Alexei Ashikhmin
Info - 3 points
Quantum Optimization and Machine Learnin
QUANTUM OPT & ML
An introduction to the recent development in quantum optimization and quantum machine learning using gate-based Noisy Intermediate Scale Quantum (NISQ) computers. IBM’s quantum programming framework Qiskit is utilized. Qbits, quantum gates and quantum measurements, quantum algorithms (Grover’s search, Simon’s algorithm, quantum Fourier transform, quantum phase estimation) quantum optimization (quantum annealing, QAOA, variational quantum eigensolver), quantum machine learning (quantum support vector machine, quantum neural networks).
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Xiaodong Wang
CULPA: 15 Info - 3 points
RANDOM SIGNALS & NOISE
Characterization of stochastic processes as models of signals and noise; stationarity, ergodicity, correlation functions, and power spectra. Gaussian processes as models of noise in linear and nonlinear systems; linear and nonlinear transformations of random processes; orthogonal series representations. Applications to circuits and devices, to communication, control, filtering, and prediction.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Irving Kalet
CULPA: 3
h-index: 12
Info
- 3 points
DIGITAL IMAGE PROCESSING
Introduction to the mathematical tools and algorithmic implementation for representation and processing of digital pictures, videos, and visual sensory data. Image representation, filtering, transform, quality enhancement, restoration, feature extraction, object segmentation, motion analysis, classification, and coding for data compression. A series of programming assignments reinforces material from the lectures.
- R 10:10 a.m.-12:40 p.m.
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Instructor:
Christine P Hendon
Wikipedia h-index: 21 Info - 3 points
TOPICS IN EE & CE
Stat Learning Quant Tradi
Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 4900 to 4909.
TPC NEUROSCI & DEEP LEARN
Comp. w. Brain Cir of MO
Selected advanced topics in neuroscience and deep learning. Content varies from year to year, and different topics rotate through the course numbers 6070 to 6079.
- M 7 p.m.-9:30 p.m.
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Instructor:
Aurel A Lazar
h-index: 53 Info - 3 points
ADV ANALOG INTEGRATED CIRCUITS
Integrated circuit device characteristics and models; temperature- and supply-independent biasing; IC operational amplifier analysis and design and their applications; feedback amplifiers, stability and frequency compensation techniques; noise in circuits and low-noise design; mismatch in circuits and low-offset design. Computer-aided analysis techniques are used in homework(s) or a design project.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
Harish Krishnaswamy
Info - 3 points
ADV DIGITAL ELECTRONIC CIRCUIT
Advanced topics in the design of digital integrated circuits. Clocked and non-clocked combinational logic styles. Timing circuits: latches and flip-flops, phase-locked loops, delay-locked loops. SRAM and DRAM memory circuits. Modeling and analysis of on-chip interconnect. Power distribution and power-supply noise. Clocking, timing, and synchronization issues. Circuits for chip-to-chip electrical communication. Advanced technology issues that affect circuit design. The class may include a team circuit design project.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Mingoo Seok
h-index: 31 Info - 3 points
SEMICONDUCTOR DEVICE PHYSICS
Physics and properties of semiconductors. Transport and recombination of excess carriers. Schottky, P-N, MOS, and heterojunction diodes. Field effect and bipolar junction transistors. Dielectric and optical properties. Optical devices including semiconductor lamps, lasers, and detectors.
- R 4:10 p.m.-6:40 p.m.
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Instructor:
Xiang Meng
h-index: 8 Info - 3 points
PHOTONIC INTEGRATED CIRCUITS
Photonic integrated circuits are important subsystem components for telecommunications, optically controlled radar, optical signal processing, and photonic local area networks. An introduction to the devices and the design of these circuits. Principle and modeling of dielectric waveguides (including silica on silicon and InP based materials), waveguide devices (simple and star couplers), and surface diffractive elements. Discussion of numerical techniques for modeling circuits, including beam propagation and finite difference codes, and design of other devices: optical isolators, demultiplexers.
- W 4:10 p.m.-6:40 p.m.
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Instructor:
Michal Lipson
CULPA: 1 Wikipedia h-index: 114 Info - 3 points
Future Energy: Economics, Systems, Polic
Holistic understanding of the science and socioeconomic modelling of climate change, the properties of renewable energy sources, their economic and engineering
implications, and the policy decisions in the transition to future energy. Fundamentals of systems needed to support deep penetration of renewable energy, including grid stability, transmission and storage systems, and the design and operations of carbonaware datacenters.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Debasis Mitra
Wikipedia h-index: 61 Info - 3 points
TOPICS DATA-DRIVEN ANAL & COMP
Advanced Deep Learning
Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.
- W 10:10 a.m.-12:40 p.m.
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Instructor:
Zoran Kostic
CULPA: 1 h-index: 24 Info - 3 points
TOPICS DATA-DRIVEN ANAL & COMP
Deep Learning on the Edge
Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.
- W 1:10 p.m.-3:40 p.m.
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Instructor:
Zoran Kostic
CULPA: 1 h-index: 24 Info - 3 points
BAYESIAN MOD MACHINE LEARNING
Basic statistics and machine learning strongly recommended. Bayesian approaches to machine learning. Topics include mixed-membership models, latent factor models, Bayesian nonparametric methods, probit classification, hidden Markov models, Gaussian mixture models, model learning with mean-field variational inference, scalable inference for Big Data. Applications include image processing, topic modeling, collaborative filtering and recommendation systems.
- W 4:10 p.m.-6:40 p.m.
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Instructor:
John W Paisley
CULPA: 1 Info - 3 points
Quantum Sensing Theory
QUANTUM SENSING THEORY
Introduction to quantum detection and estimation theory and its applications to quantum communications, quantum radar, quantum metrology, and quantum tomography. Background on quantum mechanics, quantum detection, composite quantum systems, Gaussian states, and quantum estimation.
- F 1:10 p.m.-3:40 p.m.
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Instructor:
Xiaodong Wang
CULPA: 15 Info - 3 points
TOPICS IN NETWORKING
MachineLearn for Networks
Further study of areas such as communication protocols and architectures, flow and congestion control in data networks, performance evaluation in integrated networks. Content varies from year to year, and different topics rotate through the course numbers 6770 to 6779.
- W 7 p.m.-9:30 p.m.
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Instructor:
Anwar I Walid
CULPA: 1 Info - 3 points
Statistical signal processing and learni
STATISTICAL SIGNAL PROCES
Introduction to the fundamental principles of statistical signal processing related to detection and estimation. Hypothesis testing, signal detection, parameter estimation, signal estimation, and selected advanced topics. Suitable for students doing research in communications, control, signal processing, and related areas.
- M 10:10 a.m.-12:40 p.m.
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Instructor:
Guido H Jajamovich
Info - 3 points
Sparse and Low-Dimensional Models for Hi
SPARSE MODELS FOR HI-D DA
Overview of theory, computation and applications for sparse and low-dimensional data modeling. Recoverability of sparse and low-rank models. Optimization methods for low-dim data modeling. Applications to imaging, neuroscience, communications, web data.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
John Wright
CULPA: 1 h-index: 44 Info - 3 points
TOPICS IN SIGNAL PROCESSING
INTRO TO BLOCKCHAIN TECH
Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889.
- F 8:10 a.m.-10 a.m.
- 3 points
TOPICS IN SIGNAL PROCESSING
LARGE-SCALE STREAM PROCESSING
Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889. Topic: Large Data Stream Processing.
- R 7 p.m.-9:30 p.m.
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Instructor:
Deepak S Turaga
Info - 3 points
TOPICS-INFORMATION PROCESSING
The class will give you a richer understanding of networking, focusing on principles and systems that have shaped the Internet. Via classic and contemporary literature, topics covered include routing, content delivery, congestion control, data centers, SDN, and the cloud.
- R 4:10 p.m.-6:40 p.m.
- 3 points
TOPICS-INFORMATION PROCESSING
Reinforcement Learning
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.
- R 10:10 a.m.-12:40 p.m.
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Instructor:
Javad Ghaderi
h-index: 17 Info - 3 points
TOPICS-INFORMATION PROCESSING
TPC: Adv Big Data & AI
Advanced topics spanning Electrical Engineering and Computer Science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899. Topic: Advanced Big Data Analytics.
- T 7 p.m.-9:30 p.m.
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Instructor:
Ching-Yung Lin
h-index: None Info - 3 points
TOPICS IN ELECTRICAL AND COMPUTER ENGINE
Embedded AI
Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.
- M 1:10 p.m.-3:40 p.m.
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Instructor:
Xiaofan Jiang
h-index: 24 Info - 3 points