The objective of this course is to present the fundamentals of electrical and electronic engineering to graduate students that may not have background or an undergraduate degree in electrical engineering.  Topics covered include KVL, KCL, resistive networks, AC circuits, transient analysis, frequency response, operational amplifiers, semiconductors, BJTs, FETs, digital logic, complex numbers, elemental signals, and discrete-time signals. Prerequisites: Math 161-165 and PHYS 121-122. Restricted to graduate students.

Location

Computer Studies Room 523 (TR 4:50PM - 6:05PM)

This course introduces modern computer architectures and their design theory for machine learning. In particular, this course will introduce (1) the algorithm of the most popular types of machine learning including Convolution Neural Networks, Recurrent Neural Networks, Graph Neural Networks, BERT, Transformer, etc from the perspective of hardware design and (2) the emerging domain-specific computer architectures in both academia and industry designed for these machine learning modalities. Students learn the challenges, opportunities, and tradeoffs involved in computer architecture development for machine learning through lectures, seminar discussions, and presentations. Projects and assignments involve architecture design for emerging machine learning algorithms, architecture implementation with HDL and HLS, and academic paper writing. Prerequisites: ECE 208/408, ECE200/400, ECE 204/404,ECE 440.

Location

Computer Studies Room 523 (TR 11:05AM - 12:20PM)

This course provides a systematic treatment of a number of related concepts in computing that are outside mainstream (von Neumann) approach. The primary goal is to help students understand the foundation of the on-going research of a particular type of von Neumann architecture: Ising machines. Topics include a basic review of thermodynamics (such as Gibbs-Boltzmann distribution, Langevin dynamics), computational methods inspired by it (such as Markov chain Monte Carlo methods, energy-based models: a subset of machine learning algorithms), and hardware design of Ising machines.

Pre-requisite: ECE 200 or equivalent 

Location

Computer Studies Room 523 (MW 2:00PM - 3:15PM)

Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks.  Directed and undirected graphical models.  Numerical parameter optimization, including gradient descent, expectation maximization, and other methods.  Introduction to reinforcement learning. Proofs covered as appropriate.  Significant programming projects will be assigned. 

Prerequisites:  This course involves a lot of math and algorithms.  You should know multivariable calculus,  linear algebra, and some algorithms.   No formal prerequisites but MATH 165, MATH 164, and CSC 242 strongly recommended. Students cannot register for CSC 446 if they have already taken CSC 246.

Location

Goergen Hall Room 109 (TR 11:05AM - 12:20PM)

This course provides a broad introduction to augmented and virtual reality (AR/VR) systems. The course involves lectures covering an overview of all aspects of the AR/VR domain, as well as individual work performed by each student aimed at providing more intensive training on various aspects of AR/VR. Topics covered in the lectures include history, conceptual origins, and design/evaluation principles of AR/VR technologies; overview of visual/auditory/haptic AR/VR interfaces and applications; visual perception; optics/platforms/sensors/displays; auditory perception and spatial audio; silicon hardware architecture and materials; graphics and computation; interfaces and user experience design; data processing and machine intelligence for AR/VR; introduction to AR/VR programming tools; societal implications and ethical aspects. At the end of the course, students will have gained familiarity with the techniques, languages, and cultures of fields integral to the convergent research theme of AR/VR. This course is co-instructed by Daniel Nikolov, Mujdat Cetin, Michele Rucci, Ross Maddox, Jannick Rolland,  Yuhao Zhu, Andrew White, Chenliang Xu, Zhen Bai, and Zhiyao Duan.

Location

Goergen Hall Room 109 (MW 2:00PM - 3:15PM)

This course primarily focuses on algorithms for large-scale optimization problems arising in machine learning and data science applications. The first part will cover first-order methods including gradient and subgradient methods, mirror descent, proximal gradient method, accelerated gradient method, Frank-Wolfe method, and inexact proximal point methods. The second part will introduce algorithms for nonconvex optimization, stochastic optimization, distributed optimization, manifold optimization, reinforcement learning, and those beyond first-order.

Location

Hylan Building Room 203 (TR 9:40AM - 10:55AM)

The focus will be to provide background and insight into some of the most active security related research areas in the field of VLSI design methodologies, side-channel attacks and countermeasures, covert communication attacks and countermeasures, physical unclonnable functions, hardware Trojans, security versus power/performance/noise/area/cost tradeoffs for corresponding countermeasures, etc Prerequisites: Basic Undergraduate Math and Physics

Location

Computer Studies Room 523 (MW 10:25AM - 11:40AM)

Modern solid state devices, their physics and principles of operation. Solid state physics fundamentals, free electrons, band theory, transport properties of semiconductors, tunneling. Semiconductor junctions and transistors. Compound and semi-magnetic semiconductors. Optoelectronic and ultrafast devices. Prerequisites:

ECE 221, ECE 230, PHY 123 or Instructor's approval

Location

Computer Studies Room 601 (TR 2:00PM - 3:15PM)

The course will cover the behavior of light in integrated waveguide devices. The course will feature in-class demonstrations, integrated photonic device design, and device testing in a laboratory setting. We will review Maxwell’s Equations and cover topics such as optical modes, planar waveguides, optical fibers, rectangular waveguides, coupled-mode theory, mode coupling, resonators, modulators, and numerical methods for integrated photonic device design. During this class you will learn the fundamentals of integrated photonics, design an integrated photonic device, and test and analyze its performance.

Location

Wilmot Room 116 (TR 9:40AM - 10:55AM)

The devices, circuits, and techniques of audio electronics are covered in this course. Included is a survey of small signal amplifier designs and small-signal analysis and characterization, operational amplifiers and audio applications of opamps, large-signal design and analysis methods including an overview of linear and switching power amplifiers. The course also covers the design of vacuum tube circuits, nonlinearity and distortion. Other important audio devices are also covered including microphones, loudspeakers, analog to digital and digital to analog converters, and low-noise audio equipment design principles.

Location

Computer Studies Room 601 (MW 12:30PM - 1:45PM)

The devices, circuits, and techniques of audio electronics are covered in this course. Included is a survey of small signal amplifier designs and small-signal analysis and characterization, operational amplifiers and audio applications of opamps, large-signal design and analysis methods including an overview of linear and switching power amplifiers. The course also covers the design of vacuum tube circuits, nonlinearity and distortion. Other important audio devices are also covered including microphones, loudspeakers, analog to digital and digital to analog converters, and low-noise audio equipment design principles.

Location

Hopeman Hall Room 202 (W 9:00AM - 12:00PM)

Various types of typical nanophotonic structures and nanomechanical structures, fundamental optical and mechanical properties: micro/nano-resonators, photonic crystals, plasmonic structures, metamaterials, nano-optomechanical structures. Cavity nonlinearoptics, cavity quantum optics, and cavity optomechanics. Fundamental physics and applications, state-of-art devices and current research trends. This class is designed primarily for graduate students. It may be suitable for senior undergraduates if they have required basic knowledge. This class is designed primarily for graduate students. It may be suitable for senior undergraduates if they have required basic knowledge

Prerequisites: ECE 230 or 235,/435; OPT 262 or 462, or 468, or 223, or 412;  PHY 237, or 407

Location

Computer Studies Room 527 (TR 9:00AM - 10:15AM)

This course covers the foundations of electroacoustics and acoustics relevant to sound capture and reproduction. Of special interest are topics in spatial sound field capture and reproduction, the creation of immersive audio environment, and applications to virtual and augmented reality.  Topics covered include: acoustic radiation from vibrating surfaces, radiation impedance, electro-mechano-acoustic circuits, loudspeaker design and characterization; the description, design and performance of microphones; the foundations of spatial audio capture, reproduction, and perception including the Kirchhoff-Helmholtz integral theorem and wave-field synthesis, solutions of the acoustic wave equation in spherical coordinates and ambisonics, and plane wave decomposition of acoustic fields and vector-based amplitude panning; microphone arrays for the capture of spatial audio and loudspeaker arrays for spatial control of acoustic radiation patterns; an introduction to the acoustics relevant to spatial audio perception including simple models of the interaction of acoustic waves with a listener’s head and torso, diffraction of sound by a sphere, and the measurement, modeling, measurement, and application of head-related transfer functions, the interaction of spatial audio systems with acoustic spaces and the loss of spatial audio perceptual cues. Course work will include a set of simulation and measurement projects assigned throughout the term and a final project. Prerequisites: ECE433 (Musical Acoustics) & ECE429 (Audio Electronics) or permission of instructor.

Location

Computer Studies Room 601 (MW 9:00AM - 10:15AM)

The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes. Prerequisites: ECE 242  or equivalent

Location

Gavett Hall Room 202 (MW 4:50PM - 6:05PM)

Analysis and design of discrete-time signals and systems, including: difference equations, discrete-time filtering, z-transforms, A/D and D/A conversions, mutli-rate signal processing, FIR and IIR filter design, the Discrete Fourier Transform (DFT), circular convolution, Fast Fourier Transform (FFT) algorithms, windowing, and classical spectral analysis. Prerequisites: ECE 241 and Matlab programming skills

Location

Lattimore Room 210 (MW 3:25PM - 4:40PM)

Analysis and design of discrete-time signals and systems, including: difference equations, discrete-time filtering, z-transforms, A/D and D/A conversions, mutli-rate signal processing, FIR and IIR filter design, the Discrete Fourier Transform (DFT), circular convolution, Fast Fourier Transform (FFT) algorithms, windowing, and classical spectral analysis.

Location

Gavett Hall Room 206 (F 2:00PM - 3:15PM)

This course will introduce the students to the basic concepts of digital image processing, and establish a good foundation for further study and research in this field. The theoretical components of this course will be presented at a level that seniors and first year graduate students who have taken introductory courses in vectors, matrices, probability, statistics, linear systems, and computer programming should be comfortable with. Topics cover in this course will include intensity transformation and spatial filtering, filtering in the frequency domain, image restoration, morphological image processing, image segmentation, image registration, and image compression. The course will also provide a brief introduction to python (ipython), the primary programming language that will be used for solving problems in class as well as take-home assignments. prerequisites: ECE 242 and ECE 440 & 446 are recommended or permission of instructor 

Location

Harkness Room 114 (MW 10:25AM - 11:40AM)

Physics and implementation of X-ray, ultrasonic, and MR imaging systems. Fourier transform relations and reconstruction algorithms of X-ray and ultrasonic-computed tomography, and MRI. Prerequisites:

ECE 242 or equivalent experience with Fourier transformer operations

Location

Computer Studies Room 601 (MW 3:25PM - 4:40PM)

Introduction to the principles and implementation of diagnostic ultrasound imaging. Topics include linear wave propagation and reflection, fields from pistons and arrays, beamforming, B-mode image formation, Doppler, and elastography. Project and final report. Prerequisits: BME 230 or ECE 241 or equivalent

Location

Lechase Room 104 (TR 12:30PM - 1:45PM)

Classical computation models and complexity classes, linear algebra formulation of quantum mechanics, quantum computation models, qubits, quantum circuits, and quantum computation complexity classes, Glover's search and Shor's factorization quantum algorithms, adiabatic quantum computation.

Prerequisites:

Linear Algebra (UR Math 165 or equivalent), College Physics (UR PHYS 122 or equivalent), or instructor permission

Location

Computer Studies Room 523 (MW 4:50PM - 6:05PM)

Introduction to high performance integrated circuit design. Semiconductor technologies. CMOS inverter. General background on CMOS circuits, ranging from the inverter to more complex logical and sequential circuits. The focus is to provide background and insight into some of the most active high performance related issues in the field of high performance integrated circuit design methodologies, such as CMOS delay and modeling, timing and signal delay analysis, low power CMOS design and analysis, optimal transistor sizing and buffer tapering, pipelining and register allocation, synchronization and clock distribution, retiming, interconnect delay, dynamic CMOS design techniques, power delivery, on-chip regulators, 3-D technology and circuit design, asynchronous vs. synchronous tradeoffs, clock distribution networks, low power design, and CMOS power dissipation. Prerequisites: ECE 112 and ECE 221

Location

Computer Studies Room 523 (TR 3:25PM - 4:40PM)

Introduction to high performance integrated circuit design. Semiconductor technologies. CMOS inverter. General background on CMOS circuits, ranging from the inverter to more complex logical and sequential circuits. The focus is to provide background and insight into some of the most active high performance related issues in the field of high performance integrated circuit design methodologies, such as CMOS delay and modeling, timing and signal delay analysis, low power CMOS design and analysis, optimal transistor sizing and buffer tapering, pipelining and register allocation, synchronization and clock distribution, retiming, interconnect delay, dynamic CMOS design techniques, power delivery, on-chip regulators, 3-D technology and circuit design, asynchronous vs. synchronous tradeoffs, clock distribution networks, low power design, and CMOS power dissipation.

Location

Computer Studies Room 523 (W 3:25PM - 4:40PM)

Introduction to high performance integrated circuit design. Semiconductor technologies. CMOS inverter. General background on CMOS circuits, ranging from the inverter to more complex logical and sequential circuits. The focus is to provide background and insight into some of the most active high performance related issues in the field of high performance integrated circuit design methodologies, such as CMOS delay and modeling, timing and signal delay analysis, low power CMOS design and analysis, optimal transistor sizing and buffer tapering, pipelining and register allocation, synchronization and clock distribution, retiming, interconnect delay, dynamic CMOS design techniques, power delivery, on-chip regulators, 3-D technology and circuit design, asynchronous vs. synchronous tradeoffs, clock distribution networks, low power design, and CMOS power dissipation.

Location

Computer Studies Room 527 (W 11:50AM - 12:20PM)

Circuitry, algorithms, and architectures used in analog and mixed-mode CMOS integrated circuits. Switched-capacitor (SC) elements, amplifier stages, and filters. Other SC circuits: S/H stages, comparators, PGAs, oscillators, modulators, voltage boosters, and dividers, Non-ideal effects in SC circuits, and correction techniques. Low-voltage SC design. Nyquist-rate data converter fundamentals; SC implementations of DACs and ADCs. Oversampling (delta-sigma) data converters: fundamentals and implementations. Prerequisities: ECE 113, ECE 221, ECE 222, ECE 246/446,ECE 467 

Location

Computer Studies Room 426 (TR 11:05AM - 12:20PM)

This is a hands-on course that teaches how signal-processing may be used to alter the temporal and tonal characteristics of audio signals. Topics may include sampling, quantization, modulation effects, filters, delay-line-based effects, audio synthesis, non-linear effects, and spectral processing. An emphasis will be placed on developing algorithms that are optimized for real-time processing on embedded hardware. Students will develop and visualize algorithms in MATLAB, and implement them on the SHARC Audio Module DSP platform. Prerequisites: FAMILIARITY WITH C/C++

Location

Computer Studies Room 703 (TR 12:30PM - 1:45PM)

This is a hands-on course that teaches how signal-processing may be used to alter the temporal and tonal characteristics of audio signals. Topics may include sampling, quantization, modulation effects, filters, delay-line-based effects, audio synthesis, non-linear effects, and spectral processing. An emphasis will be placed on developing algorithms that are optimized for real-time processing on embedded hardware. Students will develop and visualize algorithms in MATLAB, and implement them on the SHARC Audio Module DSP platform. Prerequisites: FAMILIARITY WITH C/C++

Location

Computer Studies Room 703 (T 2:00PM - 3:15PM)

This is a hands-on course that teaches how signal-processing may be used to alter the temporal and tonal characteristics of audio signals. Topics may include sampling, quantization, modulation effects, filters, delay-line-based effects, audio synthesis, non-linear effects, and spectral processing. An emphasis will be placed on developing algorithms that are optimized for real-time processing on embedded hardware. Students will develop and visualize algorithms in MATLAB, and implement them on the SHARC Audio Module DSP platform. Prerequisites: FAMILIARITY WITH C/C++

Location

Computer Studies Room 703 (R 2:00PM - 3:15PM)

This course is a sequel to AME262/ECE475/TEE475 Audio Software Design I. The first part of the course will explore designing audio plug-ins with Faust (Function AUdio STream), which is a high-level functional programming language designed for real-time audio digital signal processing (DSP) and sound synthesis. Students will learn how to design plug-ins for Pro Tools, Logic and other digital audio workstations (DAWs). The second part of the course will focus on audio programming for iOS apps in Swift, which is the new programming language for iOS and OS X. Students will learn how to make musical apps with the sound engine libpd, which turns Pure Data (Pd) into an embeddable library. A special topic will introduce audio programming for video games with Wwise and FMod.

Prerequisites: AME 262 or ECE 475  or Instructor Permission

Location

Computer Studies Room 616 (TR 10:25AM - 11:40AM)

This course is a sequel to AME262/ECE475/TEE475 Audio Software Design I. The first part of the course will explore designing audio plug-ins with Faust (Function AUdio STream), which is a high-level functional programming language designed for real-time audio digital signal processing (DSP) and sound synthesis. Students will learn how to design plug-ins for Pro Tools, Logic and other digital audio workstations (DAWs). The second part of the course will focus on audio programming for iOS apps in Swift, which is the new programming language for iOS and OS X. Students will learn how to make musical apps with the sound engine libpd, which turns Pure Data (Pd) into an embeddable library. A special topic will introduce audio programming for video games with Wwise and FMod.

Prerequisite: AME 262 or ECE 475  or Instructor Permission

Location

Computer Studies Room 616 (F 10:25AM - 11:40AM)

Computer audition is the study of how to design a computational system that can analyze and process auditory scenes. Example problems in this field include source separation (splitting audio mixtures into individual source tracks), pitch estimation (estimating the pitches played by each instrument), timbre modeling (finding features to distinguish different kinds of instruments), and source localization (finding where the sound comes from). This course will cover both fundamentals and state-of-the-art research in this field, which applies various kinds of signal processing and machine learning techniques. Multiple programming assignments will help students practice what they learn, and a final research project will lead students through the entire research process.

Prerequisites: ECE 246/446 or ECE 272/472 or other equivalent signal processing courses, and Python/Matlab programming. Knowledge of machine learning techniques such as Markov models, support vector machines and neural networks is also helpful, but not required.

Location

Computer Studies Room 601 (TR 12:30PM - 1:45PM)

Students will be required to take two courses that cover all major clinical imaging methods. The following clinical courses will be offered, broken down by imaging modality and target organs. The corresponding credit hours are shown.

These two courses will give each student an understanding of how each modality functions in a clinical setting, the application of such imaging to specific body parts, and what image characteristics are relevant to specific diseases.material.  PREREQUISITES:  Bachelor’s degree in physics/engineering and/or a medical degree. Instructor Susan Hobbs

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This is the third course offered as part of the PhD training program on augmented and virtual reality (AR/VR). The goal of the course is to provide interdisciplinary collaborative project experience in AR/VR. The course involves small teams of students from multiple departments working together on semester-long projects on AR/VR with the guidance of one or more faculty involved in the PhD training program. The expected end products of this Practicum course are tangible artifacts that represent what the students have learned, discovered, or invented. Types of artifacts include research papers; patent applications; open-source software; as well as online tutorials and videos for undergraduates, K-12 students, or the general public.

Prerequisites:

ECE 410-1 or OPT 410-1 or BME 410-1 or BCSC 570-1 or NSCI 415-1 or CSC 413-1 or CVSC 534-1

Location

Computer Studies Room 426 (TR 9:40AM - 10:55AM)

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Location

Wegmans Room 1400 (W 11:50AM - 1:05PM)

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