Spring Term Schedule
Spring 2025
| Number | Title | Instructor | Time |
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ECE 400-1
Tong Geng
TR 2:00PM - 3:15PM
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Instruction set principles; processor design, pipelining, data and control hazards; datapath and computer arithmetic; memory systems; I/O and peripheral devices; internetworking. Students learn the challenges, opportunities, and tradeoffs involved in modern microprocessor design. Assignments and labs involve processor and memory subsystem design using hardware description languages (HDL). Prerequisites:ECE114, ECE 112 or CSC 171, or permission of Instructor
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ECE 401-1
Michael Huang
TR 12:30PM - 1:45PM
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Instruction set architectures. Advanced pipelining techniques. Instruction level parallelism. Memory hierarchy design. Multiprocessing. Storage systems. Interconnection network. Prerequisites: ECE 200 or equivalent
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ECE 408-1
Zhiyao Duan
WF 10:25AM - 11:40AM
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Machine Learning (ML) is the branch of Artificial Intelligence dedicated to teaching computers how to solve tasks by learning from data. This class introduces basic concepts of machine learning through various real-world ECE applications. It will cover various learning paradigms such as supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. It will also cover classical and state-of-the-art techniques such as linear models, support vector machines, Gaussian mixture models, hidden Markov models, matrix factorization, ensemble learning, principal component analysis, and various kinds of deep neural networks. Students will learn the pros and cons of different methods and their suited application scenarios. This course is hands-on with multiple programming assignments and a final project to solve real ECE problems. Prerequisites: General programming such as ECE-114; MATH 165 linear algebra. Probability and statistics such as ECE 270 is recommended.
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ECE 409-1
Daniel Gildea
TR 11:05AM - 12:20PM
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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.
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ECE 411-1
Mujdat Cetin; Yukang Yan
MW 2:00PM - 3:15PM
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This is the second course offered as part of the PhD training program on augmented and virtual reality. It builds on the first course, Introduction to Augmented and Virtual Reality (AR/VR). The goal of the course is to provide exposure to problems in the AR/VR domain addressed by various disciplines. The course consists of three one-month long modules in a semester. Modules engage students in particular aspects of AR/VR or hands-on experience on AR/VR. Modules to be offered in various years include: fundamentals of optics for AR/VR; AR/VR in the silicon; foundations of visual perception in the context of AR/VR; computer audition and acoustic rendering; measuring the human brain; deep learning and visual recognition for AR/VR; brain-computer interfacing in a virtual environment; interaction techniques for AR/VR systems; 3D interfaces and interaction; AR/VR for collaborative education & professional training. In Spring 2025, the following three modules will be offered: 1) Fundamentals of optics for AR/VR (Daniel Nikolov and Jannick Rolland). Optics is central to near-eye displays and sensing. In this module, students will learn basic concepts and terminology of optics for AR/VR, as well as key visual requirements. Students will then learn about different optical architectures based on free-space or waveguide optics. Emerging technologies enabling compact architectures such as freeform optics and meta optics will be discussed. Students will be exposed to demonstrations of hands-on design in optical design software. 2) Interaction techniques for AR/VR systems (Yukang Yan). This module introduces the design and implementation of interaction techniques that can be applied in AR/VR systems, focusing on fundamental tasks such as target selection, navigation, object manipulation, and sensory input. Through the introduction of conceptual insights and hands-on practice, we aim to teach students how to create intuitive, effective interaction methods across different tasks and scenarios. Projects will encourage students to design and build their own interaction techniques, leading to a deeper understanding of user-centered design principles and the technical skills necessary to bring interactive concepts to life. 3) Professional encounters with leading AR/VR researchers. This module involves a series of seminars (titled Voices of XR) and discussion sessions with leading AR/VR researchers from academia and industry. This component of the course is offered in partnership with Studio X. Prerequisites: ECE 410 or OPT 410 or BME 410 or NSCI 415 or CSC 413 or CVSC 534 or equivalent experience. INSTRUCTORS: Mujdat Cetin; Daniel Nikolov; Jannick Rolland; Yukang Yan
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ECE 421-1
Gary Wicks
TR 11:05AM - 12:20PM
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Blank Description
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ECE 422-1
Stephen Wu
MW 2:00PM - 3:15PM
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Topics in semiconductor device physics, electronic band structure, materials science, and magnetism with a focus on applications to new and emerging electronic device technologies. This background will serve as a jumping off point to discuss potential future electronic devices with novel properties beyond the current status quo. Looking beyond just next-generation technology, the course will explore what electronics could look like on the 25+ year timescale. Basic trends from condensed matter physics, materials science and electrical engineering will be discussed. Topics include: 2D electronic materials/transistors, magnetic memory, spintronics, multiferroic memory, topological matter/devices. Prerequisites: ECE 223/423 or instructors approval
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ECE 426-1
Jaime Cardenas
TR 3:25PM - 4:40PM
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Propagation and interactions in optical waveguides. Topics include the Goos-Haenchen effect, coupled-mode theory, pulse broadening in optical fibers, coupling between guided-wave structures and wave-guide devices such as semiconductor lasers, fiber lasers and opto-electric devices.
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ECE 427-1
Roman Sobolewski
TR 2:00PM - 3:15PM
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Introduction to small-scale superconductor electronic devices, both analog and digital. Josephson junctions as digital circuit elements. Implementation of shunted Josephson junctions in the single flux quantum (SFQ) circuitry. SFQ circuit design and integration, simulations and appropriate simulation tools Finally, fabrication and testing of SFQ-based digital integrated circuits. PREREQUISITES: ECE 425, or instructors approval
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ECE 433-1
Michael Heilemann
TR 11:05AM - 12:20PM
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Aspects of acoustics. Review of oscillators, vibratory motion, the acoustic wave equation, reflection, transmission and absorption of sound, radiation and diffraction of acoustic waves. Resonators, hearing and speech, architectural and environmental acoustics. PREREQUISITES: Linear algebra and Differential Equations (MTH 165), and Physics (PHY 121) or equivalents.
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ECE 433-2
Michael Heilemann
M 3:25PM - 4:40PM
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Aspects of acoustics. Review of oscillators, vibratory motion, the acoustic wave equation, reflection, transmission and absorption of sound, radiation and diffraction of acoustic waves. Resonators, hearing and speech, architectural and environmental acoustics.
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ECE 433-3
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W 1:15PM - 2:15PM
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Aspects of acoustics. Review of oscillators, vibratory motion, the acoustic wave equation, reflection, transmission and absorption of sound, radiation and diffraction of acoustic waves. Resonators, hearing and speech, architectural and environmental acoustics.
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ECE 442-1
Gonzalo Mateos Buckstein
MW 3:25PM - 4:40PM
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The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks. Prerequisites: Some mathematical maturity, comfortable with linear algebra, probability, and analysis (e.g., MTH164-165). Exposure to programming and Matlab useful, but not required. For more information, please visit the class website: https://www.hajim.rochester.edu/ece/sites/gmateos/ECE442.html
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ECE 445-1
Irving Barron Martinez
TR 2:00PM - 3:15PM
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This course teaches the underlying concepts behind traditional cellular radio and wireless data networks as well as design trade-offs among RF bandwidth, transmitter and receiver power and cost, and system performance. Topics include channel modeling, digital modulation, channel coding, network architectures, medium access control, routing, cellular networks, WiFi/IEEE 802.11 networks, mobile ad hoc networks, sensor networks and smart grids. Issues such as quality of service (QoS), energy conservation, reliability and mobility management are discussed. Students are required to complete a semester-long research project in order to obtain in-depth experience with a specific area of wireless communication and networking. PREREQUISITE: ECE 242 or permission of instructor. INSTRUCTOR Z. IGNJATOVIC
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ECE 449-1
Chenliang Xu
TR 9:40AM - 10:55AM
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Introduction to computer vision, including camera models, basic image processing, pattern and object recognition, and elements of human vision. Specific topics include geometric issues, statistical models, Hough transforms, color theory, texture, and optic flow. CSC 449, a graduate-level course, requires additional readings and assignments. Prerequisites: MATH 161 and CSC 242; MATH 165 strongly recommended
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ECE 451-1
Diane Dalecki
TR 11:05AM - 12:20PM
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The course presents the physical basis for the use of high-frequency sound in medicine. Topics include acoustic properties of tissue, sound propagation (both linear and nonlinear) in tissues, interaction of ultrasound with gas bodies (acoustic cavitation and contrast agents), thermal and non-thermal biological effects of utrasound, ultrasonography, dosimetry, hyperthermia and lithotripsy.
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ECE 455-1
Sree Pai
MW 10:25AM - 11:40AM
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Programming is the automation of information processing. Program analysis and transformation is the automation of programming itself---how much a program can understand and improve other programs. Because of the diversity and complexity of computer hardware, programmers increasingly depend on automation in compilers and other tools to deliver efficient and reliable software. This course combines fundamental principles and (hands-on) practical applications. Specific topics include data flow and dependence theories; static and dynamic program transformation including parallelization; memory and cache management; type checking and program verification; and performance analysis and modeling. The knowledge and practice will help students to become experts in software performance and correctness. Students taking the graduate level will have additional course requirements and a more difficult project. Recommended prerequisites: CSC 252, CSC 254
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ECE 456-1
Sobhit Kumar Singh
MW 10:25AM - 11:40AM
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An introduction to the fascinating world of quantum materials in bulk and 2D. This course aims to unveil the quantum origin of materials-specific properties from the atomic level. Topics covered include: crystal structure and symmetries, fundamentals of electronic structure, phonons and vibrational spectroscopies, optical properties of materials, electronic and thermal transport elastic and mechanical properties of solids, superconductivity, magnetism and a brief discussion of ab-initio prediction of materials properties and molecular dynamics.
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ECE 465-02
Cantay Caliskan
MW 2:00PM - 3:15PM
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The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments. PRE-REQUISITES: DSCC 462, STAT 190 or equivalent introductory statistics background.
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ECE 472-1
Sarah Smith
TR 9:40AM - 10:55AM
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This course is a survey of audio digital signal processing fundamentals and applications. Topics include sampling and quantization, analog to digital converters, time and frequency domains, spectral analysis, vocoding, digital filters, audio effects, music audio analysis and synthesis, and other advanced topics in audio signal processing. Implementation of algorithms using Matlab and on dedicated DSP platforms is emphasized. prerequisites: ECE 114 and basic Matlab programming, ECE 241 or equivalent
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ECE 472-2
Sarah Smith
M 2:00PM - 3:00PM
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This course is a survey of audio digital signal processing fundamentals and applications. Topics include sampling and quantization, analog to digital converters, time and frequency domains, spectral analysis, vocoding, digital filters, audio effects, music audio analysis and synthesis, and other advanced topics in audio signal processing. Implementation of algorithms using Matlab and on dedicated DSP platforms is emphasized. Prerequisite: ECE 114 and basic Matlab programming, ECE 240 or other equivalent signals and systems courses.
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ECE 472-3
Sarah Smith
F 2:15PM - 3:15PM
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This course is a survey of audio digital signal processing fundamentals and applications. Topics include sampling and quantization, analog to digital converters, time and frequency domains, spectral analysis, vocoding, digital filters, audio effects, music audio analysis and synthesis, and other advanced topics in audio signal processing. Implementation of algorithms using Matlab and on dedicated DSP platforms is emphasized. Prerequisite: ECE 114 and basic Matlab programming, ECE 240 or other equivalent signals and systems courses.
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ECE 473-1
Robert LaVaque
MW 3:25PM - 4:40PM
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This course is intended to provide students a basic understanding of creating audio for gaming. The emphasis is on demonstrations and hands-on experience to enable students to gain a practical knowledge of the integration of sound and music into video games. Students will primarily work with AudioKinetic Wwise, Reaper, Pro Tools, and Logic Pro. The course will also feature guest lectures by industry leading professionals, who will share their experience and insights. For a final project, students will create their own music and sound for a provided game. OPEN ONLY TO ESM BEAL STUDENTS and AME GRADUATE STUDENTS Prerequisite: AME 193 & 194, or a working knowledge of either Pro Tools, Logic Pro, or Reaper.
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ECE 474-1
Scott Seidman
TR 9:40AM - 10:55AM
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Course will cover circuits and sensors used to measure physiological systems at an advanced level. Both signal conditioning and sensor characteristics will be addressed. Topics will include measurement of strain, pressure, flow, temperature, biopotentials, and physical circuit construction. The co-requisite laboratory will focus on the practical implementation of electronic devices for biomedical measurements. Prerequisites: BME 210, ECE113 or equivalent, or permission of instructor.
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ECE 474-2
Scott Seidman
F 8:00AM - 11:00AM
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Course will cover circuits and sensors used to measure physiological systems at an advanced level. Both signal conditioning and sensor characteristics will be addressed. Topics will include measurement of strain, pressure, flow, temperature, biopotentials, and physical circuit construction. The co-requisite laboratory will focus on the practical implementation of electronic devices for biomedical measurements. Prerequisites: BME 210, ECE113 or equivalent, or permission of instructor.
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ECE 475-1
Ming Lun Lee
TR 12:30PM - 1:45PM
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In this course, students will develop skills for designing audio/music applications and creating computer music in C and Max. We will begin with the history of computer music, a survey of audio programming languages, and a review of C. Libsndfile, a C library for reading and writing sound files, will be used to explore topics in sound synthesis, analysis, and digital signal processing. Students will use PortAudio, a C library for real-time audio input/output, to design DSP applications. Max is a visual programming language for interactive audio/music and multimedia. Students are required to watch pre-recorded lectures to learn Max and attend recitations for reviews. They will also practice their programming techniques through a series of programming assignments, a midterm drum machine project in Max, and a final research/design project. . Prerequisite: ECE 114 or instructor permission
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ECE 475-2
Ming Lun Lee
F 10:25AM - 11:40AM
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In this course, students will develop the ability to design programs in C, Python, Max, and Pure Data for audio/music research, computer music, and interactive performance. We will begin with an introduction to computer music and audio programming. After a quick review of C, we will use the PortSF library to generate and process basic envelopes and waveforms, and to explore the development of the table-lookup oscillator and other DSP tools. Max and Pure Data are similar visual programming languages for music and multimedia. We will use Max to explore topics in sound synthesis, signal processing, and sound analysis, as well as computer music. Python is a general-purpose programming language used in many application domains. We will use JythonMusic, a special version of Python, for music making, building graphical user interfaces, and for connecting external human interface devices. Students will practice their programming techniques through a series of programming assignments and a final project. Prerequisite: ECE 114 or instructor permission
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ECE 480-1
Daniel Phinney
T 8:00AM - 9:30AM
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Audio amplification concepts and design techniques focused on the use of both solid state and vacuum tubes. Shall cover concepts related to impedance matching, preamps, op amps, class A, AB, D, H and G power amplifiers, circuit board layout, power supplies and grounding techniques. PREREQUISITE: AME 295 or Instructor permission. INSTRUCTOR: DAN PHINNEY
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ECE 480-2
Daniel Phinney
W 10:25AM - 1:45PM
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Audio amplification concepts and design techniques focused on the use of vacuum tubes. Will cover some concepts related to MOSFET amplifiers as well. A mixture of lab based projects and LTSpice simulation. Shall cover concepts related to impedance matching, preamps, class A and class AB power amplifiers, power supplies and grounding techniques. Prerequisites: AME 295 or Instructor permission
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ECE 482-1
Susan Hobbs
7:00PM - 7:00PM
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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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ECE 484-1
Marvin Doyley
MW 10:25AM - 11:40AM
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Researchers are actively developing artificial intelligence (AI) techniques to improve the accuracy and efficiency of some of the most challenging components of medical imaging. These components include computer-aided diagnosis, automatic segmentation of anatomical regions, automatic lesion detection, data fusion, and image-guided surgical intervention, to name a few. This course aims to develop imaging scientists who understand the fundamentals of machine learning, how to implement different machine learning algorithms, how to select and extract features from medical images, and how to evaluate different AI learning strategies (supervised vs. non-supervised). The course will cover classical machine learning techniques and deep learning techniques. Specifically, students will learn how to evaluate and implement different deep learning architectures, convolution neural networks, recurrent neural networks, object detection networks, U-Net (segmentation networks), multi-modal architectures, and generative adversarial networks. This course will also teach students how to train neural networks for medical images, data augmentation, and domain adaptation. Students will learn how to use PyTorch, a flexible machine learning framework, to implement and evaluate these concepts. PREREQUISITES: ECE 447 (Introduction to Image Processing using Python)
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ECE 485-1
Nebojsa Duric
TR 11:30AM - 1:00PM
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This course teaches students the mathematical principles and computations that underpin modern imaging systems. The course will cover deconvolution, regularization methods, statistical methods, linear inverse imaging problems, singular value decomposition, and Fourier-based methods used in image reconstruction. Clinical examples of inverse problems and their solutions will be provided to the students during the lectures. Students will be assigned homework and administered tests to gauge their understanding of the material. Prerequisties: Bachelor’s degree in physics/engineering and/or a medical degree. INSTRUCTOR: NEBOISA DURIC
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ECE 486-1
Susan Hobbs
WF 11:50AM - 1:05PM
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This course covers the same modalities and target sites as Clinical Imaging 1 and 2. Students are expected to shadow practicing radiologists who will demonstrate real-time image reads and diagnoses. This course will give each student a practical understanding of how radiologists read images and what imaging characteristics are essential inputs in the diagnostic process. PREREQUISITES: Bachelor’s degree in physics/engineering and/or a medical degree. INSTRUCTOR: SUSAN HOBBS
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ECE 491-1
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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Blank Description
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ECE 495-02
Cristiano Tapparello
7:00PM - 7:00PM
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Blank Description
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ECE 495-03
Stephen Wu
7:00PM - 7:00PM
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Blank Description
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ECE 495-04
Hui Wu
7:00PM - 7:00PM
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No description
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ECE 495-05
Roman Sobolewski
7:00PM - 7:00PM
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Blank Description
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ECE 495-06
Gaurav Sharma
7:00PM - 7:00PM
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Blank Description
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ECE 495-07
Kevin Parker
7:00PM - 7:00PM
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Blank Description
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ECE 495-08
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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Blank Description
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ECE 495-09
Qiang Lin
7:00PM - 7:00PM
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Blank Description
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ECE 495-10
Selcuk Kose
7:00PM - 7:00PM
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Blank Description
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ECE 495-11
Zeljko Ignjatovic
7:00PM - 7:00PM
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Blank Description
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ECE 495-12
Michael Huang
7:00PM - 7:00PM
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Blank Description
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ECE 495-13
Thomas Howard
7:00PM - 7:00PM
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Blank Description
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ECE 495-14
Wendi Heinzelman
7:00PM - 7:00PM
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Blank Description
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ECE 495-15
Eby Friedman
7:00PM - 7:00PM
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Blank Description
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ECE 495-16
Marvin Doyley
7:00PM - 7:00PM
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Blank Description
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ECE 495-17
Hanan Dery
7:00PM - 7:00PM
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Blank Description
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ECE 495-18
Mujdat Cetin
7:00PM - 7:00PM
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Blank Description
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ECE 495-19
Mark Bocko
7:00PM - 7:00PM
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Blank Description
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ECE 495-20
Yuhao Zhu
7:00PM - 7:00PM
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Blank Description
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ECE 495-21
Ming Lun Lee
7:00PM - 7:00PM
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Blank Description
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ECE 495-22
Michael Heilemann
7:00PM - 7:00PM
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Blank Description
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ECE 495-23
Sarah Smith
7:00PM - 7:00PM
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Blank Description
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ECE 495-24
Daniel Phinney
7:00PM - 7:00PM
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Blank Description
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ECE 495-25
Stephen Roessner
7:00PM - 7:00PM
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Blank Description
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ECE 495-26
Zhiyao Duan
7:00PM - 7:00PM
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Blank Description
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ECE 495-27
Sree Pai
7:00PM - 7:00PM
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Blank Description
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ECE 495-28
Tre Dipassio
7:00PM - 7:00PM
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Blank Description
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ECE 496-1
Ming Lun Lee
7:00PM - 7:00PM
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Blank Description
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ECE 520-1
Hanan Dery
TR 2:00PM - 3:15PM
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Up until now CMOS scaling has given us a remarkable ride with little concern for fundamental limits. It has scaled multiple generations in feature size and in speed while keeping the same power densities. However,CMOS finally encounters fundamental limits. The course is intended for students interested in research frontiers of future electronics technologies. The course begins with introduction to the basic physics of magnetism and of quantum mechanical spin. Then it covers aspects of spin transport with emphasis on spin-diffusion in semiconductors. The second part of the course is comprised of student and lecturer presentations of selected spintronics topics which may include: spin transistors, magnetic random access memories, spin-based logic paradigms, spin-based lasers and light emitting diodes, magnetic semiconductors, spin-torque devices for memory applications and the spin Hall effect. Prerequisites: Familiarity of quantum mechanics (i.e., taking a course and getting a grade; not just auditing) or PHYS 407-Quantum Mechanics-I. (ECE 459)
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ECE 595-03
Laurel Carney
7:00PM - 7:00PM
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Blank Description
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ECE 595-10
Jaime Cardenas
7:00PM - 7:00PM
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ECE 595-12
Axel Wismueller
7:00PM - 7:00PM
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Blank Description
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ECE 595-13
Sree Pai
7:00PM - 7:00PM
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Blank Description
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ECE 595-14
William Donaldson
7:00PM - 7:00PM
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Blank Description
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ECE 595-15
Mark Bocko
7:00PM - 7:00PM
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Blank Description
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ECE 595-16
Ehsan Hoque
7:00PM - 7:00PM
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ECE 595-17
Stephen McAleavey
7:00PM - 7:00PM
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ECE 595-18
Mujdat Cetin
7:00PM - 7:00PM
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ECE 595-19
Hanan Dery
7:00PM - 7:00PM
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Blank Description
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ECE 595-2
Thomas Howard
7:00PM - 7:00PM
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Blank Description
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ECE 595-20
Michael Huang
7:00PM - 7:00PM
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Blank Description
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ECE 595-21
Marvin Doyley
7:00PM - 7:00PM
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Blank Description
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ECE 595-22
Eby Friedman
7:00PM - 7:00PM
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Blank Description
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ECE 595-23
Kevin Parker
7:00PM - 7:00PM
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Blank Description
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ECE 595-24
Zeljko Ignjatovic
7:00PM - 7:00PM
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Blank Description
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ECE 595-25
Wendi Heinzelman
7:00PM - 7:00PM
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Blank Description
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ECE 595-26
Qiang Lin
7:00PM - 7:00PM
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Blank Description
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ECE 595-27
Gaurav Sharma
7:00PM - 7:00PM
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Blank Description
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ECE 595-28
Selcuk Kose
7:00PM - 7:00PM
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Blank Description
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ECE 595-29
Hui Wu
7:00PM - 7:00PM
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Blank Description
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ECE 595-30
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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Blank Description
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ECE 595-31
Michael Heilemann
7:00PM - 7:00PM
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Blank Description
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ECE 595-32
Ming Lun Lee
7:00PM - 7:00PM
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Blank Description
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ECE 595-33
Stephen Roessner
7:00PM - 7:00PM
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Blank Description
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ECE 595-34
Mohammad Mehrmohammadi
7:00PM - 7:00PM
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No description
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ECE 595-35
Nebojsa Duric
7:00PM - 7:00PM
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No description
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ECE 595-4
Sarah Smith
7:00PM - 7:00PM
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Blank Description
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ECE 595-5
Roman Sobolewski
7:00PM - 7:00PM
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Blank Description
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ECE 595-6
Stephen Wu
7:00PM - 7:00PM
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Blank Description
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ECE 595-7
Zhiyao Duan
7:00PM - 7:00PM
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Blank Description
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ECE 595-8
Tong Geng
7:00PM - 7:00PM
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Blank Description
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ECE 595-9
Michael Huang
7:00PM - 7:00PM
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Blank Description
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ECE 597-1
Michele Foster
W 11:50AM - 1:05PM
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Blank Description
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ECE 895-1
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7:00PM - 7:00PM
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ECE 897-02
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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ECE 899-02
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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ECE 986V-1
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7:00PM - 7:00PM
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ECE 987V-1
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7:00PM - 7:00PM
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ECE 995-1
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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ECE 999-01
Gonzalo Mateos Buckstein
7:00PM - 7:00PM
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Spring 2025
| Number | Title | Instructor | Time |
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| Monday | |
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ECE 472-2
Sarah Smith
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This course is a survey of audio digital signal processing fundamentals and applications. Topics include sampling and quantization, analog to digital converters, time and frequency domains, spectral analysis, vocoding, digital filters, audio effects, music audio analysis and synthesis, and other advanced topics in audio signal processing. Implementation of algorithms using Matlab and on dedicated DSP platforms is emphasized. Prerequisite: ECE 114 and basic Matlab programming, ECE 240 or other equivalent signals and systems courses. |
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ECE 433-2
Michael Heilemann
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Aspects of acoustics. Review of oscillators, vibratory motion, the acoustic wave equation, reflection, transmission and absorption of sound, radiation and diffraction of acoustic waves. Resonators, hearing and speech, architectural and environmental acoustics. |
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| Monday and Wednesday | |
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ECE 455-1
Sree Pai
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Programming is the automation of information processing. Program analysis and transformation is the automation of programming itself---how much a program can understand and improve other programs. Because of the diversity and complexity of computer hardware, programmers increasingly depend on automation in compilers and other tools to deliver efficient and reliable software. This course combines fundamental principles and (hands-on) practical applications. Specific topics include data flow and dependence theories; static and dynamic program transformation including parallelization; memory and cache management; type checking and program verification; and performance analysis and modeling. The knowledge and practice will help students to become experts in software performance and correctness. Students taking the graduate level will have additional course requirements and a more difficult project. Recommended prerequisites: CSC 252, CSC 254 |
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ECE 456-1
Sobhit Kumar Singh
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An introduction to the fascinating world of quantum materials in bulk and 2D. This course aims to unveil the quantum origin of materials-specific properties from the atomic level. Topics covered include: crystal structure and symmetries, fundamentals of electronic structure, phonons and vibrational spectroscopies, optical properties of materials, electronic and thermal transport elastic and mechanical properties of solids, superconductivity, magnetism and a brief discussion of ab-initio prediction of materials properties and molecular dynamics. |
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ECE 484-1
Marvin Doyley
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Researchers are actively developing artificial intelligence (AI) techniques to improve the accuracy and efficiency of some of the most challenging components of medical imaging. These components include computer-aided diagnosis, automatic segmentation of anatomical regions, automatic lesion detection, data fusion, and image-guided surgical intervention, to name a few. This course aims to develop imaging scientists who understand the fundamentals of machine learning, how to implement different machine learning algorithms, how to select and extract features from medical images, and how to evaluate different AI learning strategies (supervised vs. non-supervised). The course will cover classical machine learning techniques and deep learning techniques. Specifically, students will learn how to evaluate and implement different deep learning architectures, convolution neural networks, recurrent neural networks, object detection networks, U-Net (segmentation networks), multi-modal architectures, and generative adversarial networks. This course will also teach students how to train neural networks for medical images, data augmentation, and domain adaptation. Students will learn how to use PyTorch, a flexible machine learning framework, to implement and evaluate these concepts. PREREQUISITES: ECE 447 (Introduction to Image Processing using Python) |
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ECE 411-1
Mujdat Cetin; Yukang Yan
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This is the second course offered as part of the PhD training program on augmented and virtual reality. It builds on the first course, Introduction to Augmented and Virtual Reality (AR/VR). The goal of the course is to provide exposure to problems in the AR/VR domain addressed by various disciplines. The course consists of three one-month long modules in a semester. Modules engage students in particular aspects of AR/VR or hands-on experience on AR/VR. Modules to be offered in various years include: fundamentals of optics for AR/VR; AR/VR in the silicon; foundations of visual perception in the context of AR/VR; computer audition and acoustic rendering; measuring the human brain; deep learning and visual recognition for AR/VR; brain-computer interfacing in a virtual environment; interaction techniques for AR/VR systems; 3D interfaces and interaction; AR/VR for collaborative education & professional training. In Spring 2025, the following three modules will be offered: 1) Fundamentals of optics for AR/VR (Daniel Nikolov and Jannick Rolland). Optics is central to near-eye displays and sensing. In this module, students will learn basic concepts and terminology of optics for AR/VR, as well as key visual requirements. Students will then learn about different optical architectures based on free-space or waveguide optics. Emerging technologies enabling compact architectures such as freeform optics and meta optics will be discussed. Students will be exposed to demonstrations of hands-on design in optical design software. 2) Interaction techniques for AR/VR systems (Yukang Yan). This module introduces the design and implementation of interaction techniques that can be applied in AR/VR systems, focusing on fundamental tasks such as target selection, navigation, object manipulation, and sensory input. Through the introduction of conceptual insights and hands-on practice, we aim to teach students how to create intuitive, effective interaction methods across different tasks and scenarios. Projects will encourage students to design and build their own interaction techniques, leading to a deeper understanding of user-centered design principles and the technical skills necessary to bring interactive concepts to life. 3) Professional encounters with leading AR/VR researchers. This module involves a series of seminars (titled Voices of XR) and discussion sessions with leading AR/VR researchers from academia and industry. This component of the course is offered in partnership with Studio X. Prerequisites: ECE 410 or OPT 410 or BME 410 or NSCI 415 or CSC 413 or CVSC 534 or equivalent experience. INSTRUCTORS: Mujdat Cetin; Daniel Nikolov; Jannick Rolland; Yukang Yan |
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ECE 422-1
Stephen Wu
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Topics in semiconductor device physics, electronic band structure, materials science, and magnetism with a focus on applications to new and emerging electronic device technologies. This background will serve as a jumping off point to discuss potential future electronic devices with novel properties beyond the current status quo. Looking beyond just next-generation technology, the course will explore what electronics could look like on the 25+ year timescale. Basic trends from condensed matter physics, materials science and electrical engineering will be discussed. Topics include: 2D electronic materials/transistors, magnetic memory, spintronics, multiferroic memory, topological matter/devices. Prerequisites: ECE 223/423 or instructors approval |
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ECE 465-02
Cantay Caliskan
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The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments. PRE-REQUISITES: DSCC 462, STAT 190 or equivalent introductory statistics background. |
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ECE 442-1
Gonzalo Mateos Buckstein
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The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks. Prerequisites: Some mathematical maturity, comfortable with linear algebra, probability, and analysis (e.g., MTH164-165). Exposure to programming and Matlab useful, but not required. For more information, please visit the class website: https://www.hajim.rochester.edu/ece/sites/gmateos/ECE442.html |
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ECE 473-1
Robert LaVaque
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This course is intended to provide students a basic understanding of creating audio for gaming. The emphasis is on demonstrations and hands-on experience to enable students to gain a practical knowledge of the integration of sound and music into video games. Students will primarily work with AudioKinetic Wwise, Reaper, Pro Tools, and Logic Pro. The course will also feature guest lectures by industry leading professionals, who will share their experience and insights. For a final project, students will create their own music and sound for a provided game. OPEN ONLY TO ESM BEAL STUDENTS and AME GRADUATE STUDENTS Prerequisite: AME 193 & 194, or a working knowledge of either Pro Tools, Logic Pro, or Reaper. |
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| Monday, Wednesday, and Friday | |
| Tuesday | |
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ECE 480-1
Daniel Phinney
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Audio amplification concepts and design techniques focused on the use of both solid state and vacuum tubes. Shall cover concepts related to impedance matching, preamps, op amps, class A, AB, D, H and G power amplifiers, circuit board layout, power supplies and grounding techniques. PREREQUISITE: AME 295 or Instructor permission. INSTRUCTOR: DAN PHINNEY |
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| Tuesday and Thursday | |
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ECE 449-1
Chenliang Xu
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Introduction to computer vision, including camera models, basic image processing, pattern and object recognition, and elements of human vision. Specific topics include geometric issues, statistical models, Hough transforms, color theory, texture, and optic flow. CSC 449, a graduate-level course, requires additional readings and assignments. Prerequisites: MATH 161 and CSC 242; MATH 165 strongly recommended |
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ECE 472-1
Sarah Smith
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This course is a survey of audio digital signal processing fundamentals and applications. Topics include sampling and quantization, analog to digital converters, time and frequency domains, spectral analysis, vocoding, digital filters, audio effects, music audio analysis and synthesis, and other advanced topics in audio signal processing. Implementation of algorithms using Matlab and on dedicated DSP platforms is emphasized. prerequisites: ECE 114 and basic Matlab programming, ECE 241 or equivalent |
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ECE 474-1
Scott Seidman
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Course will cover circuits and sensors used to measure physiological systems at an advanced level. Both signal conditioning and sensor characteristics will be addressed. Topics will include measurement of strain, pressure, flow, temperature, biopotentials, and physical circuit construction. The co-requisite laboratory will focus on the practical implementation of electronic devices for biomedical measurements. Prerequisites: BME 210, ECE113 or equivalent, or permission of instructor. |
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ECE 409-1
Daniel Gildea
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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. |
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ECE 421-1
Gary Wicks
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Blank Description |
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ECE 433-1
Michael Heilemann
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Aspects of acoustics. Review of oscillators, vibratory motion, the acoustic wave equation, reflection, transmission and absorption of sound, radiation and diffraction of acoustic waves. Resonators, hearing and speech, architectural and environmental acoustics. PREREQUISITES: Linear algebra and Differential Equations (MTH 165), and Physics (PHY 121) or equivalents. |
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ECE 451-1
Diane Dalecki
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The course presents the physical basis for the use of high-frequency sound in medicine. Topics include acoustic properties of tissue, sound propagation (both linear and nonlinear) in tissues, interaction of ultrasound with gas bodies (acoustic cavitation and contrast agents), thermal and non-thermal biological effects of utrasound, ultrasonography, dosimetry, hyperthermia and lithotripsy. |
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ECE 485-1
Nebojsa Duric
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This course teaches students the mathematical principles and computations that underpin modern imaging systems. The course will cover deconvolution, regularization methods, statistical methods, linear inverse imaging problems, singular value decomposition, and Fourier-based methods used in image reconstruction. Clinical examples of inverse problems and their solutions will be provided to the students during the lectures. Students will be assigned homework and administered tests to gauge their understanding of the material. Prerequisties: Bachelor’s degree in physics/engineering and/or a medical degree. INSTRUCTOR: NEBOISA DURIC |
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ECE 401-1
Michael Huang
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Instruction set architectures. Advanced pipelining techniques. Instruction level parallelism. Memory hierarchy design. Multiprocessing. Storage systems. Interconnection network. Prerequisites: ECE 200 or equivalent |
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ECE 475-1
Ming Lun Lee
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In this course, students will develop skills for designing audio/music applications and creating computer music in C and Max. We will begin with the history of computer music, a survey of audio programming languages, and a review of C. Libsndfile, a C library for reading and writing sound files, will be used to explore topics in sound synthesis, analysis, and digital signal processing. Students will use PortAudio, a C library for real-time audio input/output, to design DSP applications. Max is a visual programming language for interactive audio/music and multimedia. Students are required to watch pre-recorded lectures to learn Max and attend recitations for reviews. They will also practice their programming techniques through a series of programming assignments, a midterm drum machine project in Max, and a final research/design project. . Prerequisite: ECE 114 or instructor permission |
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ECE 400-1
Tong Geng
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Instruction set principles; processor design, pipelining, data and control hazards; datapath and computer arithmetic; memory systems; I/O and peripheral devices; internetworking. Students learn the challenges, opportunities, and tradeoffs involved in modern microprocessor design. Assignments and labs involve processor and memory subsystem design using hardware description languages (HDL). Prerequisites:ECE114, ECE 112 or CSC 171, or permission of Instructor |
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ECE 427-1
Roman Sobolewski
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Introduction to small-scale superconductor electronic devices, both analog and digital. Josephson junctions as digital circuit elements. Implementation of shunted Josephson junctions in the single flux quantum (SFQ) circuitry. SFQ circuit design and integration, simulations and appropriate simulation tools Finally, fabrication and testing of SFQ-based digital integrated circuits. PREREQUISITES: ECE 425, or instructors approval |
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ECE 445-1
Irving Barron Martinez
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This course teaches the underlying concepts behind traditional cellular radio and wireless data networks as well as design trade-offs among RF bandwidth, transmitter and receiver power and cost, and system performance. Topics include channel modeling, digital modulation, channel coding, network architectures, medium access control, routing, cellular networks, WiFi/IEEE 802.11 networks, mobile ad hoc networks, sensor networks and smart grids. Issues such as quality of service (QoS), energy conservation, reliability and mobility management are discussed. Students are required to complete a semester-long research project in order to obtain in-depth experience with a specific area of wireless communication and networking. PREREQUISITE: ECE 242 or permission of instructor. INSTRUCTOR Z. IGNJATOVIC |
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ECE 520-1
Hanan Dery
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Up until now CMOS scaling has given us a remarkable ride with little concern for fundamental limits. It has scaled multiple generations in feature size and in speed while keeping the same power densities. However,CMOS finally encounters fundamental limits. The course is intended for students interested in research frontiers of future electronics technologies. The course begins with introduction to the basic physics of magnetism and of quantum mechanical spin. Then it covers aspects of spin transport with emphasis on spin-diffusion in semiconductors. The second part of the course is comprised of student and lecturer presentations of selected spintronics topics which may include: spin transistors, magnetic random access memories, spin-based logic paradigms, spin-based lasers and light emitting diodes, magnetic semiconductors, spin-torque devices for memory applications and the spin Hall effect. Prerequisites: Familiarity of quantum mechanics (i.e., taking a course and getting a grade; not just auditing) or PHYS 407-Quantum Mechanics-I. (ECE 459) |
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ECE 426-1
Jaime Cardenas
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Propagation and interactions in optical waveguides. Topics include the Goos-Haenchen effect, coupled-mode theory, pulse broadening in optical fibers, coupling between guided-wave structures and wave-guide devices such as semiconductor lasers, fiber lasers and opto-electric devices. |
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| Wednesday | |
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ECE 480-2
Daniel Phinney
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Audio amplification concepts and design techniques focused on the use of vacuum tubes. Will cover some concepts related to MOSFET amplifiers as well. A mixture of lab based projects and LTSpice simulation. Shall cover concepts related to impedance matching, preamps, class A and class AB power amplifiers, power supplies and grounding techniques. Prerequisites: AME 295 or Instructor permission |
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ECE 597-1
Michele Foster
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ECE 433-3
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Aspects of acoustics. Review of oscillators, vibratory motion, the acoustic wave equation, reflection, transmission and absorption of sound, radiation and diffraction of acoustic waves. Resonators, hearing and speech, architectural and environmental acoustics. |
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| Wednesday and Friday | |
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ECE 408-1
Zhiyao Duan
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Machine Learning (ML) is the branch of Artificial Intelligence dedicated to teaching computers how to solve tasks by learning from data. This class introduces basic concepts of machine learning through various real-world ECE applications. It will cover various learning paradigms such as supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. It will also cover classical and state-of-the-art techniques such as linear models, support vector machines, Gaussian mixture models, hidden Markov models, matrix factorization, ensemble learning, principal component analysis, and various kinds of deep neural networks. Students will learn the pros and cons of different methods and their suited application scenarios. This course is hands-on with multiple programming assignments and a final project to solve real ECE problems. Prerequisites: General programming such as ECE-114; MATH 165 linear algebra. Probability and statistics such as ECE 270 is recommended. |
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ECE 486-1
Susan Hobbs
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This course covers the same modalities and target sites as Clinical Imaging 1 and 2. Students are expected to shadow practicing radiologists who will demonstrate real-time image reads and diagnoses. This course will give each student a practical understanding of how radiologists read images and what imaging characteristics are essential inputs in the diagnostic process. PREREQUISITES: Bachelor’s degree in physics/engineering and/or a medical degree. INSTRUCTOR: SUSAN HOBBS |
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| Thursday | |
| Friday | |
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ECE 474-2
Scott Seidman
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Course will cover circuits and sensors used to measure physiological systems at an advanced level. Both signal conditioning and sensor characteristics will be addressed. Topics will include measurement of strain, pressure, flow, temperature, biopotentials, and physical circuit construction. The co-requisite laboratory will focus on the practical implementation of electronic devices for biomedical measurements. Prerequisites: BME 210, ECE113 or equivalent, or permission of instructor. |
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ECE 475-2
Ming Lun Lee
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In this course, students will develop the ability to design programs in C, Python, Max, and Pure Data for audio/music research, computer music, and interactive performance. We will begin with an introduction to computer music and audio programming. After a quick review of C, we will use the PortSF library to generate and process basic envelopes and waveforms, and to explore the development of the table-lookup oscillator and other DSP tools. Max and Pure Data are similar visual programming languages for music and multimedia. We will use Max to explore topics in sound synthesis, signal processing, and sound analysis, as well as computer music. Python is a general-purpose programming language used in many application domains. We will use JythonMusic, a special version of Python, for music making, building graphical user interfaces, and for connecting external human interface devices. Students will practice their programming techniques through a series of programming assignments and a final project. Prerequisite: ECE 114 or instructor permission |
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ECE 472-3
Sarah Smith
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This course is a survey of audio digital signal processing fundamentals and applications. Topics include sampling and quantization, analog to digital converters, time and frequency domains, spectral analysis, vocoding, digital filters, audio effects, music audio analysis and synthesis, and other advanced topics in audio signal processing. Implementation of algorithms using Matlab and on dedicated DSP platforms is emphasized. Prerequisite: ECE 114 and basic Matlab programming, ECE 240 or other equivalent signals and systems courses. |
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