| | 01 | M-W---- | 4:00P-5:20P | Seigle / 304 | Ching, Lew | No final | 70 | 76 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| F | ----F-- | 1:00P-1:50P | Steinberg / 105 | Ching, Lew | Default - none | 70 | 76 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| Description: | Electrical energy, current, voltage, and circuit elements. Resistors, Ohm's Law, power and energy, magnetic fields and dc motors. Circuit analysis and Kirchhoff's voltage and current laws. Thevenin and Norton transformations and the superposition theorem. Measuring current, voltage, and power using ammeters and voltmeters. Energy and maximum electrical power transfer. Computer simulations of circuits. Reactive circuits, inductors, capacitors, mutual inductance, electrical transformers, energy storage, and energy conservation. RL, RC and RLC circuit transient responses, biological cell action potentials due to Na and K ions. AC circuits, complex impedance, RMS current and voltage. Electrical signal amplifiers and basic operational amplifier circuits. Inverting, non-inverting, and difference amplifiers. Voltage gain, current gain, input impedance, and output impedance. Weekly laboratory exercises related to the lectures are an essential part of the course. Prerequisites: Phys 118A. Corequisite: Math 217. |
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| | 01 | -T-R--- | 1:00P-2:20P | Remote / EN | Nussinov | Exam last day of class | 160 | 140 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| A | -T----- | 2:30P-3:50P | Remote / EN | Nussinov | Default - none | 22 | 15 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| C | --W---- | 2:30P-3:50P | Remote / EN | Nussinov | Default - none | 24 | 13 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| D | --W---- | 4:00P-5:20P | Remote / EN | Nussinov | Default - none | 20 | 12 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| E | ---R--- | 2:30P-3:50P | Remote / EN | Nussinov | Default - none | 23 | 15 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| F | ---R--- | 4:00P-5:20P | Remote / EN | Nussinov | Default - none | 23 | 3 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| H | --W---- | 1:00P-2:20P | Remote / EN | Nussinov | Default - none | 23 | 13 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| I | -T----- | 2:30P-3:50P | Whitaker / 135 | Widder | Default - none | 24 | 16 | 0 | Desc: | Priority to BME students. This section will be offered in-person for students who will be on campus this fall; however, in-person participation is not required for enrollment. This section will meet in Brauer 2011 and Whitaker 135. |
| | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| J | ----F-- | 10:00A-10:50A | Remote / EN | Nussinov | Default - none | 60 | 54 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| K | ----F-- | 1:00P-1:50P | Remote / EN | Nussinov | Default - none | 60 | 49 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| L | ----F-- | 7:00P-7:50P | Remote / EN | Nussinov | Default - none | 60 | 22 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | M-W---- | 1:00P-2:20P | Remote / EN | Richard | Jan 8 2021 1:00PM - 3:00PM | 75 | 42 | 0 | | | |
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| | 01 | -T-R--- | 11:30A-12:50P | Remote / EN | Khanmohammadi | Jan 6 2021 1:00PM - 3:00PM | 70 | 47 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| 02 | -T-R--- | 10:00A-11:20A | Wilson / 214 | Hasting | Jan 7 2021 6:00PM - 8:00PM | 70 | 64 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | M-W-F-- | 9:00A-9:50A | McMillan / G052 | Hasting | Jan 5 2021 8:00AM - 10:00AM | 60 | 52 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| 02 | -T-R--- | 2:30P-3:50P | Simon / 1 | Seidel | Jan 8 2021 3:30PM - 5:30PM | 60 | 45 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| Description: | Study of probability and statistics together with engineering applications. Probability and statistics: random variables, distribution functions, density functions, expectations, means, variances, combinatorial probability, geometric probability, normal random variables, joint distribution, independence, correlation, conditional probability, Bayes theorem, the law of large numbers, the central limit theorem. Applications: reliability, quality control, acceptance sampling, linear regression, design and analysis of experiments, estimation, hypothesis testing. Examples are taken from engineering applications. Prerequisites: Math 233 or equivalent. |
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| | 01 | M-W---- | 10:00A-11:20A | Remote / EN | Zhang, J. | Jan 6 2021 10:30AM - 12:30PM | 115 | 112 | 0 | Desc: | Both sections will satisfy ESE 326 degree requirements for all engineering majors, but Section 01 will have greater emphasis on probability and is preferred for ESE and CSE students. |
| | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| 02 | -T-R--- | 1:00P-2:20P | Remote / EN | Krone | Jan 7 2021 1:00PM - 3:00PM | 90 | 81 | 0 | Desc: | Both sections will satisfy ESE 326 degree requirements for all engineering majors, but Section 02 will have greater emphasis on statistics and is preferred for EECE, MEMS and BME students. Students in this section may also register for the optional recitation section A. |
| | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| A | ----F-- | 2:30P-3:20P | Remote / EN | Krone | Default - none | 130 | 77 | 0 | Desc: | This optional discussion section is only for students enrolled in section 02 with Prof. Krone. |
| | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| B | ----F-- | 11:00A-11:50A | Remote / EN | Zhang | Default - none | 110 | 76 | 0 | Desc: | This is a recitation section specifically for E35-326-01 with Prof. Zhang. |
| | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | M-W---- | 1:00P-2:20P | Remote / EN | Dorothy Wang | Jan 6 2021 10:30AM - 12:30PM | 35 | 40 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| A | ----F-- | 11:00A-11:50A | Remote / EN | Dorothy Wang | Default - none | 35 | 14 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| Description: | Laboratory exercises provide students with a combination of hands-on experience involving electronic circuits. Students will use a variety of real instruments, analysis techniques and circuit simulation tool to summarize measurement results in written reports that clearly communicate laboratory results. A sequence of lab experiments provide hands-on experience in: properties of diodes and transistors, realistic operational amplifier characteristics, grounding and shielding techniques, signal analysis, and op amp based active filter design and characterization. Students will gain experience working with: sampling oscilloscopes to make measurements in the time and frequency domains, signal generators, digital multimeter and frequency measurements, and in creating circuits and making connections on contemporary circuit boards. The course concludes with a hands-on project to design, demonstrate and document the design of an electronic component. Prerequisite: ESE 232, Co-requisite of 232 with consent of instructor.
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| | 01 | -T----- | 10:00A-11:20A | Crow / 201 | Feher | No final | 24 | 17 | 0 | | | |
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| Description: | Introduction to concepts and methodology of discrete- and continuous-time signals in relation to linear dynamic systems. Representation of signals and systems. Fourier, Laplace, and Z-transforms. Input-output description of linear systems: impulse response, convolution, transfer function. Time-domain and frequency-domain system analysis: transient and steady-state responses, stability, frequency spectra and frequency responses. Matlab-based case studies highlight the role of this material in key ESE areas of signal processing, control systems, and communication. Prerequisites: Physics 117A-118A, Math 217, CSE 131, Matlab, matrix addition and multiplication, and ESE 105 or ESE 230; Corequisite: ESE 318. |
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| | 01 | M-W---- | 11:30A-12:50P | McDonnell / 361 | Trobaugh | Jan 7 2021 10:30AM - 12:30PM | 45 | 17 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| A | ----F-- | 4:00P-4:50P | Remote / EN | Trobaugh | Default - none | 52 | 2 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | -T-R--- | 11:30A-12:50P | Remote / EN | Richard | Jan 6 2021 1:00PM - 3:00PM | 50 | 24 | 0 | | | |
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| | 01 | TBA | | TBA | Feher | Default - none | 0 | 0 | 0 | | | |
| 02 | TBA | | TBA | Richter | Default - none | 0 | 1 | 0 | | | |
| 03 | TBA | | TBA | Ching | Default - none | 0 | 0 | 0 | | | |
| 04 | TBA | | TBA | Zeng | Default - none | 0 | 1 | 0 | | | |
| 05 | TBA | | TBA | Genin | Default - none | 0 | 0 | 0 | | | |
| 06 | TBA | | TBA | Zhang, Silvia | Default - none | 0 | 0 | 0 | | | |
| 07 | TBA | | TBA | Lew | Default - none | 0 | 0 | 0 | | | |
| 08 | TBA | | TBA | Kamilov | Default - none | 0 | 0 | 0 | | | |
| 10 | TBA | | TBA | Min | Default - none | 0 | 0 | 0 | | | |
| 12 | TBA | | TBA | Mukai | Default - none | 0 | 0 | 0 | | | |
| 13 | TBA | | TBA | O'Sullivan | Default - none | 0 | 0 | 0 | | | |
| 14 | TBA | | TBA | Pickard | Default - none | 0 | 0 | 0 | | | |
| 16 | TBA | | TBA | Trobaugh | Default - none | 0 | 1 | 0 | | | |
| 17 | TBA | | TBA | Schaettler | Default - none | 0 | 0 | 0 | | | |
| 18 | TBA | | TBA | Shrauner | Default - none | 0 | 0 | 0 | | | |
| 19 | TBA | | TBA | Snyder | Default - none | 0 | 0 | 0 | | | |
| 20 | TBA | | TBA | Spielman | Default - none | 0 | 0 | 0 | | | |
| 21 | TBA | | TBA | Tarn | Default - none | 0 | 0 | 0 | | | |
| 27 | TBA | | TBA | Nehorai | Default - none | 0 | 0 | 0 | | | |
| 28 | TBA | | TBA | Yang | Default - none | 0 | 0 | 0 | | | |
| 29 | TBA | | TBA | Li | Default - none | 0 | 0 | 0 | | | |
| 30 | TBA | | TBA | Shen | Default - none | 0 | 0 | 0 | | | |
| 31 | TBA | | TBA | Wang | Default - none | 0 | 0 | 0 | | | |
| 32 | TBA | | TBA | Kurenok | Default - none | 0 | 0 | 0 | | | |
| 34 | TBA | | TBA | Mell | Default - none | 0 | 1 | 0 | | | |
| 37 | TBA | | TBA | Chakrabartty | Default - none | 0 | 0 | 0 | | | |
| 38 | TBA | | TBA | Bhan | Default - none | 0 | 0 | 0 | | | |
| 39 | TBA | | TBA | La Rosa | Default - none | 999 | 0 | 0 | | | |
| 40 | TBA | | TBA | Sinopoli | Default - none | 0 | 1 | 0 | | | |
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| | 01 | M-W---- | 5:30P-7:00P | Simon / 1 | La Rosa | Jan 6 2021 6:00PM - 8:00PM | 65 | 53 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| A | ----F-- | 11:00A-11:50A | Remote / EN | La Rosa | Default - none | 50 | 28 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| B | ----F-- | 4:00P-4:50P | Remote / EN | La Rosa | Default - none | 50 | 5 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | M-W---- | 1:00P-2:20P | Remote / EN | Min | Jan 8 2021 1:00PM - 3:00PM | 90 | 61 | 0 | | | |
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| | 01 | M-W---- | 1:00P-2:20P | Remote / EN | Li | Jan 4 2021 10:30AM - 12:30PM | 0 | 60 | 0 | | | |
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| Description: | Machine learning is the scientific study of algorithms and statistical models that computer systems use to automatically extract patterns from large volumes of data to provide insight information for decision making. This pervasive field sits at the intersection of computer science, statistics, linear algebra and optimization. This course provides a broad introduction to machine learning and statistical pattern classification with emphasis on Electrical and Systems Engineering applications. Students will study theoretical foundations of learning and several important supervised and unsupervised machine learning methods including linear model of regression and classification, logistic regression, Bayesian learning methods, neural networks, nearest neighbor method, principal component analysis, support vector machines methods and clustering. The course will take the format of lectures and small chapter projects in areas such as imaging, signal processing and control. Python programming language will be used for demonstration and course projects. Pre-requisites: ESE326, Math 233, linear algebra and CSE 131 or equivalent |
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| | 01 | -T-R--- | 11:30A-12:50P | Remote / EN | Zhang | Jan 6 2021 1:00PM - 3:00PM | 37 | 33 | 0 | | | |
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| Description: | This course provides an accessible introduction to quantum optics and quantum engineering for undergraduate students. This course covers the following topics: Concept of photons, quantum mechanics for quantum optics, radiative transitions in atoms, lasers, photon statistics (photon counting, Sub-/Super-Poissionian photon statistics, bunching, anti-bunching, theory of photodetection, shot noise), entanglement, squeezed light, atom-photon interactions, cold atoms, atoms in cavities. The course will also provide an overview for quantum information processing: quantum computing, quantum cryptography, and teleportation. Prerequisite Course: Engineering Mathematics 318 or equivalent. |
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| | 01 | M-W---- | 1:00P-2:20P | Remote / EN | Shen | Jan 8 2021 1:00PM - 3:00PM | 25 | 22 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | M-W---- | 4:00P-5:20P | Remote / EN | Collins | No final | 15 | 6 | 0 | | | | |
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| Description: | This course covers the fundamentals of semiconductor physics and operation principles of modern solid-state devices such as homo- or hetero-junction diodes, solar cells, inorganic/organic light-emitting diodes, bipolar junction transistors, and metal-oxide-semiconductor field-effect transistors. These devices form the basis for today's semiconductor and integrated circuit industry. In additional to device physics, semiconductor device fabrication processes, new materials, and novel device structures will also be briefly introduced. At the end of this course, students will be able to understand the characteristics, operation, limitations and challenges faced by state-of-the-art semiconductor devices. This course will be particularly useful for students who wish to develop careers in the semiconductor industry. Prerequisite: ESE 232 |
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| | 01 | -T-R--- | 10:00A-11:20A | Remote / EN | Wang | Jan 7 2021 6:00PM - 8:00PM | 15 | 14 | 0 | | | |
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| | 01 | M-W---- | 2:30P-3:50P | Remote / EN | Zhang | Jan 6 2021 10:30AM - 12:30PM | 65 | 24 | 0 | | | |
| A | ----F-- | 4:00P-4:50P | Remote / EN | Zhang | No final | 65 | 4 | 0 | | | |
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| Description: | The course provide engineering students with basic understanding of two of the main components of any modern electrical or electromechanical system; sensors as inputs and actuators as outputs. The covered topics include transfer functions, frequency responses and feedback control. Component matching and bandwidth issues. Performance specification and analysis, Sensors: analog and digital motion sensors, optical sensors, temperature sensors, magnetic and electromagnetic sensors, acoustic sensors, chemical sensors, radiation sensors, torque, force and tactile sensors. Actuators: stepper motors, DC and AC motors, hydraulic actuators, magnet and electromagnetic actuators, acoustic actuators. Introduction to interfacing methods: bridge circuits, A/D and D/A converters, microcontrollers. This course is useful for those students interested in control engineering, robotics and systems engineering. Prerequisites: one of the following 4 conditions:(1) prerequisite of ESE 230 and corequisite of ESE 351; (2) prerequisites of ESE 230, ESE 318 and MEMS 255 (Mechanics II); (3) prerequisite of ESE 351; (4) permission of instructor. |
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| | 01 | -T-R--- | 5:30P-7:00P | Seigle / L002 | Becnel | Jan 7 2021 6:00PM - 8:00PM | 36 | 27 | 0 | Desc: | Occasional labs on Thursdays in Urbauer 208. |
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| | 01 | -T-R--- | 11:00A-12:50P | Remote / EN | Mell | No final | 24 | 19 | 0 | | | |
| 02 | -T-R--- | 4:00P-5:50P | Remote / EN | Mell | No final | 24 | 5 | 0 | | | |
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| | 01 | -T-R--- | 6:00P-8:00P | Green Hall / 1157 | Bhan | No final | 24 | 10 | 0 | Desc: | Lectures will be held via zoom with breakout sessions to coordinate with lab groups. Assigned class space, Green 0161, will be available for students to test-fly drones. The space will be reserved individually on an as needed basis. |
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| Description: | Integrated circuit systems provide the core technology that power today's most advanced devices and electronics: smart phones, wearable devices, autonomous robots, and cars, aerospace or medical electronics.
These systems often consist of silicon microchips made up by billions of transistors and contain various components such as microprocessors, DSPs, hardware accelerators, memories, and I/O interfaces, therefore design automation is critical to tackle the design complexity at the system level. The objectives of this course is to 1) introduce transistor-level analysis of basic digital logic circuits; 2) provide a general understanding of hardware description language (HDL) and design automation tools for very large scale integrated (VLSI) systems; 3) expose students to the design automation techniques used in the best-known academic and commercial systems. Topics covered include device and circuits for digital logic circuits, digital IC design flow, logic synthesis, physical design, circuit simulation and optimization, timing analysis, power delivery network analysis. Assignments include homework, mini-projects, term paper and group project. Prerequisites:
ESE 232; ESE 260.
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| | 01 | M-W---- | 10:00A-11:20A | Remote / EN | Zhang, Silvia | Jan 6 2021 10:30AM - 12:30PM | 17 | 11 | 0 | | | |
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| | 01 | -T----- | 1:00P-2:20P | Urbauer / 115 | Richter | Jan 7 2021 1:00PM - 3:00PM | 24 | 15 | 0 | | | |
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| | 01 | M-W---- | 5:30P-7:00P | Remote / EN | Sutton | Jan 6 2021 6:00PM - 8:00PM | 27 | 27 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 02 | TBA | | TBA | Arthur | Default - none | 0 | 0 | 0 | | | |
| 03 | TBA | | TBA | Ching | Default - none | 0 | 0 | 0 | | | |
| 04 | TBA | | TBA | Feinstein | Default - none | 0 | 0 | 0 | | | |
| 06 | TBA | | TBA | Zhang, Silvia | Default - none | 0 | 0 | 0 | | | |
| 07 | TBA | | TBA | Lew | Default - none | 0 | 1 | 0 | | | |
| 08 | TBA | | TBA | Kamilov | Default - none | 0 | 0 | 0 | | | |
| 10 | TBA | | TBA | Min | Default - none | 0 | 0 | 0 | | | |
| 12 | TBA | | TBA | Mukai | Default - none | 0 | 0 | 0 | | | |
| 13 | TBA | | TBA | O'Sullivan | Default - none | 0 | 0 | 0 | | | |
| 14 | TBA | | TBA | Pickard | Default - none | 0 | 0 | 0 | | | |
| 16 | TBA | | TBA | Rodin | Default - none | 0 | 0 | 0 | | | |
| 17 | TBA | | TBA | Schaettler | Default - none | 0 | 0 | 0 | | | |
| 18 | TBA | | TBA | Shrauner | Default - none | 0 | 0 | 0 | | | |
| 19 | TBA | | TBA | Snyder | Default - none | 0 | 0 | 0 | | | |
| 20 | TBA | | TBA | Spielman | Default - none | 0 | 0 | 0 | | | |
| 21 | TBA | | TBA | Tarn | Default - none | 0 | 0 | 0 | | | |
| 27 | TBA | | TBA | Nehorai | Default - none | 0 | 0 | 0 | | | |
| 28 | TBA | | TBA | Yang | Default - none | 0 | 0 | 0 | | | |
| 29 | TBA | | TBA | Li | Default - none | 0 | 0 | 0 | | | |
| 30 | TBA | | TBA | Shen | Default - none | 0 | 0 | 0 | | | |
| 31 | TBA | | TBA | Wang | Default - none | 0 | 0 | 0 | | | |
| 32 | TBA | | TBA | Kurenok | Default - none | 0 | 0 | 0 | | | |
| 34 | TBA | | TBA | Mell | Default - none | 0 | 0 | 0 | | | |
| 36 | TBA | | TBA | [TBA] | Default - none | 0 | 0 | 0 | | | |
| 37 | TBA | | TBA | Chakrabartty | Default - none | 0 | 0 | 0 | | | |
| 38 | TBA | | TBA | Patwari | Default - none | 0 | 1 | 0 | | | |
| 39 | TBA | | TBA | Wang | Default - none | 0 | 0 | 0 | | | |
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| Description: | Capstone design project supervised by the course instructor. The project must use the theory, techniques, and concepts of the student's major: electrical engineering or systems science & engineering. The solution of a real technological or societal problem is carried through completely, starting from the stage of initial specification, proceeding with the application of engineering methods, and terminating with an actual solution. Collaboration with a client, typically either an engineer or supervisor from local industry or a professor or researcher in university laboratories, is encouraged. A proposal, an interim progress update, and a final report are required, each in the forms of a written document and oral presentation, as well as a Web page on the project. Weekly progress reports and meetings with the instructor are also required. Prerequisite: ESE senior standing and instructor's consent. Note: this course will meet at the scheduled time only during select weeks. If you cannot attend at that time, you may still register for the course. |
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| | 01 | TBA | | Remote / EN | Wang | No final | 0 | 7 | 0 | | | |
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| Description: | Capstone design project supervised by the course instructor. The project must use the theory, techniques, and concepts of the student's major: electrical engineering or systems science & engineering. The solution of a real technological or societal problem is carried through completely, starting from the stage of initial specification, proceeding with the application of engineering methods, and terminating with an actual solution. Collaboration with a client, typically either an engineer or supervisor from local industry or a professor or researcher in university laboratories, is encouraged. A proposal, an interim progress update, and a final report are required, each in the forms of a written document and oral presentation, as well as a Web page on the project. Weekly progress reports and meetings with the instructor are also required. Prerequisite: ESE senior standing and instructor's consent. Note: this course will meet at the scheduled time only during select weeks. If you cannot attend at that time, you may still register for the course. |
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| | 01 | TBA | | Remote / EN | Wang | No final | 0 | 10 | 0 | | | |
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| | 02 | TBA | | TBA | Arthur | No final | 0 | 0 | 0 | | | |
| 06 | TBA | | TBA | Zhang, Silvia | No final | 0 | 1 | 0 | | | |
| 08 | TBA | | TBA | Kamilov | No final | 0 | 0 | 0 | | | |
| 13 | TBA | | TBA | O'Sullivan | No final | 0 | 0 | 0 | | | |
| 14 | TBA | | TBA | Pickard | No final | 0 | 0 | 0 | | | |
| 17 | TBA | | TBA | Schaettler | No final | 0 | 0 | 0 | | | |
| 18 | TBA | | TBA | Shrauner | No final | 0 | 0 | 0 | | | |
| 19 | TBA | | TBA | Snyder | No final | 0 | 0 | 0 | | | |
| 20 | TBA | | TBA | Spielman | No final | 0 | 0 | 0 | | | |
| 27 | TBA | | TBA | Nehorai | No final | 0 | 0 | 0 | | | |
| 32 | TBA | | TBA | Kurenok | No final | 0 | 0 | 0 | | | |
| 37 | TBA | | TBA | Chakrabartty | No final | 0 | 0 | 0 | | | |
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| Description: | Matrix algebra: systems of linear equations, vector spaces, linear independence and orthogonality in vector spaces, eigenvectors and eigenvalues; Vector calculus: gradient, divergence, curl, line and surface integrals, theorems of Green, Stokes, and Gauss; Elements of Fourier analysis and its applications to solving some classical partial differential equations, heat, wave, and Laplace equation. Prerequisite: ESE 318 and ESE 319 or equivalent or consent of instructor. This course will not count toward the ESE doctoral program. |
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| | 01 | M-W---- | 4:00P-5:20P | Hillman / 60 | Kurenok | Jan 5 2021 6:00PM - 8:00PM | 55 | 66 | 0 | | | |
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| Description: | Large-scale optimization is an essential component of modern data science, artificial intelligence, and machine learning. This graduate-level course rigorously introduces optimization methods that are suitable for large-scale problems arising in these areas. We will learn several algorithms suitable for both smooth and nonsmooth optimization, including gradient methods, proximal methods, mirror descent, Nesterov's acceleration, ADMM, quasi-Newton methods, stochastic optimization, variance reduction, as well as distributed optimization. Throughout the class, we will discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models. Students will be required to program in python or MATLAB. Prerequisites: CSE 247, Math 309, Math 3200 or ESE 326. |
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| | 01 | M-W---- | 1:00P-2:20P | Wilson / 214 | Kamilov | Jan 8 2021 1:00PM - 3:00PM | 32 | 35 | 0 | | | |
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| | 01 | -T-R--- | 4:00P-5:20P | Wrighton / 300 | Kurenok | Jan 8 2021 6:00PM - 8:00PM | 60 | 56 | 0 | | | |
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| Description: | This course focuses on fundamental theory, modeling, structure, and analysis methods in network science. The first part of the course includes basic network models and their mathematical principles. Topics include a review of graph theory, random graph models, scale-free network models and dynamic networks. The second part of the course includes structure and analysis methods in network science. Topics include network robustness, community structure, spreading phenomena and clique topology. Applications of the topics covered by this course include social networks, power grid, internet, communications, protein-protein interactions, epidemic control, global trade, neuroscience, etc.
Prerequisite courses: ESE 520 (Probability and Stochastic Processes), Math 429 (Linear Algebra) or equivalent.
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| | 01 | -T-R--- | 4:00P-5:20P | Remote / EN | Nehorai | Jan 8 2021 6:00PM - 8:00PM | 30 | 17 | 0 | | | |
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| Description: | Advanced design and analysis of control systems by state-space methods: classical control review, Laplace transforms, review of linear algebra (vector space, change of basis, diagonal and Jordan forms), linear dynamic systems (modes, stability, controllability, state feedback, observability, observers, canonical forms, output feedback, separation principle and decoupling), nonlinear dynamic systems (stability, Lyapunov methods). Frequency domain analysis of multivariable control systems. State space control system design methods: state feedback, observer feedback, pole placement, linear optimal control. Design exercises with CAD (computer-aided design) packages for engineering problems. Prerequisite: ESE 351 and ESE 441, or permission of instructor.
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| | 01 | ----F-- | 1:00P-3:50P | Remote / EN | Wise | Exam last day of class | 40 | 33 | 0 | | | |
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| | 01 | M-W---- | 4:00P-5:20P | Remote / EN | Zeng | Jan 5 2021 6:00PM - 8:00PM | 56 | 44 | 0 | | | | Actions: | | Books | | Syllabus | | Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use. |
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| | 01 | M-W---- | 11:30A-12:50P | Remote / EN | Chakrabartty | Jan 7 2021 10:30AM - 12:30PM | 22 | 16 | 0 | | | |
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| | 01 | TBA | | Remote / EN | Feher | No final | 180 | 148 | 0 | | | |
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| | 01 | -T-R--- | 2:30P-3:50P | Cupples I / 115 | O'Sullivan | Jan 8 2021 3:30PM - 5:30PM | 15 | 17 | 0 | | | |
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| | 01 | ----F-- | 8:30A-9:50A | Remote / EN | O'Sullivan | No final | 40 | 13 | 0 | | | |
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| | 02 | TBA | | TBA | Arthur | No final | 0 | 0 | 0 | | | |
| 06 | TBA | | TBA | Zhang, Silvia | No final | 0 | 0 | 0 | | | |
| 08 | TBA | | TBA | Kamilov | No final | 0 | 0 | 0 | | | |
| 13 | TBA | | TBA | O'Sullivan | No final | 0 | 1 | 0 | | | |
| 14 | TBA | | TBA | Pickard | No final | 0 | 0 | 0 | | | |
| 17 | TBA | | TBA | Schaettler | No final | 0 | 0 | 0 | | | |
| 18 | TBA | | TBA | Shrauner | No final | 0 | 0 | 0 | | | |
| 19 | TBA | | TBA | Snyder | No final | 0 | 0 | 0 | | | |
| 20 | TBA | | TBA | Spielman | No final | 0 | 0 | 0 | | | |
| 27 | TBA | | TBA | Nehorai | No final | 0 | 0 | 0 | | | |
| 31 | TBA | | TBA | Wang, Lihong | No final | 0 | 0 | 0 | | | |
| 32 | TBA | | TBA | Kurenok | No final | 0 | 0 | 0 | | | |
| 37 | TBA | | TBA | Chakrabartty | No final | 0 | 2 | 0 | | | |
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| | 02 | TBA | | TBA | Arthur | No final | 9 | 0 | 0 | | | |
| 04 | TBA | | TBA | Sinopoli | No final | 9 | 1 | 0 | | | |
| 06 | TBA | | TBA | Zhang, Silvia | No final | 9 | 5 | 0 | | | |
| 08 | TBA | | TBA | Kamilov | No final | 9 | 1 | 0 | | | |
| 13 | TBA | | TBA | O'Sullivan | No final | 9 | 6 | 0 | | | |
| 14 | TBA | | TBA | Pickard | No final | 9 | 0 | 0 | | | |
| 17 | TBA | | TBA | Schaettler | No final | 9 | 0 | 0 | | | |
| 18 | TBA | | TBA | Shrauner | No final | 9 | 0 | 0 | | | |
| 19 | TBA | | TBA | Snyder | No final | 9 | 0 | 0 | | | |
| 20 | TBA | | TBA | Spielman | No final | 9 | 0 | 0 | | | |
| 27 | TBA | | TBA | Nehorai | No final | 20 | 3 | 0 | | | |
| 31 | TBA | | TBA | Wang, Chuan | No final | 9 | 1 | 0 | | | |
| 32 | TBA | | TBA | Kurenok | No final | 9 | 0 | 0 | | | |
| 37 | TBA | | TBA | Chakrabartty | No final | 9 | 1 | 0 | | | |
| 38 | TBA | | TBA | Zeng | No final | 999 | 3 | 0 | | | |
| 39 | TBA | | TBA | Patwari | No final | 9 | 2 | 0 | | | |
| 40 | TBA | | TBA | Sotiras | No final | 999 | 2 | 0 | | | |
| 42 | TBA | | TBA | Raman | No final | 999 | 0 | 0 | | | |
| 45 | TBA | | TBA | Zhou | No final | 999 | 1 | 0 | | |
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| | 01 | TBA | | TBA | O'Sullivan | No final | 999 | 12 | 0 | | | |
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| | 01 | TBA | | TBA | Registrar | Default - none | 0 | 0 | 0 | | | |
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| | 01 | TBA | | TBA | Registrar | Default - none | 999 | 0 | 0 | | | |
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| | 01 | TBA | | TBA | Registrar | Default - none | 0 | 3 | 0 | | | |
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