Weill Medical College of Cornell University · Courses
ECE
158 courses with the subject ECE, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
ECE 1210The Computing Technology Inside Your Smartphone3
The organization of the computer system found within devices used in everyday living, such as smartphones and tablets. Computer systems are presented in a bottom up fashion, from bits to digital logic, computer organization, instruction sets, assembly language, and the connection to high-level languages. Discussion of the computing engines found in smart phones, tablets, and wearables.
ECE 1410How Machines Learn: AI from the Perceptron to GPT3
This course covers learning, deep learning, and neural networks from the perceptron through modern architectures such as GPT. Students will build intuition for how machines learn, explore foundational neural architectures, implement learning algorithms on neural networks, learn about small-scale and large-scale hardware architectures for leaning and AI, efficiency of and energy cost of AI systems, and reflect on the ethical and societal implications of AI. More advanced topics such as recurrent neural networks (RNNs), long short-term memory (LSTMs), Transformers and Large Language Models will be introduced at a high level with emphasis on intuition and demonstrations rather than mathematical details. The course emphasizes concepts and applications with mathematical tools that freshmen can wield to connect engineering tools with AI methods. The course also discusses AI hardware topics related to power consumption, computational infrastructure requirements, and the role of large-scale data centers in enabling AI systems. Some programming experience in Python (or an equivalent programming language) will be useful in completing assignments and design projects.
ECE 2100Introduction to Circuits for Electrical and Computer Engineers4
This course is an introduction to electronic circuits. We start with the basic quantities used to characterize circuit operation (like current, voltage, and power) and then enforce several physical laws to form the basis of our approach to circuit analysis. Networks comprising passive circuit elements such as resistors, inductors, and capacitors will be examined under constant dc, transient, and sinusoidal steady-state conditions. Active components including transistors and Op-Amps will be introduced and used to build simple amplifiers and switching power converters. Many of these ideas will be unified mathematically through the use of Laplace transforms and associated transfer functions. In the lab part of the course, we will learn how to use modern instruments to test circuits, and explore the concepts from lecture applied to real circuits. Finally, we will develop some simple modeling software in MATLAB to numerically predict the results from analysis and experiment.
This course provides an introduction to the design and implementation of digital circuits and microprocessors. Topics include transistor network design, Boolean algebra, combinational circuits, sequential circuits, finite state machine design, processor pipelines, and memory hierarchy. Design methodology using both discrete components and hardware description languages is covered in the laboratory portion of the course.
Computer systems programming involves developing software to connect the low-level computer hardware to high-level, user-facing application software. This course will provide a strong foundation in the principles, practices, and art of computer systems programming using the C and C++ programming languages. Students will learn procedural programming in C and how to theoretically analyze and practically implement basic data structures and algorithms. Students will transition to C++ to explore object-oriented, generic, functional, and concurrent programming before exploring advanced data structures and algorithms involving trees, tables, and graphs. Students will explore systems programming using the POSIX standard library. The course includes a series of programming assignments for students to put the principles they have learned into practice. For more information, see https://www.csl.cornell.edu/courses/ece2400.
An introduction to data science for engineers. The data science workflow: acquisition and cleansing, exploration and modeling, prediction and decision making, visualization and presentation. Tools for data science including numerical optimization, the Discrete Fourier Transform, Principal Component Analysis, and probability with a focus on statistical inference and correlation methods. Techniques for different steps in the workflow including outlier detection, filtering, regression, classification, and techniques for avoiding overfitting. Methods for combining domain-agnostic data analysis tools with the types of domain-specific knowledge that are common in engineering. Ethical considerations. Optional topics include classification via neural networks, outlier detection, and Markov chains. Programming projects in Python.
Covers static, quasi-static, and dynamic electromagnetic fields and waves. Topics include Maxwell's equations (integral and differential forms), fields of charge and current distributions, boundary conditions, fields near conductors, method of images, material polarization and dielectrics; energy, work, and power in electromagnetic systems; wave propagation and polarization, waves in media (dielectrics, conductors, and anisotropic materials); reflection, transmission, and refraction at media interfaces; guided waves in transmission lines, Smith charts, transients; metallic and dielectric waveguides; radiation and antennas, antenna arrays, electric circuits for transmission and reception, aperture antennas and diffraction.
ECE 3100Introduction to Probability and Inference for Random Signals and Systems4
Probability theory is a mathematical discipline that allows one to reason about uncertainty: it helps us to predict uncertain events, to make better decisions under uncertainty, and to design and build systems that must operate in uncertain environments. This course will serve as an introduction to the subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference in the presence of uncertainty. Topics include probability models, combinatorics, countable and uncountable sample spaces, discrete random variables, probability mass functions, continuous random variables, probability density functions, cumulative distribution functions, expectation and variance, independence and correlation, conditioning and Bayess rule, concentration inequalities, the multivariate Normal distribution, limit theorems (including the law of large numbers and the central limit theorem), Monte Carlo methods, random processes, and the basics of statistical inference. Applications to communications, networking, circuit design, computer engineering, finance, and voting will be discussed throughout the semester.
An introduction to the design of embedded systems, with an emphasis on understanding the interaction between hardware, software, and the physical world. Topics covered include assembly language programming, interrupts, I/O, concurrency management, scheduling, resource management, and real-time constraints.
This is a comprehensive undergraduate level course on microelectronics. Topics covered include basic semiconductor physics, electrons and holes in semiconductors, electrical transport in semiconductors, PN junctions and diodes, photodetectors and solar cells, Metal-Oxide-Semiconductor (MOS) capacitors, MOS field effect transistors (FETs), bipolar junction transistors (BJTs), large signal and small signal models of electronic devices, single stage amplifiers, multistage amplifiers, differential amplifiers, analog circuit analysis and design, high-frequency models of devices, high-frequency circuit analysis, digital logic and MOS logic devices, complimentary MOS (or CMOS) logic gates, fundamental trade-offs in high speed analog and digital circuit design. The coursework includes labs and a final project.
This is an introductory course in machine learning (ML) that covers basic theory, algorithms, and applications. The class will develop a principled understanding of the various facets of ML and encompass fundamental (supervised and unsupervised) ML primitives that underpin modern technologies. Specifically, the learning theory content will cover the statistical learning paradigm, empirical risk minimization, generalization, bias-variance tradeoff, regularization, and validation. The supervised learning chapter will cover regression, the maximum likelihood principle, generalized linear models, support vector machines, and naïve Bayes. Unsupervised learning methods will include clustering, k-means, EM algorithm, factor analysis, and other dimensionality reduction techniques. The final few lectures will be devoted to large language models and the generative pre-trained transformer (GPT) architecture, as well as topics in ethics and fairness in machine learning. Our treatment of the material will start from theoretical principles, and build up towards implementation and applications dealing with text data, handwriting, music, images, etc. To that end, the course will incorporate a programming.
Course aims to provide students with a rigorous treatment of the fundamentals of discrete- and continuous-time signals and systems. The course makes use of sophisticated tools such as vector spaces of signals (e.g. bounded, summable, and square-summable signals) and orthogonal expansions in Hilbert space in addition to covering standard material on time- and frequency-domain analysis of signals and systems, including discrete- and continuous-time convolution, Fourier series, continuous- and discrete-time Fourier transforms, sampling theory, the DFT and FFT, and spectrograms. Homework assignments include a computational component where appropriate. A background in sophomore-level linear algebra commensurate with MATH 2940 is strongly recommended.
This course includes topics on microcontrollers, sensors, motors & actuators, and circuit & software debugging, with an end-of-semester fully operational robot demonstration. Students will learn the value and trade-offs between theory/simulation and physical implementations, and gain familiarity with teamwork, leadership skills, time management, and how to disseminate work to a broader audience through wiki-pages.
Introduction to quantum physics and engineering for advanced undergraduate and beginning graduate students. Topics covered include historical developments, quantum postulates, Schrodinger equation, quantum states and observables, measurement in quantum mechanics, quantum confined states in potential wells and atoms, quantum tunneling, uncertainty relations, Dirac notation, angular momentum and spin, quantum dynamics, time-independent and time-dependent perturbation theories, quantum two-level systems, quantum information and the qubit, quantum computation and quantum circuits, identical particles, quantum statistics for fermions and bosons, fundamentals of quantum statistical physics, quantization of light and the photon, quantization of simple mechanical and electrical superconducting circuits. The course will enable students to take advanced courses in areas related to electronic and optical devices, solid state physics and material science, and quantum information and computation.
ECE 4070Physics of Semiconductors and Nanostructures4
Covers basic solid state and semiconductor physics relevant for understanding electronic and optical devices. Topics include crystalline structures, bonding in atoms and solids, energy bands in solids, electron statistics and dynamics in energy bands, effective mass equation, carrier transport in solids, Boltzmann transport equation, semiconductor homo- and hetero-junctions, optical processes in semiconductors, electronic and optical properties of semiconductor nanostructures, semiconductor quantum wells, wires, and dots, electron transport in reduced dimensions, semiconductor lasers and optoelectronics, high-frequency response of electrons in solids and plasmons.
ECE 4110Random Signals in Communications and Signal Processing4
Introduction to models for random signals in discrete and continuous time; Markov chains, Poisson process, queuing processes, power spectral densities, Gaussian random process. Response of linear systems to random signals. Elements of estimation and inference as they arise in communications and digital signal processing systems.
ECE 4130Introduction to Nuclear Science and Engineering3
Introduces the fundamental concepts of nuclear science and engineering, including nuclear structure, radioactivity, nuclear reactions and the interaction of neutrons, charged particles, x-rays and gamma-rays with matter. Discusses the neutron chain reaction and its control in the core of a fission reactor. Different reactor designs are introduced and discussed along with their safety features. Other topics include radiation shielding and aspects of the nuclear fuel cycle, including isotope separation, fuel reprocessing, waste disposal and sustainability
Analysis of GPS operating principles and engineering practice with a culminating design exercise. GPS satellite orbital dynamics, navigation data modeling, position/navigation/timing solution algorithm, receiver and antenna characteristics, analysis of error and accuracy, differential GPS.
The course focus is on systems level design and implementation of fast and dynamic autonomous robots. With the recent DIY movement, design of kinematic robots is largely becoming a software challenge. In dynamic robots, however, any latency or noise can be detrimental. We will design a fast autonomous car, explore dynamic behaviors, acting forces, sensors, and reactive control on an embedded processor, as well as the benefit of partial off-board computation. Students will learn how to derive design specifications from abstract problem descriptions and gain familiarity with rapid prototyping techniques, system debugging, system evaluation, and online dissemination of work.
Creating robots capable of performing complex tasks autonomously requires one to address a variety of different challenges such as sensing, perception, control, planning, mechanical design, and interaction with humans. In recent years many advances have been made toward creating such systems, both in the research community (different robot challenges and competitions) and in industry (industrial, military, and domestic robots). This course gives an overview of the challenges and techniques used for creating autonomous mobile robots. Topics include sensing, localization, mapping, path planning, motion planning, obstacle and collision avoidance, and multi-robot control.
The course will be devoted to understanding, implementation, and applications of various machine learning primitives. This course is intended to have three modules, and within each we will cover basic theory, and implementations. The modules will be supervised learning, unsupervised learning, and finally topics that are motivated by engineering applications such as speech recognition, and recommendation systems. Supervised learning will include regression, support vector machines, decision trees, random forests, naive Bayes, boosting and bagging. Unsupervised learning includes clustering, k-means, k-NN, principal components analysis and other dimensionality reduction methods. We will give particular emphasis on engineering applications, e.g., text data, hand-writing, music, image, and time series data, and categorical datasets such those in recommendation systems. The course will have a programming component, which will be administered in the form of assignments, and in-class-kaggle competitions.
Network systems pervade our society in both social and technological contexts. On the one hand, social networks play a central role in the transmission of information and viruses with fundamental consequences for product marketing, technology adoption, voting decisions, spread of false news and epidemiology. On the other hand, network topology fundamentally affects the performance and resilience properties of large-scale multi-agent systems, such as the electric power grid, the internet of things, traffic or robotic sensor networks.
A first course in computer vision for advanced undergraduate students; that is, the analysis by computer of multidimensional signals provided by imaging sensors including image sequences and three-dimensional images for primarily autonomous applications. Basic techniques for image processing and feature extraction are covered in lectures; topics include image formation, image filtering, edge detection, region growing, shape description, and machine learning for computer vision. Machine learning methods including convolution neural networks, deep learning models for focus attention and image segmentation. The course focus is on the machine interpretation of images for autonomous decision making.
An introductory course on robot perception techniques for modeling, fusing, and interpreting heterogeneous and dynamic sensor measurements in the context of robot motion and uncertain environments. The course covers sensor modeling, artificial vision, acoustic sensing, and probabilistic filtering methods. Emphasis is placed on intelligent sensor fusion, object detection and classification, tracking, localization and mapping, exploration, and information-driven motion planning. Algorithms inspired by neural networks, Bayesian networks, graphical models, and information theory are examined. Students investigate perception-driven decision making through benchmark problems such as coverage, target search, tracking, and pursuit-evasion. Applications are drawn from environmental monitoring, surveillance, sensing-and-pursuit games, and human-robot interaction.
ECE 4250Digital Signal Processing and Statistical Inference4
This course introduces discrete-time signal and system models in deterministic and stochastic settings and develops signal processing and statistical inference methodologies for real-time sensing and control applications. The course is intended for upper-level undergraduate and beginning graduate engineering students in engineering departments.The course covers both deterministic and stochastic techniques. Specific topics include time and frequency domain representation of signals and systems, state-space representation, feedback, stability, linear and nonlinear filtering, signal and state estimation and tracking, hypothesis testing, and signal detection. Applications in communications and control system design are integrated into the course material.
Course addresses a collection of topics relevant to the modeling, analysis, simulation, and optimization of large complex multi-agent systems. Course provides a standalone introduction to discrete-time Markov chains; covers the Metropolis algorithm and its generalizations; gives an introduction to the theory of genetic algorithms; and provides an introduction to evolutionary game theory, including the ESS concept, replicator dynamics, and dynamic probabilistic approaches.
Introduction to the operation, physics, and application of lasers. The course covers diffraction-limited optics, Gaussian beams, optical resonators, the interaction of radiation with matter, stimulated emission, rate equations, and laser design. Examples of coherent radiation to nonlinear optics, communication, and leading-edge research are frequently used. Course concludes with a lab where students design and then build a laser.
ECE 4320Integrated Micro Sensors and Actuators: Bridging the Physical and Digital Worlds4
Introduction to micro and nano devices that allow the digital world to both sense and actuate in the physical world. Design and analysis of modern MEMS/NEMS (Micro/Nano Electromechanical Systems) touch, accelerometers, gyroscopes, pressure, microphones, neural probe sensors. Design and analysis of electrostatic, piezoelectric, thermal, and magnetic actuators for frequency control and micro robotic applications. This is an interdisciplinary course drawing from mechanics, materials, solid state devices, CMOS electronics, and micro and nano fabrication. The students design, fabricate, and test a microsensor chip to implement class concepts.
This course covers techniques to design and analyze microwave circuits. These techniques, in general, can be used for electrical devices with dimensions in the order of the operating wavelength. These devices are commonly used in RF/mm- wave wireless communication systems, quantum computing/sensing systems, and electromagnetic imaging/radar systems. Topics include planar transmission lines, network analysis, S-parameters, matching networks, resonators, power dividers/combiners, filters, and non-reciprocal devices. The course emphasizes physical understanding, intuitive design methods, and hands-on experience in using E+M simulation tools and microwave measurements.
ECE 4360Nanofabrication and Characterization of Electronics3
This course will give an introduction to modern nanofabrication technologies with emphasis on integrated circuits manufacturing. Thermal budget, scaling of geometry, pitch and registry and control of parametric yield will be used for integration guidelines. Physical principles and process modeling will be covered in lectures and labs will include a series of fabrication steps of lithography, metallization, plasma etching and annealing to produce semiconductor devices (Schottky diodes, pn junction diodes, MOS capacitors, and MOSFETs). Recent advances in nanofabrication will be briefly reviewed for their possible technology insertion and main integration challenges.
ECE 4361Nanofabrication and Characterization of Electronics Laboratory1
This course provides an introduction to nanofabrication technologies with emphasis on Si-based integrated circuits manufacturing as well as modern electronics based on GaN, 2D materials etc. The lab, primarily taught in the Cornell Teaching Cleanroom, includes basic fabrication steps of lithography, metallization, plasma etching and annealing. A series of devices will be fabricated: solar cells, MOS capacitors and transistors, 2D transistors, GaN HEMTs and LEDs.
Introduction to optical waveguides and photonic devices. Review of optical wave propagation in uniform media and at interfaces. Gaussian beams and diffraction. Modes, dispersion, and fundamental relations/symmetries. Interferometers and resonators. Guided waves in optical fibers and planar dielectric systems. Perturbation theory, coupling of modes, adiabatic variation, and parallels to quantum phenomena. Electro- and acousto-optics. Survey of system applications, including photonic interconnects and quantum technologies. Numerical methods, and project design of planar optical components.
ECE 4380Electromagnetic and Optical Metamaterials3
Introductory Senior-level course on electromagnetic and optical metamaterials. The properties of matter can be molded and tailored on subwavelength spatial scales yielding 'metamaterials' with properties very different from naturally occurring materials, thereby opening up new directions for applications. The course introduces the electromagnetic and optical properties of surface plasmons and polaritons, artificial magnetic materials, negative-index materials, nanostructured optical materials, etc. The course also discusses the applications of these materials in diverse areas including electromagnetic cloaking and invisibility, stealth technologies, optical super-lensing, bio-chemical sensing, conformal optics, meta-surfaces, and non-reciprocal devices.
This is a senior-level course in information networking with an emphasis on wireless technology. Examples of topics include: 5G cellular, the latest WiFi standards, Bluetooth, routing and switching, congestion control, network security, and user privacy. Though the focus will be on communication networking technologies, downstream legal and social issues that arise from design decisions will be discussed.
Acquaints students with modern electric power system modeling, analysis and computation. Stresses analysis techniques appropriate for power system modeling, analysis and power flow computation. Topics include transmission line models, transformers and per unit system, generator models, network matrices, power flow analysis and computation, real and reactive power control, voltage control, economic dispatch.
Examines the operations of electric power systems, the smart grid, and electricity markets. Topics include modeling of power systems, power flow analysis, economic dispatch, optimal power flow, unit commitments, electricity markets, demand response, smart grid technology, and transactive energy.
Overview of devices available to analog integrated-circuit designers in modern CMOS and BiCMOS processes: resistors, capacitors, MOS transistors, and bipolar transistors. Basic building blocks for linear analog integrated circuits: single-stage amplifiers, current mirrors, and differential pairs. Transistor-level design of linear analog integrated circuits, such as operational amplifiers and operational transconductance amplifiers. Layout techniques for analog integrated circuits. Throughout the course, emphasis is placed on design-oriented analysis techniques.
Power electronics is an enabling technology for sustainable and energy efficient systems, as all renewable energy sources (including solar and wind) and efficient electrical loads (including LED lighting and electric vehicles) require a power electronic converter to interface them with the electrical grid. Power electronic converters, being inherently non-linear/time-variant circuits, cannot be analyzed using linear time-invariant (LTI) circuit analysis techniques, nor conventional small-signal linearization techniques taught in introductory circuits (ECE 2100) and microelectronics (ECE 3150) courses. This course, which will follow ECE 3150, will introduce advanced circuit analysis and modeling techniques required to predict the steady-state and dynamic behavior of power electronic converters. The course will also introduce seniors and first-year graduate students to other advanced topics which are required for the analysis and design of power converters, including power semiconductor device modeling, thermal modeling, magnetic component modeling, electromagnetic interference (EMI) filter design, and switching converter control design, at a level appropriate for them. This course will equip ECE students with the theoretical and practical skills needed to innovate in the area of power electronics, and allow them to succeed in graduate school and in the rapidly growing job market for power electronic engineers.
ECE 4570Semiconductor Electronic and Photonic Devices4
The class develops the fundamentals of semiconductor electronic and photonic devices that power today's computation, communication, and memory industries. It relates the basics of pn junctions to their applications in solar cells, light emitting diodes, and lasers. Majority and minority carrier transport in heterostructure bipolar transistors is related to gain and speed limits of amplifiers for 5G communications and beyond. Schottky diodes and their applications in power electronics, and in field effect transistors of many flavors ranging from Silicon CMOS and FinFETs to GaN high electron mobility transistors are covered. Tunneling transport, flash memory, and DRAM devices are discussed. The course uses industrially relevant simulation tools, and the laboratory component gives students firsthand experience of measuring and appreciating the power and the limitations of semiconductor devices, and the reason for their revolutionary influence on our lives and society.
Provides a fundamental understanding of processing/structure/property relation in thin films, with an emphasis on behaviors that differ from those observed in classical bulk materials. Topics include vacuum technology, deposition techniques, surface energies, surface kinetics, nucleation and growth, homoepitaxy, heteroepitaxy, microstructure, stress in thin films, and electromigration. The course will impart a basic perspective on thin film synthesis and properties and will develop your ability to analyze materials issues and formulate solutions.
The goal of this course is to understand how state-of-the-art digital communication systems are designed and why they are designed in the way they are. The course will cover communication theory, transceiver algorithms that enable reliable communication, wireless channels, and modern communication standards (such as 3GPP LTE and WiFi). The students will design a working audio-band communication system that relies on orthogonal frequency-division multiplexing (OFDM).
Design of microcontroller systems using embedded Linux. Student teams design and debug example solutions on a target microcontroller (for example, Raspberry Pi) during lab sessions. Emphasis will be on application and Linux programming skills, processor and microcontroller architecture with some discussion of hardware interfaces. Students create a final, end-of-semester project using the microcontroller platform and development techniques discussed during the semester. This is a culminating design experience (CDE) course. Students need to be comfortable with digital hardware design, embedded systems, C programming, and they need to be familiar with basic Linux concepts.
An introduction to digital very large-scale integration (VLSI) design from architectural considerations down to the layout level. Lecture, labs, and homework topics emphasize disciplined synchronous VLSI design and include topics of CMOS logic, layout, and timing; computer-aided design and analysis tools; electrical and performance considerations.
This course aims to provide a strong foundation for students to understand the principle and practice of designing, implementing, testing, and evaluating standard-cell ASIC chips using automated state-of-the-art electronic design automation (EDA) tools. Topic include both basic and advanced CMOS devices, standard cells, memories, front-end flows, and back-end flows. The course includes three design projects completed in small groups. In the first design project, students build their own simple EDA toolflow including a small standard cell library, synthesis algorithm, and place-and-route algorithm to enable mapping a combinational hardware design to layout. In the second design project, students transition to using a commercial standard-cell library and commercial EDA tools for simulation, synthesis, place-and-route, static-timing analysis, power analysis, design rule checking, and layout-vs-schematic checking of a hardware accelerator of their choosing. In the third open-ended design project, students will design, implement, test, and evaluate an interesting technique in computer architecture in the context of a more complex system-on-chip.
This course aims to provide a strong foundation for students to understand modern computer system architecture and to apply these insights and principles to future computer designs. The course is structured around the three primary building blocks of general-purpose computing systems: processors, memories, and networks. The first half of the course focuses on the fundamentals of each building block. Topics include processor microcoding and pipelining; cache microarchitecture and optimization; and network topology, routing, and flow control. The second half of the course delves into more advanced techniques and will enable students to understand how these three building blocks can be integrated to build a modern shared-memory multicore system. Topics include superscalar execution, branch prediction, out-of-order execution, register renaming and memory disambiguation; VLIW, vector, and multithreaded processors; memory protection, translation, and virtualization; and memory synchronization, consistency, and coherence. This course includes a significant project decomposed into five lab assignments. Throughout the semester, students will gradually design, implement, test, and evaluate a complete multicore system capable of running real parallel applications at the register-transfer level.
ECE 4760Digital Systems Design Using Microcontrollers4
Design of real-time digital systems using microprocessor-based embedded controllers. Students working in pairs design, debug, and construct several small systems that illustrate and employ the techniques of digital system design acquired in previous courses. The content focuses on the laboratory work. The lectures are used primarily for the introduction of examples, description of specific modules to be designed, and instruction in the hardware and high-level design tools to be employed.
In this course, students design system-on-chip applications for hardware acceleration via FPGAs. Students working in groups of 2-3 design, debug, and construct several systems that illustrate the design of parallel FPGA hardware linked to ARM processors running a LINUX operating system. The content focuses on laboratory work. The lectures are used primarily for the introduction of examples, description of specific modules to be designed, and instruction in the hardware and high-level design tools to be employed in the lab. Each laboratory assignment baselines the speed of execution for an algorithm of interest on the ARM processors, students are evaluated on the acceleration they can achieve by way of hardware acceleration via the FPGA. This is a culminating design experience (CDE) course. Students need to be comfortable with hardware design using Verilog, they need to be comfortable writing C, they need to be familiar with basic Linux concepts, and they are expected to understand the solution of ordinary differential equations, partial differential equations, and other iterated systems in order to implement them numerically and in parallel on the FPGA.
Robotics is interdisciplinary and draws inspiration from many different fields towards solving a variety of tasks in real-world environments using physical systems. This course is a challenging introduction to basic computational concepts used broadly in robotics. By the end of this course, students should have a fundamental understanding of how the different sub-fields of robotics such as kinematics, state-estimation, motion planning, and controls come together to develop intelligent behaviors in physical robotic systems. The mathematical basis of each area will be emphasized, and concepts will be motivated using common robotics applications. Students will be evaluated using a mixture of theoretical and programming exercises throughout the semester.
Introduction to optimization theory and algorithms for system analysis and design. Topics include linear programming, convex programming, duality. We may touch dynamic programming around the end if time permits. Application will be discussed in various areas including geometric problems, networks, control, circuits, signal processing, and communications.
ECE 4840Introduction to Controlled Fusion: Principles and Technology3
Introduction to the physical principles and various engineering aspects underlying power generation by controlled fusion. Topics include: fuels and conditions required for fusion power and basic fusion-reactor concepts, fundamental aspects of plasma physics relevant to fusion plasmas and basic engineering problems for a fusion reactor, and an engineering analysis of proposed magnetic and/or inertial confinement fusion-reactor designs.
Fundamentals of radar, antennas, and remote sensing. Exposes students to the principles underlying the analysis and design of antennas used for communication and for radar-related applications. Students also encounter both a mathematical and a practical description of how radars function, how their performance can be optimized for different applications, and how signals acquired by them can be processed. The objective is to familiarize students with a wide variety of radars rather than turn them into practicing radar engineers. Each topic is developed from basic principles so students with a wide variety of backgrounds are able to take the course. Emphasizes radar applications in geophysics, meteorology and atmospheric sciences, and astronomy and space sciences. Gives special attention to radar remote sensing of the Earth from spacecraft.
This course addresses the design of radio-frequency links in the component view to enable eventual full-duplex, multi-access wireless network. The analysis will reside mostly in the signal (SIMULINK) level instead of the circuit implementation, although the nonideal circuit characteristics will be reflected in the signal representation. Federal Communications Commission (FCC) and Occupational Safety & Health Administration (OSHA) standards will be introduced. Existing standard protocols will be selectively introduced including FM, TV broadcast, Bluetooth, Z-wave, Zigbee and Wi-fi to enable intuitive understanding of RF system tradeoffs.
Laboratory-oriented course designed to teach the concepts and tools of cellular neurophysiology through hands-on experience with extracellular and intracellular electrophysiological techniques, and computer acquisition and analysis of laboratory results. Students explore signal transmission in the nervous system by examining the cellular basis of resting and action potentials, and synaptic transmission and optogenetic control of behavior and physiology. Lecture time is used to review nervous system physiology, introduce laboratory exercises, discuss lab results and primary research papers, and for presentation of additional experimental preparations and methods. Invertebrate preparations are used as model systems.
Technical writing aimed at having students write up their ECE projects in a formal fashion, with a view to publication. Projects may be existing, or may be created as part of the independent study, but the writing requirement will match the current College technical writing requirement.
This course is designed to give qualified undergraduates experience through actual involvement in planning and teaching courses under the supervision of department faculty. Students cannot receive both pay and credit for the same hours of preparation and teaching. Enrollment is with permission of instructor only. One credit of ECE 4980 is equivalent to 5 weekly work hours, including both student contact hours and preparatory/other time.
ECE 4998Electrical and Computer Engineering Group Projects1-4
Group study, analysis, and, usually, experimental tests in connection with a special engineering project chosen by the students after consultation with the faculty member directing the project. New projects are added upon faculty request. Written progress reports are required.
ECE 4999Electrical and Computer Engineering Independent Projects1-4
Individual study or directed reading in connection with a special engineering problem chosen by the student after consultation with the faculty member directing the project. An engineering report on the project is required. Students must make individual arrangements with a faculty sponsor.
This seminar introduces students to professional issues that are important as they begin their professional career. Topics include M.Eng. ECE project options; professional expectations; project planning and management; effective teamwork; technical writing and communications; impact of environmental, economic, and societal constraints in design; entrepreneurship possibilities; and career planning issues.
Laboratory-oriented course designed to teach the concepts and tools of cellular neurophysiology through hands-on experience with extracellular and intracellular electrophysiological techniques, and computer acquisition and analysis of laboratory results. Students explore signal transmission in the nervous system by examining the cellular basis of resting and action potentials, and synaptic transmission. Lecture time is used to review nervous system physiology, introduce laboratory exercises, discuss lab results and primary research papers, and for presentation of additional experimental preparations and methods. Invertebrate preparations are used as model systems. Students must complete and additional semester-long project. The project includes exploration of experimental neuroscience questions not covered in the undergraduate laboratory class, novel, low cost instrumentation design, computational approaches to nervous system function and development of active learning activities. It will require a project proposal early in the semester, and a final project presentation and research journal style paper at the end of the semester.
ECE 5110Random Signals in Communications and Signal Processing3-4
Introduction to models for random signals in discrete and continuous time; Markov chains, Poisson process, queuing processes, power spectral densities, Gaussian random process. Response of linear systems to random signals. Elements of estimation and inference as they arise in communications and digital signal processing systems. Extra homework problems, team homework disallowed, higher expectations for mini-projects.
Fundamental ideas of systems engineering, and their application to design and development of various types of engineered systems. Defining system requirements, creating effective project teams, mathematical tools for system analysis and control, testing and evaluation, economic considerations, and the system life cycle.Content utilizes model-based systems engineering, which is the integration of systems modeling tools, such as SysML, with tools for systems analysis, such as Matlab and Modelica. The vision for this integration is the ability to create and analyze complete parametric representations of complex products and systems. These systems make it possible to investigate the impact of changing one aspect of a design on all other aspects of design and performance. This course will familiarize students with these modeling languages. Off-campus students must provide their own Windows 7, internet-connected, computer with administrator access in order to install the commercial software used in this course.
ECE 5130Systems Analysis Behavior and Optimization3
This is an advanced course in the application of analytical methodologies and tools to the analysis and optimization of complex systems. On completion of this course, students should be able to use probability and statistics as a modeling and analysis tool for systems exhibiting uncertainty; be able to use algorithms and dynamic programming to model and optimize systems with a recursive structure; be able to use optimization tools to optimize complex systems and tune parameters.
The course focus is on systems level design and implementation of fast and dynamic autonomous robots. With the recent DIY movement, design of kinematic robots is largely becoming a software challenge. In dynamic robots, however, any latency or noise can be detrimental. We will design a fast autonomous car, explore dynamic behaviors, acting forces, sensors, and reactive control on an embedded processor, as well as the benefit of partial off-board computation. Students will learn how to derive design specifications from abstract problem descriptions and gain familiarity with rapid prototyping techniques, system debugging, system evaluation, and online dissemination of work.
This is an introductory course in machine learning (ML) that covers basic theory, algorithms, and applications. The class will develop a principled understanding of the various facets of ML and encompass fundamental (supervised and unsupervised) ML primitives that underpin modern technologies. Specifically, the learning theory content will cover the statistical learning paradigm, empirical risk minimization, generalization, bias-variance tradeoff, regularization, and validation. The supervised learning chapter will cover regression, the maximum likelihood principle, generalized linear models, support vector machines, and naïve Bayes. Unsupervised learning methods will include clustering, k-means, EM algorithm, factor analysis, and other dimensionality reduction techniques. The final few lectures will be devoted to large language models and the generative pre-trained transformer (GPT) architecture, as well as topics in ethics and fairness in machine learning. Our treatment of the material will start from theoretical principles, and build up towards implementation and applications dealing with text data, handwriting, music, images, etc. To that end, the course will incorporate a programming.
Network systems pervade our society in both social and technological contexts. On the one hand, social networks play a central role in the transmission of information and viruses with fundamental consequences for product marketing, technology adoption, voting decisions, spread of false news and epidemiology. On the other hand, network topology fundamentally affects the performance and resilience properties of large-scale multi-agent systems, such as the electric power grid, the internet of things, traffic or robotic sensor networks.
ECE 5235Sustainable Urban and Energy Delivery Systems3
The course focuses on how to transition from legacy energy delivery infrastructures dependent on fossil fuel to a sustainable decarbonized grid that harnesses distributed renewable energy resources and responsive demand from buildings, electrified transportation systems, and industrial loads. The content includes models and abstractions for the architecture of the cyber-physical energy system, its economics, and future evolution, and numerical optimization and learning methods in support of the infrastructure's safety critical operations in the legacy system and in the future architecture. At the MSc level the students will focus on learning how to use tools and data while at the graduate level the students will be asked to also solve problems, formulate novel solutions, interpret results. Similarly, to differentiate the MSc from PhD level and course outcomes, the final project will require the MSc students to pick one out of a set of predefined problems while the PhD students will have to define an original problem and solution.
An introductory course on robot perception techniques for modeling, fusing, and interpreting heterogeneous and dynamic sensor measurements in the context of robot motion and uncertain environments. The course covers sensor modeling, artificial vision, acoustic sensing, and probabilistic filtering methods. Emphasis is placed on intelligent sensor fusion, object detection and classification, tracking, localization and mapping, exploration, and information-driven motion planning. Algorithms inspired by neural networks, Bayesian networks, graphical models, and information theory are examined. Students investigate perception-driven decision making through benchmark problems such as coverage, target search, tracking, and pursuit-evasion. Applications are drawn from environmental monitoring, surveillance, sensing-and-pursuit games, and human-robot interaction.
How can intelligent machines perceive, make decisions, and execute their plans in an uncertain, dynamic world? This course will cover methods and algorithms for sensory perception, planning, and control with a focus on real-time adaptation and learning. Students should have prior experience with data processing and machine learning methods. Topics covered include probabilistic methods for sensory integration, latent models for dynamical systems, navigation and reinforcement learning. Students will learn about fundamental principles underlying algorithms for intelligent systems as well as how to implement these algorithms for real-time processing.
ECE 5245Digital Fabrication: Manipulating the Matter of Tech3
Historically, computing encompassed one-dimensional Input/Output using command line interfaces. The GUI and mouse brought 2D I/O to computing, and later VR and 3D Scanning allowed for 3D I/O. The next thing is interacting with matter as I/O for computing: Digital Fabrication. This course teaches fabrication techniques, and how to leverage it to control physical matter. This will allow you to develop hardware or understand how to talk to hardware developers within your team. We will dive down to the bare bones of fabrication machines by creating custom slicers to control 3D printers using basic coding. We will take apart existing products to study how they were made and build entirely new products from scratch.
ECE 5250Digital Signal Processing and Statistical Inference4
This course introduces discrete-time signal and system models in deterministic and stochastic settings and develops signal processing and statistical inference methodologies for real-time sensing and control applications. The course is intended for upper-level undergraduate and beginning graduate engineering students in engineering departments.The course covers both deterministic and stochastic techniques. Specific topics include time and frequency domain representation of signals and systems, state-space representation, feedback, stability, linear and nonlinear filtering, signal and state estimation and tracking, hypothesis testing, and signal detection. Applications in communications and control system design are integrated into the course material.ECE5250 co-meets with ECE4250. The lecture part of the two courses is the same. The homework, projects, and exams are different for the two courses. Students taking ECE5250 are given extra homework problems and project assignments to gain a deeper analytical understanding of the material.
ECE 5260Graph-based Data Science and AI Models for Networked Systems3
The goal of this course is to introduce data structural and computational models that are indexed by the irregular support of a graph. The graph represents the network that couples the dynamics of many agents, or it can be a more abstract Bayesian graphical model that explains how observations are conditionally dependent. The course will start from introducing basic concepts in graph theory followed by an introduction to random graphs models. This part will be followed by network dynamical models that model the observations from these processes. Bayesian graphical models will be briefly covered as a more general statistical abstraction and computational framework to perform inferences. The course will then introduce the students to the emerging field of graph signal processing, a theory that generalizes digital and image processing to graph signals.
Introduction to optimization theory and algorithms for system analysis and design. Topics include linear programming, convex programming, duality. We may touch dynamic programming around the end if time permits. Application will be discussed in various areas including geometric problems, networks, control, circuits, signal processing, and communications. This course is ideal for students who have not had an optimization course but want to have an idea of the subject within one semester.Added final project.
ECE 5290Distributed Optimization for Machine Learning and AI3
This is a graduate-level course about theory, algorithms and applications of distributed optimization and machine learning. The course covers the basics of distributed optimization and learning algorithms and their performance analyses when they are used to solve large-scale distributed problems arising in AI, machine learning, signal processing, communication networks, and power systems.
ECE 5320Integrated Micro Sensors and Actuators: Bridging the Physical and Digital Worlds4
Introduction to micro and nano devices that allow the digital world to both sense and actuate in the physical world. Design and analysis of modern MEMS/NEMS (Micro/Nano Electromechanical Systems) touch, accelerometers, gyroscopes, pressure, microphones, neural probe sensors. Design and analysis of electrostatic, piezoelectric, thermal, and magnetic actuators for frequency control and micro robotic applications. This is an interdisciplinary course drawing from mechanics, materials, solid state devices, CMOS electronics, and micro and nano fabrication. The students design, fabricate, and test a microsensor chip to implement class concepts.
Introduction to quantum physics and engineering for advanced undergraduate and beginning graduate students. Topics covered include historical developments, quantum postulates, Schrodinger equation, quantum states and observables, measurement in quantum mechanics, quantum confined states in potential wells and atoms, quantum tunneling, uncertainty relations, Dirac notation, angular momentum and spin, quantum dynamics, time-independent and time-dependent perturbation theories, quantum two-level systems, quantum information and the qubit, quantum computation and quantum circuits, identical particles, quantum statistics for fermions and bosons, fundamentals of quantum statistical physics, quantization of light and the photon, quantization of simple mechanical and electrical superconducting circuits. The course will enable students to take advanced courses in areas related to electronic and optical devices, solid state physics and material science, and quantum information and computation.More advanced homework; added term paper.
Introduction to optical waveguides and photonic devices. Review of optical wave propagation in uniform media and at interfaces. Gaussian beams and diffraction. Modes, dispersion, and fundamental relations/symmetries. Interferometers and resonators. Guided waves in optical fibers and planar dielectric systems. Perturbation theory, coupling of modes, adiabatic variation, and parallels to quantum phenomena. Electro-and acousto-optics. Survey of system applications, including photonic interconnects and quantum technologies. Numerical methods, and project design of planar optical components.
Covers basic solid state and semiconductor physics relevant for understanding electronic and optical devices. Topics include crystalline structures, bonding in atoms and solids, energy bands in solids, electron statistics and dynamics in energy bands, effective mass equation, carrier transport in solids, Boltzmann transport equation, semiconductor homo- and hetero-junctions, optical processes in semiconductors, electronic and optical properties of semiconductor nanostructures, semiconductor quantum wells, wires, and dots, electron transport in reduced dimensions, semiconductor lasers and optoelectronics, high-frequency response of electrons in solids and plasmons.
ECE 5360Semiconductor Electronic and Photonics Devices4
The class develops the fundamentals of semiconductor electronic and photonic devices that power to-day's computation, communication, and memory industries. It relates the basics of pn junctions to their applications in solar cells, light emitting diodes, and lasers. Majority and minority carrier transport in heterostructure bipolar transistors is related to gain and speed limits of amplifiers for 5G communications and beyond. Schottky diodes and their applications in power electronics, and in field effect transistors of many flavors ranging from Silicon CMOS and FinFETs to GaN high electron mobility transistors are covered. Tunneling transport, flash memory, and DRAM devices are discussed. The course uses industrially relevant simulation tools, and the laboratory component gives students firsthand experience of measuring and appreciating the power and the limitations of semiconductor devices, and the reason for their revolutionary influence on our lives and society. MEng students and ECE PhD students who enroll in the 5000 level will be required to complete one extra design laboratory project compared to the 4000 level class.
ECE 5365Nanofabrication and Characterization of Electronics3
This course will give an introduction to modern nanofabrication technologies with emphasis on integrated circuits manufacturing. Thermal budget, scaling of geometry, pitch and registry and control of parametric yield will be used for integration guidelines. Physical principles and process modeling will be covered in lectures and labs will include a series of fabrication steps of lithography, metallization, plasma etching and annealing to produce semiconductor devices (Schottky diodes, pn junction diodes, MOS capacitors, and MOSFETs). Recent advances in nanofabrication will be briefly reviewed for their possible technology insertion and main integration challenges.
ECE 5366Nanofabrication and Characterization of Electronics Laboratory1
This course provides an introduction to nanofabrication technologies with emphasis on Si-based integrated circuits manufacturing as well as modern electronics based on GaN, 2D materials etc. The lab, primarily taught in the Cornell Teaching Cleanroom, includes basic fabrication steps of lithography, metallization, plasma etching and annealing. A series of devices will be fabricated: solar cells, MOS capacitors and transistors, 2D transistors, GaN HEMTs and LEDs.
ECE 5380Electromagnetic and Optical Metamaterials3
Introductory Senior-level course on electromagnetic and optical metamaterials. The properties of matter can be molded and tailored on subwavelength spatial scales yielding 'metamaterials' with properties very different from naturally occurring materials, thereby opening up new directions for applications. The course introduces the electromagnetic and optical properties of surface plasmons and polaritons, artificial magnetic materials, negative-index materials, nanostructured optical materials, etc. The course also discusses the applications of these materials in diverse areas including electromagnetic cloaking and invisibility, stealth technologies, optical super-lensing, bio-chemical sensing, conformal optics, meta-surfaces, and non-reciprocal devices.
ECE 5413Developing and Designing Interactive Devices3
This course covers the human-centered and technical workings behind interactive devices ranging from cell phones and video game controllers to household appliances and smart cars. This is a hands-on, lab-based course. For the final project, students will build a functional IoT prototype of their own design, using Python, single-board Linux computer, embedded microcontrollers, and/or other electronic components. Topics include electronics prototyping, interface design, sensors and actuators, microcontroller development, physical prototyping, and user testing.
Learn and apply key concepts of modeling, analysis and validation from machine learning, data mining and signal processing to analyze and extract meaning from data. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, and dimensionality reduction.
This course covers fundamentals of digital signal and image processing (DSP); and its connections to modern machine learning, using a balanced mix between math and hands-on experiments. The course will teach basic concepts in signals and systems, including convolutions, frequency analysis, sampling, compressed sensing, image segmentation, image registration, neural network-based machine learning, and representation learning. We will use multiple hands-on programming assignments to demonstrate the concepts we cover in class and problem sets that will help students practice with the theory. A background in linear algebra and probability will be critical to follow the theoretical concepts. All coding will be done in Python. We will offer supplementary material to help get started with Python coding and reinforce the background in linear algebra and probability.
The course will be devoted to understanding, implementation, and applications of various machine learning primitives. This course is intended to have three modules, and within each we will cover basic theory, and implementations. The modules will be supervised learning, unsupervised learning, and finally topics that are motivated by engineering applications such as speech recognition, and recommendation systems. Supervised learning will include regression, support vector machines, decision trees, random forests, naive Bayes, boosting and bagging. Unsupervised learning includes clustering, k-means, k-NN, principal components analysis and other dimensionality reduction methods. We will give particular emphasis on engineering applications, e.g., text data, hand-writing, music, image, and time series data, and categorical datasets such those in recommendation systems. The course will have a programming component, which will be administered in the form of assignments, and in-class-kaggle competitionExtra homework problems, report required for projects.
Covers computer acquisition and analysis of image data with emphasis on techniques for robot vision. Concentrates on descriptions of objects at three levels of abstraction: segmented images (images organized into subimages that are likely to correspond to interesting objects), geometric structures (quantitative models of image and world structures), and relational structures (complex symbolic descriptions of images and world structures). The programming of several computer-vision algorithms is required.
Acquaints students with modern electric power system modeling, analysis and computation. Stresses analysis techniques appropriate for power system modeling, analysis and power flow computation. Topics include transmission line models, transformers and per unit system, generator models, network matrices, power flow analysis and computation, real and reactive power control, voltage control, economic dispatch.Added homework, added design project.
Overview of devices available to analog integrated-circuit designers in modern CMOS and BiCMOS processes: resistors, capacitors, MOS transistors, and bipolar transistors. Basic building blocks for linear analog integrated circuits: single-stage amplifiers, current mirrors, and differential pairs. Transistor-level design of linear analog integrated circuits, such as operational amplifiers and operational transconductance amplifiers. Layout techniques for analog integrated circuits. Throughout the course, emphasis is placed on design-oriented analysis techniques.More extensive lab requirements.
This Master's level course will take a hardware-centric view of machine learning systems. From constrained embedded microcontrollers to large distributed multi-GPU systems, we will investigate how these platforms run machine learning algorithms. We will look at different levels of the hardware/software/algorithm stack to make modern machine learning systems possible. This includes understanding different hardware acceleration paradigms, common hardware optimizations such as low-precision arithmetic and sparsity, compilation methodologies, model compression methods such as pruning and distillation, and multi-device federated and distributed training. Through hands-on assignments and an open-ended project, students will develop a holistic view of what it takes to train and deploy a deep neural network.
Examines the operations of electric power systems, the smart grid, and electricity markets. Topics include modeling of power systems, power flow analysis, economic dispatch, optimal power flow, unit commitments, electricity markets, demand response, smart grid technology, and transactive energy.
Power electronics is an enabling technology for sustainable and energy efficient systems, as all renewable energy sources (including solar and wind) and efficient electrical loads (including LED lighting and electric vehicles) require a power electronic converter to interface them with the electrical grid. Power electronic converters, being inherently non-linear/time-variant circuits, cannot be analyzed using linear time-invariant (LTI) circuit analysis techniques, nor conventional small-signal linearization techniques taught in introductory circuits (ECE 2100) and microelectronics (ECE 3150) courses. This course, which will follow ECE 3150, will introduce advanced circuit analysis and modeling techniques required to predict the steady-state and dynamic behavior of power electronic converters. The course will also introduce seniors and first-year graduate students to other advanced topics which are required for the analysis and design of power converters, including power semiconductor device modeling, thermal modeling, magnetic component modeling, electromagnetic interference (EMI) filter design, and switching converter control design, at a level appropriate for them. This course will equip ECE students with the theoretical and practical skills needed to innovate in the area of power electronics, and allow them to succeed in graduate school and in the rapidly growing job market for power electronic engineers.Added final project.
This course covers techniques to design and analyze microwave circuits. These techniques, in general, can be used for electrical devices with dimensions in the order of the operating wavelength. These devices are commonly used in RF/mm- wave wireless communication systems, quantum computing/sensing systems, and electromagnetic imaging/radar systems. Topics include planar transmission lines, network analysis, S-parameters, matching networks, resonators, power dividers/combiners, filters, and non-reciprocal devices. The course emphasizes physical understanding, intuitive design methods, and hands-on experience in using E+M simulation tools and microwave measurements.
ECE 5620Information Theory and Generative Modeling3
A graduate-level introduction to information theory, data compression, and generative modeling. An introduction to information measures: entropy, mutual information, relative entropy, differential entropy, and their properties. Lossless compression and its connection to prediction and generative modeling. The Minimum Description Length (MDL) principle in model selection. Practical lossless compression using arithmetic coding. The rate-distortion theorem and its connection to lossy compression standards such as JPEG, mp3, and AAC as well as generative modeling techniques such as autoencoders and variational inference. The Nonlinear Transform Coding framework. Practical methods for lossy compression such as Trellis-Coded Quantization (TCQ) and entropy-constrained dithered quantization. ECE 5620 is intended for M.Eng. students and advanced undergraduates with assignments focused on the development of practical data compression algorithms.
This is a senior-level course in information networking with an emphasis on wireless technology. Examples of topics include: 5G cellular, the latest WiFi standards, Bluetooth, routing and switching, congestion control, network security, and user privacy. Though the focus will be on communication networking technologies, downstream legal and social issues that arise from design decisions will be discussed. Added midterm, longer final report, more advanced homework.
Principles of modeling communication baseband and passband channels. Methods for communicating in the presence of intersymbol interference, noise, channel uncertainty, and asynchrony. Design, in software, of a transmitters and receivers to communicate over audio-band and RF channels.
This course addresses the design of radio-frequency links in the component view to enable eventual full-duplex, multi-access wireless network. The analysis will reside mostly in the signal (SIMULINK) level instead of the circuit implementation, although the nonideal circuit characteristics will be reflected in the signal representation. Federal Communications Commission (FCC) and Occupational Safety & Health Administration (OSHA) standards will be introduced. Existing standard protocols will be selectively introduced including FM, TV broadcast, Bluetooth, Z-wave, Zigbee and Wi-fi to enable intuitive understanding of RF system tradeoffs.Added design project.
The course provides a strong foundation for students to understand the entire system stack in cloud computing systems today, from hardware to OS, resource management, and application frameworks, as well as considerations such as reliability, privacy, and security. The course consists of a mix of lectures and discussions of the latest papers in datacenter design and management. Topics include, but are not limited to: server design, hardware accelerators, cloud OS, cluster management, distributed programming frameworks, microservices, serverless computing, and cloud security and privacy.
Design of microcontroller based systems using embedded Linux. Student teams design and debug example solutions on a target microcontroller (for example, Raspberry Pi) during lab sessions. Emphasis will be on application and Linux programming skills, processor and microcontroller architecture with some discussion of hardware interfaces. Students create a final, end-of-semester project using the microcontroller platform and development techniques discussed during the semester.
ECE 5730Digital Systems Design Using Microcontrollers4
Design of real-time digital systems using microprocessor-based embedded controllers. Students working in pairs design, debug, and construct several small systems that illustrate and employ the techniques of digital system design acquired in previous courses. The content focuses on the laboratory work. The lectures are used primarily for the introduction of examples, description of specific modules to be designed, and instruction in the hardware and high-level design tools to be employed.Added requirements for each lab.
This course aims to provide a strong foundation for students to understand modern computer system architecture and to apply these insights and principles to future computer designs. The course is structured around the three primary building blocks of general-purpose computing systems: processors, memories, and networks. The first half of the course focuses on the fundamentals of each building block. Topics include processor microcoding and pipelining; cache microarchitecture and optimization; and network topology, routing, and flow control. The second half of the course delves into more advanced techniques and will enable students to understand how these three building blocks can be integrated to build a modern shared-memory multicore system. Topics include superscalar execution, branch prediction, out-of-order execution, register renaming and memory disambiguation; VLIW, vector, and multithreaded processors; memory protection, translation, and virtualization; and memory synchronization, consistency, and coherence. This course includes a significant project decomposed into five lab assignments. Throughout the semester, students will gradually design, implement, test, and evaluate a complete multicore system capable of running real parallel applications at the register-transfer level.Added problem set and added lab.
This graduate-level course provides an introduction to digital very large-scale integration (VLSI) design from architectural considerations down to the layout level. Lecture, labs, and homework topics emphasize disciplined synchronous VLSI design and include topics of CMOS logic, layout, and timing; computer-aided design and analysis tools; electrical and performance considerations.
This course aims to provide a strong foundation for students to understand the principle and practice of designing, implementing, testing, and evaluating standard-cell ASIC chips using automated state-of-the-art electronic design automation (EDA) tools. Topic include both basic and advanced CMOS devices, standard cells, memories, front-end flows, and back-end flows. The course includes three design projects completed in small groups. In the first design project, students build their own simple EDA toolflow including a small standard cell library, synthesis algorithm, and place-and-route algorithm to enable mapping a combinational hardware design to layout. In the second design project, students transition to using a commercial standard-cell library and commercial EDA tools for simulation, synthesis, place-and-route, static-timing analysis, power analysis, design rule checking, and layout-vs-schematic checking of a hardware accelerator of their choosing. In the third open-ended design project, students will design, implement, test, and evaluate an interesting technique in computer architecture in the context of a more complex system-on-chip.
This course introduces contemporary design of digital very-large scale integration (VLSI) circuits and hands-on experience in digital VLSI design using state-of-the-art computer aided design (CAD) tools. The lectures will cover FinFET CMOS technology and scaling, design principles of core VLSI building units, including arithmetic circuits, sequential circuits, memory system, I/O circuitries, etc. Students will learn how digital VLSI implementations in CMOS are overarched by considerations of key design metrics, such as timing, power, area, and reliability. The lab assignments provide hands-on experience on how to implement a given algorithm onto an application specific integrated circuit (ASIC), progressively going through the processes of hardware description language (HDL) coding on register transfer level (RTL), synthesis, automatic place-and-route, and post-layout verification processes (i.e. overall RTL-to-GDS flow). For the lab assignment, the process design kit and standard cell / memory libraries of TSMC 16nm ADFP (Academic Design Foster Package) will be used.
This Master's level course is designed to provide a hardware-centric overview of computer systems used in modern computing platforms. From the bottom up we will study the architecture of processor architectures (e.g., pipelined CPUs, ISA, RISC vs. CISC, out-of-order execution) and memory systems (e.g., memory hierarchy, caching, DRAM memories). We will understand how to evaluate the performance of modern processors and exploit parallelism in applications. This includes parallelization across multi-core CPUs, GPUs, and specialized hardware. Through ands-on assignments and an open-ended project students will develop a holistic understanding of modern computer systems and how they are designed.
Design of system-on-chip applications. Students working in pairs design, debug, and construct several systems that illustrate the design of embedded processors with custom peripherals running a real-time operating system. The content focuses on laboratory work. The lectures are used primarily for the introduction of examples, description of specific modules to be designed, and instruction in the hardware and high-level design tools to be employed.
Creating robots capable of performing complex tasks autonomously requires one to address a variety of different challenges such as sensing, perception, control, planning, mechanical design, and interaction with humans. In recent years many advances have been made toward creating such systems, both in the research community (different robot challenges and competitions) and in industry (industrial, military, and domestic robots). This course gives an overview of the challenges and techniques used for creating autonomous mobile robots. Topics include sensing, localization, mapping, path planning, motion planning, obstacle and collision avoidance, and multi-robot control.
This course introduces modern machine learning, neural network, and artificial intelligence methods for the modeling, prediction, estimation, and control of multivariable dynamical systems. The course emphasizes the integration of data-driven learning techniques with classical multivariable systems and control theory to address challenges arising in complex nonlinear systems for which first-principles models are incomplete, uncertain, or unavailable. Topics include supervised, unsupervised, and reinforcement learning methods; neural networks for system identification and state estimation; neural ordinary differential equations; physics-informed machine learning; optimal control; model predictive control; adaptive and learning-based control; and reinforcement learning for autonomous decision making. Applications include robotics, autonomous vehicles, aerospace systems, manufacturing, energy systems, biological systems, and cyber-physical systems.
Topics include plasma state; motion of charged particles in fields; drift-orbit theory; coulomb scattering, collisions; ambipolar diffusion; elementary transport theory; two-fluid and hydromagnetic equations; plasma oscillations and waves, CMA diagram; hydromagnetic stability; and elementary applications to space physics, plasma technology, and controlled fusion.
ECE 5815Introduction to Nuclear Science and Engineering3
Introduces the fundamental concepts of nuclear science and engineering, including nuclear structure, radioactivity, nuclear reactions and the interaction of neutrons, charged particles, x-rays and gamma-rays with matter. Discusses the neutron chain reaction and its control in the core of a fission reactor. Different reactor designs are introduced and discussed along with their safety features. Other topics include radiation shielding and aspects of the nuclear fuel cycle, including isotope separation, fuel reprocessing, waste disposal and sustainability.
ECE 5840Introduction to Controlled Fusion: Principles and Technology3
Introduction to the physical principles and various engineering aspects underlying power generation by controlled fusion. Topics include: fuels and conditions required for fusion power and basic fusion-reactor concepts, fundamental aspects of plasma physics relevant to fusion plasmas and basic engineering problems for a fusion reactor, and an engineering analysis of proposed magnetic and/or inertial confinement fusion-reactor designs.
Energy Seminars will explore energy-related topics of emerging, contemporary and historical interest. An abbreviated list of subjects explored in the seminars includes: global energy resources, energy generation technologies (present and future), energy storage options, environmental impacts and climate change mitigation, energy policy, and energy delivery economics and systems. Seminar speakers will be distinguished practicing engineers and executives from industry and government as well as faculty members from several departments at Cornell, and other academic institutions. Students from any department in Engineering or the Physical Sciences should find these talks informative.
Energy Seminars will continue to explore energy-related topics of emerging, contemporary and historical interest. An abbreviated list of subjects explored in the seminars includes: global energy resources, energy generation technologies (present and future), energy storage options, environmental impacts and climate change mitigation, energy policy, and energy delivery economics and systems. Seminar speakers will be distinguished practicing engineers and executives from industry and government as well as faculty members from several departments at Cornell, and other academic institutions.
Two-semester Design Project: must enroll in both semesters; students will receive a letter grade for each individual semester. For students enrolled in M.Eng. (Electrical and Computer Engineering) degree program. Uses real engineering situations to present fundamentals of engineering design. Each professor is assigned a section for the fall and spring. Students who do not complete the Design Project in the spring receive a grade only for the work completed up to the point of the required submission date, which is the last day of Finals Week. Students must then enroll for the summer with the ECE M.Eng. director to complete the work remaining for the design project.
ECE 5996Master of Engineering Design Project II1-4
Two-semester Design Project: must enroll in both semesters; students will receive a letter grade for each individual semester. For students enrolled in M.Eng. (Electrical and Computer Engineering) degree program. Uses real engineering situations to present fundamentals of engineering design. Each professor is assigned a section for the fall and spring. Students who do not complete the Design Project in the spring receive a grade only for the work completed up to the point of the required submission date, which is the last day of Finals Week. Students must then enroll for the summer with the ECE M.Eng. director to complete the work remaining for the design project.
Independent study supervised by a faculty member in Electrical and Computer Engineering. A final report or exam is required. Students must make individual arrangements with a faculty sponsor.
Research directed by a faculty member in Electrical and Computer Engineering. A final report or exam is required. Students must make individual arrangements with a faculty sponsor.
This course offers M.Eng. students the opportunity to receive credit for ECE relevant work occurring as part of a summer (or semester) internship. Normally taken in the semester following the internship, students in the course will be required to write a detailed report describing the ECE related technical work performed during internship.
State-space and multi-input-multi-output linear systems in discrete and continuous time. The state transition matrix, the matrix exponential, and the Cayley-Hamilton theorem. Controllability, observability, stability, realization theory. At the level of Linear Systems by Kailath.
ECE 6230Stochastic Systems: Estimation and Control3
The problem of sequential decision making in the face of uncertainty is ubiquitous. Examples include: dynamic portfolio trading, operation of power grids with variable renewable generation, air traffic control, livestock and fishery management, supply chain optimization, internet ad display, data center scheduling, and many more. In this course, we will explore the problem of optimal sequential decision making under uncertainty over multiple stages - stochastic optimal control. We will discuss different approaches to modeling, estimation, and control of discrete time stochastic dynamical systems (with both finite and infinite state spaces) over finite horizons. Solution techniques based on dynamic programming will play a central role in our analysis. Topics include: Fully and Partially Observed Markov Decision Processes, Linear Quadratic Gaussian control, Bayesian Filtering, and Approximate Dynamic Programming. Applications to various domains will be discussed throughout the semester.
ECE 6240Power System Economics and Electricity Markets3
This graduate-level course covers the fundamentals of power systems economics and electricity markets, emphasizing the application of microeconomics and optimization tools. The course introduces basic microeconomic concepts in the context of retail and wholesale electricity markets: consumer preference, consumer choice, production costs and profit maximization, individual and market demand and supply functions, competitive markets and equilibria, welfare optimization, market power, monopoly and price discrimination, regulated monopoly and utility pricing. The course covers electricity market design: energy markets, reserve markets, ancillary service markets, capacity markets, ancillary service markets, scarcity pricing, capacity markets, financial transmission rights, inter-regional transaction markets, and virtual transaction markets. Advanced topics on distributed energy resources and energy aggregation are discussed and assigned as part of class projects.
ECE 6310Quantum Optics for Photonics and Optoelectronics3
This is a graduate level course on quantum optics. Topics covered include density matrices, semi-classical light matter interactions, Rabi oscillations, electromagnetically induced transparency and interference effects, dressed states, quantization of fields in cavity and free space, properties of photons, field commutation relations, number states, coherent states, quadrature and amplitude-phase squeezed states, vacuum fluctuations, matter-photon interactions, stimulated and spontaneous emission processes, classical/quantum descriptions of photon detection, coherent homodyne and heterodyne detection, coherence functions, cavity quantum optics and microcavity effects in spontaneous emission, cavity quantum optics in strong coupling and weak coupling limits, generation of quantum states of light, phase sensitive and phase insensitive optical amplifiers, parametric amplifiers, fundamental performance limits of optical amplifiers, photon statistics and characteristic functions, laser oscillators, numberand phase characteristics of laser light, parametric oscillators and squeezing, quantum opto-mechanics, opto-mechanical cooling and squeezing, noise penalty in simultaneous detection of two non-commuting field quadratures, bit error rates in optical communication systems, classical and quantum information and information carrying capacity of quantum states of light, quantum communications.
Fundamentals of magnetism, magnetic materials, and their applications. Topics include: sources of magnetism, types of magnetism, theory of electronic magnetism, magnetic anisotropy energies, domain theory, magnetic resonance, magnetostriction and magnetoelasticity, spin torques, thin-film phenomena, spin caloritronics, and permanent magnets. Current device and industry applications relevant to each topic will be discussed.
Advanced analog integrated circuit and system design. Topics include integrated continuous-time filter design, translinear circuits and systems, dynamic analog techniques, integrated discrete-time filter design, and Nyquist-rate data converter design.
ECE 6550Advanced High-Speed and RF Integrated Circuits2
Principles of analog integrated circuit design in the Giga-Hertz frequency range. This course covers the fundamental understanding of high-frequency circuit building blocks such as low noise amplifiers, mixers, oscillators, phase locked loops, frequency synthesizers, clock and data recoveries, and power amplifiers. Additionally, because some of the traditional microwave building blocks such as transmission lines and distributed circuit elements are essential parts of today's high speed integrated circuits, the course briefly covers them. Throughout the course, a systematic review of advanced wireless and wireline applications is covered. The course emphasizes physical understanding and intuitive design methods as well as qualitative techniques and computer simulations. The course has collaborative class projects, based on real-world problems.
Advanced topics in analysis and design of power electronics. Topics covered include advanced topologies for power conversion, including soft-switching, quasi-resonant, resonant, switched-capacitor and hybrid-switched-capacitor converters, advanced steady-state analysis methods, including state plane analysis and fundamental frequency analysis, zero-current and zero-voltage switching techniques, coupled magnetics, planar magnetics, matching networks, advanced dynamic modeling and control methods. Applications emphasized include high-power-density high-efficiency converters for emerging applications such as electric vehicles, data centers, robotics and wireless power transfer. Students should have knowledge of power electronics at the level of ECE 4560/5560 but no course is a prerequisite.
ECE 6630Information Theory for Data Transmission, Security and Machine Learning3
This is a graduate-level introduction to mathematics of information theory. We will cover both classical and modern topics, starting from f-divergences, information measures and relations between them. With these tools we will study the fundamental limits of data transmission over noisy channels. Wiretap channels, where information-theoretic security versus a malicious eavesdropped must be ensured, will also be covered. Passive and adversarial models will be considered. Finally, we will explore connections between information theory and machine learning, examining how they can cross-fertilize each other.
Graduate level introduction to fundamentals of detection and statistical decision theory. Specific topics include simple and composite hypothesis testing, sequential detection and decision theory, nonparametric and robust detection, and change detection. Basic elements of game theory and its connections with statistical decision theory will be covered if time permits. Emphasis will be placed on discrete-time models and results.
ECE 6660Control and Optimization of Information Networks3
Introduction to current research directions in networking featuring the Internet. Assumes basic knowledge of signals and systems as well as random processes. Necessary tools from optimization, dynamical systems, and related economic theory are introduced. Research topics may include layering, routing, medium access control, flow control, and peer-to-peer networks. Students are expected to hand in homework assignments, discuss one set of papers in class, and participate in a course project.
ECE 6680Bio-inspired Coordination of Multi-Agent Systems4
Autonomous multi-agent systems are becoming an integral part of our daily lives with thousands of robots and drones operating in places from warehouses to entertainment lightshows. With robot costs dropping to mainstream consumer levels, the current technological wave is driven by industry, researchers, and ambitious hobbyists, and spans applications in autonomous vehicles, construction, agriculture, and more. With this eminent technological revolution, it is critical that we look beyond robots working efficiently in parallel, and towards actual swarm intelligence for superior system ability and robustness beyond the sum of its parts. As a proof of concept, natural swarms exhibit efficient, error tolerant, and adaptive properties far beyond the individual scope. Beyond serving as an introduction to research, reading and review of scientific papers, this course also offers insights into both biology and the systems they have inspired.
Powerful imaging modalities with attending computer image processing methods are evolving for the evaluation of health and the detection of disease. This course focuses on the quantitative analysis of such images and Computer Aided Diagnosis (CAD), i.e., the automatic identification and classification of abnormalities by the computer.
Principles and practices involved in the design, implementation, testing, and evaluation of complex standard-cell ASIC chips using automated state-of-the-art CAD tools. Topics include hardware description languages; CMOS logic, state, and interconnect fundamentals; chip design methodologies; automated cell-based design; CAD algorithms; details of accurately modeling ASIC delay, energy, and area; robustness issues; testing, verification, and debugging; power distribution and clocking; packaging and I/O. Includes a six-week open ended project where small groups of students design, implement, test, and evaluate an interesting technique in computer architecture using functional-, microarchitectural-, registertransfer-, and layout-level modeling.
This course discusses advanced topics in computer architecture beyond the material that is covered in undergraduate courses such as ECE 4750/CS 4420. In particular, the course places special focus on multicore and multiprocessor architectures (coherence, consistency, synchronization, interconnects, OS support, etc.), as well as advanced architecture techniques (simultaneous multithreading, speculative loads and stores, neural branch predictors, hardware resource management, memory scheduling, etc.) Students work on parallel programming assignments that emphasize hardware-aware performance optimization.
ECE 6760Memory Technologies and Memory-Centric AI Computing3
The first half of this course will cover semiconductor memory technologies including SRAM, DRAM/eDRAM, and embedded non-volatile memories (RRAM, ferroelectric RAM, etc.). The lectures will encompass bitcell designs, array architecture, peripheral circuits, scaling trend, and target applications. The second half of this course will teach memory-centric AI computing, including SRAM/eDRAM/eNVM-based compute-in-memory designs and DRAM-based processing-in-memory designs for AI workloads, which have been demonstrated in both academia and industry in recent years.
The course will cover the state of the art in hardware and software architectures of datacenters that power the AI boom, as well as the cloud services we use every day. It discusses the evolution of datacenters, from early designs built using redundant arrays of inexpensive, desktop-like computers to today’s highly specialized AI datacenters. The course focuses on the requirements of datacenter hardware and software and how these requirements shape datacenter design. We will explore emerging trends in processor architecture (both general-purpose and domain-specific), memory, storage, networking, system software, and AI software architectures for datacenters.
The course starts with an introduction to modern electronic system design automation flow, before delving into high-level synthesis (HLS) design methodologies and tools for enabling digital system design above the register transfer level. Specific topics include C-based HLS design methods, hardware specialization, scheduling, pipelining, resource sharing, reconfigurable computing, and hardware/software co-design. This course also discusses the applications of a number of important optimization techniques, such as graph algorithms, dynamic programming, local search, and linear programming. In addition, commercial C-to- FPGA tools will be provided to the students to implement real-life image/video processing and machine learning applications on programmable system-on- chips that tightly integrate a dual-core processor and FPGAs.
Introduction to multivariable feedback control theory in both time and frequency domain. Topics include model-based control, performance limitations, Linear Quadratic and H-infinity optimal control, control synthesis vis convex optimization, and model predictive control. Additional topics at the discretion of the instructor.
ECE 6790Neuromorphic Computing Algorithm and Hardware Design3
Industry and academia have shown large interest in low-power hardware designs for neuromorphic computing (e.g. IBM TrueNorth, Intel Loihi) and deep learning algorithms for a wide range of image, speech, and biomedical applications. In this course, we will learn the underlying theory, basic algorithms, and efficient circuit/architecture design of neuromorphic computing.
Network systems pervade our society in both social and technological contexts. On the one hand, social networks play a central role in the transmission of information and viruses with fundamental consequences for product marketing, technology adoption, voting decisions, spread of false news and epidemiology. On the other hand, network topology fundamentally affects the performance and resilience properties of large-scale multi-agent systems, such as the electric power grid, the internet of things, traffic or robotic sensor networks.
ECE 7230Bayesian Estimation and Stochastic Optimization4
Covers essential topics in high dimensional statistical inference, stochastic optimization, Bayesian statistical signal processing and Markov Chain Monte-Carlo stochastic simulation. The course is four inter-related parts. Part 1 covers the basics of probabilistic models, Markov chain Monte-Carlo simulation and regression with sparsity constraints. Part 2 covers Bayesian filtering including the Kalman filter, Hidden Markov Model filter and sequential Markov chain Monte-Carlo methods such as the particle filter. Part 3 covers maximum likelihood estimation and numerical methods such as the Expectation Maximization algorithm. Part 4 covers stochastic gradient algorithms and stochastic optimization. The course focuses on the deep fundamental ideas that underpin signal processing, data science and machine learning. The discussion sections will focus on more advanced aspects in statistical inference.
ECE 7235Sustainable Urban and Energy Delivery Systems3
The course focuses on how to transition from legacy energy delivery infrastructures dependent on fossil fuel to a sustainable decarbonized grid that harnesses distributed renewable energy resources and responsive demand from buildings, electrified transportation systems, and industrial loads. The content includes models and abstractions for the architecture of the cyber-physical energy system, its economics, and future evolution, and numerical optimization and learning methods in support of the infrastructure's safety critical operations in the legacy system and in the future architecture. At the MSc level the students will focus on learning how to use tools and data while at the graduate level the students will be asked to also solve problems, formulate novel solutions, interpret results. Similarly, to differentiate the MSc from PhD level and course outcomes, the final project will require the MSc students to pick one out of a set of predefined problems while the PhD students will have to define an original problem and solution.
ECE 7260Graph-based Data Science for Networked Systems3
Complex networks are often the source of high-dimensional data. The goal of this course is to introduce structural and computational models for data that are indexed by the irregular support of a graph. The graph represents the network that couples the dynamics of many agents, or it can be a more abstract Bayesian graphical model that explains how observations are conditionally dependent. The interest in these models spans many fields.
ECE 7290Distributed Optimization for Machine Learning and AI3
This is a graduate-level course about theory, algorithms and applications of distributed optimization and machine learning. The course covers the basics of distributed optimization and learning algorithms and their performance analyses when they are used to solve large-scale distributed problems arising in AI, machine learning, signal processing, communication networks, and power systems.
ECE 7300Advanced Electrodynamics of Complex Media3
Graduate-level course on advanced topics in applied electrodynamics, metamaterials, and nanophotonics. The flow of electromagnetic waves can be controlled and molded with engineered (meta)materials and wave-guiding structures, for applications ranging from radio frequencies to visible light, and from large antenna/radar systems to nanoscale optics. Topics include the optical/electromagnetic properties of materials; conservation laws and theorems; causality and Kramers-Kronig relations; wave propagation in complex continuous media (anisotropic, dissipative, dispersive, chiral, nonlocal, nonreciprocal, hyperbolic, etc.) and periodic structures; coupled-mode theory in space and time; mode degeneracies and singularities; geometric phase and topology in wave physics. The course also introduces several topics at the forefront of applied research in this field, including nonreciprocal devices, super-resolution imaging, non-Hermitian physics, topological and time-varying photonics.
ECE 7620Information Theory and Generative Modeling3
A graduate-level introduction to information theory, data compression, and generative modeling. An introduction to information measures: entropy, mutual information, relative entropy, differential entropy, and their properties. Lossless compression and its connection to prediction and generative modeling. The Minimum Description Length (MDL) principle in model selection. Practical lossless compression using arithmetic coding. The rate-distortion theorem and its connection to lossy compression standards such as JPEG, mp3, and AAC as well as generative modeling techniques such as autoencoders and variational inference. The Nonlinear Transform Coding framework. Practical methods for lossy compression such as Trellis-Coded Quantization (TCQ) and entropy-constrained dithered quantization. ECE 7620 is intended for students intending to undertake Ph.D.-level research in information theory, statistics, or machine learning. Knowledge of probability and linear algebra at the advanced undergraduate level is required.
For students enrolled in master's or doctoral program. Each professor is assigned a section number. To register, see roster for appropriate six-digit course ID numbers.
For students enrolled in master's or doctoral program. Each professor is assigned a section number. To register, see roster for appropriate six-digit course ID numbers.
This course will assist new graduate students in their transition to Cornell, by introducing them to on-campus resources and equipping them with the skills needed to succeed in research, academia, and their future careers. The course will address specific issues pertaining to diversity and minority resources. Guest panelists throughout the semester will include graduate students, administrators, and faculty across engineering and computer science.
