Electrical and Electronic Engineering

The walls between art and engineering exist only in our minds.
Theo Jansen
TRENDING BUBBLES

by Anant Agarwal

Electrical and Electronic Engineering

by Shaoul Ezekiel

Electrical and Electronic Engineering

by Saman Amarasinghe

Electrical and Electronic Engineering

by John Tsitsiklis

Electrical and Electronic Engineering

SELECT YOUR LEVEL

University


Introduction to Electrical Engineering and Computer Science I

by MIT

This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of contexts from electrical engineering and computer science. Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making.


Electrical Machines and Drives

by TU Delft

The course gives an overview of different types of electrical machines and drives. Different types of mechanical loads are discussed. Maxwell's equations are applied to magnetic circuits including permanent magnets. DC machines, induction machines, synchronous machines, switched reluctance machines, brushless DC machines and single-phase machines are discussed with the power electronic converters used to drive them.


Measurement Science

by TU Delft

This course is an introduction to measurement science. It describes the theoretical foundations and practical examples of measurement systems. The course covers the analysis of measurement problems and the specification of measurement systems. Various common sources of measurement errors and the concept of uncertainty in measurement results are introduced. Several important instruments for electrical measurements are discussed. Moreover a number of commonly-used sensors for the measurement of non-electronic quantities are introduced, as well as electronic circuits for the readout of these sensors.


Computer Systems Colloquium

by Stanford

Teaching is formalized in the classroom, but constantly challenged and enriched through the hands-on work of the various CSL project groups and the wider influence of Silicon Valley technology. Of special interest is the Stanford Electrical Engineer Computer Systems Colloquium which is open to the public. The Colloquium is an ongoing guest lecture series touching on many elements of computer systems, the technologies they employ, and the systems they enable. Outstanding and sometimes controversial speakers are drawn form academia, commercial research labs, and industry.


Probabilistic Systems Analysis and Applied Probability

by MIT

Welcome to probabilistic systems analysis and applied probability, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: 1)The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science".2) A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions.3) Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget. The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.


Discrete Stochastic Processes

by MIT

Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.


Signals and Systems

by MIT

This course was developed in 1987 by the MIT Center for Advanced Engineering Studies. It was designed as a distance-education course for engineers and scientists in the workplace.Signals and Systems is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, defense electronics, consumer electronics, and consumer products.The course presents and integrates the basic concepts for both continuous-time and discrete-time signals and systems. Signal and system representations are developed for both time and frequency domains. These representations are related through the Fourier transform and its generalizations, which are explored in detail. Filtering and filter design, modulation, and sampling for both analog and digital systems, as well as exposition and demonstration of the basic concepts of feedback systems for both analog and digital systems, are discussed and illustrated.Lectures 1, 5 and 18 are not available due to copyright restrictions.


Introduction to MEMS Design

by Berkeley

Physics, fabrication, and design of micro-electromechanical systems (MEMS). Micro and nanofabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. Lecture 1 is unavailable due to copyright restrictions.


Performance Engineering of Software Systems

by MIT

Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard practice.This class is a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems.


Synchrotron Radiation for Materials Science Applications

by Berkeley

Synchrotron Radiation for Materials Science Applications with professor David Attwood of the University of California, Berkeley.


Soft X Rays and Extreme Ultraviolet Radiation

by Berkeley

Professor David T. Attwood, Electrical Engineering Professor in Residence, Professor Attwood's research interests include short wavelength electromagnetics, soft x-ray microscopy, coherence, and EUV lithography.


Multicore Programming Primer

by MIT

Multicore Programming Primer by professor Rodric Rabbah of the Massachusetts Institute of Technology.


Understanding Lasers and Fiberoptics

by MIT

Lasers are essential to an incredibly large number of applications. Today, they are used in bar code readers, compact discs, medicine, communications, sensors, materials processing, computer printers, data processing, 3D-imaging, spectroscopy, navigation, non-destructive testing, chemical processing, color copiers, laser "shows", and in the military. There is hardly a field untouched by the laser. But what exactly is so unique about lasers that makes them so effective? This brief video course is designed for engineers, scientists, medical personnel, managers, and others who work with lasers and/or fiberoptics, or who anticipate working with lasers and/or fiberoptics, yet have little or no background in laser or fiberoptic basics. The course focuses on fundamentals and emphasizes a physical intuitive interpretation of laser and fiberoptic phenomena and their applications. Because Prof. Ezekiel keeps mathematics to a minimum, the topics covered are easily understood, without the need for a strong technical background. Prof. Ezekiel uses plain language, graphic illustrations, and video demonstrations to explain the basic characteristics of lasers and fiberoptics.


Underactuated Robotics

by MIT

Underactuated Robotics by professor Russell Tedrake of the Massachusetts Institute of Technology.


Analysis and Design of VLSI

by Berkeley

Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. RF integrated electronics including synthesizers, LNA's, and baseband processing. Low power mixed signal design. Data communications functions including clock recovery. CAD tools for analog design including simulation and synthesis.


Machine Structures

by Berkeley

The internal organization and operation of digital computers. Machine architecture, support for high-level languages (logic, arithmetic, instruction sequencing) and operating systems (I/O, interrupts, memory management, process switching). Elements of computer logic design. Tradeoffs involved in fundamental architectural design decisions.


Principles of Digital Communication

by MIT

This course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451, is offered in the spring.Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM modulation, signal constellations, finite-energy waveform spaces, detection, and modeling and system design for wireless communication.


Principles of Digital Communication II

by MIT

This course is the second of a two-term sequence. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise ( AWGN) channels, their performance analysis, and design principles. After a review of Principles of Digital Communication I and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm.More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms; the BCJR algorithm; turbo codes, LDPC codes and RA codes; and performance of LDPC codes with iterative decoding. Finally, the course addresses coding for the bandwidth-limited regime, including lattice codes, trellis-coded modulation, multilevel coding and shaping. If time permits, it covers equalization of linear Gaussian channels.


Introduction to Robotics

by Stanford

The purpose of this course is to introduce you to basics of modelling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.The course is presented in a standard format of lectures, readings and problem sets. Lectures will be based mainly, but not exclusively, on material in the Lecture Notes. Lectures will follow roughly the same sequence as the material presented in the notes, so it can be read in anticipation of the lectures.Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design. Prerequisites: matrix algebra.


Computer System Engineering

by MIT

This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. It also looks at case studies of working systems and readings from the current literature provide comparisons and contrasts, and do two design projects.


Circuits and Electronics

by MIT

This course is designed to serve as a first course in an undergraduate electrical engineering (EE), or electrical engineering and computer science (EECS) curriculum. The course introduces the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; digital abstraction; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. Design and lab exercises are also significant components of the course. The course content was created collaboratively by Profs. Anant Agarwal and Jeffrey H. Lang.