Fundamentals of Electrical Engineering

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Description

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This course probes fundamental ideas in electrical engineering, seeking to understand how electrical signals convey information, how bits can represent smooth signals like music and how modern communication systems work.

About the Course

The course focuses on the creation, manipulation, transmission, and reception of information by electronic means. The topics covered include elementary signal theory; time- and frequency-domain analysis of signals; conversion of analog signals to a digital form; and how information can be represented with signals. Signal processing, both analog and digital, allow information to be extracted and manipulated. The course then turns to information theory, which…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

This course probes fundamental ideas in electrical engineering, seeking to understand how electrical signals convey information, how bits can represent smooth signals like music and how modern communication systems work.

About the Course

The course focuses on the creation, manipulation, transmission, and reception of information by electronic means. The topics covered include elementary signal theory; time- and frequency-domain analysis of signals; conversion of analog signals to a digital form; and how information can be represented with signals. Signal processing, both analog and digital, allow information to be extracted and manipulated. The course then turns to information theory, which demonstrates the technological advantages of digital transmission. The course text was written by the instructor for this course and is entirely online. You can print your own hard copy or view the material entirely online.

About the Instructor(s)

Don H. Johnson is the J.S. Abercrombie Professor Emeritus of Electrical and Computer Engineering at Rice University, Houston, Texas. He received his undergraduate and graduate degrees in electrical engineering, all from the Massachusetts Institute of Technology. He joined M.I.T. Lincoln Laboratory as a staff member in 1974 and then, in 1977, he joined the faculty of Rice University. At MIT and at Rice, he received several institution-wide teaching awards, including Rice’s highest award, the George R. Brown Award for Excellence in Teaching. Professor Johnson is a Fellow of the IEEE, a recipient of the IEEE Millennium Medal and the Signal Processing Society’s Meritorious Service Award, and former President of the Signal Processing Society. He co-authored the graduate-level book Array Signal Processing, published in 1993, and holds nine patents. His present research activities concern art forensic analysis from x-ray images of paintings and statistical signal processing. Since 2011, he is an Adjunct Research Fellow of the Van Gogh Museum, Amsterdam.

Course Syllabus

Elements of signal and system theoryWeek 1: Digital and analog information; block diagrams: sources, systems, sinks. Simple signals and systems. Complex numbers.

Analog Signal Processing Weeks 2-3: Representation of signals by electrical quantities (electric currents and electromagnetic radiation). Elementary circuit theory: resistors and sources, KVL and KCL, power, equivalent circuits. Circuits with memory: impedance, transfer functions, Thévenin and Mayer-Norton equivalent circuits.

Frequency Domain Ideas Weeks 4-5: Fourier series and Fourier transforms. Signals in time and frequency domains. Encoding information in the frequency domain. Filtering signals. Modeling the speech signal.

Digital Signal Processing Weeks 6-8: Analog-to-digital (A/D) conversion: Sampling Theorem, amplitude quantization, data rate. Discrete-time signals and systems. Discrete-time Fourier transform, discrete Fourier transform and the fast Fourier transform. Digital implementation of analog filtering.

Communicating information Weeks 9-10: Fundamentals of communication: channel models, wireline and wireless channels. Analog (AM) communication: modulation and demodulation, noise (signal-to-noise ratio, white noise models), linear filters for noise reduction.

Weeks 11-12: Digital communication: binary signal sets, digital channel models. Entropy and Shannon's Source Coding Theorem: lossless and lossy compression; redundancy. Error-correcting codes: Shannon’s Noisy Channel Coding Theorem, channel capacity, Hamming codes. Comparison of analog and digital communication.

Recommended Background

Knowledge of Calculus

Suggested Readings

The course text is online at http://cnx.org/content/col10040 provides additional exercises and problems. Every lecture contains links to the relevant sections in the text. Be sure to read them!

Course Format

The class will consist of lecture videos, which are between 8 and 12 minutes in length. These contain 3-4 integrated quiz questions per video. There will also be standalone homeworks that are not part of video lectures.

FAQ

  • Will I get a certificate after completing this class?

    Rice University does not award certificates or issue transcripts or other credentials for student work in this course. However, Coursera will maintain limited data regarding student progress and performance in this course and, with your permission, provide authorized third parties with access to such data.

  • What is the format of the class?

    In addition to the lecture videos, short quizzes will make sure you are on top of the material. Homework is an essential part of the course and will occur roughly once a week.

  • Is there a textbook?

    Yes!! The text, especially written for this course, is available online at Connexions. In addition to supplementing the course, the online book contains interactive exercises to probe your mastery of the course.

  • What are the prerequisites?

    Calculus, both differential and integral, is necessary. Previous familiarity with complex numbers is important, but not essential.

  • How was the course designed?

    This course covers virtually every topic in electrical engineering, but is more than a survey course. Topics are covered at some depth but not completely, preparing students for virtually any succeeding course in electrical engineering.

  • How difficult is this course?

    This course is routinely taken by second-year electrical engineering students at Rice as their first electrical engineering course. Its reputation can be summarized as “the hardest course I have ever taken but I learned a lot.”

Provided by:

University: Rice University

Instructor(s): Don H. Johnson

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