About the content
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.
Week 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.
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.
- Don Johnson
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