LearnGuide03 - Learning Guide and Examples Information Theory and Coding Prerequisite courses Continuous Mathematics Probability Discrete Mathematics

# LearnGuide03 - Learning Guide and Examples Information...

This preview shows page 1 - 2 out of 31 pages.

Learning Guide and Examples: Information Theory and Coding Prerequisite courses: Continuous Mathematics, Probability, Discrete Mathematics Overview and Historical Origins: Foundations and Uncertainty. Why the movements and transformations of information, just like those of a fluid, are law-governed. How concepts of randomness, redundancy, compressibility, noise, bandwidth, and uncertainty are intricately connected to information. Origins of these ideas and the various forms that they take. Mathematical Foundations; Probability Rules; Bayes’ Theorem. The meanings of proba- bility. Ensembles, random variables, marginal and conditional probabilities. How the formal concepts of information are grounded in the principles and rules of probability. Entropies Defined, and Why They Are Measures of Information. Marginal entropy, joint entropy, conditional entropy, and the Chain Rule for entropy. Mutual information between ensembles of random variables. Why entropy is a fundamental measure of information content. Source Coding Theorem; Prefix, Variable-, & Fixed-Length Codes. Symbol codes. Binary symmetric channel. Capacity of a noiseless discrete channel. Error correcting codes. Channel Types, Properties, Noise, and Channel Capacity. Perfect communication through a noisy channel. Capacity of a discrete channel as the maximum of its mutual information over all possible input distributions. Continuous Information; Density; Noisy Channel Coding Theorem. Extensions of the dis- crete entropies and measures to the continuous case. Signal-to-noise ratio; power spectral density. Gaussian channels. Relative significance of bandwidth and noise limitations. The Shannon rate limit and efficiency for noisy continuous channels. Fourier Series, Convergence, Orthogonal Representation. Generalized signal expansions in vector spaces. Independence. Representation of continuous or discrete data by complex expo- nentials. The Fourier basis. Fourier series for periodic functions. Examples. Useful Fourier Theorems; Transform Pairs. Sampling; Aliasing. The Fourier transform for non-periodic functions. Properties of the transform, and examples. Nyquist’s Sampling Theo- rem derived, and the cause (and removal) of aliasing. Discrete Fourier Transform. Fast Fourier Transform Algorithms. Efficient algorithms for computing Fourier transforms of discrete data. Computational complexity. Filters, correla- tion, modulation, demodulation, coherence. The Quantized Degrees-of-Freedom in a Continuous Signal. Why a continuous signal of fi- nite bandwidth and duration has a fixed number of degrees-of-freedom. Diverse illustrations of the principle that information, even in such a signal, comes in quantized, countable, packets. Gabor-Heisenberg-Weyl Uncertainty Relation. Optimal “Logons.” Unification of the time- domain and the frequency-domain as endpoints of a continuous deformation. The Uncertainty Principle and its optimal solution by Gabor’s expansion basis of “logons.” Multi-resolution wavelet codes. Extension to images, for analysis and compression.  #### You've reached the end of your free preview.

Want to read all 31 pages?

• • •  