Digital Fountain Codes

Common methods for communicating over such channels employ a feedback channel from receiver to sender that is used to control the retransmission of erased packets. For example, the receiver might send back messages that identify the missing packets, which are then retransmitted. Alternatively, the receiver might send back messages that acknowledge each received packet; the sender keeps track of which packets have been acknowledged and retransmits the others until all packets have been acknowledged.

These simple retransmission protocols have the advantage that they will work regardless of the erasure probability f, but purists who have learned their Shannon theory will feel that these retransmission protocols are wasteful. If the erasure probability f is large, the number of feedback messages sent by the first protocol will be large. Under the second protocol, it's likely that the receiver will end up receiving multiple redundant copies of some packets, and heavy use is made of the feedback channel. According to Shannon, there is no need for the feedback channel: the capacity of the forward channel is (1 – f)l bits, whether or not we have feedback.

Efficient coding. Shannon coding in the channel without interference.

Enhancements to soft K-means

Entropy. Basic properties of entropy.

Exact inference for continuous hypothesis spaces

Explain the difference in the levels of communication problems.

Finding the lowest-cost path

Functional diagram of the transmission of information, the purpose of its components.

Further applications of arithmetic coding

Gaussian distribution

Generalized parity-check matrices

Give the definition of a stationary random process in the narrow and broad sense.

Hash codes

How much can we compress?

How to measure the information content of a random variable?

How to measure the information content of a random variable?

Inferring the input to a real channel

Inferring the mean and variance of a Gaussian distribution

Information content defined in terms of lossy compression

Information content of independent random variables

Information conveyed by a channel

Information types

Introduction to convolutional codes

Joint entropy

Jointly-typical sequences

K-means clustering

Lempel–Ziv coding

Low-Density Parity-Check Codes

Low-Density Parity-Check Codes

Maximum Likelihood and Clustering

Maximum likelihood for a mixture of Gaussians

Maximum likelihood for one Gaussian

Message classification

Message Passing

More on trellises

More than two variables

Noise and distortion in the channels of information transmission.

Noisy channels

Optimal source coding with symbol codes: Huffman coding

Other roles for hash codes

Parity-check matrices of convolutional codes and turbo codes

Periods of information circulation

Pictorial demonstration of Gallager codes

Planning for collisions

Probabilities and Inference

Relative entropy and mutual information

Repeat–Accumulate Codes

Review of probability and information

Simple language models

Soft K-means clustering

Solving the decoding problems on a trellis

Source Models of discrete messages.

Symbol codes

The binary entropy function

The burglar alarm

The capacity of a continuous channel of information transfer.

The decoder

The differential entropy and its properties.

The entropy of a discrete source. Full and partial entropy.

The Gaussian channel

The general problem

The information-retrieval problem

The junction tree algorithm

The junction tree algorithm

The main types of signals used in the transmission of information.

The min–sum algorithm

The noisy-channel coding theorem

The Noisy-Channel Coding Theorem

The set, which objects of an information transmission system?

The sum–product algorithm

Turbo codes

Typicality

Units of information content

What are the capabilities of practical error-correcting codes?

What are the capabilities of practical error-correcting codes?

What are the different forms of representation models of signals.

What are the main problems of the theory of information?

What are the main stages of treatment information?

What Information system is?

What is meant by the message and the signal?

What is said to be centered random process?

What is the difference between a line and a channel of communication?

What is the difficulty of exact mathematical description of a random process?

What is the essence of fundamental differences in the interpretation of the concept of information?

What limit is imposed by unique decode ability?

What's the most compression that we can hope for?

Date: 2015-01-29; view: 1011