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Channel capacity is the theoretical maximum rate at which data can be transmitted over a communication channel without error. It is determined by the channel bandwidth, the signal-to-noise ratio, and other factors.

The SNR is a measure that compares the level of the desired signal to the level of background noise. It's expressed in decibels (dB) and a higher SNR means a clearer signal with better quality.

In the context of information theory, entropy is a measure of the uncertainty or randomness of a system. Higher entropy implies a greater potential for information content. It is a central concept for assessing the capacity for a communication channel to transmit data.

Redundancy in information theory refers to the repetition of information or the presence of additional information that is not needed for accurate transmission. Redundancy can improve communication reliability by allowing error detection and correction.

In information theory, coding refers to the conversion of data into a specific format for efficient transmission or storage. Coding can also imply the insertion of error-control codes to facilitate reliable communication.

An information cascade is a process in communication where a piece of information or behavior is rapidly adopted by individuals in a network sequentially, often irrespective of their own information or initial beliefs.

Error detection and correction techniques in information theory allow the identification and correction of errors in transmitted data. This enhances the reliability of communication systems, especially over noisy channels.

In communication, noise refers to any unwanted interference that disrupts the signal being transmitted. Noise can degrade the quality of the transmission and reduce the efficiency of communication channels.

The Nyquist Rate is the minimum sampling rate, twice the highest frequency present in the signal, that is necessary to successfully reconstruct a sampled analog signal without aliasing during ADC.

Shannon's Theorem, or the Noisy Channel Coding Theorem, establishes the maximum possible efficiency of error-correcting methods versus levels of noise interference. It defines the maximum rate at which information can be sent over a noisy channel without errors.

Source coding is a process in information theory that involves encoding information from a source in an efficient form to minimize bits without losing the integrity of the original data. This compression improves transmission rates over a channel.

Channel coding, also known as forward error correction, is the process of adding redundancy to the information being transmitted to detect and correct errors at the receiver end. It improves the robustness of communication over noisy channels.

Digital modulation is the process of varying a carrier wave in order to digitally encode information for transmission over a communication channel, allowing for the efficient transfer of data over various media.

In the field of information theory, information is a quantifiable measure of uncertainty reduction. It is conveyed through messages and can be calculated as the logarithmic measure of the number of possible states of a communication system.

ADC is the process of converting an analog signal into a digital signal by sampling and then quantizing it. This allows for the analog information to be processed, stored, and transmitted using digital techniques.

Data compression is the process of encoding information using fewer bits than the original representation. Effective compression reduces the amount of data required to represent a piece of information, making transmission more efficient.

Mutual Information is a measure of the amount of information that one random variable contains about another. It is a key concept in evaluating how much information can be obtained about one communication by observing another.

Rate-Distortion Theory is a framework in information theory that deals with the trade-off between data compression (rate) and the quality of the approximation or fidelity (distortion) of the decompressed signal.

A bit is the basic unit of information in information theory and digital communications. It represents a binary choice or a two-state system, such as a 0 or a 1.