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Principle of PAM: (1) original signal, (2) PAM signal, (a) amplitude of signal, (b) time Pulse-amplitude modulation , acronym PAM , is a form of signal modulation where the message information is encoded in the amplitude of a series of signal pulses. It is an analog pulse modulation scheme in which the amplitude of train of carrier pulse are varied according to the sample value of the message signal. http://en.wikipedia.org/wiki/Pulse-amplitude_modulation

Pulse-amplitude modulation

EMI

Code of Federal Regulations , Title 47, Part 15 (47 CFR 15) is an oft-quoted part of Federal Communications Commission (FCC) rules and regulations regarding unlicensed transmissions . It is a part of Title 47 of the Code of Federal Regulations (CFR), and regulates everything from spurious emissions to unlicensed low-power broadcasting . Nearly every electronics device sold inside the United States radiates unintentional emissions, and must be reviewed to comply with Part 15 before it can be advertised or sold in the US market. [ edit ] Subparts http://en.wikipedia.org/wiki/Title_47_CFR_Part_15

Title 47 CFR Part 15

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Viterbi algorithm

http://en.wikipedia.org/wiki/Viterbi_algorithm The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models . The terms Viterbi path and Viterbi algorithm are also applied to related dynamic programming algorithms that discover the single most likely explanation for an observation. For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation (parse) of a string, which is sometimes called the Viterbi parse .

"The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states", like hidden markov. by nomel Jun 7

http://en.wikipedia.org/wiki/Hidden_Markov_model#Learning A hidden Markov model ( HMM ) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved ( hidden ) states. An HMM can be considered as the simplest dynamic Bayesian network . The mathematics behind the HMM was developed by L. E.

Hidden Markov model

In markov model, state is visible. In Hidden markov model, only result is visible. By looking at the sequence of results, the change in state and therefore hidden model can be inferred.
From the link: "Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states." by nomel Jun 7