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A low code-rate close to zero implies a strong code that uses many redundant bits to achieve a good performance, while a large code-rate close to 1 implies a weak code. The redundant bits that protect the information have to be transferred using the same communication resources that they are trying to protect.
The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay, contains chapters on elementary error-correcting codes; on the theoretical limits of error-correction; and on the latest state-of-the-art error-correcting codes, including low-density parity-check codes, turbo codes, and fountain codes.
In this example, x 1 =2 and the tentative assignment x 2 =1 is considered. Forward checking only checks whether each of the unassigned variables x 3 and x 4 is consistent with the partial assignment, removing the value 2 from their domains. The simpler technique for evaluating the effect of a specific assignment to a variable is called forward ...
The algorithm is named after its inventors: Bahl, Cocke, Jelinek and Raviv. [1] This algorithm is critical to modern iteratively-decoded error-correcting codes, including turbo codes and low-density parity-check codes.
The analysis of errors computed using the global positioning system is important for understanding how GPS works, and for knowing what magnitude errors should be expected.
Hardware-wise, this turbo code encoder consists of two identical RSC coders, C 1 and C 2, as depicted in the figure, which are connected to each other using a concatenation scheme, called parallel concatenation: In the figure, M is a memory register. The delay line and interleaver force input bits d k to appear in different sequences.
The correct number of sections for a fence is n − 1 if the fence is a free-standing line segment bounded by a post at each of its ends (e.g., a fence between two passageway gaps), n if the fence forms one complete, free-standing loop (e.g., enclosure accessible by surmounting, such as a boxing ring), or n + 1 if posts do not occur at the ends ...
The general algorithm involves message passing and is substantially similar to the belief propagation algorithm (which is the generalization of the forward-backward algorithm). With an algorithm called iterative Viterbi decoding , one can find the subsequence of an observation that matches best (on average) to a given hidden Markov model.