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Table 1: The capacity threshold for each of the decoding algorithms. The optimal threshold is given by the Shannon limit for a BSC multiplied by the rate of the code. (From [14, Tbl. 1])
Table 1: The capacity threshold for each of the decoding algorithms. The optimal threshold is given by the Shannon limit for a BSC multiplied by the rate of the code. (From [14, Tbl. 1])
Table 1: The capacity threshold for each of the decoding algorithms. The optimal threshold is given by the Shannon limit for a BSC multiplied by the rate of the code. (From [14, Tbl. 1])
2001
Table 3.3: Two degree sequences yielding codes of rate 1=2 with a = 8. For each sequence, the actual sum-product decoding threshold, and the corresponding ( Eb N0 ) in dB are given. Also listed is the Shannon limit.
2002
Table 1. Maximum background light that can be handled while operating with a coded BER of The table indicates that codes operating at the Shannon limit can withstand 2.3 to 7.6dB higher levels of background light, compared to RS codes. Parameters: M = 256,64,2, R, = 718 or 112, fi, = 100, T, = 31.25 ns, SLiK detector.
"... In PAGE 7: ...ith a RS(255,224) code we required fib 5 7.1; and capacity implies fib 5 16.0. Note in Table1 that when M = 64, a RS code is further from capacity than when M = 256. Table 1.... ..."
Table 3.2: Good degree sequences yielding codes of rate approximately 1=3 for the BIAGN channel and with a = 2; 3; 4. For each sequence, the Gaussian approxima- tion noise threshold GA, the actual sum-product decoding threshold , and the corresponding ( Eb N0 ) in dB are given. Also listed is the Shannon limit (S.L.).
2002
Table 2: Compression Performance. \Compression Factor quot; gives the size of the input divided by the size of the output. \Statistics quot; identi es the data used to build the multialphabet encoder apos;s probability model; \Self quot; means the source le itself provided the statistics.
1993
"... In PAGE 9: ... Note that our implementation processes a 257-symbol alphabet non-adaptively, whereas the Q-coder processes a binary alphabet adaptively. Table2 reports the compression performance of our system, and compares it with the Q-coder [9]. Observe that the Q-coder performance typically exceeds the Shannon limit 1=Hbit, where Hbit = ?p0 log2 p0?p1 log2 p1 is the zero-order bit entropy.... ..."
Cited by 4
Table 2: Compression Performance. \Compression Factor quot; gives the size of the input divided by the size of the output. \Statistics quot; identi es the data used to build the multialphabet encoder apos;s probability model; \Self quot; means the source le itself provided the statistics.
1993
"... In PAGE 9: ... Note that our implementation processes a 257-symbol alphabet non-adaptively, whereas the Q-coder processes a binary alphabet adaptively. Table2 reports the compression performance of our system, and compares it with the Q-coder [9]. Observe that the Q-coder performance typically exceeds the Shannon limit 1=Hbit, where Hbit = ?p0 log2 p0?p1 log2 p1 is the zero-order bit entropy.... ..."
Cited by 4
Table 3: Jensen-Shannon Divergence
Table 3: Comparison of OBDD-size and -OBDD-size for variable orders created by weight propagation heuristic. -OBDD-size equals OBDD-size for circuits not containing EXOR(EQU)-gates if Boole-Shannon expan- sion and ITE- algorithm is used.
"... In PAGE 10: ... In Table 2 - 4, all obtained results are listed depending on the heuristics used to create the variable order. For Table 2, fanin heuristics is used, for Table3 , weight propagation heuristics, and for Table 4, the original order of the circuit description le. A bar in a cell of a table denotes that the computation exceeded the memory limitations, which were chosen rather low to allow a larger number of experiments within the intended timeframe.... ..."
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