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Thus, the need for efficient error-correcting codes is increasing.
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The approach also provides a normative, convex, biologically plausible learning mechanism for discovering these networks from small amounts of data and reveals new connections between binary McCulloch Pitts neural networks, efficient error-correcting codes, and computational graph theory.
We propose a first of its kind methodology for novel transient fault correction using efficient error correcting codes with hardware scheduling for FPGAs.
Technology developments such as MIMO-OFDM together with high order modulation schemes and efficient error correcting codes, allow for a spectrum efficiency of up to 15bps/Hz [1].
These applications require resource efficient error correction hardware to detect and correct errors introduced by the communication channel.
Two other sets of paired-end short reads were obtained by correcting the set SR-E: one (SR-CE1, for Short Reads with Corrected Errors 1) by using the trimming tool proposed by SSAKE [ 41] and the other one (SR-CE2 for Short Reads with Corrected Errors 2) by using the correcting tool proposed by ALLPATHS [ 44], one of the most efficient error correction methods for short reads [ 16].
Hence, the need for complex error correcting codes is reduced.
Standard errors corrected for clustering within states.
Error Correcting Output Codes (ECOC) framework is efficient approach for multiclass classification problems by reducing them to binary classifiers.
In this paper, we have proposed efficient single bit as well as multi-bit error correcting methods to correct errors in the CM of FPGAs using simple parity equations and Erasure code.
They either confirmed the feedback for errors, for corrects, for both the errors and corrects or neither.
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