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We present a hierarchical decomposition scheme for GPUs based on decomposition of the output matrix and input data.
A discrete-time sliding mode controller, which is insensitive to the perturbed state matrix and input matrix and disturbances, is proposed for LPM drive system.
Similarly the HNN MLSE decoder correlation matrix and input vector relating to (10), as derived in (36) and (37), are now X D and I D, respectively, and will henceforth be referred to as "decoder correlation matrix" and "decoder input vector".
For the HNN MLSE equalizer the correlation matrix and input vector relating to (10), as derived in (22) and (27), are now X E and I E, respectively, and will henceforth be referred to as "equalizer correlation matrix" and "equalizer input vector".
The extended cross-correlation matrix and input vector in (36) and (37) can now be used to estimate the ML sequence of transmitted codewords, after which each detected codeword (of length 2 n ) can be mapped back to n bits for BPSK modulation and 2n bits for 4-QAM modulation.
Define the controllability matrix of the system as H = [B, AB,.., An−1B], where A and B are the state transition matrix and input to the state matrix in the linear dynamic system, respectively.
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More precisely, the statistical models will be regression models with input-dependent design matrix and input-correlated errors.
The general case where both signature matrices and input vectors are complex is also discussed in [20, 21].
In this section, we will also survey the most popular case when the signature matrices and input vector entries are binary including active user detection.
Such classifiers are designed based on cross-correlation in the frequency domain between the input matrix and the input weights of neural networks.
Such processors are designed based on cross correlation in the frequency domain between the input matrix and the input weights of neural networks.
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