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Matrices A i,X i,Y i,Z i are arbitrary matrices with dimensions of Rr,i×Rs,i, Rr,i×(Nr-Rs,i), (Nr-Rr,i ×Rs,i, (Nr-Rr,i)×(Nr-Rs,i), respectively.
The outputs are matrices with dimensions 27 × 27 × 4 × 6 (i.e. Listener's channels × Speaker's channels × frequency bands × blocks corresponding to the five topics plus the resting condition).
Table 3 includes average values over 10,000 realizations for sensing matrices with dimensions 20×120 and 30×120.
LDPCs are linear block codes (N,K) and can be described by sparse binary parity-check H matrices with dimensions (N - K) × N.
Further, the computational complexity to compute the channel inversion (13) and N QR decompositions (15) of matrices with dimensions (N_{t_{n}}times N_{r}) is much lower than the computational cost of computing N SVD transformations (19) of matrices with dimensions (phantom {dot {i}!}N_{r}times (N_{t}-N_{t_{n}})).
The gain matrix is replaced by: F = [F_{text{K}},F_{text{C}} ],, (17 where F k and F C are the stiffness and damping type of matrices with dimensions of (n × n).
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The MSP algorithm requires K compact SVDs for matrices with dimension M × d and K with dimension N × d, whose complexity is K d 2(14M + 14N + 16d). .
The MSP algorithm requires K compact SVDs for matrices with dimension M × d and K with dimension N × d, whose complexity is K d 2(14M + 14N + 16d).
I r and O r represent identity and null matrices with dimension r × r.
The final simplified form only involves multiplications and additions of matrices with dimension I k × I k, where I k is the number of genes in cluster k given current parameters.
where H p1 and H p2 are sub-matrices with dimensions (frac {N}{9}times frac {2N}{9}).
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