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The algorithms are based on simplifying matrix computation in the AIC.
Apart from using the difference matrix in subsequent covariance matrix computations, these results are stored in the DDR3-SDRAM via BRAM, to be reused for the PC matrix computation in stage 4. Similar to the mean design, the numerator of the covariance is computed for an element of the covariance matrix and only the final covariance result goes through the divider.
The three packages handle matrix computation in different ways.
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Specific vectors are constructed with GPS measurements and used for attitude matrix computation like in analytic alignment algorithm.
For this reason, we have excluded it from the correlation matrix computation represented in Figure 10.
Theory and algorithm development for large matrix computation problems arising in computational science and engineering.
The data path of the PC matrix computation, as depicted in Fig. 9, consists of a subtractor, a multiplier, and an accumulator.
To this end, we triangularize the companion matrix constructed from the high-degree polynomial using iterative Gram-Schmidt QR transformations, an eigenvalue problem in matrix computation, coupled with another iterative correction process to produce a frequency offset estimate to good accuracy.
We describe matrix computations available in the cluster programming framework, Apache Spark.
Preliminary version in Distributed Matrix Computation with NeurIPS14.
Most scaling algorithms in the matrix computation field (Knight, 2008; Knight and Ruiz, 2012) suffer from this scalability issue, because their main focus is improving the convergence rate and numerical stability.
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