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Taking sparsity into account, this section develops sparsity-cognizant trackers.
By taking sparsity into consideration, channel estimation can be simplified and/or made more accurate.
Another possibility for improving the performance of DBM is the incorporation of sparsity into the learned representation.
If we take channel sparsity into consideration when designing the blind equalizer, both computational efficiency and performance can be improved.
We present two general approaches to incorporate sparsity into time frequency analysis, leading to what we refer to as sparsity-aware quadratic time frequency distributions.
Using the sparse channel model described in Section 2, we are able to incorporate prior information about channel sparsity into the path metric of the SA.
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In Section 3, we look into sparsity in Costas waveform and present mathematical framework of A/I model for low-rate sampling of the continuous-time waveform and its subsequent reconstruction in transform basis.
The steps (8) are known to compensate for the non-orthogonality of the columns of F p while the thresholding operator takes the sparsity constraint into account.
In this paper, we incorporate both Hessian regularization and sparsity constraints into auto-encoders and then propose a new auto-encoder algorithm called Hessian regularized sparse auto-encoders (HSAE).
However, it does not take into account the sparsity in the copy number variations.
The results illustrate that if one incorporates the block-sparsity structure into the recovery procedure, the block version of convex l 1 minimization does also reduce the number of measurement as the standard non-convex l q minimization with some q < 1. Figure 2 Recovery results with M = 225.
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