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In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class, the classification accuracy is guaranteed by a constraint that the ratio of variances of signals in two different classes is lower...
In this paper, a novel common sub-expression elimination (CSE) method is presented for the design of linear phase finite impulse response (FIR) filters with low hardware cost. Based on weight criteria, this method eliminates the common sub-expressions of unequal weight with a transposed direct structure. By relaxing the word length while limiting its search range, the least cost coefficient sets can...
A novel algorithm is presented in this paper to design sparse FIR filters in the weighted least-squares (WLS) sense. The original design problem is cast as a constrained l0-norm optimization problem. To tackle the nonconvexity, an efficient iterative procedure is developed. In each iterative step, a subproblem in a simpler form is constructed. It can be demonstrated that in each iteration an optimal...
In this paper, we present a novel algorithm to design sparse FIR digital filters in the minimax sense. To tackle the nonconvexity of the design problem, an efficient iterative procedure is developed to find a potential sparsity pattern. In each iteration, a subproblem in a simpler form is constructed. Instead of directly resolving these nonconvex subproblems, we resort to their respective dual problems...
In this paper, an iterative algorithm is proposed to design IIR variable fraction delay (VFD) digital filters in the weighted least-squares (WLS) sense. The original IIR VFD filter design problem is nonconvex. As an attempt to tackle this difficulty, an iterative procedure is introduced, and the Steiglitz-McBride (SM) reweighting technique is employed at each iteration to transform the original approximation...
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