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In this paper, a new algorithm is proposed for the design of sparse FIR filters. Traditional l1-optimization-based methods take all the coefficients into l1-norm minimization. However, it is unnecessary since some of them can only take nonzero values to satisfy design specifications. Furthermore, minimizing l1 norm of all the coefficients could drive the design results to deviate from the optimal...
In this paper, a novel algorithm is developed for the design of minimum-phase FIR filters with sparse impulse responses. Traditional minimum-phase FIR filter design approaches are based on autocorrelation sequences of impulse responses and design problems are formulated as a semidefinite program. To achieve sparse designs, l1 norm of filter coefficients is incorporated in the objective function as...
Artificial bee colony (ABC) algorithm is a swarm based meta-heuristic algorithm inspired by the foraging behavior of honey bees. Due to its simplicity, the ABC algorithm is used for minimax design of linear phase FIR fullband digital differentiators in this paper. Results in term of peak error obtained from designed digital differentiator examples indicate that the approach can reach smaller peak...
In this paper, teaching-learning-based optimization (TLBO) is used for minimax design of linear phase finite impulse response (FIR) digital Hilbert transformers. TLBO is a population-based and heuristic search algorithm which is parameter-free and exhibits a strong convergence ability. The results obtained from using TLBO to design Type 4 linear phase FIR highpass digital Hilbert transformers indicate...
In this paper, a novel algorithm is presented for the design of sparse linear-phase FIR filters. Compared to traditional l1-optimization-based methods, the proposed algorithm minimizes l1 norm of a portion (instead of all) of nonzero coefficients. In this way, some nonzero coefficients at crucial positions are not affected by l1 norm utilized in the objective function. The proposed algorithm employs...
In this paper, a novel algorithm is developed for the minimax design of IIR digital filters. Using a partial second-order factorization (PSOF), the denominator polynomial of an IIR digital filter is decomposed as a cascade of second-order factors (SOFs) and a single higher-order factor (HOF). This is inspired by the fact that, when some poles are closer to the boundary of the stability domain, the...
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...
This paper presents a new algorithm using semidefinite programming (SDP) relaxation to design infinite impulse response digital filters in the minimax sense. Unlike traditional design algorithms that try to directly minimize the error limit, the proposed algorithm employs a bisection searching procedure to locate the minimum error limit of the approximation error. Given a fixed error limit at each...
In this paper, a new iterative algorithm is proposed to design IIR digital filters in the minimax sense. Instead of directly minimizing the error limit of the approximation error, the proposed algorithm employs a bisection searching procedure to locate the minimum error limit. At each iteration, a feasibility problem with a given error limit is to be solved, which is constructed by applying the semidefinite...
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