The design of variable fractional delay (VFD) finite-impulse response (FIR) filters with group delay error constraints in the least squares (LS) sense is investigated in this paper. Most of methods in the literature f...
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ISBN:
(纸本)9789881563903
The design of variable fractional delay (VFD) finite-impulse response (FIR) filters with group delay error constraints in the least squares (LS) sense is investigated in this paper. Most of methods in the literature for designing VFD filters with constraints are based on vector variables, leading to a heavy computational load. To solve this problem more efficiently, we propose a matrix-based iterative constrained least squares (CLS) algorithm. By linearizing the highly nonlinear constraints as linear ones, the proposed algorithm transforms the original nonconvex design problem into a series of CLS subproblems. Each CLS subproblem is solved by using an efficient matrix-based active-set method. As a result, the proposed algorithm can fast converge to the LS solution with described group delay errors. Finally, design examples are provided to show the good performance of the proposed algorithm.
This paper considers the minimax design of two-dimensional (2D) finite impulse response (FIR) half-band filters. First, the design problem is formulated in a matrix form, where the half-band constraints are expressed ...
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This paper considers the minimax design of two-dimensional (2D) finite impulse response (FIR) half-band filters. First, the design problem is formulated in a matrix form, where the half-band constraints are expressed as a pair of matrix equations. By matrix transformations, the constrained minimax problem is transformed into an unconstrained one. Then, we propose an efficient iterative reweighted least squares (IRLS) algorithm to solve this problem. The weighted least squares (WLS) subproblems arising from the IRLS algorithm are solved using a generalized conjugate gradient (GCG) algorithm. Moreover, the GCG algorithm is guaranteed to converge in a finite number of iterations. In the proposed algorithm, the design coefficients of filters are solved in their matrix form, leading to a great saving in computations and memory space. Design examples and comparisons with existing methods are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.
The design of variable fractional delay（VFD） finite-impulse response（FIR） filters with group delay error constraints in the least squares（LS） sense is investigated in this paper. Most of methods in the literatur...
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The design of variable fractional delay（VFD） finite-impulse response（FIR） filters with group delay error constraints in the least squares（LS） sense is investigated in this paper. Most of methods in the literature for designing VFD filters with constraints are based on vector variables, leading to a heavy computational load. To solve this problem more efficiently, we propose a matrix-based iterative constrained least squares（CLS） algorithm. By linearizing the highly nonlinear constraints as linear ones, the proposed algorithm transforms the original nonconvex design problem into a series of CLS *** CLS subproblem is solved by using an efficient matrix-based active-set method. As a result, the proposed algorithm can fast converge to the LS solution with described group delay errors. Finally, design examples are provided to show the good performance of the proposed algorithm.
Fast design of two-dimensional FIR filters in the least lp-norm sense is investigated in this brief. The design problem is first formulated in a matrix form and then solved by a matrix-based iterative reweighted least...
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Fast design of two-dimensional FIR filters in the least lp-norm sense is investigated in this brief. The design problem is first formulated in a matrix form and then solved by a matrix-based iterative reweighted least squares algorithm. The proposed algorithm includes two loops: one for updating the weighting function and the other for solving the weighted least squares (WLS) subproblems. These WLS subproblems are solved using an efficient matrix-based WLS algorithm, which is an iterative procedure with its initial iterative matrix being the solution matrix in the last iteration, resulting in a considerable CPU-time saving. Through analysis, the new algorithm is shown to have a lower complexity than existing methods. Three design examples are provided to illustrate the high computational efficiency and design precision of the proposed algorithm.
An efficient algorithm is proposed for the minimax design of two-dimensional（2-D） linear-phase FIR filters with frequency inequality constraints. The method converts the constrained minimax design problem into a ser...
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An efficient algorithm is proposed for the minimax design of two-dimensional（2-D） linear-phase FIR filters with frequency inequality constraints. The method converts the constrained minimax design problem into a series of unconstrained weighted minimax design problems, where the weighting function is appropriately updated in each iteration. Further, these unconstrained weighted minimax design problems are solved efficiently using a matrix-based iterative reweighted least-squares（IRLS） algorithm. Moreover, the proposed algorithm is guaranteed to converge to the optimal solution provided it exists. Finally,a design example and comparison with existing method are provided to demonstrate the effectiveness and efficiency of the proposed method.
An efficient algorithm is presented in this paper for the constrained least-squares design of centrally symmetric two dimensional (2-D) finite impulse response filters. The problem is basically a quadratic programming...
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ISBN:
(纸本)9781479953417
An efficient algorithm is presented in this paper for the constrained least-squares design of centrally symmetric two dimensional (2-D) finite impulse response filters. The problem is basically a quadratic programming with positive-definite quadratic cost and linear constraint functions. This paper formulates both the cost and constraint functions in terms of two coefficient matrices of small size and then presents an efficient algorithm to solve the quadratic programming directly for the two coefficient matrices rather than vectorizing it first as in conventional methods. Design example and comparisons demonstrate the effectiveness and high efficiency of the proposed algorithm.
A new matrix-based algorithm is presented for the weighted least squares (WLS) design problem of two-dimensional (2-D) finite impulse response (FIR) filters with centrally (anti) symmetric response. Firstly, the optim...
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ISBN:
(纸本)9789881563897
A new matrix-based algorithm is presented for the weighted least squares (WLS) design problem of two-dimensional (2-D) finite impulse response (FIR) filters with centrally (anti) symmetric response. Firstly, the optimality condition of such optimization problem is obtained and expressed as a pair of matrix equations involving two matrix variables. Then, by introducing a parameter, we develop a matrix-based iterative algorithm to solve the optimality condition equations. Further, the convergence of the algorithm is established by using linear operators theory. Because matrix iterative operations are used and the coefficients of filters are in their natural matrix forms, great savings in computations and memory space required are achieved. Finally, a design example and comparisons to existing methods are provided to illustrate the effectiveness and efficiency of the proposed algorithm.
Two-dimensional (2-D) nonlinear-phase finite impulse response (FIR) filters have found many applications in signal processing and communication systems. This paper considers the elliptic-error and phase-error constrai...
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Two-dimensional (2-D) nonlinear-phase finite impulse response (FIR) filters have found many applications in signal processing and communication systems. This paper considers the elliptic-error and phase-error constrained least-squares design of 2-D nonlinear-phase FIR filters, and develops a matrix-based algorithm to solve the design problem directly for the filter's coefficient matrix rather than vectorizing it first as in the conventional methods. The matrix-based algorithm makes the design to consume much less design time than existing algorithms. Design examples and comparisons with existing methods demonstrate the effectiveness and high efficiency of the proposed design method.
A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recentl...
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A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first. This process induces a high computational complexity and may also lead to loss of the geometric feature of samples. The proposed BDFLA is a matrix-based algorithm. It aims to minimise the within-class scatter and maximise the between-class scatter based on a two-dimensional (2D) NFL. Experimental results on two-image databases confirm the effectiveness.
In a total grouping of dynamic interconnection networks, safe and quick routing is so important. The Benes network is one of the dynamic interconnection networks that are good for telephone networks, multi-processor s...
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In a total grouping of dynamic interconnection networks, safe and quick routing is so important. The Benes network is one of the dynamic interconnection networks that are good for telephone networks, multi-processor systems, parallel computers, ATM switches and Navigation and radio communication between robots. In this paper, two models of Benes routing algorithm is introduced then compare them with looping;Hassan-Jose and fast algorithms in the speed of running time and implementation then introduce the optimum algorithm. (C) 2014 The Authors. Published by Elsevier B.V.
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