The Experts below are selected from a list of 10788 Experts worldwide ranked by ideXlab platform

Amit Konar - One of the best experts on this subject based on the ideXlab platform.

  • brief paper a swarm intelligence approach to the synthesis of two dimensional iir filters
    Engineering Applications of Artificial Intelligence, 2007
    Co-Authors: Swagatam Das, Amit Konar
    Abstract:

    The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple. This paper investigates a novel approach to the designing of two-dimensional zero phase infinite impulse response (IIR) digital filters using the PSO algorithm. The design task is reformulated as a Constrained Minimization Problem and is solved by a modified PSO algorithm. Numerical results are presented. The paper also demonstrates the superiority of the proposed design method by comparing it with two recently published filter design methods and two other state of the art optimization techniques.

  • two dimensional iir filter design with modern search heuristics a comparative study
    International Journal of Computational Intelligence and Applications, 2006
    Co-Authors: Swagatam Das, Amit Konar
    Abstract:

    In the past few years, there has been a massive growth in the field of biologically inspired global search heuristics. Computational cost having been reduced almost dramatically, researchers from all corners are taking more interset in following the underlying principles of nature to solve nearly intractable search Problems. In this paper, we attempt to solve one very important optimization Problem arising in the field of two-dimensional IIR (infinite impulse response) filter design, with three naturally inspired global search algorithms. We have used a state-of-the-art real coded genetic algorithm (GA), one very recent and modified version of the particle swarm opimization (PSO) and finally an improved version of the differential evolution (DE) algorithm. The DE algorithm has been modified by us to prevent its premature convergence to some suboptimal region of the search space. The design task is formulated as a Constrained Minimization Problem and solved by the three metaheuristics. Numerical results are presented over three difficult instances of the design Problem. The study also compares the results with two recently published filter design methods. Our experiments reveal that the DE family of algorithms should receive primary attention in solving the Constrained multidimensional filter design tasks.

M N S Swamy - One of the best experts on this subject based on the ideXlab platform.

  • design of two dimensional recursive filters using genetic algorithms
    IEEE Transactions on Circuits and Systems I-regular Papers, 2003
    Co-Authors: Nikos E Mastorakis, I F Gonos, M N S Swamy
    Abstract:

    In this paper, we examine a new design method for two-dimensional (2-D) recursive digital filters using genetic algorithms (GAs). The design of the 2-D filter is reduced to a Constrained Minimization Problem the solution of which is achieved by the convergence of an appropriate GA. Theoretical results are illustrated by a numerical example. Also, comparison with the results of some previous design methods is attempted.

Swagatam Das - One of the best experts on this subject based on the ideXlab platform.

  • brief paper a swarm intelligence approach to the synthesis of two dimensional iir filters
    Engineering Applications of Artificial Intelligence, 2007
    Co-Authors: Swagatam Das, Amit Konar
    Abstract:

    The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple. This paper investigates a novel approach to the designing of two-dimensional zero phase infinite impulse response (IIR) digital filters using the PSO algorithm. The design task is reformulated as a Constrained Minimization Problem and is solved by a modified PSO algorithm. Numerical results are presented. The paper also demonstrates the superiority of the proposed design method by comparing it with two recently published filter design methods and two other state of the art optimization techniques.

  • two dimensional iir filter design with modern search heuristics a comparative study
    International Journal of Computational Intelligence and Applications, 2006
    Co-Authors: Swagatam Das, Amit Konar
    Abstract:

    In the past few years, there has been a massive growth in the field of biologically inspired global search heuristics. Computational cost having been reduced almost dramatically, researchers from all corners are taking more interset in following the underlying principles of nature to solve nearly intractable search Problems. In this paper, we attempt to solve one very important optimization Problem arising in the field of two-dimensional IIR (infinite impulse response) filter design, with three naturally inspired global search algorithms. We have used a state-of-the-art real coded genetic algorithm (GA), one very recent and modified version of the particle swarm opimization (PSO) and finally an improved version of the differential evolution (DE) algorithm. The DE algorithm has been modified by us to prevent its premature convergence to some suboptimal region of the search space. The design task is formulated as a Constrained Minimization Problem and solved by the three metaheuristics. Numerical results are presented over three difficult instances of the design Problem. The study also compares the results with two recently published filter design methods. Our experiments reveal that the DE family of algorithms should receive primary attention in solving the Constrained multidimensional filter design tasks.

Akira Terui - One of the best experts on this subject based on the ideXlab platform.

  • GPGCD: An iterative method for calculating approximate GCD of univariate polynomials
    Theoretical Computer Science, 2013
    Co-Authors: Akira Terui
    Abstract:

    We present an iterative algorithm for calculating approximate greatest common divisor (GCD) of univariate polynomials with the real or the complex coefficients. For a given pair of polynomials and a degree, our algorithm finds a pair of polynomials which has a GCD of the given degree and whose coefficients are perturbed from those in the original inputs, making the perturbations as small as possible, along with the GCD. The Problem of approximate GCD is transferred to a Constrained Minimization Problem, then solved with the so-called modified Newton method, which is a generalization of the gradient-projection method, by searching the solution iteratively. We demonstrate that, in some test cases, our algorithm calculates approximate GCD with perturbations as small as those calculated by a method based on the structured total least norm (STLN) method and the UVGCD method, while our method runs significantly faster than theirs by approximately up to 30 or 10 times, respectively, compared with their implementation. We also show that our algorithm properly handles some ill-conditioned polynomials which have a GCD with small or large leading coefficient.

  • GPGCD, an iterative method for calculating approximate GCD, for multiple univariate polynomials
    ACM Communications in Computer Algebra, 2011
    Co-Authors: Akira Terui
    Abstract:

    We present an extension of our GPGCD method, an iterative method for calculating approximate greatest common divisor (GCD) of univariate polynomials, to multiple polynomial inputs. For a given pair of polynomials and a degree, our algorithm finds a pair of polynomials which has a GCD of the given degree and whose coefficients are perturbed from those in the original inputs, making the perturbations as small as possible, along with the GCD. In our GPGCD method, the Problem of approximate GCD is transferred to a Constrained Minimization Problem, then solved with the so-called modified Newton method, which is a generalization of the gradient-projection method, by searching the solution iteratively. In this paper, we extend our method to accept more than two polynomials with the real coefficients as an input.

  • an iterative method for calculating approximate gcd of univariate polynomials
    International Symposium on Symbolic and Algebraic Computation, 2009
    Co-Authors: Akira Terui
    Abstract:

    We present an iterative algorithm for calculating approximate greatest common divisor (GCD) of univariate polynomials with the real coefficients. For a given pair of polynomials and a degree, our algorithm finds a pair of polynomials which has a GCD of the given degree and whose coefficients are perturbed from those in the original inputs, making the perturbations as small as possible, along with the GCD. The Problem of approximate GCD is transfered to a Constrained Minimization Problem, then solved with a so-called modified Newton method, which is a generalization of the gradient-projection method, by searching the solution iteratively. We demonstrate that our algorithm calculates approximate GCD with perturbations as small as those calculated by a method based on the structured total least norm (STLN) method, while our method runs significantly faster than theirs by approximately up to 30 times, compared with their implementation. We also show that our algorithm properly handles some ill-conditioned Problems with GCD containing small or large leading coefficient.

Ginmo Jason Chung - One of the best experts on this subject based on the ideXlab platform.

  • a fast algorithm for euler s elastica model using augmented lagrangian method
    Siam Journal on Imaging Sciences, 2011
    Co-Authors: Xuecheng Tai, Jooyoung Hahn, Ginmo Jason Chung
    Abstract:

    Minimization of functionals related to Euler's elastica energy has a wide range of applications in computer vision and image processing. A high order nonlinear partial differential equation (PDE) needs to be solved, and the gradient descent method usually takes high computational cost. In this paper, we propose a fast and efficient numerical algorithm to solve Minimization Problems related to Euler's elastica energy and show applications to variational image denoising, image inpainting, and image zooming. We reformulate the Minimization Problem as a Constrained Minimization Problem, followed by an operator splitting method and relaxation. The proposed Constrained Minimization Problem is solved by using an augmented Lagrangian approach. Numerical tests on real and synthetic cases are supplied to demonstrate the efficiency of our method.