Performance of swarm based optimization techniques for designing digital FIR filter: A comparative study

Sharma, Ila; Kuldeep, B.; Kumar, Arun; Singh, Vineet Kumar

doi:10.1016/j.jestch.2016.05.013

Cited by 25 publications

(12 citation statements)

References 33 publications

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“…Modern finite impulse response digital filter design techniques demands optimization methods aimed to minimize the difference between the actual and the desired filter response and also the optimization of the impulse response coefficients. The optimization techniques spans across several approaches . Moreover, the literature reports on a wide array of optimization techniques used to reduce the number of arithmetic operations required for hardware implementation of FIR filters .…”

Section: Introductionmentioning

confidence: 99%

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Intensity‐curvature functional‐based digital high‐pass filters

Ciulla

Rechkoska

Veljanovski³

et al. 2017

Int J Imaging Syst Tech

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The intensity-curvature functional (ICF) of a model polynomial function is defined on a pixel-by-pixel basis by the ratio between the intensity-curvature term before interpolation and the intensity-curvature term after interpolation. Through the comparison with the traditional high-pass filter (HPF), this work presents evidence that the ICFs of three model polynomial functions can be tuned as HPFs. The evidence consists of the mathematical characterization of the ICF-based HPFs, qualitative comparisons in magnetic resonance imaging (MRI) of the human brain, and the determination of the finite impulse response (FIR) of the filters. The ICF-based HPFs can remove periodic noise in the low-frequency band. K E Y W O R D S finite impulse response, high-pass filter, intensity-curvature functional, model polynomial function, magnetic resonance imaging wileyonlinelibrary.com/journal/ima Int J Imaging Syst Technol.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, the literature reports on a wide array of optimization techniques used to reduce the number of arithmetic operations required for hardware implementation of FIR filters . Nonetheless, the most recent class of filter design techniques are inspired by natural phenomena and are classifiable as evolutionary and swarm optimization …”

Section: Introductionmentioning

confidence: 99%

Intensity‐curvature functional‐based digital high‐pass filters

Ciulla

Rechkoska

Veljanovski³

et al. 2017

Int J Imaging Syst Tech

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“…The design of digital filter algorithms can be classified into two main categories: finite impulse response (FIR) and infinite impulse response (IIR). The FIR filter design is characterized by the product of the ideal impulse response with a window function (Butterworth, Chebyshev, Kaiser, and Hamming among others) or a gradient-based optimization method [3][4][5]. In contrast, the IIR filter design is characterized by a non-zero impulse response function of an infinite time duration [3].…”

Section: Literature On Icfmentioning

confidence: 99%

“…The FIR filters can be optimally designed by a careful selection of coefficients for the frequency response, which can be written as a trigonometric function of the frequency [6,7]. The state-of-the-art design of optimization techniques for implementing FIR digital filters includes the evolutionary and swarm optimization approaches, such as the PSO algorithm [5,8]. ICF-based digital HPFs have been recently realized [1], where they were tested and characterized using two-dimensional images, and were observed to be belonging to the category of HPFs based on the polynomial functions [9].…”

Section: Literature On Icfmentioning

confidence: 99%

“…The research work reported in ref. [5] focused on a linear phase infinite-impulse response filter, which was designed through the nature inspired population-based global optimization algorithm known as Cuckoo Search (CS) [22]. The main aspect of the study was that the controlling parameter, which quantitatively determined the ripple (and thus the amount of energy) in the desired frequency bands (both stop and pass), was experimentally set in such a way that the best performance of the filter could be obtained.…”

Section: Applications Of Digital Filters Literaturementioning

confidence: 99%

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Intensity-curvature functional based digital high pass filter of the bivariate cubic B-spline model polynomial function

Ciulla

Agyapong

2019

Vis. Comput. Ind. Biomed. Art

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This research addresses the design of intensity-curvature functional (ICF) based digital high pass filter (HPF). ICF is calculated from bivariate cubic B-spline model polynomial function and is called ICF-based HPF. In order to calculate ICF, the model function needs to be second order differentiable and to have non-null classic-curvature calculated at the origin (0, 0) of the pixel coordinate system. The theoretical basis of this research is called intensitycurvature concept. The concept envisions to replace signal intensity with the product between signal intensity and sum of second order partial derivatives of the model function. Extrapolation of the concept in two-dimensions (2D) makes it possible to calculate the ICF of an image. Theoretical treatise is presented to demonstrate the hypothesis that ICF is HPF signal. Empirical evidence then validates the assumption and also extends the comparison between ICF-based HPF and ten different HPFs among which is traditional HPF and particle swarm optimization (PSO) based HPF. Through comparison of image space and k-space magnitude, results indicate that HPFs behave differently. Traditional HPF filtering and ICF-based filtering are superior to PSO-based filtering. Images filtered with traditional HPF are sharper than images filtered with ICF-based filter. The contribution of this research can be summarized as follows: (1) Math description of the constraints that ICF need to obey to in order to function as HPF; (2) Math of ICF-based HPF of bivariate cubic B-spline; (3) Image space comparisons between HPFs; (4) K-space magnitude comparisons between HPFs. This research provides confirmation on the math procedure to use in order to design 2D HPF from a model bivariate polynomial function.

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