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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Tariq Tashan
Mustansiriyah University
Electrical & Electronics Engineering

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Dual channel speech enhancement using particle swarm optimization Dalal Hamza; Tariq Tashan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp821-828

Abstract

Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.
Particle swarm optimization based multilevel MRI compression using compressive sensing Tariq Tashan; Ahmed K. Kadhim
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3873

Abstract

A multilevel compression method, for magnetic resonance imaging (MRI) images, is presented in this paper. First, the image is segmented into frames of equal size. Then, the sparsity of each frame is computed. Based on the sparsity index value, each frame is compressive sensing (CS) compressed/reconstructed at one level of four. Particle swarm optimization (PSO) is used to optimize the amount of information to be used in the CS reconstruction process, and to optimize the sparsity thresholds, that separate the different compression levels. Two-dimensional sigmoid function is suggested as a fitness function for the PSO. Six MRI images are used to evaluate the performance of the proposed method. The results show considerable gain in both peak signal to noise ratio (PSNR) and compression level (CL), when compared to single level compression, which is commonly considered in the literature.
Co-Authors Ahmed K. Kadhim Dalal Hamza