research-article

Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization

Authors:

Bharat Bhushan Sharma,

Naveen Kumar Sharma,

Fausto Pedro Garcia MarquezAuthors Info & Claims

Volume 624, Issue C

Pages 686 - 708

https://doi.org/10.1016/j.ins.2022.11.137

Published: 01 May 2023 Publication History

Highlights

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New group theory based PSO learning for optimal parameters of fuzzy inference system.

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Concept and an overview of the new algorithm for determining the filter coefficients.

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Three-process analytical design for filter coefficient extraction to design person independent wavelet.

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The energy compaction ratio is lower than the db4 coefficients.

Abstract

This paper presents a new method for designing matched digital filters with discrete valued coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been applied to obtain the optimum codebook in design of matched wavelet function. Abelian group has been used to extract the similarity present in the input voiced signal. Fuzzy particle swarm optimization (FPSO) process is used to find approximate ideal vector quantization (VQ) codebook to be carried out for compression of data. FPSOVQ scheme utilises features of fuzzy inference method (FIM) and expert particle swarm optimization (PSO). The generated codebook consists of set of highly ideal features, which are considered as the filter coefficients. These coefficients are used in the designing of the filter. All of the phonemes in the American English language were included in the 30 sentences that were chosen from the IEEE database. The sentences were originally down-sampled from 25 kHz to 8 kHz. The magnitude responses of each filter have been drawn, which indicates the characteristics of the filter. A comparison has been provided using energy compaction ratio as a parameter to judge the performance of matched designed filter with db4 filter. Experimental results show the advantage of the developed algorithm as the average value of energy compaction ratio for sampled voice signals is 2.8046 times lower for matched designed filter.

References

[1]

M.D. Kusljevic, IIR cascaded-resonator-based filter design for recursive frequency analysis, IEEE Trans. Circuits Syst. Express Briefs 69 (9) (2022) 3939–3943.

[2]

C. Yu, L.Z. Hou, Realization of a real-time image denoising system for dashboard camera applications, IEEE Trans. Consum. Electron. 68 (2) (2022) 181–190.

[3]

N.Y. Ko, G. Song, W. Youn, S.H. You, Lie group approach to dynamic-model-aided navigation of multirotor unmanned aerial vehicles, IEEE Access 10 (2022) 72717–72730.

[4]

M.P.V. Shifas, C. Zorilă, Y. Stylianou, End-to-end neural based modification of noisy speech for speech-in-noise intelligibility improvement, IEEE/ACM Trans. Audio Speech Lang. Process. 30 (2022) 162–173.

[5]

S. Brindha, A robust and adaptive fuzzy logic based differential evolution algorithm using population diversity tuning for multi-objective optimization, Eng. Appl. Artif. Intel. 102 (2021).

[6]

S. Hu, L. Guo, J. Zhou, An iterative nonlinear filter based on posterior distribution approximation via penalized Kullback-Leibler divergence minimization, IEEE Signal Process Lett. 29 (2022) 1137–1141.

[7]

V. Elvira, L. Martino, P. Closas, Importance Gaussian quadrature, IEEE Trans. Signal Process. 69 (2021) 474–488.

[8]

S. Duan, H. Luo, H. Liu, A multi-strategy seeker optimization algorithm for optimization constrained engineering problems, IEEE Access 10 (2022) 7165–7195.

[9]

W. Ding, M. Abdel-Basset, H. Hawash, A.M. Ali, Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey, Inf. Sci. 615 (2022) 238–292.

[10]

H.-M. Feng, C.-Y. Chen, F. Ye, Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression, Expert Syst. Appl. 32 (2007) 213–222.

[11]

M. Fernández-Díaz, A. Gallardo-Antolín, An attention long short-term memory based system for automatic classification of speech intelligibility, Eng. Appl. Artif. Intel. 96 (2020).

[12]

M. Golubitsky, I. Stewart, D.G. Schaeffer, Singularities and groups in bifurcation theory: Volume ii, Springer Science & Business Media 69 (2012).

[13]

X. Pan, Y. Wang, S. He, K.S. Chin, A dynamic programming algorithm based clustering model and its application to interval type-2 fuzzy large-scale group decision-making problem, IEEE Trans. Fuzzy Syst. 30 (1) (2022) 108–120.

[14]

K.A. Dib, On fuzzy and fuzzy Group theory, Information Science 80 (1994) 253–282.

[15]

J. Dai, T. Chen, K. Zhang, The intuitionistic fuzzy concept-oriented three-way decision model, Inf. Sci. 619 (2022) 52–83.

[16]

G. Jin, C. Liu, X. Chen, An efficient deep neural network framework for COVID-19 lung infection segmentation, Inf. Sci. 612 (2022) 745–758.

[17]

M. Jian, J. Wang, H. Yu, G. Wang, X. Meng, L. Yang, J. Dong, Y. Yin, Visual saliency detection by integrating spatial position prior of object with background cues, Expert Syst. Appl. 168 (2021).

[18]

D.J. Kalita, S. Singh, SVM hyper-parameters optimization using quantized multi-pso in dynamic environment, Soft. Comput. 24 (2020) 1225–1241.

[19]

W. Assawinchaichote, C. Angeli, J. Pongfai, Proportional-Integral-Derivative Parametric Autotuning by Novel Stable Particle Swarm Optimization (NSPSO). IEEE, Access 10 (2022) 40818–40828.

[20]

X. Zhou, Y. Wu, M. Zhong, M. Wang, Artificial bee colony algorithm based on adaptive neighborhood topologies, Inf. Sci. 610 (2022) 1078–1101.

[21]

Q. Liu, Y.C. Lim, Z. Lin, Design of pipelined iir filters using two-stage frequency-response masking technique, IEEE Trans. Circuits Syst. Express Briefs 66 (2019) 873–877.

[22]

E. Egrioglu, E. Bas, M. Chen, Recurrent dendritic neuron model artificial neural network for time series forecasting, Inf. Sci. 607 (2022) 572–584.

[23]

S.Y. Chang, H.C. Wu, Tensor Wiener filter, IEEE Trans. Signal Process. 2022 (70) (2022) 410–422.

[24]

S. Zhao, D. Wang, Elite-ordinary synergistic particle swarm optimization, Inf. Sci. 609 (2022) 1567–1587.

[25]

S.R. Jondhale, V. Mohan, B.B. Sharma, J. Lloret, S.V. Athawale, Support vector regression for mobile target localization in indoor environments, Sensors 22 (1) (2022) 358.

[26]

X. Gu, Q. Shen, A self-adaptive fuzzy learning system for streaming data prediction, Inf. Sci. 579 (2021) 623–647.

[27]

P.J. McCarthy, C. Nielsen, Global sampled-data regulation of a class of fully actuated invariant systems on simply connected nilpotent matrix lie groups, IEEE Trans. Autom. Control 67 (1) (2022) 436–442.

[28]

Z. Pan, H. Fan, L. Zhang, Texture classification using local pattern based on vector quantization, IEEE Trans. Image Process. 24 (2015) 5379–5388.

[29]

T. Zhang, S. Weng, Z. Wu, J. Lin, W. Hong, Adaptive encoding based lossless data hiding method for VQ compressed images using tabu search, Inf. Sci. 602 (2022) 128–142.

[30]

J.G. Proakis, Digital signal processing: Principles algorithms and applications, Pearson Education India, 2001.

[31]

X. Wang, C. Liu, D. Jiang, An efficient double-image encryption and hiding algorithm using a newly designed chaotic system and parallel compressive sensing, Inf. Sci. 610 (2022) 300–325.

[32]

M. Rostami, K. Berahmand, E. Nasiri, S. Forouzande, Review of swarm intelligence-based feature selection methods, Eng. Appl. Artif. Intel. 100 (2021).

[33]

Bursuc S., Mauw S. Contingent payments from two-party signing and verification for abelian groups. 2022 IEEE 35th Computer Security Foundations Symposium (CSF) 2022: 195-210.

[34]

K.A. Benatti, L.G. Pedroso, A.A. Ribeiro, Theoretical analysis of classic and capacity constrained fuzzy clustering, Inf. Sci. 616 (2022) 127–140.

[35]

B.B. Sharma, et al., Designing and implementing a smart transplanting framework using programmable logic controller and photoelectric sensor, Energy Rep. 8 (2022) 430–444.

[36]

K. Schab, J.T. Bernhard, A group theory rule for predicting eigenvalue crossings in characteristic mode analyses, IEEE Antennas Wirel. Propag. Lett. 16 (2016) 944–947.

[37]

F. Wang, X. Wang, S. Sun, A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization, Inf. Sci. 602 (2022) 298–312.

Digital Library

[38]

H. Si, B.L. Ng, M.S. Rahman, J. Zhang, A novel and efficient vector quantization based cpri compression algorithm, IEEE Trans. Veh. Technol. 66 (2017) 7061–7071.

[39]

W. Fan, Y. Si, W. Yang, M. Sun, Class-specific weighted broad learning system for imbalanced heartbeat classification, Inf. Sci. 610 (2022) 525–548.

[40]

G. Stacey, R. Mahony, The role of symmetry in rigidity analysis: A tool for network localization and formation control, IEEE Trans. Autom. Control 63 (2017) 1313–1328.

[41]

G. Liu, M. Dang, J. Liu, R. Xiang, Y. Tian, N. Luo, True wide convolutional neural network for image denoising, Inf. Sci. 610 (2022) 171–184.

[42]

H. Taheri, Z.C. Xia, Slam; Definition and evolution, Eng. Appl. Artif. Intel. 97 (2021).

[43]

O. Teke, P.P. Vaidyanathan, IIR filtering on graphs with random node-asynchronous updates, IEEE Trans. Signal Process. 68 (2020) 3945–3960.

[44]

M. Zhou, M.M. Hassan, A. Goscinski, Emerging edge-of-things computing for smart cities: Recent advances and future trends, Inf. Sci. 600 (2022) 440–445.

[45]

Y. Wang, Z. Pan, R. Li, Performance re-evaluation on codewords distribution-based optimal combination of equal-average equal-variance equal-norm nearest neighbor fast search algorithm for vector quantization encoding, IEEE Trans. Image Process. 27 (2017) 718–720.

[46]

Y. Wu, M. Carricato, Identification and geometric characterization of lie triple screw systems and their exponential images, Mech. Mach. Theory 107 (2017) 305–323.

[47]

J. Zhang, X. Hu, M. Wang, H. Qiao, X. Li, T. Sun, Person re-identification via group symmetry theory, IEEE Access 7 (2019) 133686–133693.

[48]

S.U. Wood, J.K. Stahl, P. Mowlaee, Binaural codebook-based speech enhancement with atomic speech presence probability, IEEE/ACM Trans. Audio Speech Lang. Process. 27 (2019) 2150–2161.

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Yang XLi H(2023)Evolutionary-state-driven multi-swarm cooperation particle swarm optimization for complex optimization problemInformation Sciences: an International Journal10.1016/j.ins.2023.119302646:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119302
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Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 624, Issue C

May 2023

924 pages

ISSN:0020-0255

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Elsevier Science Inc.

United States

Publication History

Published: 01 May 2023

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Cited By

Tian YSong SBao SZhou DWei C(2024)Canonical triangular interval type-2 fuzzy linguistic distribution assessment EDAS approach with its application to production supplier evaluation and rankingApplied Soft Computing10.1016/j.asoc.2024.111309154:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111309
Yang XLi H(2023)Evolutionary-state-driven multi-swarm cooperation particle swarm optimization for complex optimization problemInformation Sciences: an International Journal10.1016/j.ins.2023.119302646:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119302
Tuncer OCirpan H(2023)Adaptive fuzzy based threat evaluation method for air and missile defense systemsInformation Sciences: an International Journal10.1016/j.ins.2023.119191643:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119191

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