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

Sharma, Bharat Bhushan; Sharma, Naveen Kumar; Banshwar, Anuj; Malik, Hitendra K.; Márquez, Fausto Pedro Garcı́a

doi:10.1016/j.ins.2022.11.137

Cited by 6 publications

(1 citation statement)

References 47 publications

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“…These disadvantages cause destruction unavoidable within the progression and facilitate the catastrophe of locating the worldwide top-quality effects. These features revealed via messy mind display that messy sequence chains perform to be unsystematic, the pathways of messy variables are ordinary, and the search space is continuously searched without replication; consequently, messy or chaotic sequences are grander to blind random search and remove the downside of the algorithm [65][66][67][68].The PSO algorithm includes a set of rules, which is intended for the multi-objective purpose of optimization layout [72,73]. When we compared the results of the PSO algorithm with the traditional PSO algorithm, the effects display that the latest one is more advanced than the traditional particle swarm optimization, which contains a different set of rules in phrases for optimization impact and velocity.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Novel Approach for Image Localization Using SVM Classifier and PSO Algorithm for Vehicle Tracking

Chinju R Nair

2023

IJRITCC

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In this paper, we propose a novel methodology for vehicular image localization, by incorporating the surveillance image object identification, using a local gradient model, and vehicle localization using the time of action. The aerial images of different traffic densities are obtained using the Histograms of Oriented Gradients (HOG) Descriptor. These features are acquired simply based on locations, angles, positions, and height of cameras set on the junction board. The localization of vehicular image is obtained based on the different times of action of the vehicles under consideration. Support Vector Machines (SVM) classifier, as well as Particle Swarm Optimization (PSO), is also proposed in this work. Different experimental analyses are also performed to calculate the efficiency of optimization methods in the new proposed system. Outcomes from experimentations reveal the effectiveness of the classification precision, recall, and F measure.

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