Improved T-Matrix Method for Simultaneous Reconstruction of Dielectric and Perfectly Conducting Scatterers

Gan, Yongji; Yin, Chengyou; Fan, Qimeng; Li, Anqi

doi:10.1109/access.2020.3014257

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Given these factors, chromosomes are picked and mating operations are carried out on them for the new generational population. The fitness variable surveys the idea of each successor [31]. The study here will utilize an HGA approach which is an adjusted GA approach for greater legitimacy.…”

Section: Genetic Algorithm (Ga) In Iomt-cloudmentioning

confidence: 99%

IoMT-Cloud Task Scheduling Using AI

Hussain¹,

Al‐Turjman²

2023

Computer Modeling in Engineering &Amp; Sciences

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The internet of medical things (IoMT) empowers patients to get adaptable, and virtualized gear over the internet. Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it. Thus, a proposition is being made for a distinct scheduling technique to suitably meet these solicitations. To manage the scheduling issue, an artificial intelligence (AI) method known as a hybrid genetic algorithm (HGA) is proposed. The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches. The CloudSim is utilized to quantify its effect on various parameters like time, resource utilization, cost, and throughput. The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47 ms and a reduced time of 40.16 ms. Thus, the experimented outcomes show that the HGA reduces cost as well as time profoundly.

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Section: Genetic Algorithm (Ga) In Iomt-cloudmentioning

confidence: 99%

IoMT-Cloud Task Scheduling Using AI

Hussain¹,

Al‐Turjman²

2023

Computer Modeling in Engineering &Amp; Sciences

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“…However, the computational cost of the required singular value decomposition is large and the reconstruction accuracy of the T-matrix method is limited. In 2020, Gan et al [12] proposed an improved T-matrix method that uses a genetic algorithm to optimise the truncation and assignment parameters before the start of iteration. In the same year, they proposed a fast algorithm [13] that applies qualitative results from the linear sampling method to the virtual experiment circles in the framework of the T-matrix method, transforming the parameter inversion into solving a series of univariate equations, thus significantly improving the imaging efficiency.…”

Section: Introductionmentioning

confidence: 99%

An inverse scattering reconstruction method for perfect electric conductor‐dielectric hybrid target based on physics‐inspired network

Zhang

Yin

et al. 2023

IET Radar Sonar & Navi

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To improve the imaging accuracy in solving inverse scattering problems with mixed boundary conditions, the authors propose an inverse scattering imaging method based on a physics‐inspired neural network. First, the contrast source inversion and the T‐matrix method are unified to establish a combined parameter model, in which the characteristics of perfect electric conductor and dielectric scatterers are represented by the T‐matrix coefficients t and the dielectric contrast respectively. Considering the influence of singularity of conductor contrast, the PEC region and the DIE contrast are reconstructed separately. Then, an alternative parameter updating neural network method, called APU‐Net, is proposed to update the contrast of dielectric scatterers and the T‐matrix of perfect electric conductor scatterers alternately. The experimental results demonstrate the strong performance of APU‐Net for inverse scattering imaging of PEC‐DIE hybrid targets as well as its improved generalisation ability over existing methods.

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“…Given these factors, chromosomes are picked, and mating operations are carried out on them for the new generational population. The fitness variable surveys the idea of each successor [25]. This paper will utilize an HGA approach which is an adjusted GA approach for greater legitimacy.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Algorithm for IOMT-Cloud Task Scheduling

Hussain¹,

Al‐Turjman²

2022

Wireless Communications and Mobile Computing

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Task scheduling for the cloud is one of the main advances in IoMT stage, which impacts the whole execution of the cloud resource. Cloud is a proficient headway for computation, and it encompasses data storage, management, and manipulation in large volumes. Thus, a proposition is being made a better approach to proffer task scheduling in the cloud. In this case, a new hybrid genetic algorithm (HGA) is proposed. The proposed HGA method will be justified by contrasting it with the previous researches and approaches. The CloudSim is utilized to quantify their effect on various metrics like timing factors and resource utilization. The proposed HGA technique enhanced the viability of task scheduling with a better execution rate of 32.57 ms. Thus, the experimented outcomes show that the HGA also reduces cost profoundly.

show abstract

Improved T-Matrix Method for Simultaneous Reconstruction of Dielectric and Perfectly Conducting Scatterers

Cited by 5 publications

References 28 publications

IoMT-Cloud Task Scheduling Using AI

IoMT-Cloud Task Scheduling Using AI

An inverse scattering reconstruction method for perfect electric conductor‐dielectric hybrid target based on physics‐inspired network

Hybrid Genetic Algorithm for IOMT-Cloud Task Scheduling

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