Application of Quasi Monte Carlo Integration Technique in Efficient Capacitance Computation

Mishra, M. K.; Gupta, Nisha; Dubey, Ankhuri; Shekhar, Shashank

doi:10.2528/pier09011310

Cited by 11 publications

(10 citation statements)

References 17 publications

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“…Moreover, there is no strong evidence that when the dimension of the problem is moderate (e.g., d ≤ 15) it makes a great deal of difference whether one uses Halton, Faure or Sobol sequence. The suitability of the proposed technique is demonstrated for charge density problem for several examples in [10].…”

Section: Qmci Technique Implementationmentioning

confidence: 99%

“…Since Halton sequence points are self-avoiding and no two Halton sequences are same, the QMCI tackles the singularity problem effectively for integrals in (9). This can be further elaborated with the help of Table 1 [10]. As evident, for a three dimensional Halton point, the x, y and z-coordinates are generated using three different bases such as base 2, base 3 and base 5 respectively.…”

Section: Qmci Technique Implementationmentioning

confidence: 99%

“…It is also proved that the use of Halton sequences due to their inherent property automatically makes the kernel non-singular. The mathematical concept of the generation of Halton sequences is elaborated in [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Quasi Monte Carlo Integration Technique in Em Scattering From Finite Cylinders

Mishra¹,

Gupta²

2009

PIER Letters

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Abstract-In this work, a Quasi Monte Carlo (QMC) IntegrationTechnique using Halton Sequence is proposed for the Electric Field Integral Equation (EFIE) in the Method of Moments (MoM) solution for scattering problems. It is found that the Halton Sequence used in QMC integration scheme is capable of handling the singularity issue in the EFIE automatically and at the same time provides solution to the scattering problems very easily. Finally the proposed technique is applied to solve the scattering problem from a finite cylinder employing the entire domain basis function expansions. The results obtained show a good agreement between the proposed and conventional technique.

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Section: Qmci Technique Implementationmentioning

confidence: 99%

Section: Qmci Technique Implementationmentioning

confidence: 99%

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Application of Quasi Monte Carlo Integration Technique in Em Scattering From Finite Cylinders

Mishra¹,

Gupta²

2009

PIER Letters

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“…Magnetic resonance imaging (MRI) is an imaging technique that produces high quality images of the anatomical structures of the human body, especially in the brain, and provides critical information for clinical diagnosis and biomedical research [1][2][3][4][5][6]. The diagnostic values of MRI are greatly magnified by the automated and accurate classification of the MR images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Magnetic Resonance Brain Image Classification Based on Adaptive Chaotic Pso

Zhang¹,

Wang²,

Wu³

2010

PIER

117

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is an integral component of the analysis and interpretation of neuroimaging. Many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. This method first employs a wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to an FNN, and these parameters are optimized via adaptive chaotic particle swarm optimization (ACPSO). K-fold stratified cross validation was used to enhance generalization. We applied the proposed method on 160 images (20 normal, 140 abnormal), and found that the classification accuracy is as high as 98.75% while the computation time per image is only 0.0452 s.

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“…However, if two separate quasi random sequences spread over the domain are chosen for the source and the field points, then they are not equal to each other, and thus the integration will not be singular. The QMCI technique [5][6][7] using Halton sequence is employed here for this purpose for the solution of singular integral equation, which uses prime numbers as bases to generate uniformly distributed quasi random points in the domain of integration for function evaluation. This is because of the fact that sample points in a quasi -random sequence are, in a precise sense, "maximally avoiding" of each other.…”

Section: Introductionmentioning

confidence: 99%

Computation of Protection Zone of a Lightning Rod Using Method of Moments and Monte Carlo Integration Technique

Srivastava¹,

Dubey²,

Shekhar³

et al. 2011

JEMAA

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Application of Quasi Monte Carlo Integration Technique in Efficient Capacitance Computation

Cited by 11 publications

References 17 publications

Application of Quasi Monte Carlo Integration Technique in Em Scattering From Finite Cylinders

Application of Quasi Monte Carlo Integration Technique in Em Scattering From Finite Cylinders

A Novel Method for Magnetic Resonance Brain Image Classification Based on Adaptive Chaotic Pso

Computation of Protection Zone of a Lightning Rod Using Method of Moments and Monte Carlo Integration Technique

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