Applying Shannon Wavelet Basis Functions to the Method of Moments for Evaluating the Radar Cross Section of the Conducting and Resistive Surfaces

Danesfahani, R.; Hatamzadeh, Saeed; Babolian, E.; Masouri, and Zahra

doi:10.2528/pierb08062601

Cited by 17 publications

(9 citation statements)

References 58 publications

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“…However, it requires large storage and is computationally more expensive [6,7]. In order to reduce the feature vector dimensions and increase the discriminative power, the principal component analysis (PCA) [8] method has been used.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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“…The method of moments (MoM) has already been successfully used in various electromagnetic scattering problems [1][2][3][4][5][6]. In the MoM, the electric field integral equation (EFIE) is reduced to a matrix equation solved for the induced currents The radar cross section (RCS) is computed from the knowledge of the induced currents The generation of the matrix equation and its solution are the two major computationally intensive operations in MoM, especially for frequencysweeping cases.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Radar Cross Section Computation Using Chebyshev Approximation in Both Broad Frequency Band and Angular Domains Simultaneously

Jin¹,

Gong²,

Lu³

et al. 2010

PIER Letters

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Abstract-To predict the three-dimensional radar cross section (RCS) pattern of an arbitrary shaped perfectly electric conductor objects in both a broad frequency band and angular domains simultaneously, the method of moments (MoM) combined with the Chebyshev polynomial approximation is presented. The induced current is expanded by a bivariate Chebyshev series. Using this function, the induced current can be obtained at any frequency and angle within the desired frequency band and angular domains. Numerical results show that the proposed method is found to be superior in terms of the CPU time to obtain the three-dimensional RCS pattern compared with the direct solution by MoM repeating the calculations at each frequency and angle. Good agreement between the presented method and the direct MoM is observed.

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“…Integral equation (IE) is widely used for the numerical analysis of electromagnetic radiation and scattering [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Especially the time domain integral equation (TDIE) has been receiving much interest in studying many practical transient electromagnetic problems [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Tdie/Tdpo Hybrid Method for the Analysis of Tm Transient Scattering From Two-Dimensional Combinative Conducting Cylinders

Qin¹,

Gong²,

Wang³

et al. 2010

PIER

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Applying Shannon Wavelet Basis Functions to the Method of Moments for Evaluating the Radar Cross Section of the Conducting and Resistive Surfaces

Cited by 17 publications

References 58 publications

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Fast and Accurate Radar Cross Section Computation Using Chebyshev Approximation in Both Broad Frequency Band and Angular Domains Simultaneously

A Tdie/Tdpo Hybrid Method for the Analysis of Tm Transient Scattering From Two-Dimensional Combinative Conducting Cylinders

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