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Patel S.K.,Chandubhai S Patel Institute Of Technology | Kosta Y.P.,Marwadi Education Foundations Group Of Institutions
Waves in Random and Complex Media | Year: 2012

Magnetic properties can be imparted to a naturally nonmagnetic material by metallic inclusions. A multiband meandered square microstrip patch antenna loaded with such a metamaterial is reported. Metamaterials exhibit qualitatively new electromagnetic response functions that cannot be found in nature. The inclusion of these structures allows simultaneous operation over several frequencies. The antenna was designed to function in multiple bands in the frequency range 0.6-2.2GHz. The antenna has eight working frequency bands and its centre frequencies are 670MHz, 1185 MHz, 1293MHz, 1747MHz, 1909MHz, 1999MHz, 2063MHz and 2134MHz. The metamaterial also enhances the gain of the antenna, which is applicable for several wireless applications. Design results were obtained by a high frequency structure simulator which is used for simulating microwave passive components. © 2012 Taylor and Francis Group, LLC. Source


Chaudhuri A.,Marwadi Education Foundations Group Of Institutions
AI Communications | Year: 2014

In the recent past, credit approval is a significant problem in credit risk management. Making decision to approve a credit has been a source of major concern for financial institutions. As such the problem is formulated as classification problem where making correct decision yields maximum returns. The classification task is taken care of by modified fuzzy support vector machine (MFSVM). It is variant of fuzzy support vector machine (FSVM) developed by Chaudhuri et al. The inherent vagueness and uncertainty in training samples are handled by new fuzzy membership function with hyperbolic tangent kernel. The success of classification lies in considering fuzzy membership function as function of center and radius of each class in feature space and representing it with kernel. In nonlinear training samples, input space is mapped into high dimensional feature space to compute separating surface using linear separating method. The different input points make unique contributions to decision surface. MFSVM produces significant results for Australian Credit Approval dataset. The model is tested with both linear and nonlinear kernels. MFSVM performance is also assessed in light of number of support vectors required to model the data. The effect of variability in prediction and generalization of MFSVM is studied with respect to parameters C and δ2. The area under curve helps to reduce imbalance issues in the dataset considered. The training samples are either linear or nonlinear separable. MFSVM effectively handles the issue of nonlinear classification problem. Experimental results on both artificial and real datasets support the fact that MFSVM achieves superior performance in reducing outliers' effects than FSVM. © 2014 - IOS Press and the authors. All rights reserved. Source


Patel S.K.,Gujarat University | Kosta Y.P.,Marwadi Education Foundations Group Of Institutions
International Journal of Applied Electromagnetics and Mechanics | Year: 2013

Magnetic properties were imparted to a naturally nonmagnetic material by metallic inclusions. In this paper, microstrip based meandered radiating structure using artificial substrate has been reported. Here metallic split ring resonators are added in the conventional dielectric substrate to make artificial substrate material. The patch size is reduced 50% using the artificial material. Comparison of artificial substrate material design and conventional material design is shown in this paper. Artificial substrate Design has one band at 0.64 GHz centre frequency. Design results are obtained by a HFSS (High Frequency Structure Simulator) which is used for simulating microwave passive components. © 2013-IOS Press and the authors. All rights reserved. Source


Patel S.K.,Gujarat University | Kosta Y.P.,Marwadi Education Foundations Group Of Institutions
Journal of Modern Optics | Year: 2013

A square multiband truncated microstrip patch antenna was investigated using a square-tooth split ring resonator for multiband applications in both S- and C-bands. The square-tooth split ring resonator is formed from metallic inclusions in a substrate to create a metamaterial. We introduce a new square-tooth split ring resonator which increases the radiation of the antenna as well as the bandwidth. This new design creates a slow wave structure. The square-tooth addition to the split ring resonator works like a slow wave structure. The square-tooth split ring resonator design is compared with the simple split ring resonator design. The square-tooth design has four bands with center frequencies of 3.88, 4.81, 5.4, and 5.62 GHz, whereas design with the simple split ring resonator has just three bands with center frequencies of 3.88, 4.74, and 5.50 GHz. The bandwidth is increased by 20% to 30% using the square-tooth split ring resonator compared to the simple split ring resonator. © 2014 Taylor & Francis. Source


Chaudhuri A.,Marwadi Education Foundations Group Of Institutions
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Support vector regression (SVR) is a promising regression tool based on support vector machine (SVM). It is a paradigm for identifying estimated models that are based on minimizing Vapnik's loss function of residuals. It is based on linear combination of displaced replicas of kernel function. Single kernel is ineffective when function approximated is non stationary. This problem is taken care of by hierarchical modified regularized least squares fuzzy support vector regression (HMRLFSVR). It is developed from modified regularized least squares fuzzy support vector regression (MRLFSVR) and regularized least squares fuzzy support vector regression (RLFSVR). HMRLFSVR consists of a set of hierarchical layers each containing MRLFSVR with Gaussian kernel at given scale. On increasing scale layer by layer details are incorporated inside regression function. It adapts local scale to data keeping number of support vectors and configuration time comparable with classical SVR. It considers disadvantages when approximating non stationary function using single kernel approach where it is not able to follow variations in frequency content in different regions of input space. The approach is based on interleaving regression estimate with pruning activity. It denoises original data obtaining an effective multiscale reconstruction. The tuning of SVR configuration parameters becomes simplified in HMRLFSVR. Favourable results over noisy synthetic and real datasets are obtained when compared with multikernel approaches. © 2013 Springer-Verlag Berlin Heidelberg. Source

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