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Inamdar K.,Sardar Vallabhbhai National Institute of Technology, Surat | Kosta Y.P.,Marwadi Education Foundation Group of Institutions | Patnaik S.,Xavier Institute of Engineering
Radioelectronics and Communications Systems | Year: 2015

Metamaterials have been an attractive topic for research in the field of electromagnetics in recent years. In this paper, a criss-cross structure has been suggested; this shape has been inspired from the famous Jerusalem Cross. The software analysis of the proposed unit cell structure has been validated experimentally thus giving negative response of ɛ and μ. Following this, a microstrip patch antenna based on suggested metamaterial has been designed. The theory and design formulas to calculate various parameters of the proposed antenna have been presented. The design of a metamaterial based microstrip patch antenna has been optimized for providing of an improved gain, bandwidth and multiple frequency operations. All the antenna performance parameters are compared and presented in table and response-graphs. Also it has been observed that the physical dimensions of the metamaterial based patch antenna are smaller compared to its conventional counterpart operating in the same frequency band. The response of the patch antenna has been verified experimentally either. The important part of the research was to develop metamaterial based on some signature structures and techniques that would offer advantage in terms of bandwidth and multiple frequency operation, that is demonstrated in the paper. The unique shape suggested in this paper provides an improvement in bandwidth without reducing the gain of the antenna. © 2015, Allerton Press, Inc. Source


Deshpande A.M.,Sardar Vallabhbhai National Institute of Technology, Surat | Deshpande A.M.,College of Engineering, Pune | Patnaik S.,Xavier Institute of Engineering
11th IEEE India Conference: Emerging Trends and Innovation in Technology, INDICON 2014 | Year: 2015

This paper presents an effective single image spatially variant motion blur removal technique. Motion blur during the image capture occurs due to the relative motion between the capturing device and image being captured. This blur becomes spatially variant if it varies with position in an image. Removal of such space/shift variant blur from a single image is a challenging problem. To solve this problem, the blurred image is divided into smaller subimages assuming that each subimage is uniformly blurred. In the proposed technique each subimage is transformed into frequency domain for estimating motion blur parameters. Proposed blur parameter estimation implies dual Fourier spectrum computation and Radon transformation steps to obtain estimated values of blur length and blur angle respectively. These estimated motion blur parameters are used to prepare a local parametric blur model. These local parametric blur models are deconvolved with the blurry subimages and thus restores the original image. Restoration step is performed using parametric Wiener filtering. However, due to piecewise uniform consideration, proposed technique introduces some blocking artifacts which are later removed by applying post processing steps on the restored image. To demonstrate the usefulness of this technique for natural scenes and real blurred images it is tested on Berkeley Segmentation dataset and standard test images. The proposed technique shows effective deblurring of images under spatial variance conditions. © 2014 IEEE. Source


Morade S.S.,SVNIT | Patnaik S.,Xavier Institute of Engineering
2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015 | Year: 2015

In lip reading, selection of features play crucial role. In lip reading applications database is video, so 3 Dimensional transformation is appropriate to extract lip motion information. State of art the lip reading is based on frame normalization and frame wise feature extraction. However this is not appropriate due to chances of information loss during frame normalization. Also all the frames cannot be considered equally as they bear varying motion information. In this paper 3D transform based method is proposed for feature extraction. These features are the input to Genetic Algorithm (GA) model for discriminative analysis. Genetic Algorithm is used for dimensionality reduction and to improve the performance of the classifiers at low cost of computation. Both testing and training time for classifier is reduced by compact feature size. For experimentation of digit utterances CUAVE and Tulips database are used. The results obtained are compared with various feature selectors from WEKA software. It is found that from classification accuracy point of view proposed method is better than others. © 2015 IEEE. Source


Jose J.,Sardar Vallabhbhai National Institute of Technology, Surat | Patel J.N.,Sardar Vallabhbhai National Institute of Technology, Surat | Patnaik S.,Xavier Institute of Engineering
Optik | Year: 2015

Recent past has witnessed the use of sparse coding of images using learned dictionaries for image compression, denoising and deblurring applications. Though, few of the works reported in literature have addressed the issue of determining the optimum size of the dictionary to be learned and the extent of learning required and largely depends on trial and error approach for finding it. This paper analyses the dictionary learning process and models it using multiple regression analysis, a mathematical tool for determining the statistical relationship among variables. The model can be used as a reference for learning dictionaries from the same training set for different applications. Though the analysis returns a fit model, it lacks generality due to the specific training image set used. However, while using a larger or content specific image set for learning a dictionary, such an analysis is extremely useful. © 2015 Elsevier GmbH. Source


Patel J.N.,Sardar Vallabhbhai National Institute of Technology, Surat | Jose J.,Sardar Vallabhbhai National Institute of Technology, Surat | Patnaik S.,Xavier Institute of Engineering
IEICE Transactions on Information and Systems | Year: 2015

The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression. Copyright © 2015 The Institute of Electronics, Information and Communication Engineers Source

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