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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
D. Naresh Kumar
MLR Institute of Technology
Economics, Econometrics & Finance

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Verilog based efficient convolution encoder and viterbi decoder Md. Abdul Rawoof; Umasankar Ch.; D. Naresh Kumar; D. Khalandar Basha; N. Madhur
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.907 KB) | DOI: 10.11591/ijres.v8.i1.pp75-80

Abstract

In the today’s digital communication Systems, transmission of data with more reliability and efficiency is the most challenging issue for data communication through channels. In communication systems, error correction technique plays a vital role. In error correction techniques, The capacity of data can be enhanced by adding the redundant information for the source data while transmitting the data through channel. It mainly focuses on the awareness of convolution encoder and Viterbi decoder. For decoding convolution codes Viterbi algorithm is preferred.
A Novel Face Recognition Algorithm Using Gabor - based KPCA Umasankar Ch; D. Naresh Kumar; Md. Abdul Rawoof; D. Khalandar Basha; N. Madhu
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 7, No 2: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (317.557 KB) | DOI: 10.11591/ijres.v7.i2.pp124-130

Abstract

The Gabor wavelets are used to extract facial features, and then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this work, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical properties of the input features, but also adopts an Eigen mask to emphasize those important facial feature points The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database.
Co-Authors D. Khalandar Basha Md. Abdul Rawoof N. Madhu N. Madhur Umasankar Ch Umasankar Ch.