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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
D. Khalandar Basha
Institute of Aeronautical Engineering
Economics, Econometrics & Finance

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ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving B. Naresh; S. Rambabu; D. Khalandar Basha
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 6, No 3: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.686 KB) | DOI: 10.11591/ijres.v6.i3.pp127-132

Abstract

This paper discussed about EEG-Based Drowsiness Tracking during Distracted Driving based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity commands through controller device in real time. With these signals from brain in mat lab signals spectrum analyzed and estimates driver concentration and meditation conditions. If there is any nearest vehicles to this vehicle a voice alert given to driver for alert. And driver going to sleep gives voice alert for driver using voice chip. And give the information about traffic signal indication using RFID. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human feelings, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) is used to receive the raw data from brain wave sensor and it is used to extract and process the signal using Mat lab platform. The nearest vehicles information is information is taken through ultrasonic sensors and gives voice alert. And traffic signals condition is detected through RF technology.
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 B. Naresh D. Naresh Kumar Md. Abdul Rawoof N. Madhu N. Madhur S. Rambabu Umasankar Ch Umasankar Ch.