Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Facial expression recognition using Gabor filter and multi-layer artificial neural network

Abstract: This paper deciphers the Facial Expression Recognition (FER) using Gabor Filter and Artificial Neural Network (ANN), extracts the facial expression using Gabor filter and then classify the facial expressions using the multi-layer artificial neural network. Recognizing facial expressions of human beings in an image processing by a computer is an interesting and challenging research work. This paper is based on detection and classification of facial emotion expressions. At first, we design an algorithm to detect the face reason image in the whole image using Viola-Jones detection algorithm, then by using Gabor filter extracts the facial features in the spatial domain. The Gabor filter is used to capture the whole frequency spectrum in all directions. And then extract meaningful facial features using Gabor Filter. Finally, they have been successfully classified the facial expressions using the extracted Gabor features of face image used as an input to the Artificial Neural Network classifier. The experimental results on database images of JAFFE show the robustness and better recognition rates of the proposed approach.

Design and Analysis of Novel Dispersion Compensating Model with Chirp Fiber Bragg Grating for Long-Haul Transmission System

Abstract: In this work, a novel dispersion compensation model has been presented with chirped fiber Bragg grating (CFBG) for long-haul transmission system. The proposed model has been designed for 20 Gbps non-return to zero (NRZ) transmission system over 210 km long single-mode fiber (SMF). The proposed model is applied to Dense Wavelength Division Multiplexing (DWDM) also. Performance of the model is optimized through linear-chirped CFBG having 90 mm short grating length and it plays a significant role as a module of dispersion compensation. The proposed model enhance/improves the performance in terms of bit error rate (BER) and quality factor is ≥18 at the receiver end of systems. Further, it has also been compared with existing reported work on the basis of quality factor, BER, and eye-diagrams. The simulations of the proposed model have been carried out through OptiSystem 7.

Tunicate swarm Grey Wolf optimization for multi-path routing protocol in IoT assisted WSN networks

Abstract: Internet of Things (IoTs) have become popular for connected people as well as objects for collecting and exchanging data based on embedded sensors. In IoT-assisted Wireless sensor Networks (WSN), the nodes are considered as the resource parameters in several ways, like computing resources, energy resources, and storage resources such that the robust multipath routing protocols are needed for maintaining long network lifetime and for achieving higher energy utilization. Hence, this paper presents the multipath routing protocol using the proposed optimization method, named Tunicate swarm Grey Wolf optimization (TSGWO) algorithm in the IoT assisted WSN network. By multipath routing protocol, the multipath is designed by multipath source node to several destinations. The multipath source node forwarding packet to multiple destinations simultaneously. At first, the nodes in IoT-assisted WSN network is simulated together and performs the cluster head selection using Fractional Gravitational Search algorithm (FGSA), and then the multipath routing process is done on the basis of proposed TSGWO in which the routing path is selected by considering the fitness parameters, like QoS parameters and trust factors. The QoS parameters include the delay, energy, link lifetime, as well as distance. The path with the minimum distance is selected as optimal path using fitness parameter. The proposed optimization algorithm effectively performs the multipath routing mechanism by integrating the parametric features from both the optimization algorithm. After that, the route maintenance process is carried out in the simulated IoT network to recover the link breakage based on DRINA. The proposed TSGWO outperformed other methods with maximal average Residual energy of 2.161 J, maximal link lifetime of 0.075 s, maximal PDR of 96.38%, and maximal throughput of429.49 Kbps.