Tripura Institute of Technology

About: Tripura Institute of Technology is a based out in . It is known for research contribution in the topics: Electric power system & Renewable energy. The organization has 63 authors who have published 92 publications receiving 510 citations.

An Improved Route Maintenance Strategy for QoS Based AODV Routing Protocol

Abstract: The challenges of mobile ad hoc network are to maintain quality of services and performance. Many routing protocols like AODV discover routes whenever required by the source node and the benefit in doing so is that no prior knowledge of the topology is required. The AODV routing protocol is commonly and effectively used for mobile, ad-hoc nodes [4, 7, 8, 15, 22, 23]. So far, many maintenance strategies have been proposed on AODV to provide better QoS services [7, 10, 19, 21] performances. Some has used the radio propagation characteristics of mobile nodes in ad hoc network [12, 23] during route maintenance [22]. Some modification has been done on computation of delay parameter [16, 19, 22]. The other modification was on multi route selection during route discovery considering only delay as QoS parameter [5, 23]. This paper aims towards proposing a new route maintenance strategy with multiple options that will lead to better performance during link failure on a route. During route discovery and route reply procedure [26] two different QoS parameters considered here are delay and bandwidth. The routes should be selected during route discovery process. The route maintenance strategy may be applied to both the routes independently and simultaneously by the internal nodes where the link failure has occurred.

Operation and control of a DFIG-based grid-connected WECS using NSC during grid fault and with unbalanced non-linear load

Abstract: This paper proposes a combined feed-forward and selective harmonic compensation-based control scheme for a grid-connected doubly fed induction generator Wind Turbine system using a nine-switch converter (NSC) to improve the performance during grid faults and unbalanced non-linear loading. This is achieved through desired line voltage compensation and/or selective harmonic reduction technique. The two outputs of NSC are controlled such that one functions as a series grid-side converter to inject appropriate voltage in series with stator terminals to maintain the desired stator voltage profile, while the other one functions as a parallel grid-side converter to regulate the DC-link voltage to its reference value irrespective of the direction of flow of rotor power with the conventional rotor side converter. Simulation has been carried out in a MATLAB/Simulink environment. The results show that the proposed scheme ensures the feasibility of improving the Fault Ride Through capability and reducing the ...

An Integrated Image Processing and Deep Learning Approach for Diabetic Retinopathy Classification

Abstract: Diabetic retinopathy is referred to as eye sight damage and permanent blindness because of diabetic condition in humans. Diabetic patients are growing up in numbers every year around the globe. Because of modern day life style with elevated stress and tension the risk is even higher. Hence it is crucial for diabetic patients to look for its effect on other body parts as diabetic condition hit eye more likely and if left unchecked, may lead to serious eye related issues. But it can be easily monitored by regular checkups and proper health care and prevented from further degradation, while an automatic screening system to identify whether an individual need follow up or referral for supplementary action to avoid blindness can ease the task of detection at early stages to a great extent. In this paper a model for automatic detection of diabetic retinopathy is proposed using low complexity image processing technique and modified Convolutional Neural Network (CNN) with better accuracy and precision to help an ophthalmologist through detection of change in retina features. The proposed model is used to classify the fundus images into two categories, viz. healthy and infected and tested on EyePACS dataset which obtained classification accuracy of 82% shows the robustness of the system.