Nitte Meenakshi Institute of Technology

About: Nitte Meenakshi Institute of Technology is a based out in . It is known for research contribution in the topics: Computer science & Ultimate tensile strength. The organization has 846 authors who have published 644 publications receiving 2702 citations.

Design of a generic network on chip frame work for store & forward routing for 2D mesh topology

Abstract: The need of high performaning, mega functionality solutions are becoming important day by day. The implementation of these mega functional modules which was done using common bus architecture, parallel bus architecture, pipelining are becoming ineffective and posing a bottleneck in terms of performance and throughput in this billion transistor era. To overcome these performance issues, a new paradigm in interconnect technology was proposed. The idea was to implant the concept of data transfer in data communication networks into silicon thus providing advantages of low power scalable high performing architecture with a small increase in die area for routing resources. This paper discusses the design of a generic frame work for network on chip based systems using store and forward strategy.

New Composite Membrane of Carboxylated Polysulfone (CPS)– Polyphenylene Ether Ether Sulfone (PPEES) as Potential Material for Direct Methanol Fuel Cell

Abstract: In the present study, carboxylated polysulfone (CPS) and Poly (1,4–phenylene ether ether–sulfone) (PPEES) NF membranes for their application in proton exchange membrane fuel cells (PEMFCs) were investigated. New NF membranes were prepared by blending PPEES with CPS solution in NMP (5 wt. %) by the solution casting procedure using K–control coater. The membranes have been characterized by thermal analysis, water uptake, IEC measurements, proton conductivity performance and methanol crossover. Electrochemical impedance spectroscopy (EIS) was at length used to decide proton conductivity. The addition of CPS increased the water uptake capability of the synthesized membrane and resulted in enhanced proton conductivity. The conductivity values in the range of 10 –4 –10 –2 S/cm were obtained for the new CPS–PPEES membranes. The conductivities of the membranes showed increasing trend as a function of operating temperature and wt. % of CPS. Membrane of the type CM1 gave the conductivity in the range of 0.12 S/cm at 100°C. The results showed that, CPS/PPEES is a promising membrane material for possible use in proton exchange membrane fuel cells.

Growth temperature influenced electrical properties of copper-oxide thin films

Abstract: Copper-o xide thin films are deposited by using reactive magnetron sputtering at different te mperatures and investigated the variation in the bonding configuration. We in -turn investigated how this bonding configuration influences its optical and electrica l properties such as bandgap, resistivity mobility etc. Even the room te mperature (RT) deposited films are crystalline in nature. Bandgap of 2.3 e V has been observed for the films deposited at 400°C. A decreasing trend was observed in hole concentration fro m 2 x 1018 to 7 x 1016 per cm3 with increase in substrate temperature from RT to 400°C. XPS investigations were also done to understand the bonding behavior.

A Risk Assessment Model for Patients Suffering from Coronary Heart Disease Using a Novel Feature Selection Algorithm and Learning Classifiers

Abstract: The aim of this research is to develop an efficient risk assessment model to assess the risk in patients suffering from coronary heart disease. The proposed technique classifies patients as having low risk or high risk of coronary heart disease. CVD dataset from Cleveland database is used to develop the model. The various parameters considered are cholesterol, blood pressure, electrocardiogram and echocardiogram tests among others as well. The patients suffering from coronary heart diseases are labeled as lower or higher risk. Feature selection is one of the critical tasks in developing predictive models. It reduces the computational cost by removing insignificant features. This leads to a simpler, accurate and comprehensible model. Here, two feature selection techniques namely Mean selection (MS) and a novel feature selection technique, VAS-CHD, a variance-based attribute selection for coronary heart disease diagnosis is implemented to capture the right features for the purpose of prediction. The decision tree has resulted in accuracy of 80.5% and lower False Negatives for features obtained from MS. The multilayer perceptron has resulted in 73.7% accuracy and lower False Negatives for features obtained from VAS-CHD.

A Comparative Study on the Mitigation of Seismic Response of Two Adjacent Buildings by Using Viscous Dampers Study

Abstract: For a long time, the dampers have been used as a method to mitigate earthquake force in structures. These dampers are traditionally provided inside the buildings. Thereby, in this paper, a different approach is being adapted by carrying out a comparative study between two adjacent RCC buildings of fifteen and ten story buildings in which: (i) No dampers are Provided in Buildings (ii) The dampers are being provided between the two buildings, (iii) dampers are being provided inside the buildings. Further, parameters such as variation in base shear and displacement of the buildings are taken under consideration to draw a proper comparison between the above three cases. The results were obtained for the optimized placement of the damper case after carrying out an extensive trial and error process. Results have shown that connecting the adjacent buildings of different fundamental frequencies by these dampers can effectively reduce the earthquake-induced responses of either building. Providing dampers inside the building gave better results than providing the dampers between the buildings.