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.

End-to-End Verifiable Electronic Voting System Using Delegated Proof of Stake On Blockchain

Abstract: This paper aims to introduce a scalable blockchain based system for balloting. The system can maintain privacy of the individual voter, remaining publicly accountable, and tamper proof. Blockchain offers a distributed ledger of votes across the voting nodes such that no duplicate votes are allowed. We use Consensus algorithm to facilitate fast propagation of blocks of votes. Increased transaction speeds are achieved by delegating certain nodes as block producers, relying on them for delegated proof of stake. For increased accountability, we include the voter’s biometric data in the transaction. The pilot implementation showcased significant improvement with respect to currently followed balloting process in India. Response time of the proposed system is evaluated on different load levels on network, experimental results are promising, and we recommend the system for the large-scale implementation.

Synthesis, Characterization & Impedance Studies of some New Nano Filtration Membranes

Abstract: In the recent years membrane technology has gained significant attention from polymer chemists all around the world due to their attractive features such as efficiency, low costs, low energy costs and as effective solutions to longstanding problems in the chemical industries. Membrane technologies have been widely applied in the separation of liquids and even gases. Many separation problems can be solved economically by nanofiltration alone or in combination with other separation processes. This study aimed to synthesize polysulfone based nanofiltration membranes using DIPS (diffusion induced phase separation) technique. Newly synthesized polymer membranes were subjected to Infra red spectral and water uptake studies. Membranes were also characterized using electrochemical spectroscopy for their proton conducting property. Their surface morphology is visualized by SEM.

Quantifying the classification of exoplanets: in search for the right habitability metric

Abstract: What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone running into billions, possibly a number greater than the number of stars, it is high time to start characterizing them, sorting them into classes/types just like stars, to better understand their formation paths, their properties and, ultimately, their ability to beget or sustain life. After all, we do have life thriving on one of these billions of planets, why not on others? Which planets are better suited for life and which ones are definitely not worth spending expensive telescope time on? We need to find sort of quick assessment score, a metric, using which we can make a list of promising planets and dedicate our efforts to them. Exoplanetary habitability is a transdisciplinary subject integrating astrophysics, astrobiology, planetary science, and even terrestrial environmental sciences. It became a challenging problem in astroinformatics, an emerging area in computational astronomy. Here, we review the existing metrics of habitability and the new classification schemes (machine learning (ML), neural networks, activation functions) of extrasolar planets, and provide an exposition of the use of computational intelligence techniques to evaluate habitability scores and to automate the process of classification of exoplanets. We examine how solving convex optimization techniques, as in computing new metrics such as Cobb–Douglas habitability score (CDHS) and constant elasticity earth similarity approach (CEESA), cross-validates ML-based classification of exoplanets. Despite the recent criticism of exoplanetary habitability ranking, we are sure that this field has to continue and evolve to use all available machinery of astroinformatics, artificial intelligence (AI) and machine learning. It might actually develop into a sort of same scale as stellar types in astronomy, to be used as a quick tool of screening exoplanets in important characteristics in search for potentially habitable planets (PHPs), or Earth-like planets, for detailed follow-up targets.

Computational Analysis of Conjugate Buoyant Convective Transport in an Annulus

Abstract: In the present work, the convective flow and thermal pattern, associated heat transport rates of buoyant convection in an annular geometry is theoretically analyzed. The inner cylindrical wall has finite thickness and is kept at high temperature, while the outer cylindrical wall is held at low temperature. The vorticity-stream function form of model equations are solved using FDM based on ADI and SLOR techniques. The numerical simulations for various parameters are presented. In particular, this analysis focused on the effects of conjugate heat transport characteristics.