National Institute of Technology, Meghalaya

About: National Institute of Technology, Meghalaya is a education organization based out in Shillong, India. It is known for research contribution in the topics: Control theory & Computer science. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

Water absorption and mechanical behaviour of Borassus fruit fibre-reinforced composites

Abstract: The application of natural fibre composites has increased manifold due to their environment-friendliness and sustainable characteristics. In this study, short Borassus fruit fibre (natural fibre)-b...

Falsification of hybrid systems using symbolic reachability and trajectory splicing

Abstract: The falsification of a hybrid system aims at finding trajectories that violate a given safety property. This is a challenging problem, and the practical applicability of current falsification algorithms still suffers from their high time complexity. In contrast to falsification, verification algorithms aim at providing guarantees that no such trajectories exist. Recent symbolic reachability techniques are capable of efficiently computing linear constraints that enclose all trajectories of the system with reasonable precision. In this paper, we leverage the power of symbolic reachability algorithms to improve the scalability of falsification techniques. Recent approaches to falsification reduce the problem to a nonlinear optimization problem. We propose to reduce the search space of the optimization problem by adding linear state constraints obtained with a reachability algorithm. We showcase the efficiency of our approach on a number of standard hybrid systems benchmarks demonstrating the performance increase in speed and number of falsifyable instances.

Hardware implementation of methodologies of fixed point division algorithms

Abstract: This paper describes the hardware implementation methodologies of fixed point binary division algorithms. The implementations have been extended for the execution of the reciprocal of the binary numbers. Radix-2 (binary) implementations of digit recurrence and multiplicative based methods have been considered for comparison. Functionality of the algorithms have been verified in Verilog hardware description language (HDL) and synthesized in Xilinx ISE 8.2i targeting the device xc4vlx15-12sf363 of Virtex4 family. Implementation was done for both signed and unsigned number systems, having bit width of operands vary as an exponential function of , where =2 to 5. Performance parameters have been calculated in terms of clock frequency, FPGA slice utilization, latency and power consumption. Implementation results indicate that multiplicative based algorithm is superior in terms of latency, while digit recurrence algorithms are consuming low power along-with less area overhead.

Deep Learning Based Sentiment Analysis On COVID-19 Public Reviews

Abstract: Sentiment Analysis is a classification task in order to identify public reviews about different issues like product reviews, movie reviews, restaurant reviews, political opinions, and other current issues by extracting the public reviews from Social Media, and other Micro blogging sites. As we all know Coronavirus Disease 2019 (COVID-19) is still a global issue for entire world and people are expressing their emotions, thoughts, and opinions about this issue with help of Twitter, Facebook, and other Media. In this paper we have collected public tweets from Twitter which are talked about the COVID-19 global pandemic and applied a Convolutional Neural Network with Bidirectional Long-Short Term Memory (CNN-Bi-LSTM) hybrid Deep Learning algorithm to detect the user’s outlook on this pandemic whether they have positive feelings, negative feelings, or neutral feelings. The proposed method used preprocessing techniques to clean the data and used a word embedding pre-trained model to extract word embedding for rare words in our corpus with the help of FastText and Globe pre-trained models. The CNN-Bi-LSTM hybrid model evaluated using accuracy, precision, recall, and f1 evaluation techniques. The experimental result has been shown 99.33% accuracy using CNN-Bi-LSTM with FastText pre-trained model, and 97.55% accuracy using CNN-Bi-LSTM with GloVe pre-trained model.