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.

Parallel simulation of cyber-physical systems

Abstract: Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems.

Performance evaluation of floating point Differential Evolution hardware accelerator on FPGA

Abstract: Differential Evolution (DE) is a meta-heuristic algorithm widely being used for solving optimization problems. The computation time of the DE scales up with complexity of the problems. This limits its usage in real time embedded applications. This work compares the execution time of DE floating point algorithm implemented on a 32-bit PowerPC440 (PPC440) and a MicroBlaze (MB) processors. Further, the execution time of the DE hardware accelerator in SoC is compared with the execution time of floating point unit (FPU) enabled MicroBlaze and PPC440 processors operating at 125 MHz. For performance comparison, the DE hardware accelerator is used for solving three benchmark test functions of different complexities. It is observed that the hardware accelerator attained an acceleration of 6–8×, and 40–100× compared to PPC440 with hardware and software FPU respectively, whereas it attained an acceleration of 124–328× and 120–264× compared to MicroBlaze with software and hardware FPU respectively. Furthermore, resource utilization and power analysis of MicroBlaze and PPC440 with soft/hard FPU based SoC system on Virtex-5 ML507 platform are also reported.

Steady state model of Dual Rotor Motor for electric vehicle application

Abstract: Dual Rotor Motor (DRM) can be a very suitable option for electric vehicle application due to its compact size, high torque and high power density. In this paper, an analytical model of DRM is developed, from where the steady state torque equation is investigated for different firing angle of the inverter feeding the motor. The finite element analysis of the same machine is performed to validate the developed analytical model. Speed-torque characteristics for different operating modes have also been analyzed for the better understanding of DRM behavior, which are required to control the DRM.

A Game Theoretic Group Coordination Strategy for Multi Robot Navigation

Abstract: Navigating multiple robots across a terrain to achieve their individual goals is a challenging research problem and has been addressed by incorporating a myriad of soft computing techniques. This article discusses this problem from the perspective of enacting a group coordination strategy among a group of robots. Such a scheme can go a long way in paving the path for quick and automatic disaster recovery through sharing critical information. Experiments have been carried out with four groups of robots with their respective sources and destinations and varying the number of robots between 8 to 20 with a novel game-theoretic group coordination strategy. Results obtained using the proposed approach have been compared with those obtained by traditional Potential Field Method (PFM) and standard soft computing based strategy such as Genetic Algorithm-Fuzzy (GA-Fuzzy) for performance metrics, both individual as well as for group, traveling time and the obtained results demonstrate the efficacy of the proposed method.

Sentiment Analysis of Train Derailment in India: A Case Study from Twitter Data

Abstract: The services of Indian Railway are availed by many people in the country. It is an important mode of transportation. Most of the users of Indian Railway express their views about it on different social media sites like Twitter, Facebook etc. It leads to generation of large amount of data and sentimental analysis of that data can be very helpful in understanding public opinions towards Indian Railway and in decision making. In this paper, the lexicon based sentimental analysis technique has been applied to the twitter data collected corresponding to three train accidents namely Puri-Haridwar-Kalinga Utkal Express, Delhi-bound Kaifiyat Express and Mumbai-Nagpur Duranto Express which took place on 19/08/2017, 23/08/2017 and 29/08/2017 respectively. Further, tweets are classified into different categories and analyzed in terms of percentage frequency. The results present the pattern how the sentiments of the public fluctuate with time as when derailment happens the negative tweets has high frequency of occurrence but with passage of time frequency of occurrence of neutral tweets become high.