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.

Reliable Transmission of Critical Packets in IEEE 802.15.4-based Body Area Networks

Abstract: In this paper, we propose a mechanism to ensure reliable transmission of critical packets in Wireless Body Area Networks (WBANs) As WBANs are responsible for real-time health monitoring, thus, high success rate is necessary in packet transmissions Especially, packets that possess critical health information must be dealt with utmost priority To ensure this, in this work we express the probability of successful packet transmission as a function of maximum number of backoff stage - an important MAC parameter - so that we can judiciously tune its value to achieve maximum transmission reliability for critical nodes The IEEE 802154 is used as the underlying communication standard We use a simple two-dimensional Markov chain to model the slotted CSMA-CA based channel access mechanism of IEEE 802154, which acts as the basis of the whole work We compare the proposed approach with random assignment of maximum number of backoff stage to the body sensor nodes and show that judicious tuning of this value yields 25% improvement in reliable packet transmission, in comparison with random assignment

A Comparative Analysis of Flicker Index and Modulation Depth on Few Existing Dimmable LED Drivers

Abstract: This paper presents a computed comparative analysis of the standard metrics of Flicker Index and Modulation Depth defined in IEEE Std. 1789–2015, which are used to quantify the variation of light intensity, between few existing dimmable LED driver's LED current output. The standard metrics are the indices of flicker which is associated with LED dimming functionality and basically depends on the LED driver topology. The IEEE Std. 1789–2015 recommends the standard metrics to be kept low to avoid health hazards associated with flicker thereby contributing to the faster adoption of LED technology in the market. Three existing LED driver topologies with dimming capability are investigated in this paper and from their corresponding LED current outputs the standard metrics are evaluated and compared for different dimming ratios.

Integer division technique for signal processing applications

Abstract: Algorithmic implementation of integer division technique based on ancient Vedic mathematics is reported in this paper. The potentiality of the 'Nikhilam Navatascaramam Dasatah (NND)' (all from 9 and last from 10)' sutra of Vedic mathematics was adopted to implement the high speed integer division. Optimized 4221 BCD encoding technique was incorporated with Vedic mathematics, to implement such divider for practical signal processing applications. Propagation delay and dynamic switching power consumption of division circuitry were minimized significantly through stage reduction techniques of such sutra (formulae). The functionality of the division circuitry was checked and performance parameters like propagation delay and dynamic power consumption were calculated by Xilinx tool (VHDL language). The propagation delay of the resulting 6÷3 digit divisor circuitry was only ~41ns and consumed ~93mW power. Amalgam-nation of BCD arithmetic with ancient Vedic mathematics, substantial amount of iterations were eliminated owing to ~20% reduction in delay and ~12% reduction in power from its counterpart.

Estimation of Support Vector Machine Parameters Based on Breakdown Voltage Characteristics

Abstract: In this work, breakdown voltage characteristics of vegetable oil as insulation medium is studied. Breakdown voltage is measured under different electrode gap and ramp rate of applied voltage. Weibull distribution is used to analyse the measured results. The time to failure and the corresponding voltage magnitude depends on electrode gap and ramp rate. The breakdown results are further processed to train Support Vector Machine (SVM), a machine learning algorithm. The electric field features corresponding to breakdown voltage are extracted from electric field distribution and are used to train Support Vector Machine. The parameters that configure SVM during training process for prediction or classification of new data are estimated. The breakdown mechanism influences values of these parameters.