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.

ASAC: automatic sensitivity analysis for approximate computing

Abstract: The approximation based programming paradigm is especially attractive for developing error-resilient applications, targeting low power embedded devices It allows for program data to be computed and stored approximately for better energy efficiency The duration of battery in the smartphones, tablets, etc is generally more of a concern to users than an application's accuracy or fidelity beyond certain acceptable quality of service Therefore, relaxing accuracy to improve energy efficiency is an attractive trade-off when permissible by the application's domain Recent works suggest source code annotations and type qualifiers to facilitate safe approximate computation and data manipulation It requires rewriting of programs or the availability of source codes for annotations This may not be feasible as real-world applications tend to be large, with source code that is not readily availableIn this paper, we propose a novel sensitivity analysis that automatically generates annotations for programs for the purpose of approximate computing Our framework, ASAC, extracts information about the sensitivity of the output with respect to program data We show that the program output is sensitive to only a subset of program data that we deem critical, and hence must be precise The rest of the data can be computed and stored approximatelyWe evaluated our analysis on a range of applications, and achieved a 86% accuracy compared to manual annotations by programmers We validated our analysis by showing that the applications are within the acceptable QoS threshold if we approximate the non-critical data

Combination of Clustering and Ranking Techniques for Unsupervised Band Selection of Hyperspectral Images

Abstract: Curse of dimensionality is a major disadvantage for classification of hyperspectral imagery since a large number of bands need to be dealt with. Band selection is a task to reduce the number of bands. An unsupervised band selection method is proposed in this article. It is a three-step procedure. In the first step, characteristics (attributes) of the bands are found out. Next, redundancy among the bands is removed by executing clustering operation. At last, the remaining bands, which are nonredundant among themselves, are ranked according to their discriminating capability. Discriminating capability is calculated by measuring the capacitory discrimination of the bands. Results are compared with four state-of-the-art methods: a band elimination method, a ranking-based, and two clustering-based band selection methods to demonstrate the effectiveness of the proposed method. Four evaluation measures, namely: 1) classification accuracy; 2) Kappa coefficient; 3) class separability, and 4) entropy, are calculated over the selected bands to assess the efficiency of the selected bands. The proposed method shows promising results compared to them.

Frequency Regulation in Deregulated Market Using Vehicle-to-Grid Services in Residential Distribution Network

Abstract: Progress in vehicle-to-grid technology opens market for electric vehicle (EV) users to participate in the emergency reliability services, such as frequency regulation (FR). EVs can be considered as a mobile energy storage, which has the potential to compensate the uncontracted power if the contracts between the market players are breached. As all the EVs will be penetrated to the distribution network, distribution power loss along with the power limit of transformer and lines must be incorporated in the EV model. In this paper, the accuracy of conventional EV model is improved by introducing distributed network characteristics to participate in FR under deregulated environment in the presence of diverse transmission links such as ac/dc links. Fractional order plus proportional plus integral plus derivative (FOPID) controller also abbreviated as PI λ D μ controller is used for coordinated control of conventional units and EVs. Flower pollination algorithm is employed to choose the controller parameters under different scenarios. Extensive simulations have been performed to validate the superiority of the proposed control strategy. Obtained results strongly suggest that FOPID controller is far superior to conventional PID controller.