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 & Electric power system. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

PIPP: Person Identification from Palm-surface Polygons

Abstract: Automatic person identification from palmprint images is a well-established technology in biometrics. In biometrics, a person is identified based on some characteristic parameters. Characterization of the palmprint images is done mainly using principal lines, wrinkles, and ridges. In this paper, we have proposed a person identification method based on a novel set of geometric features highlighting the characteristics of the palm surface as biometric behavior. Performance of the proposed method has been evaluated on publicly available palm-image datasets, and the results establish the applicability of the method. On VERA left-hand, right-hand, and COEP image datasets, the proposed method shows 96.5%, 97.5%, and 95.3% accuracy, respectively.

Exploiting Negative Control Lines and Nearest Neighbor for Improved Comparator Design

Abstract: In this paper, improved reversible comparator designs are investigated using Multiple Control Toffoli(MCT) gates, improving Quantum Cost(QC) and also Nearest Neighbor Cost(NNC) metrics of circuits. The proposed designs are compared with recent works and found to be efficient in terms of cost by making these nearest neighbor compliant using Swap gate insertions.

Evolving Social Networks via Friend Recommendations

Abstract: A social network grows over a period of time with the formation of new connections and relations. In recent years we have witnessed a massive growth of online social networks like Facebook, Twitter etc. So it has become a problem of extreme importance to know the destiny of these networks. Thus predicting the evolution of a social network is a question of extreme importance. A good model for evolution of a social network can help in understanding the properties responsible for the changes occurring in a network structure. In this paper we propose such a model for evolution of social networks. We model the social network as an undirected graph where nodes represent people and edges represent the friendship between them. We define the evolution process as a set of rules which resembles very closely to how a social network grows in real life. We simulate the evolution process and show, how starting from an initial network, a network evolves using this model. We also discuss how our model can be used to model various complex social networks other than online social networks like political networks, various organizations etc..

Stochastic Modeling of the Spatial Variability of Soil

Abstract: Disparate sources of uncertainties contribute to the variability in geotechnical properties. Inherent variability in soil property, error in measurement and uncertainty in transformation models are the three major sources of geotechnical uncertainties. This paper mainly focusses on inherent soil variability; while the rest are beyond the scope. The inherent variability in soil originates mainly from the natural geological phenomena that result in the origination and the continual modification of the in situ or residual soil mass. Inherent soil variability is generally modelled as a random field. Simulating geotechnical variability as random process incorporates significant statistical results to be inferred from field test data and helps in including such variability in reliability analysis of geotechnical design. In this study, a stochastic model for inherent soil variability in terms of SPT-N value is developed for IIT Guwahati campus (26.1903ο N, 91.6920ο E) situated in Guwahati city of state Assam in India. Total 146 borehole SPT-N profile of the campus are used for developing the model. SPT-N value is observed to have an increasing trend with depth i.e. the data is nonstationary. In this model, the original data is first detrended and the trend function can be treated as deterministic in nature. The trend function is then evaluated using linear regression; a linear model is found to be good enough for present data. The residual part (error) is modelled as a one-dimensional random field. The squared exponential correlation structure is found to be best fitted in this case. The scale of fluctuation of the random field characterizing the error has been estimated from the fitted autocorrelation structure. The errors can be simulated easily once the correlation structure is known and added to the deterministic part to generate desire number of N profiles for further geotechnical analyses of the site.

Region of Interest-Based Encryption of Biomedical Image

Abstract: Encrypting the particular region of an image is of wiser than encrypting the entire image, which helps in a decrement of the computational time in the encryption process. In this paper, the biomedical images of baby, brain, lungs, and retina are taken, and by utilizing Otsu thresholding and contours, extraction of the region of interest (ROI) is done. After that, generated region of interest is encrypted using RSA, Elgamal, and LEA techniques. The computational times of above mentioned encryption techniques are performed and compared in this manuscript. Python programming is used for compilation and calculating computational times.