National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Modelling of dielectric resonator antennas using numerical methods: a review

Abstract: This article presents an inclusive review of notable research carried out on modelling of dielectric resonator (DR)-antenna using numerical methods in last more than two decades. Dielectric resonator antenna (DRA) has created its individual existence in antenna engineering because of its captivating characteristics like; small size, low loss, high efficiency, wide bandwidth, three-dimensional design flexibility as compared to conventional antennas, etc. The DR antennas are being widely modelled using numerical methods nowadays. The triple-folded intention of this review article is to: (1) give an overview on DRA modelling using single and hybrid numerical methods, (2) give a compressive review of notable numerical modelling researches carried out on DRAs and (3) give some favourable future concentration for the antenna researchers in order to apply the numerical methods on some innovative geometries of DRAs.

A multihop routing protocol for wireless sensor network based on grid clustering

Abstract: Due to limited sensor nodes energy in wireless sensor network, it is very important to save the energy for prolonging networks lifetime. Clustering is a successful approach in wireless sensor network, which can increases network lifetime and scalability. A multihop routing protocol based on grid clustering is proposed to improve networks performance in wireless sensor network. Detailed analysis and simulation result reveals that the proposed algorithm when compared with some existing algorithm increases the wireless network lifetime.

Automatic characterization of masses in mammograms

Abstract: The classification of benign and malignant masses in digital mammogram is an important yet challenging step for the early detection of breast cancer. This paper presents statistical measures of the orientation of texture to classify malignant and benign masses. Since the presence of mass in mammogram may change the orientation of normal breast tissues, two types of co-occurrence matrices are derived to estimate the joint occurrence of the angles of oriented structures for characterizing them. Haralick's 14 features are then extracted from each of the matrices derived from different regions related to mass. A total of 444 mass regions from 434 scanned-film images of the DDSM database are selected to evaluate the performance of the proposed features to differentiate the masses. The features are also compared with Haralick's features, obtained from well-known gray-level co-occurrence matrix. The best Az value of 0.77 is achieved with the stepwise logistic regression method for feature selection, an Fisher linear discriminant analysis for classification, and the leave-one-ROI-out approach for cross validation.