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: Control theory & Electric power system. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

An efficient scheduling scheme for MIMO-OFDM broadcast networks

Abstract: A new limited feedback user/antenna scheduling algorithm is proposed for multiple-input multiple-output orthogonal frequency division multiplexing broadcast networks. Feedback of channel state information at users to base station (BS) is inevitable for efficient spectrum utilization by performing the best possible scheduling of users/antennas. The feedback overhead increases with number of users and subcarriers. This enormous feedback information consumes useful bandwidth in uplink channel. Hence, in literature scheduling schemes with clustering of adjacent subcarriers (per-cluster scheduling) are proposed to reduce feedback overhead by considering channel condition of only the center subcarrier of cluster. According to proposed algorithm, channel conditions of all subcarriers are taken into consideration in a novel manner for efficient scheduling. The benefit of this scheme is attaining higher system throughput than the per-cluster scheduling scheme without incurring any additional feedback overhead. Moreover, this scheme achieves higher system throughput for large cluster size and more number of channel taps as compared to per-cluster scheduling scheme. Further throughput is improved by assigning previously unscheduled users randomly to transmit antennas which were not sending data packets to any users previously.

Comparative Analysis of the Effects of Trap Charges on Single- and Double-Gate Extended-Source Tunnel FET with δp+ SiGe Pocket Layer

Abstract: This paper investigates the trap analysis of a double-gate extended-source tunnel field-effect transistor (DG-ESTFET) and single-gate extended-source tunnel field-effect transistor (SG-ESTFET) with a δp+ SiGe pocket layer. The trap analysis of both structures is compared in terms of the currents, average subthreshold swing, threshold voltage, and switching ratio. In addition, the impact of interface trap charges at different interfaces on analog/RF performance, transfer characteristics, and slope are investigated and compared. It is observed that the trap charges between the silicon and front gate oxide interface (Si–HfO2) have a greater effect on the DG-ESTFET than the SG-ESTFET, whereas the reverse is true when trap charges are at the back gate oxide interface (Si–SiO2). In the case of analog/RF performance, the SG-ESTFET is found to be more affected by the trap charges at the silicon and front gate oxide interface (Si–HfO2).

Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images

Abstract: Malaria, being a life-threatening disease caused by parasites, demands its rapid and accurate diagnosis. In this paper, we develop a computer-assisted malaria-infected life-cycle stages classification based on a hybrid classifier using thin blood smear images. The major issues are: feature extraction, feature selection and classification of erythrocytes infected with different life-cycle stages of malaria. Feature set (134 dimensional features) has been defined by the combination of the proposed features along with the existing features. Features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R–G color channel difference histogram and Gabor features are the newly proposed features in our system. In the feature selection, a two-stage algorithm utilizing the filter method to rank the feature, along with the incremental feature selection technique, has been analyzed. Moreover, the performance of all the individual classifiers (Naive Bayes, support vector machine, k-nearest neighbors and artificial neural network) is evaluated. Finally, the three individual classifiers are combined to develop a hybrid classifier using different classifier combining techniques. From the experimental results, it may be concluded that hybrid classifier formed by the combination of SVM, k-NN and ANN with majority voting technique provides satisfactory results compared to other individual classifiers as well as other hybrid model. An accuracy of 96.54 ± 0.73% has been achieved on the collected clinical database. The results show an improvement in accuracy (11.62, 6.7, 3.39 and 2.39%) as compared to the state-of-the-art individual classifiers, i.e., Naive Bayes, SVM, k-NN and ANN, respectively.

Compound influence of topological defects and heteroatomic inclusions on the mechanical properties of SWCNTs

Abstract: The utility of carbon nanotubes as the reinforcement agents in polymer and metal matrix composites has opened up a new avenue in the development of novel composite materials with exceptional strength and stiffness to weight ratios. Such exploitation of superior mechanical properties of carbon nanotubes depends on their inherent irregularities and structural integration. The nanotubular structures of carbon are prone to topological defects and heteroatom dopants due to the inevitable complexities in nano-synthesis. The objective of this article is to quantify the compound influence of such inherent structural irregularities (such as single vacancy defects and nanopores) and foreign atom inclusions (such as nitrogen and boron atoms) on the mechanical characteristics (like constitutive relation, fracture strength, failure strain and Young’s moduli) of single-walled carbon nanotubes (SWCNT) under various multi-physical influences (such as temperature, strain rate, diameter and chirality) based on molecular dynamics (MD) simulations. The current investigation also includes a detailed analysis on the variation in mechanical characteristics of CNTs under different spatial distributions of defects and doping.