BCET Gurdaspur

About: BCET Gurdaspur is a based out in . It is known for research contribution in the topics: Cognitive radio & Electrical discharge machining. The organization has 63 authors who have published 97 publications receiving 1231 citations.

Fabrication and Machining of Metal Matrix Composites: A Review

Abstract: Intrinsically smart, metal matrix composites (MMCs) are lightweight and high-performance materials having ever expanding industrial applications. The structural and the functional properties of these materials can be altered as per the industrial demands. The process technologies indulged in fabrication and machining of these materials attract the researchers and industrial community. Hybrid electric discharge machining is a promising and the most reliable nonconventional machining process for MMCs. It exhibits higher competence for machining complex shapes with greater accuracy. This paper presents an up-to-date review of progress and benefits of different routes for fabrication and machining of composites. It reports certain practical analysis and research findings including various issues on fabrication and machining of MMCs. It is concluded that polycrystalline tools and diamond-coated tools are best suitable for various conventional machining operations. High speed, small depth of cut and low feed ra...

Throughput of a Cognitive Radio Network With Energy-Harvesting Based on Primary User Signal

Abstract: In this letter, we analyze an energy-harvesting-based cognitive radio (CR) system. The CR system harvests energy from the radio frequency (RF) signal of primary user (PU) during sensing time as well as the transmission time of a detection cycle if PU is present. The CR accesses the spectrum band of PU opportunistically using the energy harvested over the frames with PU present, while maintaining a quality of service (QoS) constraint on PU in terms of a collision probability. An optimal sensing time is found, which maximizes the harvested energy. The performance is investigated in terms of harvested energy, outage probability, and throughput of the network. Novel analytical expressions for average harvested energy and average throughput are developed under such a scenario, which are validated by simulation.

Forest Type Classification: A Hybrid NN-GA Model Based Approach

Abstract: Recent researches have used geographically weighted variables calculated for two tree species, Cryptomeria japonica (Sugi, or Japanese Cedar) and Chamaecyparis obtusa (Hinoki, or Japanese Cypress) to classify the two species and one mixed forest class. In machine learning context it has been found to be difficult to predict that a pixel belongs to a specific class in a heterogeneous landscape image, especially in forest images, as ground features of nearly located pixel of different classes have very similar spectral characteristics. In the present work the authors have proposed a GA trained Neural Network classifier to tackle the task. The local search based traditional weight optimization algorithms may get trapped in local optima and may be poor in training the network. NN trained with GA (NN-GA) overcomes the problem by gradually optimizing the input weight vector of the NN. The performance of NN-GA has been compared with NN, SVM and Random Forest classifiers in terms of performance measures like accuracy, precision, recall, F-Measure and Kappa Statistic. The results have been found to be satisfactory and a reasonable improvement has been made over the existing performances in the literature by using NN-GA.

Video Shot Boundary Detection: A Review

Abstract: Video image processing is a technique to handle the video data in an effective and efficient way. It is one of the most popular aspects in the video and image based technologies such as surveillance. Shot change boundary detection is also one of the major research areas in video signal processing. Previous works have developed various algorithms in this domain. In this paper, a brief literature survey is presented that establishes an overview of the works that has been done previously. In this paper we have discussed few algorithms that were proposed previously which also includes histogram based, DCT based and motion vector based algorithms as well as their advantages and their limitations.