http://20.198.91.3:8080/jspui/handle/123456789/91 Sun, 05 Apr 2026 19:31:26 GMT 2026-04-05T19:31:26Z http://20.198.91.3:8080/jspui/handle/123456789/9104 Title: A novel multiple relay selection technique for rf energy harvested relay networks Authors: Saha, Madhushree Abstract: The utilization of Radio Frequency (RF) for Energy Harvesting (EH) in wireless communication networks has become popular in recent time. RF can be used for powering the network nodes which has limited energy, as well as to perform data transmission. Signal transmission is very much affected by the channel fading. Cooperative communication could be used to reduce the effects of channel fading. In cooperative communication, relaying technique is used to transmit the data from source to destination in smaller hops. This improves the transmission reliability and the radio coverage of the wireless network. The performance of a relay-assisted cooperative network greatly depends on the selection strategies of a set of best performing relays or a best relay, whichever applicable, from a given set of relays. However, only one selected relay based on optimized performance may not provide the best performance constantly in the network. Keeping this point in mind, a dual-hop relay-assisted network with energy harvesting capability is taken into consideration in this thesis. The best relays are chosen to serve the high-quality communication in a set transmission block time. In order to address afore mentioned problem, a new relay selection algorithm to select best four relays, has been discussed in this thesis work. The proposed model has been simulated using MATLAB R2015a platform. The throughput performances of this new algorithm have been analyzed. A comparative study of the proposed algorithm and the existing algorithms has also been presented. Sun, 01 Jan 2023 00:00:00 GMT http://20.198.91.3:8080/jspui/handle/123456789/9104 2023-01-01T00:00:00Z http://20.198.91.3:8080/jspui/handle/123456789/9103 Title: Design and development of graphene based metasurface for x-band applications Authors: Dumpala, Mahesh Sun, 01 Jan 2023 00:00:00 GMT http://20.198.91.3:8080/jspui/handle/123456789/9103 2023-01-01T00:00:00Z http://20.198.91.3:8080/jspui/handle/123456789/9102 Title: Analysis of fss prototype using deep neural network and its synthesis implementing evolutionary algorithm Authors: Mitra, Chameli Sun, 01 Jan 2023 00:00:00 GMT http://20.198.91.3:8080/jspui/handle/123456789/9102 2023-01-01T00:00:00Z http://20.198.91.3:8080/jspui/handle/123456789/9101 Title: Some studies on object tracking using kalman filter and tuning of process noise covariance parameter for optimal system performance Authors: Sk Babul Akhtar Abstract: Object Detection and Tracking are important and challenging tasks in many computer vision applications such as surveillance, vehicle navigation, and autonomous robot navigation. Video surveillance in a dynamic environment, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. The present Thesis work involves designing an efficient video surveillance system in complex environments. The Kalman Filter (KF) is a widely used algorithm for tracking objects in a noisy and uncertain environment. This Thesis explores the application of the Kalman filter in object tracking and presents a comprehensive review of the theory and implementation of the Kalman filter algorithm. The Thesis also investigates different techniques for modeling the motion and measurement of objects, and examines the impact of different model parameters on tracking performance. The Thesis presents experimental results on a variety of object tracking scenarios, including tracking a single object in one and two dimensions, multiple objects, and objects with complex motion patterns. The results demonstrate the effectiveness and versatility of the Kalman filter in tracking objects in challenging environments. Finally, the Thesishighlights the importance of accurate Process Noise Covariance modeling and discusses the tuning of the Kalman filter using RMS indexwhich is verified using Sensitivity and Robustness metrics.Upon tuning of the Kalman filter using RMS index, the system performance was evaluated, showcasing a significant improvement in its overall effectiveness. In the context of a simulated one-dimensional (1D) tracking system, a comparison between an un-tuned Kalman filter and a properly tuned counterpart revealed notable enhancements. Specifically, the mean error experienced a substantial decrease from -8.51m (for an un-tuned Kalman Filter) to -0.20m (for a tuned KF), while the standard deviation reduced from 11.34m to 0.94m for the un-tuned and tuned systems, respectively. This remarkable transformation resulted in a 17-fold improvement in the system's performance for the 1D tuned tracking system. Overall, this Thesis provides a comprehensive overview of the Kalman filter algorithm and its application in object tracking, anserves as a useful reference for researchers and practitioners in the field of computer vision. Sun, 01 Jan 2023 00:00:00 GMT http://20.198.91.3:8080/jspui/handle/123456789/9101 2023-01-01T00:00:00Z