Title: Bayesian-based binary compression with bandwidth optimisation for UAV aerial images

Authors: Pankaj Agarwal; Sapna Yadav; J. Pradeep Kandhasamy; A. Balaji; S. Markkandan; D. Vijendra Babu

Addresses: Department of Computer Science and Engineering, NorthCap University, Gurgoan, Haryana, India ' CS and IT Department, KIET Group of Institutions, Ghaziabad, India ' School of Computing, Kalasalingam Academy of Research and Education, Tamil Nadu, India ' Department of Computer Science and Engineering, Guntur Engineering College, Guntur, India ' Department of ECE, SRM TRP Engineering College, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation, Paiyanoor-603 104. Tamil Nadu, India

Abstract: This article proposes a new Bayesian-based binary compression model for UAV aerial pictures. This technique utilises inter-signal correlations to extract several sparse signals simultaneously. BKF-based approach employs both intra- and inter-signal correlations. The Bessel K-form (BKF) also features a higher zero peak with longer tails. Consumers may use UAV-borne base stations for temporary or emergency services. The effectiveness of low-bandwidth wireless Bayesian UAV communication BS still a challenge. This study's aim is to enhance UAV-BS spectrum usage while maintaining user fairness. Through aerial picture quality, we propose adjusting the distribution of bandwidth, power, and UAV-BS trajectory to capture the object image. The proposed method outperforms other approaches in aerial picture detection. To get high quality aerial images, Bayesian-based binary compression lowers picture size and minimises noise. The advantages of UAVs using the Bayesian approach have spurred research interest in novel communication systems.

Keywords: Bayesian method; binary compression; bandwidth optimisation; UAV aerial images; Bessel K-form; BKF.

DOI: 10.1504/IJESMS.2024.136971

International Journal of Engineering Systems Modelling and Simulation, 2024 Vol.15 No.2, pp.53 - 62

Received: 14 Sep 2021
Accepted: 28 Jan 2022
Published online: 01 Mar 2024 *

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