Browsing by Author "Al Imran, Md Abdullah"

Now showing 1 - 2 of 2

Citation - WoS: 5
Citation - Scopus: 8
Optimal Operation Mode Selection for Energy-Efficient Light-Weight Multi-Hop Time Synchronization in Linear Wireless Sensor Networks
(Springer, 2020) Al Imran, Md Abdullah; Dalveren, Yaser; Tavli, Bulent; Kara, Ali; Department of Electrical & Electronics Engineering
We explored the joint effect of synchronization window and offset/drift mode selection on the time synchronization of linear wireless sensor networks (LWSNs). Recent advances in the field along with the availability of capable hardware led to adoption of LWSNs in diverse areas like monitoring of roads, pipelines, and tunnels. The linear topology applications are susceptible to single point of failure; therefore, energy efficient operation of LWSNs is even more important than the traditional WSNs. To address the challenge, we investigate the time synchronization mode selection for the optimum operation of a multi-hop and low-overhead LWSN. We investigate two modes of synchronization: synchronization by using only offset and synchronization by using offset in addition to the clock drift. Furthermore, we investigate the effects of synchronization window size. Our experimental results reveal that computation of offset alone for smaller window sizes and resynchronization periods is sufficient in achieving acceptable degree of synchronization.
Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning
(Ieee-inst Electrical Electronics Engineers inc, 2024) Catak, Ferhat Ozgur; Al Imran, Md Abdullah; Dalveren, Yaser; Yildiz, Beytullah; Kara, Ali; Department of Electrical & Electronics Engineering; Software Engineering
In this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.