ORCID Profile
0000-0002-7380-7767
Current Organisation
Khulna University of Engineering and Technology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Hindawi Limited
Date: 30-11-2022
DOI: 10.1155/2022/6284158
Abstract: To localize the position and perfect autonomous navigation, building up a map is essential for mobile robots. The map becomes very important when the weather is not appropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors with emotional accuracy. The loop-closure detection is the process through which a robot can acknowledge the location visited previously, which can identify the ultimate solution to the previous problem. The robot faced difficulty identifying its previously visited path when the environment underwent an extreme change. The main motive of our work is to promote a model capable of understanding the scenes that are presented robustly. Moreover, during seasonal changes, this model provides an appropriate loop-closure detection result. Independent component analysis (ICA) and auto-encoder are proposed to complete our research work. ICA is a powerful tool to describe invariant images perfectly. Especially, when the robot moves through a changing environment, ICA provided more accurate outcomes than the other algorithm (baseline algorithm). On the other hand, the auto-encoder can distinguish between two features of scene variant condition and invariant condition. The encoder takes our work’s next steps by discovering possible routes. To analyze the performance, this work uses the baseline method with a precision-recall curve and a fraction of correct matches. The proposed algorithm ICA showed a 91.05% accuracy rate, which is better than the baseline algorithms, and the appropriate route-finding rate using an auto-encoder is also acceptable.
Publisher: IEEE
Date: 2019
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 12-2019
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Academy and Industry Research Collaboration Center (AIRCC)
Date: 30-04-2014
Publisher: IEEE
Date: 11-2015
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 2023
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 14-09-2021
Publisher: IEEE
Date: 02-2017
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 09-2018
Publisher: IEEE
Date: 24-02-2022
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 24-02-2022
Publisher: Lattice Science Publication (LSP)
Date: 10-08-2021
DOI: 10.54105/IJSP.C1008.081321
Abstract: The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 28-11-2020
Publisher: MECS Publisher
Date: 08-10-2021
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 09-2017
Publisher: Begell House
Date: 2012
Publisher: MECS Publisher
Date: 08-04-2018
Publisher: MECS Publisher
Date: 08-10-2019
Publisher: Springer Science and Business Media LLC
Date: 05-06-2020
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 02-2018
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 05-2012
Publisher: IEEE
Date: 12-2018
Publisher: Academy and Industry Research Collaboration Center (AIRCC)
Date: 28-02-2015
Publisher: IEEE
Date: 07-2018
Publisher: Journal of Engineering Research and Sciences
Date: 11-2022
DOI: 10.55708/JS0111002
Publisher: Springer Science and Business Media LLC
Date: 22-11-2023
Publisher: IEEE
Date: 14-09-2021
Publisher: IEEE
Date: 09-2019
Publisher: MECS Publisher
Date: 08-08-2021
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 18-11-2021
Publisher: Springer Science and Business Media LLC
Date: 30-06-2023
Publisher: Elsevier BV
Date: 09-2022
Publisher: MECS Publisher
Date: 08-06-2017
Publisher: Elsevier BV
Date: 08-2017
Publisher: American Chemical Society (ACS)
Date: 12-12-2016
DOI: 10.1021/JACS.6B10782
Abstract: High-power sodium-ion batteries (SIBs) with long-term cycling attract increasing attention for large-scale energy storage. However, traditional SIBs toward practical applications still suffer from low rate capability and poor cycle induced by pulverization and amorphorization of anodes at high rate (over 5 C) during the fast ion insertion/extraction process. The present work demonstrates a robust strategy for a variety of (Sb-C, Bi-C, Sn-C, Ge-C, Sb-Bi-C) freestanding metal-carbon framework thin films via a space-confined superassembly (SCSA) strategy. The sodium-ion battery employing the Sb-C framework exhibits an unprecedented performance with a high specific capacity of 246 mAh g
No related grants have been discovered for Dr. Sheikh Md. Rabiul Islam.