Applications of machine learning and deep learning in antenna design, optimization, and selection : a review
- Sarker, Nayan, Podder, Prajoy, Mondal, M., Shafin, Sakib, Kamruzzaman, Joarder
- Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
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- Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.
- Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
- Full Text:
- Reviewed:
- Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.
Decentralized content sharing in mobile ad-hoc networks : a survey
- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Rashid, Md Mamunur
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Rashid, Md Mamunur
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Digital Communications and Networks Vol. 9, no. 6 (2023), p. 1363-1398
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- Description: The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic. To address this issue and take advantage of the short-range communication capabilities of smart mobile devices, the decentralized content sharing approach has emerged as a suitable and promising alternative. Decentralized content sharing uses a peer-to-peer network among co-located smart mobile device users to fulfil content requests. Several articles have been published to date to address its different aspects including group management, interest extraction, message forwarding, participation incentive, and content replication. This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration. © 2022 Chongqing University of Posts and Telecommunications
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Rashid, Md Mamunur
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Digital Communications and Networks Vol. 9, no. 6 (2023), p. 1363-1398
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- Description: The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic. To address this issue and take advantage of the short-range communication capabilities of smart mobile devices, the decentralized content sharing approach has emerged as a suitable and promising alternative. Decentralized content sharing uses a peer-to-peer network among co-located smart mobile device users to fulfil content requests. Several articles have been published to date to address its different aspects including group management, interest extraction, message forwarding, participation incentive, and content replication. This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration. © 2022 Chongqing University of Posts and Telecommunications
Deep learning and federated learning for screening COVID-19 : a review
- Mondal, M., Bharati, Subrato, Podder, Prajoy, Kamruzzaman, Joarder
- Authors: Mondal, M. , Bharati, Subrato , Podder, Prajoy , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: BioMedInformatics Vol. 3, no. 3 (2023), p. 691-713
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- Description: Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. © 2023 by the authors.
- Authors: Mondal, M. , Bharati, Subrato , Podder, Prajoy , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: BioMedInformatics Vol. 3, no. 3 (2023), p. 691-713
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- Description: Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. © 2023 by the authors.
Attacks on self-driving cars and their countermeasures : a survey
- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Jolfaei, Alireza, Das, Rajkumar
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
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- Description: Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE.
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
- Full Text:
- Reviewed:
- Description: Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE.
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