Applications of machine learning and deep learning in antenna design, optimization, and selection : a review
- 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.
Deep learning and federated learning for screening COVID-19 : a review
- 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.
A novel OFDM format and a machine learning based dimming control for lifi
- Authors: Nowrin, Itisha , Mondal, M. , Islam, Rashed , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 17 (2021), p.
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- Description: This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC‐biased pulse amplitude modulated optical OFDM (DPO‐OFDM) by combining the ideas of the existing DC‐biased optical OFDM (DCO‐OFDM) and pulse amplitude modulated discrete multitone (PAM‐DMT). The analysis indicates that the required DC‐bias for DPO‐OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO‐OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO‐OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO‐OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO‐OFDM component of DPO‐OFDM for a given constellation size of PAM‐DMT. With the component switching and the machine learning algorithms, DPO‐OFDM‐based LiFi is power efficient at a wide dimming range. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.