Efficient AI-Driven Design of Microwave Antennas Using PSADEA

Akinsolu, Mobayode O.; Danjuma, Isah Musa; Mistry, Keyur K.; Liu, Bo; Abd‐Alhameed, Raed A.; Lazaridis, Pavlos I.; Zaharis, Zaharias D.; Excell, Peter S.

doi:10.1109/menacomm46666.2019.8988550

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…The future of microwave imaging will rely on the AI-driven design of microwave antennas specific to medical applications. The parallel surrogate modelassisted hybrid differential evolution for antenna optimization (PSADEA) method has shown good performance for AI-assisted ultra-wide band (UWB) antennas suitable for medical applications [144]. Machine-learning approaches offer promise for accelerated antenna designs for the medical applications of interest.…”

Section: Microwave Imaging Hardware Design With Aimentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

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Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.

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Section: Microwave Imaging Hardware Design With Aimentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

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“…As concrete resources, smart designing blends: (i) human creativity, intents, abilities, competencies, and experiences, (ii) networked physical and virtualized computing environments (such as edge, fog, and cloud computing), (iii) penetration into real-life processes and generation of lifecycle-related big data, information, knowledge, and meta-knowledge, (iv) integrated warehouse of manual and digital ideation, conceptualization, architecting, modeling, analysis, simulation, tools, etc., and (v) tailored problem solving intellect offered by artificial intelligence approaches. In combination with traditional design methods, the framework of generative design utilizes ANI and knowledge representation techniques to generate better designs in a shorter time (Akinsolu et al, 2019).…”

Section: Essence Of Smart Designingmentioning

confidence: 99%

Connectors of smart design and smart systems

Horváth

2021

AIEDAM

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Though they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

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“…The parallel surrogate model-assisted differential evolution for antenna synthesis (PSADEA) method [16][17][18] has been employed to find the optimal design parameters, so as to satisfy the specifications. PSADEA is the state-of-the-art in the SADEA algorithm series [18][19][20]; which offers 3 to 20 times optimization efficiency improvement and higher optimization ability compared to several popular antenna optimizers [21,22].…”

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confidence: 99%