A review on machine learning and deep learning for various antenna design applications

Khan, Mohammad Monirujjaman; Hossain, Sazzad; Majumder, Puza; Akter, Sonia; Salam, Zainal

doi:10.1016/j.heliyon.2022.e09317

Cited by 43 publications

(24 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several researchers have used deep-learning models to identify novel frequency-selective surfaces for sensor designs [170,171]. Moreover, novel metamaterials can be designed using machine-learning methods to customize sensors with high sensitivity and specificity [121,[172][173][174][175]. Characterizing the dielectric properties of target tissues using dielectric spectroscopy of the target tissues will provide the contrast needed for the specific sensor design.…”

Section: Microwave-based Medical Sensors With Aimentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Microwave-based Medical Sensors With Aimentioning

confidence: 99%

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

et al. 2023

View full text Add to dashboard Cite

show abstract

“…There are several survey papers in the literature that discuss different aspects of ML application to future wireless networks. The topic of antenna design for future networks using ML is discussed by the authors in [ 10 ]. In contrast, the topic of ML applications in vehicular networks from the networking point of view is examined in [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

Chatzoglou

Goudos

2023

Sensors

View full text Add to dashboard Cite

A challenging problem in millimeter wave (mmWave) communications for the fifth generation of cellular communications and beyond (5G/B5G) is the beam selection problem. This is due to severe attenuation and penetration losses that are inherent in the mmWave band. Thus, the beam selection problem for mmWave links in a vehicular scenario can be solved as an exhaustive search among all candidate beam pairs. However, this approach cannot be assuredly completed within short contact times. On the other hand, machine learning (ML) has the potential to significantly advance 5G/B5G technology, as evidenced by the growing complexity of constructing cellular networks. In this work, we perform a comparative study of using different ML methods to solve the beam selection problem. We use a common dataset for this scenario found in the literature. We increase the accuracy of these results by approximately 30%. Moreover, we extend the given dataset by producing additional synthetic data. We apply ensemble learning techniques and obtain results with about 94% accuracy. The novelty of our work lies in the fact that we improve the existing dataset by adding more synthetic data and by designing a custom ensemble learning method for the problem at hand.

show abstract

“…In recent years, deep learning technology has been used in various sectors. Deep learning has developed human-like abilities, such as knowledge learning, problem-solving, and decision-making [1]. Big companies have tried to adopt the latest digital technologies, including the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), and Blockchain [2].…”

Section: Introductionmentioning

confidence: 99%

Understanding of Convolutional Neural Network (CNN): A Review

Purwono

Ma’arif

Rahmaniar

et al. 2023

IJRCS

View full text Add to dashboard Cite

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

show abstract

A review on machine learning and deep learning for various antenna design applications

Cited by 43 publications

References 98 publications

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

Applications of Microwaves in Medicine Leveraging Artificial Intelligence: Future Perspectives

Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

Understanding of Convolutional Neural Network (CNN): A Review

Contact Info

Product

Resources

About