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With the prevalence of breast cancer among women and the shortcomings of conventional techniques in detecting breast cancer at its early stages, microwave breast imaging has been an active area of research and has gained momentum over the past few years, mainly due to the advantages and improved detection rates it has to offer. To achieve this outcome, specifically designed antennas are needed to satisfy the needs of such systems where an antenna array is typically used. These antennas need to comply with several criteria to make them suitable for such applications, which most importantly include bandwidth, size, design complexity, and cost of manufacturing. Many works in the literature proposed antennas designed to meet these criteria, but no works have classified and evaluated these antennas for the use in microwave breast imaging. This paper presents a comprehensive study of the different array configurations proposed for microwave breast imaging, with a thorough investigation of the antenna elements proposed to be used with these systems, classified per antenna type, and per the improvements that concern the operational bandwidth, the size of the antenna, the radiation characteristics, and the techniques used to achieve the improvement. At the end of the investigation, a qualitative evaluation of the antenna designs is presented, providing a comparison between the investigated antennas, and determining whether a design is suitable or not to be used in antenna arrays for microwave breast imaging, based on the performance of each. An evaluation of the investigated arrays is also presented, where the advantages and limitations of each array configuration are discussed.

This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

Abstract-Slotted waveguide antenna (SWA) arrays offer clear advantages in terms of their design, weight, volume, power handling, directivity, and efficiency. For broadwall SWAs, the slot displacements from the wall centerline determine the antenna's sidelobe level (SLL). This paper presents a simple inventive procedure for the design of broadwall SWAs with desired SLLs. For a specified number of identical longitudinal slots and given the required SLL and operating frequency, this procedure finds the slots length, width, locations along the length of the waveguide, and displacements from the centerline. Compared to existing methods, this procedure is much simpler as it uses a uniform length for all the slots and employs closed-form equations for the calculation of the displacements. A computer program has been developed to perform the design calculations and generate the needed slots data. Illustrative examples, based on Taylor, Chebyshev and the binomial distributions are given. In these examples, elliptical slots are considered, since their rounded corners are more robust for high power applications. A prototype SWA has been fabricated and tested, and the results are in accordance with the design objectives.

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