Hilal M. El Misilmani

Computation of complex-coefficient complex-solution differential equations is a problem that arises in various domains of science and engineering. This paper aims at applying the Theory of Characteristic Modes (TCM) approach along with the Method of Moments (MoM) in solving these problems with emphasis on procedures for higher differential equations. Several available methods, known in literatures, are available for solving the problem. The complexity of the available methods differs based on the accuracy of the solution. In this paper, the general method is first presented and then a simplified version of it is proposed to solve high order differential equations. Two examples are illustrated, a third and a fourth order complex-coefficients complex-solution differential equations, to show the simplicity of the proposed method. The proposed approach can be also introduced along with other methods to solve these special occurrences differential equations and other boundary value problems.

This paper presents the use of machine learning (ML) to facilitate the design of dielectric-filled Slotted Waveguide Antennas (SWAs) with specified sidelobe levels. Conventional design methods for air-filled SWAs require the simultaneous solving of complex equations to deduce the antenna’s design parameters, which typically requires further manual simulation-based optimization to reach the desired resonance frequency and sidelobe level ratio (SLR). The few works that investigated the design of filled SWAs, did not optimize the design for a specified SLR. For an accelerated design process in the case of specified SLRs, we formulate the design of dielectric-filled SWAs as a regression problem where based on input specifications of the antenna’s SLR, reflection coefficient, frequency of operation, and relative permittivity of the dielectric material, the developed ML model predicts the filled SWA’s design parameters fast and with very low error. These parameters include the unified slots length and the non-uniform slots displacements required to achieve the desired performance. We experiment with several regressive ML algorithms and provide a comparative study of their results. Our numerical evaluations and validation experiments with the best performing ML models demonstrate the high efficiency of the proposed ML approach in estimating the dielectric-filled SWA’s design parameters in only a few milliseconds. A comparison to the design obtained through conventional optimization using the Genetic Algorithm also indicate superiority in computation time and resulting antenna performance.

Summary: This article presents a complete design procedure for planar slotted waveguide antennas (SWA). For a desired sidelobe level ratio (SLR), the proposed method provides a pencil shape pattern with a narrow half power beamwidth, which makes the proposed system suitable for high power microwave applications. The proposed planar SWA is composed of only two layers, and uses longitudinal coupling slots rather than the conventional inclined coupling slots. For a desired SLR, the slots excitation in the radiating and feeder SWAs are calculated based on a specified distribution. Simplified closed‐form equations are then used to determine the slots nonuniform displacements, for both the radiating and feeder SWAs, without the need to use optimization algorithms. Using simplified equations, the slots lengths, widths, and their distribution along the length of the radiating and feeder SWAs can be found. The feeder dimensions and slots positions are deduced from the dimensions and total number of the radiating SWAs. An 8 × 8 planar SWA has been designed and tested to show the validity of the proposed method. The obtained measured and simulated results are in accordance with the design objectives.

This paper presents a complete design procedure, with an optimized feeding method, of two-dimensional slotted waveguide antenna arrays (2D SWAs). For a desired sidelobe level ratio, the proposed system provides a pencil shape pattern with a narrow halfpower beamwidth, large sidelobe level ratio (SLR), and very low sidelobe levels (SLL), which makes it suitable for high power microwave applications. The radiating slotted waveguide antennas use longitudinal slots, designed for a specified slidelobe level ratio and resonance frequency. The resulting two-dimensional slotted waveguide antenna array is formed by stacking a number of similarly designed radiating SWAs, and fed with an additional SWA. The proposed feeding method uses longitudinal coupling slots rather than the conventional inclined coupling slots, which can provide better values of SLR and easily obtain very low SLLs, in comparison with the conventional systems. The feeder dimensions and slots positions are deduced from the dimensions and total number of the radiating SWAs. For a desired SLR, the slots excitation in the radiating and feeder SWAs are calculated based on a specified distribution. Then, using simplified closed-form equations and for a desired resonance frequency, the slots lengths, widths, and their distribution along the length of the radiating SWAs and feeder SWA can be found. Two examples are illustrated with different number of slots and radiating elements, and one is fabricated and tested. Chebyshev distribution is used to estimate the excitations of the SWA slots in the examples. The obtained measured and simulated results are in accordance with the design objectives.

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.

With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently taking a major part of the ongoing research, and expected to be the key player in today’s technologies. This paper introduces and investigates the applications of machine learning in antenna design. It covers the major aspects of machine learning, including its basic concept, differentiation with artificial intelligence and deep learning, learning algorithms, its wide applications in various technologies, with a main focus on its usage in antenna design. The review also includes a comparison of the results using machine learning in antenna design, compared to the conventional design methods.

Slotted waveguide antennas (SWAs) are widely used in high power microwave applications. In this paper, the slots displacements in broadwall SWAs, previously used to control the SWA sidelobe level ratio (SLR), are further investigated to also adjust the beamwidth of the SWA. A modified Taylor array design method is used to estimate the excitations of the SWA slots leading to independently controllable SLR and first-null beamwidth (FNBW). The slots displacements are then calculated from these excitations. An example is presented where the SWA has 7 slots and the proposed method is employed to find the displacements required for desired SLR and FNBW.

Implanted biomedical devices are witnessing great attention in finding solutions to complex medical conditions. Many challenges face the design of implantable biomedical devices including designing and implanting antennas within hostile environment due to the surrounding tissues of human body. Implanted antennas must be compact in size, efficient, safe, and can effectively work within adequate medical frequency bands. This paper presents an overview of the major aspects related to the design and challenges of in body implanted antennas. The review includes surveying the applications, design methods, challenges, simulation tools, and testing and manufacturing of implantable biomedical antennas.

This paper presents a procedure for the design of a 2D Slotted Waveguide Antenna (SWA) array having circular polarization. The 2D array is formed by a defined number of 1D branchlines broadwall SWAs, which are fed using an extra broadwall SWA. The SWA branchlines have cross-shaped slots that radiate with a circular polarization. The dimensions of these slots are optimized for best performance. The feed SWA is made with rectangular slots, which are designed to achieve a low SLL. An example SWA with 10 χ 10 slots is designed using this procedure, and the design results are reported in this paper.