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This paper presents a framework for achieving machine learning (ML)‐assisted direction‐of‐arrival (DoA) accuracy enhancement using a millimetre‐wave (mmWave) dynamic aperture. The technique used for the enhanced DoA estimation accuracy leverages an over‐sized lens‐loaded cavity antenna connected to a single RF chain in the physical layer and a computational method in the computational layer of the framework. It is shown for the first time that by introducing a reconfigurable mode‐mixing mechanism inside the over‐sized lens‐loaded cavity hardware, a greater number of spatially orthogonal radiation modes can be achieved giving rise to many cavity states. If the best cavity state is determined and selected by means of design exploration using a contemporary ML‐assisted antenna optimisation method, the computational DoA estimation accuracy can be improved. The mode‐mixing mechanism in this work is a randomly oriented metallic scatterer located inside an over‐sized constant−ϵ r lens‐loaded cavity, connected to a stepper motor that is electronically controlled by inputs from the computational layer of the presented framework. Measurement results in terms of near‐field radiation mode scans are included in this study to verify and validate that the proposed ML‐assisted framework enhances the DoA estimation accuracy. Moreover, this investigation simultaneously provides a simplification in the physical layer implementation of mmWave radio hardware, and DoA accuracy enhancement, which in turn lends itself favourably to the adoption of the proposed framework for channel sounding in mmWave communication systems.

Wireless communication systems and their supporting infrastructure continue to play a vital role in contemporary daily activities. Due to the unprecedented levels of interconnectivity achieved between wireless devices in recent times, new insights and paradigms for the robust deployment and better utilization of wireless communication systems are always of interest to many countries for socio-economic development. Present-day Nigeria is faced with the challenge of insurgencies whose financing has been linked to proceeds from livestock theft or rustling according to many scholarly works and news reports. To mitigate rustling and the sales of stolen livestock via identification and traceability from ‘herds to markets to homes’, the design considerations and data communication architecture for national animal identification and traceability system in Nigeria (NAITS) is proposed in this paper for safer and improved livestock farming and production. Particularly, technical insight into the co-use of radio frequency identification (RFID) and fifth-generation new radio (5G NR) technologies for the implementation of NAITS are highlighted and discussed in this paper for a prospective technological policy plan and development in Nigeria.

A dual-band metasurface (MTS)-based antenna can be realized by composite right-/left-handed (CRLH) structures for wireless power transfer or ambient wireless energy harvesting. The bandwidth in the right-handed (RH) region can be obtained by exciting different desired transverse magnetic (TM) modes while that in the left-handed (LH) region can be achieved by mushroom radiating structures. The position choice of shorting pins in mushroom structures is significant since it will not only affect the potential bandwidth in the RH region, but also be related to the resonant frequencies in the LH region. The positions of the shorting pins will be chosen where the Electric field distribution is minimal for modes excited in the RH region. The proposed dual-band MTS-based antenna with an overall size of 0.375 λ0×0.375λ0×0.035 λ0 can achieve 25% and 48% fractional bandwidths, and 5.0 and 9.6 dBi peak gains in the lower and higher frequency band, respectively.

High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermoelastic micro-actuator, a high-performance corrugated membrane micro-actuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability.

Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.

With the advent of artificial intelligence (AI), the design of microwave devices such as antennas has been expedited in terms of throughput and time-to-market. This is chiefly because design automation via optimization has replaced the use of time intensive manual design techniques which premise on trial and error without any guarantee of successful outcomes. For the rapid design of antennas via optimization, surrogate model-based optimization (SBO) methods tend to be at the forefront due to their efficiency improvement in terms of computational cost. The surrogate model assisted differential evolution for antenna synthesis (SADEA) algorithm family are a class of state-of-the-art SBO methods. In this paper, the use and advantages of the SADEA algorithm family is demonstrated using two cases of real-world antenna design problems as examples. The antenna design problems are the optimization of: a multi-layered compact\ud multiple-input and the multiple-output (MIMO) antenna array for wireless communications and a microwave imaging antenna for ultra wide band (UWB) body-centric applications. For both examples, the SADEA algorithm family obtained very good design solutions within an affordable time and the quality of the\ud obtained solutions are validated by the close agreement which exits between the simulated and measured results of the fabricated and ready-to-use prototypes of the antennas. In one of the cases (the microwave imaging antenna), the performance of the SADEA algorithm family when compared to 2019 Computer Simulation Technology - Microwave Studio (CST-MWS) optimizers (trust region framework (TRF) and particle swarm optimisation (PSO)) is reported. Results from the comparisons show that the SADEA algorithm family obtains very satisfactory design solutions in all runs using an affordable optimization time in each, whereas the alternative optimizers failed in all runs by not meeting the design requirements and/or generating designs with geometric incongruities.