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The purpose of this paper is to design simple and high-performing screens capable to separate circularly polarized electromagnetic waves in Ku band from the ones in Ka band.

The proposed screen consists of an inductive double resonant element FSS, i.e. a regular array of circular holes in a metal thick plate, in order to grant the robustness to mechanical stress for antenna applications in extreme conditions.

The proposed design of a multi-band frequency selective surface (FSS) is able to separate circularly polarized electromagnetic waves in Ku band from the ones in Ka band.

The paper shows the capabilities of a novel FSS that combine the transmission properties of two simple FSSs which allows us to achieve an interesting behaviour in three typical bands of the satellite communications.

In corridor environments, human-following robot encounter difficulties when the target turning around at the corridor intersections, as walls may cause complete occlusion. This paper aims to propose a collision-free following system for robot to track humans in corridors without a prior map.

In addition to following a target and avoiding collisions robustly, the proposed system calculates the positions of walls in the environment in real-time. This allows the system to maintain a stable tracking of the target even if it is obscured after turning. The proposed solution is integrated into a four-wheeled differential drive mobile robot to follow a target in a corridor environment in real-world.

The experimental results demonstrate that the robot equipped with the proposed system is capable of avoiding obstacles and following a human target robustly in the corridors. Moreover, the robot achieves a 90% success rate in maintaining a stable tracking of the target after the target turns around a corner with high speed.

This paper proposes a human target following system incorporating three novel features: a path planning method based on wall positions is introduced to ensure stable tracking of the target even when it is obscured due to target turns; improvements are made to the random sample consensus (RANSAC) algorithm, enhancing its accuracy in calculating wall positions. The system is integrated into a four-wheeled differential drive mobile robot effectively demonstrates its remarkable robustness and real-time performance.

This paper aims to concentrate on research that has been conducted in the previous decade on metamaterial (MTM)-based sensors for material characterization, which includes solid dielectrics, micro fluids and biomolecules.

There has been a vast advancement in sensors based on MTM since the past few decades. MTM elements provide a sensitive response to materials while having a tiny footprint, making them an appealing alternative for realizing diverse sensing devices.

Related research papers on MTM sensors published in reputable journals were reviewed in this report, with a specific emphasis on the structure, size and nature of the materials characterized. Because electromagnetic wave interaction excites MTM structures, sensing applications around the electromagnetic spectrum are possible.

The paper contains valuable information on MTM sensor technology for material characterization, and this study also highlights the challenges and approaches that will guide future development.

This paper aims to propose a practical design methodology of high-power wideband power amplifier.

The distributed power amplification method is used for a Gallium Nitride device to achieve wideband operation. To achieve the high power without trading-off the bandwidth and gain, a methodology to extract the package-loading effect is proposed and verified.

A maximum output power of 10 W is achieved from 100 MHz to 2 GHz with a wideband power gain of 32 dB in measurement. This performance is achieved through a single section matching network.

Measurement accuracy is dependable to the thermal behaviour of the high-power device.

The proposed technique is an excellent solution to be used in the two way radio power amplifier that minimizes the fundamental trade-off issue between power, gain, bandwidth and efficiency.

In this work, a practical distributed power amplifier (DPA) design methodology is proposed that reduces the development cycle time for industrial engineers working on high-power circuit design application.

The purpose of this paper is to validate methodologies for expedited multi-objective design optimization of complex antenna structures both numerically and experimentally.

The task of identifying the best possible trade-offs between the antenna size and its electrical performance is formulated as multi-objective optimization problem. Algorithmic frameworks are described for finding Pareto-optimal designs using auxiliary surrogate models and two alternative approaches to design refinement: response correction techniques and co-kriging. Numerical and experimental case studies are provided to demonstrate feasibility of solving real-world and complex antenna design tasks.

It is possible, through appropriate combination of the surrogate modeling techniques (both data driven and physics based) and response correction methods, to find the set of alternative designs representing the best possible trade-offs between conflicting design objectives, here, electrical performance and size. Design optimization can be performed at practically feasible computational costs.

The study demonstrates feasibility of automated multi-objective design optimization of antennas at low computational cost. The presented techniques reach beyond the commonly used design approaches based on parameter sweeps and similar hands-on methods, particularly in terms of automation, reliability and reduction of the computational costs of the design processes.

Multi-objective design of antenna structures is very challenging when high-fidelity electromagnetic simulations are utilized for performance evaluation of the structure at hand. The proposed design framework permits rapid optimization of complex structures (here, MIMO antenna), which is hardly possible using conventional methods.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

The purpose of this study is to propose a framework for expedited antenna optimization with numerical derivatives involving gradient variation monitoring throughout the optimization run and demonstrate it using a benchmark set of real-world wideband antennas. A comprehensive analysis of the algorithm performance involving multiple starting points is provided. The optimization results are compared with a conventional trust-region (TR) procedure, as well as the state-of-the-art accelerated TR algorithms.

The proposed algorithm is a modification of the TR gradient-based algorithm with numerical derivatives in which a monitoring of changes of the system response gradients is performed throughout the algorithm run. The gradient variations between consecutive iterations are quantified by an appropriately developed metric. Upon detecting stable patterns for particular parameter sensitivities, the costly finite differentiation (FD)-based gradient updates are suppressed; hence, the overall number of full-wave electromagnetic (EM) simulations is significantly reduced. This leads to considerable computational savings without compromising the design quality.

Monitoring of the antenna response sensitivity variations during the optimization process enables to detect the parameters for which updating the gradient information is not necessary at every iteration. When incorporated into the TR gradient-search procedures, the approach permits reduction of the computational cost of the optimization process. The proposed technique is dedicated to expedite direct optimization of antenna structures, but it can also be applied to speed up surrogate-assisted tasks, especially solving sub-problems that involve performing numerous evaluations of coarse-discretization models.

The introduced methodology opens up new possibilities for future developments of accelerated antenna optimization procedures. In particular, the presented routine can be combined with the previously reported techniques that involve replacing FD with the Broyden formula for directions that are satisfactorily well aligned with the most recent design relocation and/or performing FD in a sparse manner based on relative design relocation (with respect to the current search region) in consecutive algorithm iterations.

Benchmarking against a conventional TR procedure, as well as previously reported methods, confirms improved efficiency and reliability of the proposed approach. The applications of the framework include direct EM-driven design closure, along with surrogate-based optimization within variable-fidelity surrogate-assisted procedures. To the best of the authors’ knowledge, no comparable approach to antenna optimization has been reported elsewhere. Particularly, it surmounts established methodology by carrying out constant supervision of the antenna response gradient throughout successive algorithm iterations and using gathered observations to properly guide the optimization routine.

Sensing- and warning-based technologies are widely used in the construction industry for occupational health and safety (OHS) monitoring and management. A comprehensive understanding of the different types and specific research topics related to the application of sensing- and warning-based technologies is essential to improve OHS in the construction industry. The purpose of this paper is to examine the current trends, different types and research topics related to the applications of sensing- and warning-based technology for improving OHS through the analysis of articles published between 1996 and 2017 (years inclusive).

A standardized three-step screening and data extraction method was used. A total of 87 articles met the inclusion criteria.

The annual publication trends and relative contributions of individual journals were discussed. Additionally, this review discusses the current trends of different types of sensing- and warning-based technology applications for improving OHS in the industry, six relevant research topics, four major research gaps and future research directions.

Overall, this review may serve as a spur for researchers and practitioners to extend sensing- and warning-based technology applications to improve OHS in the construction industry.

A framework for reliable modeling of high-frequency structures by nested kriging with an improved sampling procedure is developed and extensively validated. A comprehensive benchmarking including conventional kriging and previously reported design of experiments technique is provided. The proposed technique is also demonstrated in solving parameter optimization task.

The keystone of the proposed approach is to focus the modeling process on a small region of the parameter space (constrained domain containing high-quality designs with respect to the selected performance figures) instead of adopting traditional, hyper-cube-like domain defined by the lower and upper parameter bounds. A specific geometry of the domain is explored to improve a uniformity of the training data set. In consequence, the predictive power of the model is improved.

Building the model in a constrained domain allows for a considerable reduction of a training data set size without a necessity to either narrow down the parameter ranges or to reduce the parameter space dimensionality. Improving uniformity of training data set allocation permits further reduction of the computational cost of setting up the model. The proposed technique can be used to expedite the parameter optimization and enables locating good initial designs in a straightforward manner.

The developed framework opens new possibilities inaccurate surrogate modeling of high-frequency structures described by a large number of geometry and/or material parameters. Further extensions can be investigated such as the inclusion of the sensitivity data into the model or exploration of the particular geometry of the model domain to further reduce the computational overhead of training data acquisition.

The efficiency of the proposed method has been demonstrated for modeling and parameter optimization of high-frequency structures. It has also been shown to outperform conventional kriging and previous constrained modeling approaches. To the authors’ knowledge, this approach to formulate and handle the modeling process is novel and permits the establishment of accurate surrogates in highly dimensional spaces and covering wide ranges of parameters.

This paper aims to focus on research work related to metamaterial-based sensors for material characterization that have been developed for past ten years. A decade of research on metamaterial for sensing application has led to the advancement of compact and improved sensors.

In this study, relevant research papers on metamaterial sensors for material characterization published in reputed journals during the period 2007-2018 were reviewed, particularly focusing on shape, size and nature of materials characterized. Each sensor with its design and performance parameters have been summarized and discussed here.

As metamaterial structures are excited by electromagnetic wave interaction, sensing application throughout electromagnetic spectrum is possible. Recent advancement in fabrication techniques and improvement in metamaterial structures have led to the development of compact, label free and reversible sensors with high sensitivity.

The paper provides useful information on the development of metamaterial sensors for material characterization.