An improved method to design compact pattern reconfigurable pixel antennas is described. The method introduces a new structure consisting of diagonal connections between pixels to enhance the pattern steering capability of the pixel antennas. These diagonal connections increase the number of independent degrees of freedom available in the design process which results in significantly improved performance as compared to designs with only horizontal and vertical connections between pixels. Furthermore the diagonal structures enable the possible formation of new “cross-over” connections which provide a structure that allows currents to cross each other in the antenna geometry. Simulation and measurement results show that an antenna with 2×6 pixels can steer a beam over 150° in the azimuth plane and 300° beam steering range can be achieved with 3×6 pixels. The use of the diagonal elements increases gains by up to 3 dB and provides a much greater steering range as compared to pixel antenna design without the diagonal connections. Utilizing these diagonal pixel connections can also potentially enhance the performance of pixel antenna designs in a wide variety of other pixel antenna configurations.

This article addresses the modeling and control of heterogeneous populations of thermostatically controlled loads (TCLs) operated by model predictive control (MPC) schemes at the level of each TCL. It is shown that the dynamics of such TCLs populations can be described by a pair of Fokker-Planck equations coupled via the actions in the domain. The technique of input-output feedback linearization is used in the design of aggregate power control, which leads to a nonlinear system in a closed loop. Well-posedness analysis is carried out to validate the developed control scheme. The closed-loop stability of the system is assessed by rigorous analysis. A simulation study is contacted, and the obtained results confirm the validity and effectiveness of the proposed approach.

The localization problem for multi-robot teams has been extensively studied with the goal of obtaining precise positioning information, such as required by a variety of robotic applications. This paper proposes a dynamic localization approach that exploits multiple robots equipped with range-only ultrawideband sensors to create and maintain a common self-adaptive coordinate system. For 2-D localization, we use three robots with relative range measurements to build a global coordinate system. We recursively apply an extended Kalman filter, which results in accurate position estimates over time. We also propose a reconfiguration approach that prevents error accumulation from ultra-wideband sensors. The applicability of our approach is tested through a campaign of simulations, which show promising results.

The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

With the development of e-commerce, the last-mile delivery has become a significant part of customers shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyds algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.

Recent research on Polycystic Ovary Syndrome (PCOS) detection increasingly employs intelligent algorithms to assist gynecologists in more accurate and efficient diagnoses. However, intelligent PCOS detection faces notable challenges: absence of standardized feature taxonomies, limited research on available datasets, and insufficient understanding of existing detection tools' capabilities. This paper addresses these gaps by introducing a novel analytical framework for PCOS diagnostic research and developing a comprehensive taxonomy comprising 108 features across 8 categories. Furthermore, we analyzed available datasets and assessed current intelligent detection tools. Our findings reveal that 12 publicly accessible datasets cover only 54% of the 108 features identified in our taxonomy. These datasets frequently lack multimodal integration, regular updates, and clear license information—constraints that potentially limit detection tool development. Additionally, our analysis of 42 detection tools identifies several limitations: high computational resource requirements, inadequate multimodal data processing, insufficient longitudinal analysis capabilities, and limited clinical validation. Based on these observations, we highlight critical challenges and future research directions for advancing intelligent PCOS detection tools.