Abstract
Accurate estimation of hand shape and position is an important task in various applications, such as human-computer interaction, human-robot interaction, and virtual and augmented reality. In this paper, it is proposed a method to estimate the hand keypoints from single and colored images utilizing the pre-trained deep convolutional neural networks VGG-16 and VGG-19. The method is evaluated on the FreiHAND dataset, and the performance of the two neural networks is compared. The best results were achieved by the VGG-19, with average estimation errors of 7.40 pixels and 11.36 millimeters for the best cases of two-dimensional and three-dimensional hand keypoints estimation, respectively.

Abstract
Recent developments in dexterous robotic manipulation technologies allowed for the design of very compact, yet capable, multi-fingered robotic hands. These can be designed to emulate the human touch and feel, reducing the aforementioned need for human expertise in highly detailed tasks. The presented work focused on the application of two simulation platforms Gazebo and MuJoCo - to a use-case of a Schunk Five Finger Robotic Hand, coupled to the UR5 collaborative manipulator. This allowed to assess the relative appropriateness of each of these platforms.

Abstract
Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Instead, Radio Frequency (RF)-based HAR relies on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) are significant for this use-case by allowing dynamic control over the wireless environment, enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach using our own 6.5 GHz RIS design, which we use to gather a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%. © 2025 IEEE.

Abstract
Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6 GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5 GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering. © 2025 European Association on Antennas and Propagation.

Abstract
Throughout this article the execution of the motion planning for a robotic manipulator by means of Reinforcement Learning methods is studied. Towards this, an implementation based on a Wire and loop game is used as an example case to be solved. The loop is controlled in a single plane as the endeffector of the manipulator. The modeling of the problem and the process of training the agent is detailed. This allowed for the verification of the capacity of a learning based method, having produced, under the considered abstractions, satisfying results by gaining the capability of completing the path imposed by the wire in 23 seconds.