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The application of lenses combined with array antennas (also known as dome arrays or dome antennas) to the next generation of terrestrial and satellite communication systems brings a wide range of advantages in terms of improved radiation performance, reconfigurability in the use case, and reduction in power consumption. To facilitate the industrial implementation of dome antennas, highly efficient simulation tools are required. In this paper, we present a streamlined implementation of ray tracing for fast and efficient numerical analysis of the far-field radiation performance of 2D multilayer dielectric lenses combined with phased arrays. Unlike commercial physical-optical methods, our proposed ray-tracing method is capable of computing the effects of internal reflections in the dome in a multilayer configuration. In addition, the method estimates the absorption losses as a result of the Joule effect. To demonstrate the effectiveness of the proposed approach, we provide comparisons of the simulated radiation patterns using our proposed ray tracing with the results obtained from commercial full-wave simulation tools.

This article provides a comprehensive analysis, including static, dynamic, and experimental validation, of a voltage-doubler Ćuk rectifier operating in the discontinuous conduction mode (DCM) for single-phase applications. The proposed topology integrates two classic Ćuk rectifiers through a three-position switch, allowing for either a doubled output voltage or reduced voltage efforts compared to the conventional topology. The voltage gain is increased while maintaining the step-up/step-down characteristic of the Ćuk family, and power components are activated only in a half cycle of the electrical grid, thus reducing the current efforts in the semiconductors. These features make the Ćuk converter suitable for higher voltage applications. The proposed topology is analyzed in DCM, where a high power factor is naturally achieved without a current control loop. An experimental prototype of 1 kW is built to verify the theoretical analysis, and the results show an efficiency of 94.68% with an input current total harmonic distortion of 1.86% at a rated power, including an output voltage control loop. Furthermore, the proposed topology is compared to the conventional Ćuk rectifier, both operating in DCM and with power factor correction.

Studies on robotic interventions for gait rehabilitation after stroke require: (i) rigorous performance evidence; (ii) systematic procedures to tune the control parameters; and (iii) combination of control modes. In this study, we investigated how stroke individuals responded to training for two weeks with a knee exoskeleton (ABLE-KS) using both Assistance and Resistance training modes together with auditory feedback to train peak knee flexion angle. During the training, the torque provided by the ABLE-KS and the biofeedback were systematically adapted based on the subject’s performance and perceived exertion level. We carried out a comprehensive experimental analysis that evaluated a wide range of biomechanical metrics, together with usability and users’ perception metrics. We found significant improvements in peak knee flexion ( ${p} = {0}.{0016}$ ), minimum knee angle during stance ( ${p} = {0}.{0053}$ ), paretic single support time ( ${p} = {0}.{0087}$ ) and gait endurance ( ${p} = {0}.{022}$ ) when walking without the exoskeleton after the two weeks of training. Participants significantly ( ${p} < {0}.{00025}$ ) improved the knee angle during the stance and swing phases when walking with the exoskeleton powered in the high Assistance mode in comparison to the No Exo and the Unpowered conditions. No clinically relevant differences were found between Assistance and Resistance training sessions. Participants improved their performance with the exoskeleton (24-55 %) for the peak knee flexion angle throughout the training sessions. Moreover, participants showed a high level of acceptability of the ABLE-KS (QUEST 2.0 score: 4.5 ± 0.3 out of 5). Our preliminary findings suggest that the proposed training approach can produce similar or larger improvements in post-stroke individuals than other studies with knee exoskeletons that used higher training intensities.

This work presents the development and field testing of a novel adaptive visual information gathering (AVIG) framework for autonomous exploration of benthic environments using AUVs. The objective is to adapt dynamically the robot exploration using the visual information gathered online. This framework is based on a novel decision-time adaptive replanning (DAR) behavior that works together with a sparse Gaussian process (SGP) for environmental modeling and a Convolutional Neural Network (CNN) for semantic image segmentation. The framework is executed in mission time. The SGP uses semantic data obtained from stereo images to probabilistically model the spatial distribution of certain species of seagrass that colonize the sea bottom forming widespread meadows. The uncertainty of the probabilistic model provides a measure of sampling informativeness to the DAR behavior. The DAR behavior has been designed to execute successive informative paths, without stopping, considering the newest information obtained from the SGP. We solve the information path planning (IPP) problem by means of a novel depth-first (DF) version of the Monte Carlo tree search (MCTS). The DF-MCTS method has been designed to explore the state-space in a depth-first fashion, provide solution paths of a given length in an anytime manner, and reward smooth paths for field realization with non-holonomic robots. The complete framework has been integrated in a ROS environment as a high level layer of the AUV software architecture. A set of simulations and field testing show the effectiveness of the framework to gather data in P. oceanica environments.

Recent studies have shown evidence of a significant decline of the Posidonia oceanica (P.O.) meadows on a global scale. The monitoring and mapping of these meadows are fundamental tools for measuring their status. We present an approach based on a deep neural network to automatically perform a highprecision semantic segmentation of the P.O. meadows in sea-floor images, offering several improvements over the state-of-the-art techniques. Our network demonstrates outstanding performance over diverse test sets, reaching a precision of 96.57% and an accuracy of 96.81%, surpassing the reliability of labeling the images manually. Moreover, the network is implemented in an autonomous underwater vehicle, performing an online P.O. segmentation, which will be used to generate real-time semantic coverage maps.

Two new three-phase high-power-factor rectifiers using an interleaved discontinuous conduction mode (DCM) boost converters in a three-level configuration are proposed in this paper. The input currents waveforms follow the input voltages waveforms without a current control loop and the input currents present low current ripple without additional input filter due to the interleaved operation. The main advantage of the proposed rectifiers is the low voltage stress with the voltage applied across all semiconductors and output filter capacitors lower than the output voltage. A high gain rectifier version is presented in a hybrid configuration, including resonant voltage multiplier cell. The structures use only two active switches and current sensors and current control loops are not necessary, becoming a possible solution for low power and low cost applications. The low voltage stress operation and the reduction of the switch current stress due to the interleaved operation allows the reduction of the switches conduction losses of the DCM operation. The experimental results obtained from two prototypes are presented and efficiency equal to 97.26% is obtained for the three-level converter (Vi = 127 V/P = 2 kW) and 94% for the hybrid configuration (Vi = 64 V/P = 1 kW).

Controlling and quantifying the presence of Posidonia Oceanica (P.O.) in the Mediterranean sea is crucial for the conservation of these endemic ecosystems and to underscore the negative impact of many anthropogenic activities. These activities, which include uncontrolled leisure anchoring or illegal drag fishing, directly affect the tourism and fishing industries. Nowadays, the control and quantification of P.O. is done by divers, in a slow and imprecise process achieved in successive missions of a duration limited by the capacity of the oxygen scuba tanks. This paper proposes the application of robotic and computer vision technologies to upgrade the current P.O. control methods, building large scale coverage maps using the imagery provided by an autonomous underwater vehicle endowed with a bottom-looking camera. The process includes four main steps: 1) training a classifier based on two different Gabor filter image patch descriptors and a support vector machine; 2) detecting P.O. autonomously, both on-line and off-line, in each individual image; 3) color photo-mosaicking the area explored by the vehicle to obtain a global view of the meadow structure; these mosaics are extremely useful to analyze the structure and extension of the meadow and to calculate some of the biological descriptors needed to diagnose its state; and 4) building a binary coverage map in which the classification results of areas with image overlap are fused according to four different strategies. The experiments, performed in coastal areas of Mallorca and Girona, evaluate and compare the proposed descriptors and fusion techniques, showing, in some cases, accuracies and precisions above 90% in the detection of different patterns of P.O., from video sequences at different locations, in different seasons and with different environmental conditions.