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The intensity of the backscattered signal collected by active radars over wet, seasonal snowpacks depends on numerous variables related to the snowpack, which are often difficult to determine accurately. In recent years, thanks to the increased availability of spaceborne synthetic aperture radars (SARs), a temporal relationship between wet-snow metamorphism and microwave backscattering has been demonstrated. However, a precise quantitative description of this phenomenon has yet to be fully determined. In this article, we propose a new mixed analytical-numerical model to describe the effect of the physical parameters related to the wet snowpack metamorphism on the intensity of the backscattering at L, C, and X bands, with a focus on high alpine snowpacks. Particular attention was paid to integrate the effects of the snow superficial roughness and the snow scattering. The model is first applied to several simulated snowpacks and then validated against a real multitemporal SAR signature acquired by Sentinel-1 over the snow station of Malga Fadner (South Tyrol, Italy) and of Torgnon (Aosta Valley, Italy). The comparison between the model outcomes and the satellite data were in good agreement, leading to the possibility of using such method for operational identification of the run-off phase from remote locations.

This paper analyses electromagnetic (EM) wave propagation within a metasurface-cladded waveguide filled with magnetized plasma. First, an analytical model is developed to understand the governing dynamics of the system. Second, a realistic numerical design is presented to assess the feasibility of the proposed concept. This technology targets microwave-driven plasma production, with the cladding metasurface enabling control over the cut-off frequency of the waveguide. Results suggest that EM propagation can be triggered regardless of the value of the static magnetic induction field. This represents a significant advance over standard implementations, promoting system miniaturization and facilitating the integration of waveguides with plasma sources.

Static Analysis (SA) and Dynamic Analysis (DA) are complementary techniques for searching web application vulnerabilities. Typically, SA detects more vulnerabilities but reports a higher number of false positives, whereas DA finds less but with better precision. In this paper, we blend SA and DA to simultaneously improve the detection and decrease the false alarms. Our approach starts with SA to identify an initial set of potential vulnerabilities. Then, the target application is executed to obtain specific runtime information. These data are used to automatically configure the DA, improving its ability to confirm if the vulnerabilities reported by the SA are indeed exploitable. We evaluated the proposed approach using 49 WordPress plugins with more than 450 SQLi vulnerabilities. Our approach was able to confirm either as a vulnerability or a false alarm 76.7% of the results reported by the SA, decreasing tremendously the usual need for manual work, which is a huge improvement for security practitioners.

Networked sensing refers to the capability of multiple wireless terminals to cooperate with the aim of enhancing specific figures of merit, e.g., positioning accuracy or imaging resolution. Regarding radio-based sensing, it is essential to understand when and how sensing terminals should cooperate, namely the best strategy that trades between performance and cost (e.g., energy consumption, communication overhead, and complexity). This tutorial paper revises networked sensing from a wavefield interaction perspective, aiming to provide a general theoretical benchmark to evaluate its imaging performance bounds and to guide the sensing cooperation accordingly. Diffraction tomography theory (DTT) is the method to quantify the imaging resolution of any radio sensing experiment from inspection of its spectral (or wavenumber) content. In networked sensing, the image formation is based on the back-projection integral, valid for any network topology and physical configuration of the terminals. The wavefield networked sensing is a framework in which multiple sensing terminals cooperate during the acquisition process to maximize the imaging quality (resolution and sidelobes suppression) by pursuing the wavenumber tessellation principle. We discuss all the coherent data fusion possibilities between sensing terminals and possible killer applications. Remarkably, we show the possibility that the proposed method allows obtaining high-quality images of the environment in limited bandwidth conditions, leveraging the coherent combination of multiple multi-static low-resolution images.

The increasing complexity and unpredictability of many ICT scenarios let us envision that future systems will have to dynamically learn how to act and adapt to face evolving situations with little or no a priori knowledge, both at the level of individual components and at the collective level. In other words, such systems should become able to autonomously (i.e., self-) develop mental models of themselves and of their environment. Autonomous mental development includes: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act in the presence of others, i.e., at the collective level. In this paper, we introduce a conceptual framework for autonomous mental development in ICT systems – at both the individual and collective levels – by framing its key concepts and illustrating suitable application domains. Then, we overview the many research areas that are contributing or can potentially contribute to the realization of the framework, and identify some key research challenges.

Among the converters currently employed for ac–dc conversion, diode rectifiers (DRs) and voltage source rectifiers are the most prevalent due to their accessibility, cost-effectiveness, and efficiency. However, current source rectifiers (CSRs) present several advantages, such as short-circuit protection, step-down capabilities, minimal output current ripple, and the elimination of dc bus capacitors. These features can lead to a reduction in overall size and an enhancement in power quality, although CSRs are often associated with higher conversion losses. Recent advancements in applications that can leverage the benefits of CSRs, along with the development of new devices to improve their efficiency, have produced renewed interest in this configuration. This article aims to review the design parameters of CSR components, modulation schemes, and control strategies, while also conducting a loss analysis using both established literature methods and innovative approaches developed by the authors. In addition, a comparison with other topologies is presented, illustrating that in certain applications and conditions, CSRs can be a compelling choice. Thus, rather than focusing on a specific aspect of CSRs, this article provides a comprehensive overview of the topic, offering practical insights for engineers.

Comparing images captured by disparate sensors is a common challenge in remote sensing. This requires image translation—converting imagery from one sensor domain to another while preserving the original content. Denoising diffusion implicit models (DDIM) are potential state-of-the-art solutions for such domain translation due to their proven superiority in multiple image-to-image translation tasks in computer vision. However, these models struggle with reproducing radiometric features of large-scale multipatch imagery, resulting in inconsistencies across the full image. This renders downstream tasks like heterogeneous change detection impractical. To overcome these limitations, we propose a method that leverages denoising diffusion for effective multisensor optical image translation over large areas. Our approach super-resolves large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors, ensuring uniformity across hundreds of patches. Our contributions lie in new forward and reverse diffusion processes that address the challenges of large-scale image translation. Extensive experiments using paired Sentinel-II (10 m) and Planet Dove (3 m) images demonstrate that our approach provides precise domain adaptation, preserving image content while improving radiometric accuracy and feature representation. A thorough image quality assessment and comparisons with the standard DDIM framework and five other leading methods are presented. We reach a mean learned perceptual image patch similarity of 0.1884 and a Fréchet Inception Distance of 45.64, expressively outperforming all compared methods, including DDIM, ShuffleMixer, and SwinIR. The usefulness of our approach is further demonstrated in two Heterogeneous Change Detection tasks.

This paper considers the tasks of detecting and recognizing bearing faults in electric motors from the signals collected from supply currents, using machine learning techniques. In particular, following recent trends in AI, the main point of interest was focused towards interpretable solutions that provide explanations on the decisions taken by the classifiers. For this reason, decision trees were chosen, since they represent a classic machine learning approach which inductively learns tree structures from a collection of observations. Paths along the learnt trees can be easily interpreted as plain classification rules. An extensive experimental comparison shows the strong generalization capabilities of such a classifier. In particular, the present work reports results obtained in a highly challenging scenario, usually overlooked in the literature, where the system is tested on configurations of radial and torsional loads that have not been observed during training. The approach achieves over 90% of accuracy even on this cross-load generalization setting.

In this article, we present cutting-edge machine learning-based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station, and pointing toward the Earth. We base our approach on a recent technique, the STACKing method plus Convolutional Neural Network (STACK-CNN), originally developed as an online trigger in an orbiting remediation system to detect space debris. Our proposed method, the refined-STACKing method plus convolutional neural network (R-Stack-CNN), makes the STACKing method plus convolutional neural network (STACK-CNN) more robust, thanks to a random forest that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case, no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data.

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this context, entanglement denotes how well a DT mirrors its counterpart and the extent to which the behavior of the physical counterpart aligns with the commands issued by the DT. In this paper we propose a concise yet expressive and original metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness, i.e., the freshness of the collected data and the ratio between collected and total data, respectively. In addition, the paper describes how we have built our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while concerns about workers’ privacy arise due to the increase of data sensed and transferred from the shop floor. This paper presents the results of a research project addressing the prediction of dangerous conditions through workplace monitoring with privacy guarantees. This work is driven by a realistic approach starting from the fact that it is entirely centered on a real 14-meter production line equipped with an extensive array of top-tier devices, including robotic arms, autonomous mobile robots, a reconfigurable moving belt, a multi-camera system, and a highly efficient data transport and computation infrastructure. This project shows safety and privacy achievements over six representative use cases such as man-on-the-ground, environmental events (e.g., fire incidents), workers’ errors that can lead to potential accidents, compliance of Personal Protective Equipment (PPE), and gatherings restrictions. The benefits of this study extend to stakeholders such as manufacturers and workers offering safety systems that can be deployed in industrial settings while addressing privacy concerns and providing compliance with regulations. The industrial laboratory at the heart of this study represents with realism a dynamic and interconnected Industry 4.0 and 5.0 environment.

The scarcity of asynchronous/self-clocked, sampling-rate ( $f_{s}$ )-reconfigurable, analog-front-end (AFE)-driven, incremental mode operation-based ADCs – highly needed for various practical event-triggered applications- pushes the need for innovative solutions. This paper is an effort in this direction and presents a $1^{\mathrm {st}}$ -order, versatile, asynchronous Incremental mode operation-based Noise-Shaping Successive-Approximation-Register (INS-SAR) Analog-to-Digital-Converter (ADC). Proposed for event-triggered multi-channel CMOS readout Application-Specific-Integrated-Circuits (ASICs), the proposed ADC is autonomously able to generate all required control signals internally by exploiting either a single externally-provided (for standalone ADC operation) or analog-front-end (AFE)-driven (for an integrated environment) trigger ‘Sample’ clock. The latter-mentioned characteristic enables an AFE-ADC co-design approach. Unlike all other proposed Incremental mode operation-based ADCs who offer sampling-rate ( $f_{s}$ )-reconfigurability with the tuning of an Over Sampling Ratio (OSR) value, this ADC features a novel $f_{s}$ -reconfigurability approach, even at a single OSR value, with an $f_{s}$ -scalable power-consumption. The ADC supports free-running/NS mode of operation too. The ADC has been designed and fabricated in a $0.35~\mu $ m CMOS process with two versions: $f_{s}$ -reconfigurable Version 1 operates at a supply voltage of 3.3 V, whereas the digital section of fixed $f_{s}$ Version 2 operates at 1.2 V. In incremental mode, both versions of the ADC achieve a measured ENOB of 10.25 bit, a peak SNDR of 63 dB, and a DR of 64 dB at an $f_{s}$ of 40 kHz with an OSR of 8. ADC Version 1 gets the same performance at all supported sampling-rates. ADC Version 1 power-consumption is $79~\mu $ W at its maximum $f_{s}$ of 167 kHz, while it is $40.56~\mu $ W for Version 2 at an $f_{s}$ of 132 kHz. The ${FoM}_{S}$ (155 dB) is constant for both versions and at all supported sampling-rates. With highly desired novel features and expected performance based on measurement results, the proposed ADC becomes a highly attractive candidate for multi-channel CMOS ASICs for a variety of practical event-triggered applications.

The Liquid State Machine (LSM) framework addresses supervised learning tasks involving spatio-temporal data streams. It relies on a randomly created, untrained Spiking Recurrent Neural Network (SRNN), called the “liquid,” to map inputs into task-independent representations. A simple readout layer then uses these representations to solve specific tasks. LSM’s computational power arises from two properties: the Separation Property (related to the liquid) and the Approximation Property (related to the readout). This research aims to enhance the liquid’s separation property to improve classification performance and enable multitask learning through swarm intelligence. The study develops a two-phase approach: first, using Particle Swarm Optimization (PSO) to optimize the liquid for distinguishing data streams of different classes in single tasks; and second, extending this optimization to multitask learning with Original Multi-Objective PSO (OMOPSO). Results from experiments on four artificial problems (one of frequency recognition and three of pattern recognition) demonstrate that optimized liquids improve separability and maintain regularized firing behaviors, even with a simple softmax readout layer. On average, the experiments show that our approach outperforms baseline methods across all four artificial datasets when using PSO and achieves superior results on three pattern recognition datasets when employing OMOPSO.

The evaluation and characterization of microwave imaging (MWI) prototype along with the optimization of imaging algorithm is a precursor step before the clinical study to measure in vivo dielectric properties. This study presents the microwave tomographic (MWT) image reconstruction of anthropomorphic calcaneus phantoms by using a specialized microwave bone imaging prototype. To this end, the 2D tomographic images of 3D experimental multi-layered (including skin, fat, cortical and trabecular bone) calcaneus-shaped phantoms were reconstructed. Liquid tissue-mimicking mixtures (TMM) for skin, fat, normal bone, osteoporotic bone, and osteoarthritic bone were prepared. The TMMs were dielectrically characterized over the frequency range of 1 - 4 GHz. The TMMs were poured into a calcaneus-shaped phantom that was placed in the imaging prototype. The imaging prototype was equipped with nine microstrip antennas, connected to a 2-port VNA through a 24-port switching matrix. The tomographic reconstructions were achieved around 3 GHz by using a Distorted Born Iterative Method (DBIM) with an Iterative Method with Adaptive Thresholding for Compressed Sensing (IMATCS). Qualitative and quantitative findings indicate that the employed method is suitable for reconstructing and distinguishing the properties of the mimicked human bone tissues. Indeed, the osteoporotic and osteoarthritic bone phantoms can be differentiated with an average percentage difference of 25%. The target region (i.e., trabecular tissue) is correctly located and its diseased condition can be determined from its retrieved properties for all the phantoms, showing a Structural Similarity Index of at least 82%. This work proves the potential clinical utility of MWI in bone health assessment.

The implementation of distributed applications is more frequently achieved through the configuration of service-oriented workflows, which are then deployed within the Edge-Cloud Continuum. This approach facilitates the support of distributed processing pipelines. In this context, there is an increasing demand for solutions that can continuously guarantee the non-functional properties (e.g. security and performance) of such applications. This demand has been boosted by the advent of 5G, where the Continuum is empowered by the ability to involve pervasive 5G Edge Nodes (i.e., Multi-access Edge Computing) and powerful and reliable connectivity links (i.e., network slices). Although capable to support Non-Functional Properties (NFPs) like performance, the current 5G Core Network (CN) is not yet ready to host and execute service workflows nor capable of providing trustworthy guarantees on more advanced NFPs, such as, integrity, security, and robustness. This delay in achieving the full promise of the 5G CN architecture is having a great impact on the capillary diffusion of key technologies such as AI-empowered workflows, which require a fully trusted execution environment to comply with AI regulations such as the AI act. In this paper, we propose an extension of the 5G CN functionalities that supports service workflow deployment and a methodology for the continuous assessment of Non-Functional Properties, beyond simple performance, implemented in a lightweight assurance framework. Our assurance framework is integrated within a 5G simulator to provide a trustworthy 5G CN test-bed and experimentally evaluated in realistic scenarios.

Total Hip Arthroplasty (THA) is one of the most performed surgical procedures to treat osteoarthritis of the hip joint or other skeletal disorders, such as Developmental Dysplasia of the Hip (DDH) or malformations. The purpose of the THA is to replace damaged bone/cartilage and recover the biomechanics of the joint in the best possible way. Placing hip prostheses is common practice in the treatment of THA, but the effect of this treatment must be evaluated before surgery. This work proposes a method to assess hip deformation with an implanted acetabular cup, using digital image processing and multivariate linear regression. Images were acquired when a hemipelvis was subjected to mechanical load ranging from 0 to 1800 Newtons. Eighteen geometric characteristics were analyzed, and only seven were significant in the multivariate linear regression model. To decide on the variables that are considered important, three criteria were used on the multivariate linear regression model established in the different characteristics. The first criterion considers the significance of the model coefficients; the second criterion considers a coefficient of determination greater than 75%, and the third criterion considers that the proposed model is significant. If all three criteria are met, the response variable is considered significant. This analysis gives specialists a tool to make decisions before performing THA treatment. Moreover, this methodology could be applied to assess the deformation of the hip with different pathologies such as DDH, malformations, and overweight conditions. Finally, a relationship is generated between the extracted geometric characteristics and the experimental results obtained in the laboratory.

Fifth Generation (5G) systems have been commercially available worldwide for at least a couple of years, with mid-band Non-Standalone (NSA) being the deployment mode preferred by Mobile Network Operators (MNOs). Empirical analyses have provided so far key insights on 5G NSA performance from different perspectives, but most of these works consider short time periods to drive conclusions. In this paper, we investigate the evolution of 5G NSA considering deployment, performance, and services, including positioning. We perform a large-scale measurement campaign in two phases (2021 and 2023), covering six MNOs in two European countries, Italy and Sweden. Our results show significant differences in network deployment and performance, with increasing network density and frequencies but, at times, decreasing downlink throughput performance. For the latter, we identify worse radio coverage and connectivity issues as root causes. By using a standardized methodology, we also evaluate the performance of new services such as real-time gaming and augmented/virtual reality, and reveal that stable 5G connectivity is key to meet their requirements. Similarly, we highlight the negative effects of roaming on performance. Finally, we evaluate 5G fingerprinting positioning systems and show that a higher accuracy is achievable in denser 5G deployments.

A architecture of reconfigurable analog computing devices is proposed for image processing and advanced wireless communication systems, such as those required for self-driving cars. This architecture is designed to work in the terahertz frequency range, and it is based on a metasurface made of VO2 brick resonators as metaatoms. The system is capable of performing first-order and second-order derivative, or it may act as a mirror for impinging signals in a broad range of terahertz frequencies. The reconfigurability between these functions is achieved by tuning the temperature of the VO2 resonators, which can be switched between the metallic and the insulator state with an electric current passed through a back metal layer. Following this approach, we present two dynamically controlled metasurfaces, one designed in k-space and the other in real-space.

This paper proposes an analytical method to calculate the switch-on sequences that provides Time Modulated Antenna Arrays (TMAA) with beam scanning in the sideband region. Such time sequences can be calculated to locate the main beam both in the azimuth and elevation planes. A comparison between H. Shanks’ method and the method proposed in this paper is presented. The radiation patterns of uniform linear, square and circular arrays are presented to demonstrate the efficacy of the proposed method. Additionally, a 6-element circular antenna array prototype for 5G applications was constructed. Such array resonates at the frequency of 3.612 GHz. The radiation patterns were measured in the anechoic chamber, demonstrating the effectiveness of the proposed method.

This paper presents a new design proposal for the simplification of linear beam-forming networks by applying a novel phase interpolation technique. This paper proposes the strategic utilization of 1-to-3 division nodes and 2-to-1 recombination nodes to reconstructing the cophasal values necessary for radiation beam scanning in linear phased array systems. This novel technique simplifies the beam-forming network, achieving a reduction of phase shifters by up to 60% and ±18° of scan angle. These benefits are obtained using uniform amplitude excitation and without additional amplitude controls. The complete antenna system considers a broadband V-slot as antenna element for obtaining a bandwidth performance of approximately 1.2 GHz. This results in a 22% of bandwidth. This is illustrated by using a prototype with experimental measurement results and simulations based on electromagnetic solver (CST Microwave Studio). These results reveal an interesting reduction of phase shifters for a good trade-off of beam-steering range and bandwidth behavior. The experimental results are analyzed and compared with other techniques and previous works cited in the literature. This novel methodology provides interesting results of a high benefit for wireless applications of phased antenna systems.

An innovative millimeter-wave (mm-wave) microstrip edge-fed antenna (EFA) for 77 GHz automotive radars is proposed. The radiator is designed under the main requirements of having (i) an horizontally-polarized pattern and (ii) a single-layer layout. Its contour is modeled with a sinusoidal spline-shaped (SS) profile characterized by a reduced number of geometrical descriptors, but still able to guarantee, thanks to a continuously non-uniform width, a high flexibility in the modeling for fulfilling challenging user-defined requirements. The SS-EFA descriptors are effectively and efficiently optimized with a customized implementation of the System-by-Design (SbD) paradigm. The synthesized EFA layout, integrated within a linear arrangement of identical replicas to account for the integration into the real radar system, exhibits suitable impedance matching, isolation, polarization purity, and stability of the beam shaping/pointing within the target band $f_{\min }=76$ [GHz] $\le f \le f_{\max }=78$ [GHz]. The experimental assessment, carried out with a Compact Antenna Test Range (CATR) system on a printed circuit board (PCB)-manufactured prototype, assesses the reliability of the outcomes from the full-wave (FW) simulations as well as the suitability of the synthesized SS-EFA for automotive radars.

To understand how Convolutional Neural Networks (ConvNets) perceive the muscular movements of the human face, known as Action Units (AUs) in this work, we introduce a new dataset named Dataset on Emotions among Mexicans (DEM), consisting of 1557 images of Mexicans labeled with twenty-six AUs and seven emotions. As a benchmark, we used the comparison with DISFA+ labeled with 12 AUs. To address the task of detecting AUs in each image, six ConvNets were employed, and we evaluated their performance using the F1 Score. The two ConvNets with the best performance were VGG19 with 0.8180% (DEM), 0.9106 % (DISFA+), and ShuffleNetV2 with 0.7154% (DEM), 0.9440% (DISFA+). Subsequently, these ConvNets were analyzed using Grad-CAM and Grad-CAM++; this algorithms allows us to observe the areas of the face considered for prediction. In most cases, these areas consider the region of the labeled AU. Considering the F1 score and the visual study, we can conclude that using DEM as a dataset to classify AUs is promising since the experiments achieved performances similar to those of the current literature that only use ConvNets.

On the road to the sixth generation of cellular networks (6G), the need to ensure a sustainable usage of natural resources, amid increased competition and cost pressures, has driven the adoption of text Self-Healing Mobile Networks to enhance operational efficiency of current and future wireless networks. This paradigm shift relies on Artificial Intelligence (AI) to increase automation of network functions, notably by applying predictive fault detection and automatic root-cause analysis. In this context, this paper proposes a Deep Learning (DL) model for text self-healing operations based on a Spatial Graph Convolutional Neural Network (SGCN), which is applied to evaluate the performance degradation of Base Stations (BSs) and uncover the underlying root-causes. The advantages of the proposed DL model are threefold. Firstly, it is especially suited for wireless network applications, leveraging the SGCN to account for spatial dependencies among BSs and their physical characteristics. Secondly, the proposed model offers the flexibility to process diverse types of predictive features, including Performance Management (PM), Fault Management (FM), or other data types. Thirdly, it incorporates an explainability module that pinpoints the input features, such as PM counters, with the most significant influence on BS performance, thereby shedding light on its root-cause factors. The proposed model was evaluated on a live 4G network dataset and the results confirmed its effectiveness in identifying BS performance degradation. An F1-score of 89.6% was achieved in the classification of performance failures, which includes a 27% reduction in false negatives compared to prior research outcomes. In a live network environment, this reduction translates into substantial improvements in Quality of Experience (QoE) for the end users and cost savings for the Mobile Network Operators (MNOs).

The safe operation of high-power solid-state transformers (SSTs) in medium-voltage grid-connected applications relies on medium-frequency transformers (MFTs). In MFTs, hybrid foil-litz windings present a cost-effective alternative to litz wire, since foil is employed as the main conductor. This innovative topology exhibits lower ohmic losses than conventional foil windings. Besides, the dielectric stress in the insulation is mitigated, facilitating a more compact design. This paper presents the experimental validation of a ${166}\; \mathrm{k}\mathrm{W}$ MFT prototype rated for the $ {17.5}\;\mathrm{k}\mathrm{V}$ AC/$ {26.3}\; \mathrm{k}\mathrm{V}$ DC insulation class. The MFT features hybrid foil-litz windings insulated with ester liquid. The design of the active parts, the insulation and the cooling system are described. The performances of alternative design concepts is evaluated. The power rating of the MFT prototype is verified experimentally by the temperature rise test at full load, operating the MFT within the cell of an SST. The insulation withstand is confirmed through dielectric tests, including AC and DC overvoltage tests up to $ {54}\; \mathrm{k}\mathrm{V}$ peak, and the lightning impulse up to $ {95}\; \mathrm{k}\mathrm{V}$.

This paper proposes a Design Space Exploration for Edge machine learning through the utilization of the novel MathWorks FPGA Deep Learning Processor IP, featured in the HDL Deep Learning toolbox. With the ever-increasing demand for real-time machine learning applications, there is a critical need for efficient and low-latency hardware solutions that can operate at the edge of the network, in close proximity to the data source. The HDL Deep Learning toolbox provides a flexible and customizable platform for deploying deep learning models on FPGAs, enabling effective inference acceleration for embedded IoT applications. In this study, our primary focus lies in investigating the impact of parallel processing elements on the performance and resource utilization of the FPGA-based processor. By analyzing the trade-offs between accuracy, speed, energy efficiency, and hardware resource utilization, we aim to gain valuable insights into making optimal design choices for FPGA-based implementations. Our evaluation is conducted on the AMD-Xilinx ZC706 development board, which serves as the target device for our experiments. We consider all the compatible Convolutional Neural Networks available within the HDL Deep Learning toolbox to comprehensively assess the performances.

Associative access is widely used in fundamental microarchitectural components, such as caches and TLBs. However, associative (or content addressable) memories (CAMs) have been traditionally considered too large, too energy-hungry, and not scalable, and therefore, have limited use in modern computer microarchitecture. This work revisits these presumptions and proposes an energy-efficient fully-associative tag array (FASTA) architecture, based on a novel complementary CAM (CCAM) bitcell. CCAM offers a full CMOS solution for CAM, removing the need for time- and energy-consuming precharge and combining the speed of NOR CAM and low energy consumption of NAND CAM. While providing better performance and energy consumption, CCAM features a larger area compared to state-of-the-art CAM designs. We further show how FASTA can be used to construct a novel aliasing-free, energy-efficient, Very-Many-Way Associative (VMWA) cache. Circuit-level simulations using 16 nm FinFET technology show that a 128 kB FASTA-based 256-way 8-set associative cache is 28% faster and consumes 88% less energy-per-access than a same sized 8-way (256-set) SRAM based cache, while also providing aliasing-free operation. System-level evaluation performed on the Sniper simulator shows that the VMWA cache exhibits lower Misses Per Kilo Instructions (MPKI) for the majority of benchmarks. Specifically, the 256-way associative cache achieves 17.3%, 11.5%, and 1.2% lower average MPKI for L1, L2, and L3 caches, respectively, compared to a 16-way associative cache. The average IPC improvement for L1, L2, and L3 caches are 1.6%, 1.4%, and 0.2%, respectively.

The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly, we will examine the three different workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.

This work presents the theoretical and numerical design of a cylindrical waveguide that supports Spoof Surface Plasmon Polaritons (SSPP) at microwave frequencies. First, a theoretical model is proposed to assess the propagation of SSPPs in a cylindrical geometry and to estimate the surface impedance required to enable these wave modes. The reliability of the theoretical model is verified against numerical results. The proposed cylindrical waveguide embeds a cladding metasurface with a dielectric substrate covered by a lattice of square metallic patches. According to numerical estimations, the proposed design allows the propagation of SSPPs in the frequency range 2.2–5.7 GHz. Notably, the cut-off frequency is lowered by approximately 3.5 GHz with respect to conventional metallic solutions. The target application of the proposed waveguide is efficient plasma generation.

This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our toolchain combines the power of ANTLR4 and Abstract Syntax Tree (AST) traversal techniques to transform the architecture and interactions within smart contracts into a specialized bipartite graph. This enables advanced network analytics to highlight operational efficiencies within the DApp’s architecture. The bipartite graph generated by the proposed tool comprises two sets of nodes: one representing smart contracts, interfaces, and libraries, and the other including functions, events, and modifiers. Edges in the graph connect functions to smart contracts they interact with, offering a granular view of interdependencies and execution flow within the DApp. This network-centric approach allows researchers and practitioners to apply complex network theory in understanding the robustness, adaptability, and intricacies of decentralized systems. Our work contributes to the enhancement of security in smart contracts by allowing the visualisation of the network, and it provides a deep understanding of the architecture and operational logic within DApps. Given the growing importance of smart contracts in the blockchain ecosystem and the emerging application of complex network theory in technology, our toolchain offers a timely contribution to both academic research and practical applications in the field of blockchain technology.

Information reconciliation (IR) is a key step in quantum key distribution (QKD). In recent years, blind reconciliation based on low-density parity-check (LDPC) codes has replaced Cascade as a standard de facto since it guarantees efficient IR without a priori quantum bit error rate estimation and with limited interactivity between the parties, which is essential in high key-rate and long-distance QKD links. In this article, a novel blind reconciliation scheme based on rateless protograph LDPC codes is proposed. The rate adaptivity, essential for blind reconciliation, is obtained by progressively splitting LDPC check nodes, which ensures a number of degrees of freedom larger than puncturing in code design. The protograph nature of the LDPC codes allows us to use the same designed codes with a large variety of sifted-key lengths, enabling block length flexibility, which is important in largely varying key-rate link conditions. The code design is based on a new protograph discretized density evolution tool.

Parkinson’s disease is a degenerative neurological disorder that impairs motor functions and is accompanied by a wide range of non-motor symptoms, such as sleep problems. Parkinsonism is assessed during clinical evaluations and via self-administered diaries and, based on these, the required medication therapies are provided to lessen symptoms. Tri-axial accelerometers and gyroscopes have the potential utility to objectively assess the patient’s condition and aid clinicians in their decision-making. People with Parkinson’s often have significant abnormalities in blood pressure due to comorbid age-related cardiovascular disease and orthostatic hypotension, which result in blurred vision, dizziness, and falls. Frequent blood pressure monitoring may aid in the evaluation of such events and differentiate Parkinson’s disease symptoms from those originated by hypotension. In the present paper, a novel technology for the remote monitoring of Parkinsonian symptoms is presented: the WESAA system. It consists of two devices, worn on the wrist and ankle; its main function is to record accelerations and angular velocities from these body parts, together with photoplethysmograph and electrocardiogram data. This information can be elaborated offline to measure common Parkinson’s disease motor symptoms (e.g., tremor, bradykinesia, and dyskinesia), as well as gait speed, sleep-wake cycles, and cuff-less blood pressure measurements. The overall system requirements, market overview, industrial design and ergonomics, system development, user experience, early results of the gathered inertial raw data, and validation of the photoplethysmograph and electrocardiogram signal waveforms are all thoroughly discussed. The developed technology satisfies all system requirements, and the sensors adopted provided outcomes comparable with gold standard techniques.

In this work, we present an open-source unmanned aerial vehicle (UAV) platform for research in swarm robotics. In swarm robotics, groups of robots collaborate using local interactions and collectively solve tasks beyond an individual robot’s capabilities. Individual robots must have onboard processing, communication, and sensing capabilities to autonomously react to their neighbors and immediate environment. Most research involving UAVs in swarm robotics presents only simulation results, while key landmark studies with real UAV swarms have used UAV platforms that were custom-built for the respective study. One important reason for this is that no commercial UAV platform comes pre-equipped with the ability to identify and track the positions and poses of nearby drones using only onboard sensors and computation, and in research platforms, the relevant sensing technologies are currently under development. Our aim is to provide a platform that allows swarm robotics researchers to test their algorithms on real UAVs, without having to develop their own custom-built UAVs or to wait until more advanced sensing technology is ready off-the-shelf. We provide a well-documented, entirely open-source UAV platform— S-drone (Swarm-drone)—to foster and support UAV swarm research in a laboratory environment. The S-drone uses fiducial markers in the environment and cooperative feature-level sensor fusion for inter-robot tracking to track the presence, identity, relative 2D position, and relative 2D orientation of neighboring peers. The S-drone is suitable for a wide range of contexts, supports quad-camera vision-based navigation and a variety of onboard sensing, and is extensible. It is especially suited for swarm robotics research because it can operate using strictly onboard processing and sensing without the need for global positioning systems, motion capture systems, or ground stations for off-board sensing.

Graph-based methods, distinguished by their resilience to noise, have demonstrated efficacy in image segmentation compared to alternative techniques. Despite their notable capabilities, these methods present over- and under segmentation problems. This paper introduces a novel graph-based approach specially designed to solve these issues effectively. Unlike conventional graph-based methodologies, which predominantly favor either local or global features, our approach considers a combination of these two. Initially, the complete graph is partitioned into multiple subgraphs considering the similarities of the local features in the nodes. Subsequently, these subgraphs are evaluated and classified as either significant or irrelevant, based on the number of nodes they contain. To mitigate over segmentation, we merged the irrelevant subgraphs with the significant subgraphs considering their global features. These mechanisms allow our method to maintain integration between the local and global features, leading to a more precise and accurate representation of the objects in the image. The proposed method was evaluated using various complex images and was benchmarked against state-of-the-art segmentation techniques. The results confirm the superiority of our approach in delivering higher-quality and more dependable segmented images than existing methods.

Repetitive controller provides a very low third harmonic dimension in the quantities under control. It exhibits an inherent issue when operated to track variable frequency references. The article deals with the analysis of the operating conditions when the controller is executed at variable frequency without any resynchronization with respect to the pulsewidth modulation carrier, which is the most common mode of operation. The delays introduced are then evaluated analytically concerning the sampling and output frequency that must be tracked. The proposed analysis allows obtaining the maximum delay affecting the control chain, which was introduced by the repetitive control desynched operation. The knowledge of the delay introduced in the control loops is at the basis of any control tuning procedure and gains selection, even when adaptive control strategies are used.

Electromagnetic interference has the potential to affect the functionality and performance of radio communication links. This work investigates the impact electromagnetic disturbances have on such wireless links, taking quadrature phase-shift keying (QPSK) as a representative example. The evaluation of the effects of interference on a QPSK radio link becomes relevant as it is used by the IEEE 802.11b protocol. Through simulations, we study a QPSK link subjected to narrowband and broadband disturbances. The bit error rate (BER) and the error vector magnitude (EVM) are our quality assessment metrics for evaluating the system. Two specific scenarios are analyzed: continuous wave and chirp interferences as in-band and out-band conditions. The results indicate that the applied electromagnetic interference can degrade the signal quality, resulting in high BER. Furthermore, it is observed that chirp interference largely affects the radio links more than continuous wave disturbances. The aforementioned findings support and reinforce the applicability of the analysis methodology in real-life scenarios like those characterizing healthcare settings.

Goal: FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. Methods: Utilizing a comprehensive dataset–the largest to date for fetal head metrics–FetSAM incorporates prompt-based learning. It distinguishes itself with a dual loss mechanism, combining Weighted DiceLoss and Weighted Lovasz Loss, optimized through AdamW and underscored by class weight adjustments for better segmentation balance. Performance benchmarks against prominent models such as U-Net, DeepLabV3, and Segformer highlight its efficacy. Results: FetSAM delivers unparalleled segmentation accuracy, demonstrated by a DSC of 0.90117, HD of 1.86484, and ASD of 0.46645. Conclusion: FetSAM sets a new benchmark in AI-enhanced prenatal ultrasound analysis, providing a robust, precise tool for clinical applications and pushing the envelope of prenatal care with its groundbreaking dataset and segmentation capabilities.

The standard CISPR 16-1-1 defines the measuring receiver using a black-box approach and sets requirements for its accuracy and spectral properties. Traditionally, such test receivers were developed using a superheterodyne architecture. Recently, time-domain electromagnetic emission measurement systems have been built employing direct sampling instruments, mainly oscilloscopes, and relying on specific signal processing to emulate the performance of compliant instruments. In these cases, the short-time Fourier transform is used for spectral estimation, but the corresponding electromagnetic compatibility standards lack details for its correct use with respect to parameters such as windowing function, overlapping factor, and frequency interpolation. Moreover, it is unclear which combination of spectral estimation parameters is best fit for this purpose. Obtaining reliable, consistent and low uncertainty spectral estimates of electromagnetic emissions measured in time-domain needs appropriate configuration and tuning of the signal processing algorithms. This paper investigates the error in the calculated spectrum for various reference signals: multitone, chirp pulses and rectangular pulses. The analysis is carried out for each CISPR band from A to D, that is, between 9 kHz and 1 GHz. After $489.6\times 10^{3}$ iterations, distributed in 1700 different digital implementations of the CISPR 16-1-1 measuring receiver, the simulations outcomes point to certain sets of parameters that showed satisfactory performance overall, being the Nutall, Kaiser, and Parzen windows with more than 75% of overlapping and using interpolation factor higher than 5, generally suitable. Calibration results are used to experimentally verify that a valid set of parameters is adequate to fulfil CISPR 16-1-1 requirements.

Online information operations (IOs) refer to organized attempts to tamper with the regular flow of information and to influence public opinion. Coordinated online behavior is a tactic frequently used by IO perpetrators to boost the spread and outreach of their messages. However, the exploitation of coordinated behavior within large-scale IOs is still largely unexplored. Here, we build a novel dataset comprising around 624K users and 4M tweets to study how online coordination was used in two recent IOs carried out on Twitter. We investigate the interplay between coordinated behavior and IOs with state-of-the-art network science and coordination detection methods, providing evidence that the perpetrators of both IOs were indeed strongly coordinated. Furthermore, we propose quantitative indicators and analyses to study the different patterns of coordination, uncovering a malicious group of users that managed to hold a central position in the discussion network, and others who remained at the periphery of the network, with limited interactions with genuine users. The nuanced results enabled by our analysis provide insights into the strategies, development, and effectiveness of the IOs. Overall, our results demonstrate that the analysis of coordinated behavior in IOs can contribute to safeguarding the integrity of online platforms.

Fiber-coupled free-space optics (FSO) communications have emerged as a promising wireless technology due to their flexibility, speed, security, and seamless integration with fiber optic systems. However, these systems are very sensitive to atmospheric conditions, particularly turbulence, necessitating robust and tailored solutions according to the application scenarios. However, the intrinsic random nature of atmospheric turbulence, together with the strong dependence on multiple external factors (e.g. varying weather conditions), makes it extremely challenging to adequately compare the merit of different turbulence mitigation techniques. In this work, resorting to the use of a custom-made atmospheric chamber, we tackle the issue of turbulence-induced power fading employing two complementary optical mitigation techniques: i) through enhanced fiber coupling efficiency using either standard single-mode fiber (SSMF) or multi-mode fiber (MMF) and ii) through tailored optical pre-amplification using either Erbium-doped fiber amplifiers (EDFAs) or semiconductor fiber amplifiers (SOAs). By carrying out a comprehensive set of repeatable and inter-comparable turbulence emulation tests, we demonstrate that MMF-based fiber coupling can be highly advantageous for less demanding FSO links, achieving 100% reliability after a real-time BER assessment at 4.5Gbps over weak-to-moderate turbulence, which represents a major improvement over the ~10-50% reliability obtained under the same circumstances for the SSMF-coupled system. In turn, for FSO links requiring high-capacity SSMF-coupled receivers, we demonstrate that EDFA-based pre-amplification operating either in saturation or automatic power control (APC) enabling 10 Gbps connectivity with 99% reliability in weak turbulence and ~96-98% in moderate turbulence regimes, in contrast with the baseline reliability of unamplified systems in the same turbulence conditions, which have shown

Robotic avatar technology has the potential to impact the future of human connectivity, transporting the sense of a human’s presence to a remote location anywhere and in real-time. In this regard, the recent ANA Avatar XPRIZE challenge fostered the development of the ultimate generation of non-autonomous robotic avatar designs. This paper is devoted to introducing our system proposal, allowing intuitive motion-based control and multi-modal feedback. The teleoperated robot endorses an anthropomorphic upper body with dual arms and dexterous hands for fine manipulation of even small objects, as well as an omnidirectional platform for improved and safe locomotion. Special focus is pointed at the crucial challenge of providing self-body perception to the operator, in particular regarding control of the arms and rendering of haptic sensations. To this end, we propose an upper-limb exoskeleton and a teleoperation architecture allowing retargeting of the operator’s skills and receiving tactile and kinesthetic force feedback with realistic and informative perception of the own’s arms. Lastly, the platform was validated during laboratory and challenge missions mimicking several social, cooperative, and cultural scenarios from which we report on the lesson learned and future improvements.

This article proposes a novel zero-voltage switching (ZVS) and zero-current switching (ZCS)-based hybrid dc circuit breaker for a radar power system. Long-range radars demand huge power, in the order of hundreds of kW. Radar's phased array antenna houses a large number of electronic devices and works primarily on a dc power supply. Typically, military systems are required to have the highest operational reliability, as a result, electrical system protection plays a crucial role. A high power 310 V dc electrical power grid in radar carries hundreds of amperes of current under nominal operating conditions, results in significant fault current due to very low impedance, and demands a very fast fault interruption device. This article proposes and demonstrates the complete operation of a hybrid dc circuit breaker topology for radar applications. The proposed dc circuit breaker employs a mechanical switch that carries the entire current during the nominal operating conditions, and a power electronic module (PEM) connected in parallel helps in diverting the fault current from the main path. Fault current transfers to the PEM branch in a fraction of a second (5 $\mu s$), which ensures faster load-side isolation. During the fault interruption process, mechanical switch contact opening experiences both ZVS and ZCS features, resulting in arcless operation, and also helps in faster contact separation. The ZVS and ZCS features greatly improve the reliability of the mechanical switch. The proposed concept does not involve any capacitors or corresponding precharging circuits for the ZVS/ZCS features. The proposed dc circuit breaker is analyzed theoretically, and also by simulations in LTspice. Additionally, an experimental prototype with a dc system rating of 310 V–10 A is developed to experimentally validate the performance of the proposed breaker topology. The article also presents a detailed design and comparative analysis, along with a discussion on the limitations of the proposed dc circuit breaker, and the scope for improvements.

Naturally occurring mass transport events can endanger human life and infrastructure integrity, especially in mountain areas often affected by landslides, debris flows, and avalanches. Currently available hazard detection systems can greatly help with the timely warning (within a few seconds) of ongoing, potentially dangerous events, but can often be inaccurate, unreliable and, due to the need for in-situ power supply, difficult to operate. Modern optical fiber sensing techniques, on the other hand, can provide exceptional accuracy but require longer measurement time and increased complexity and cost, which makes them unsuitable for such time- and cost-sensitive applications. In this paper, we present an on-field demonstration of a previously developed polarization-based optical fiber early-warning system. A mountain gully in the Valle d'Aosta region, in Italy, is used as a validation testbed to show the reliability of our approach in detecting anomalous vibration phenomena, and its robustness against the background noise associated, for instance, with car traffic or animal wildlife in the monitored area. Our findings show that the rate of variation of the state of polarization induced by real-life, hazardous rockfall events on the optical fiber is orders of magnitudes higher than the average variation rate recorded in normal conditions, thus making the proposed system a promising alternative in the mountain hazards sensors world. Moreover, we show that the system has great potential for increased accuracy in the monitoring of event dynamics by using different optical fibers in separate sections of the site, which can also be a source of additional cost reduction.

The reliable provision of entangled qubits is an essential precondition in a variety of schemes for distributed quantum computing. This is challenged by multiple nuisances, such as errors during the transmission over quantum links, but also due to degradation of the entanglement over time due to decoherence. The latter can be seen as a constraint on the latency of the quantum protocol, which brings the problem of quantum protocol design into the context of latency–reliability constraints. We address the problem through hybrid schemes that combine: indirect transmission based on teleportation and distillation, and direct transmission, based on quantum error correction (QEC). The intuition is that, at present, the quantum hardware offers low fidelity, which demands distillation; on the other hand, low latency can be obtained by QEC techniques. It is shown that, in the proposed framework, the distillation protocol gives rise to asymmetries that can be exploited by asymmetric quantum error correcting code, which sets the basis for unique hybrid distillation and coding design. Our results show that ad hoc asymmetric codes give, compared with conventional QEC, a performance boost and codeword size reduction both in a single-link and in a quantum network scenario.

Free-Space Optical (FSO) communications have emerged as a viable solution to complement conventional communication technologies, mainly due to their high transmission rates, ease of implementation, low cost, and unlicensed spectrum. However, FSO technology faces significant challenges in achieving the initial alignment between the transceivers without pre-alignment or extensive manual configuration. The manual alignment process is extremely time-consuming and current automated solutions typically employ an infrared (IR) beam followed by an IR camera, which, besides increasing the transceiver cost, also requires an initial pre-convergence stage resorting to complementary coarse alignment technologies (e.g. Global Positioning System (GPS)). To tackle this challenge, we propose a novel methodology for the automatic alignment of FSO systems that does not require pre-convergence and avoids the use of costly IR cameras. The proposed alignment method is based on the application of computer vision (CV) during the transmission of a visible beacon and the utilization of pointing, acquisition and tracking (PAT) mechanisms. This solution is cost-effective, as the initial coarse alignment is carried out using visible light equipment, such as a standard CMOS camera and a visible beacon. Furthermore, it does not require any additional pre-convergence stage, since the alignment is based on characteristics acquired through CV during the transmission of the visible beacon, specifically the angle of inclination and the count of the number of pixels associated with the beacon. Additionally, the alignment process allows for a maximum angular misalignment of ±3.3 degrees between the optical antennas (OA), corresponding to a coverage area of $5.3 \text {m}^{2}$ at a distance of 20 meters. Finally, using this method, the successful alignment of an FSO system is demonstrated in a free-space link with 20 meters distance for different ambient light conditions, with real-time transmission at 16 Gbps.

Digital Twins are becoming fundamental tools to monitor the status of entities, predict their future evolution and simulate alternative scenarios to understand the impact of possible changes for planning and design. More recently, Digital Twin solutions have been applied in the context of Smart Cities. Thanks to the large deployment of sensors, together with the increasing amount of information available for municipalities and governmental organizations, it is possible to build wide virtual reproductions of urban environments including structural data and real-time information that can undoubtfully help decision makers to face future challenges in urban development and improve the citizens’ quality of life. In this paper, the Snap4City Smart City Digital Twin framework is presented, which can respond to the requirements identified in recent literature and by international forums. The proposed architecture provides an integrated solution for data gathering, indexing, computing, and information distribution, thus realizing a continuously updated digital twin of the urban environment at global and local scales for monitoring operation and planning. It addresses 3D building models, road networks, Internet of Things entities, points of interest, paths, as well as results from analytical processes for traffic density reconstruction, pollutant dispersion, predictions, and what-if analysis for assessing impact of changes, all integrated into a freely accessible interactive 3D web interface, enabling stakeholder and citizen participation to city decision processes. As case study, the digital twin of the city of Florence (Italy) is presented, including what-if analysis. The solution is released on top of the Snap4City platform as open-source and made available through our GitHub repository (https://github.com/disit) and as Docker compose.

This paper aims to comprehensively review the literature on Gaseous Plasma Antennas (GPAs). These innovative devices transmit electromagnetic (EM) signals through ionized gas, known as plasma, instead of conventional metal structures. GPAs offer distinct advantages over their metallic counterparts, particularly in performance reconfigurability. The paper begins by evaluating the EM properties of the plasma medium, exploring methodologies for its generation, measurement, and simulation. Subsequently, the prevalent GPA architectures are presented, distinguishing between active and passive concepts based on the plasma’s primary role: either emitting EM signals or manipulating them. Additionally, unconventional GPA implementations, such as explosive or laser-based technologies, are examined. Finally, the paper assesses the most promising applications for leveraging GPAs in various fields.

A PWM converter-based power hardware-in-the-loop (P-HIL) testbed offers a rapid and cost-effective solution for prototype testing of high-power converters in electric vehicles and smart grid industries. To simplify the configuration and avoid the need for an additional bidirectional DC supply, the power amplifier (PA) can share the DC-link with the device under test (DUT). However, this leads to a zero-sequence circulating current (ZSCC) between PA and DUT due to the coupling of AC and DC ports. The ZSCC involves low and high frequency content that significantly distorts the three-phase current evaluated in the test. While the high frequency content can be mitigated by using common-mode (CM) filters, the low-frequency content has to be attenuated at a control system level. The use of classic control approaches such as proportional-integral (PI) or proportional-resonant (PR) controllers might lead to a difficult attenuation of the low-frequency ZSCC harmonic content unless high control gains are adopted, increasing the probability of unstable behavior. This paper proposes an enhanced control method, integrating a stand-alone zero-sequence voltage (ZSV) feed-forward (FF) compensation and a linear active disturbance rejection control (LADRC) strategy. This approach not only suppresses the ZSCC flowing through the PWM converters of PA and DUT but also compensates for parameter uncertainties and dynamic ZSCC variations due to power injection change, thereby enhancing the overall system performance. The effectiveness of this combined control strategy is demonstrated through experimental results, highlighting its potential to improve accuracy and reliability in the P-HIL testbed.

The loss of sensitivity of the upper limb due to neurological injuries severely limits the ability to manipulate objects, hindering personal independence. Non-invasive augmented sensory feedback techniques are used to promote neural plasticity hence to restore the grasping function. This work presents a wearable device for restoring sensorimotor hand functions based on Discrete Event-driven Sensory Control policy. It consists of an instrumented glove that, relying on piezoelectric sensors, delivers short-lasting vibrotactile stimuli synchronously with the relevant mechanical events (i.e., contact and release) of the manipulation. We first performed a feasibility study on healthy participants (20) that showed overall good performances of the device, with touch-event detection accuracy of 96.2% and a response delay of 22 ms. Later, we pilot tested it on two participants with limited sensorimotor functions. When using the device, they improved their hand motor coordination while performing tests for hand motor coordination assessment (i.e., pick and place test, pick and lift test). In particular, they exhibited more coordinated temporal correlations between grip force and load force profiles and enhanced performances when transferring objects, quantitatively proving the effectiveness of the device.

The last years have witnessed significant developments in image acquisition systems and in algorithms for extracting information from them. Nevertheless, in many scenarios, several factors can hinder the recovery of useful data from images. This is especially true and important in forensic applications, where images are often accidentally captured by an imaging system not engineered for that specific acquisition (for example, the surveillance system designed to monitor the entrance of a bank may accidentally capture the license plate of a vehicle passing outside the bank). Therefore, the acquired images often need to be processed to facilitate extracting information from them. When facing a combination of several impairment factors, such as blur and perspective distortion, several image restoration algorithms must be applied. Then, it is necessary to choose a restoration order, which means the order by which single restoration algorithms are chained together to obtain the enhanced image. This study aims to understand whether such an order may impact the final result. Of course, there exists a wide variety of image impairments; in this study, we focus on the case of an image affected by a combination of optical/motion blur, perspective distortion, and additive noise, which are all widespread artifacts in forensic image applications. To answer the question about the importance of choosing one restoration workflow over another, we first model each considered defect and its restoration operator and then analyze and compare the effects of the composition of such operators on the restored output. Such a comparison is made from both a mathematical and experimental point of view, using both images with synthetically generated impairments and pictures with real degradations. The results show that the restoration order can affect significantly the results, especially when the defects are severe.

Shared-aperture antennas are attracting a wide interest in last of years, due to their inherent compactness and low-profile layout. More specifically, in this work a shared-aperture antenna for FR1 and FR2 frequency bands is proposed. The difference in size between the radiating elements operating the two frequency bands can be exploited to embed different antennas in the same area. A $4\times 4$ patch array for FR2 is embedded inside a FR1 shaped patch antenna. The antenna system is designed by preserving a low-profile architecture suitable for planar technology. The performance of the antenna system are evaluated for both the bands achieving a 10-dB bandwidth equal to 0.14 GHz (3.8 %) for FR1 and to 2.32 GHz (8.6 %) for FR2. In FR2, a ±30° steering capability along the H plane is shown.