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A clear understanding of where humans move in a scenario, their usual paths and speeds, and where they stop, is very important for different applications, such as mobility studies in urban areas or robot navigation tasks within human-populated environments. We propose in this article, a neural architecture based on Vision Transformers (ViTs) to provide this information. This solution can arguably capture spatial correlations more effectively than Convolutional Neural Networks (CNNs). In the paper, we describe the methodology and proposed neural architecture and show the experiments' results with a standard dataset. We show that the proposed ViT architecture improves the metrics compared to a method based on a CNN., Comment: 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024

Graphs face challenges when dealing with massive datasets. They are essential tools for modeling interconnected data and often become computationally expensive. Graph embedding techniques, on the other hand, provide an efficient approach. By projecting complex graphs into a lower-dimensional space, these techniques simplify the analysis and processing of large-scale graphs. By transforming graphs into vectors, it simplifies the analysis and processing of large-scale datasets. Several approaches, such as GraphSAGE, Node2Vec, and FastRP, offer efficient methods for generating graph embeddings. By storing embeddings as node properties, it is possible to compare different embedding techniques and evaluate their effectiveness for specific tasks. This flexibilityallows for dynamic updates to embeddings and facilitates experimentation with different approaches. By analyzing these embeddings, one can extract valuable insights into the relationships between nodes and their similarities within the embedding space

When humans move in a shared space, they choose navigation strategies that preserve their mutual safety. At the same time, each human seeks to minimise the number of modifications to her/his path. In order to achieve this result, humans use unwritten rules and reach a consensus on their decisions about the motion direction by exchanging non-verbal messages. They then implement their choice in a mutually acceptable way. Socially-aware navigation denotes a research effort aimed at replicating this logic inside robots. Existing results focus either on how robots can participate in negotiations with humans, or on how they can move in a socially acceptable way. We propose a holistic approach in which the two aspects are jointly considered. Specifically, we show that by combining opinion dynamics (to reach a consensus) with vortex fields (to generate socially acceptable trajectories), the result outperforms the application of the two techniques in isolation., Comment: 7 pages, 6 figures, submitted to ICRA 2025

Sixth generation (6G) wireless networks are envisioned to include aspects of energy footprint reduction (sustainability), besides those of network capacity and connectivity, at the design stage. This paradigm change requires radically new physical layer technologies. Notably, the integration of large-aperture arrays and the transmission over high frequency bands, such as the sub-terahertz spectrum, are two promising options. In many communication scenarios of practical interest, the use of large antenna arrays in the sub-terahertz frequency range often results in short-range transmission distances that are characterized by line-of-sight channels, in which pairs of transmitters and receivers are located in the (radiating) near field of one another. These features make the traditional designs, based on the far-field approximation, for multiple-input multiple-output (MIMO) systems sub-optimal in terms of spatial multiplexing gains. To overcome these limitations, new designs for MIMO systems are required, which account for the spherical wavefront that characterizes the electromagnetic waves in the near field, in order to ensure the highest spatial multiplexing gain without increasing the power expenditure. In this paper, we introduce an analytical framework for optimizing the deployment of antenna arrays in line-of-sight channels, which can be applied to paraxial and non-paraxial network deployments. In the paraxial setting, we devise a simpler analytical framework, which, compared to those available in the literature, provides explicit information about the impact of key design parameters. In the non-paraxial setting, we introduce a novel analytical framework that allows us to identify a set of sufficient conditions to be fulfilled for achieving the highest spatial multiplexing gain. The proposed designs are validated with numerical simulations., Comment: This work has been accepted in IEEE Transactions on Green Communications and Networking

This paper presents a theoretical and mathematical framework for the design of a conformal reconfigurable intelligent surface (RIS) that adapts to non-planar geometries, which is a critical advancement for the deployment of RIS on non-planar and irregular surfaces as envisioned in smart radio environments. Previous research focused mainly on the optimization of RISs assuming a predetermined shape, while neglecting the intricate interplay between shape optimization, phase optimization, and mutual coupling effects. Our contribution, the T3DRIS framework, addresses this fundamental problem by integrating the configuration and shape optimization of RISs into a unified model and design framework, thus facilitating the application of RIS technology to a wider spectrum of environmental objects. The mathematical core of T3DRIS is rooted in optimizing the 3D deployment of the unit cells and tuning circuits, aiming at maximizing the communication performance. Through rigorous full-wave simulations and a comprehensive set of numerical analyses, we validate the proposed approach and demonstrate its superior performance and applicability over contemporary designs. This study-the first of its kind-paves the way for a new direction in RIS research, emphasizing the importance of a theoretical and mathematical perspective in tackling the challenges of conformal RISs., Comment: Submitted for journal publication

In this paper, we present two datasets that we make publicly available for research. The data is collected in a testbed comprised of a custom-made Reconfigurable Intelligent Surface (RIS) prototype and two regular OFDM transceivers within an anechoic chamber. First, we discuss the details of the testbed and equipment used, including insights about the design and implementation of our RIS prototype. We further present the methodology we employ to gather measurement samples, which consists of letting the RIS electronically steer the signal reflections from an OFDM transmitter toward a specific location. To this end, we evaluate a suitably designed configuration codebook and collect measurement samples of the received power with an OFDM receiver. Finally, we present the resulting datasets, their format, and examples of exploiting this data for research purposes.

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G- DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world., Comment: Submitted for conference publication

The podio event data model (EDM) toolkit provides an easy way to generate a performant implementation of an EDM from a high level description in yaml format. We present the most recent developments in podio, most importantly the inclusion of a schema evolution mechanism for generated EDMs as well as the "Frame", a thread safe, generalized event data container. For the former we discuss some of the technical aspects in relation with supporting different I/O backends and leveraging potentially existing schema evolution mechanisms provided by them. Regarding the Frame we introduce the basic concept and highlight some of the functionality as well as important aspects of its implementation. The usage of podio for generating different EDMs for future collider projects (most importantly EDM4hep, the common EDM for the Key4hep project) has inspired new features. We present some of those smaller new features and end with a brief overview on current developments towards a first stable version as well as an outlook on future developments beyond that., Comment: Submitted to proceedings of ACAT 2022

The Key4hep project aims to provide a turnkey software solution for the full experiment lifecycle, based on established community tools. Several future collider communities (CEPC, CLIC, EIC, FCC, and ILC) have joined to develop and adapt their workflows to use the common data model EDM4hep and common framework. Besides sharing of existing experiment workflows, one focus of the Key4hep project is the development and integration of new experiment independent software libraries. Ongoing collaborations with projects such as ACTS, CLUE, PandoraPFA and the OpenDataDector show the potential of Key4hep as an experiment-independent testbed and development platform. In this talk, we present the challenges of an experiment-independent framework along with the lessons learned from discussions of interested communities (such as LUXE) and recent adopters of Key4hep in order to discuss how Key4hep could be of interest to the wider HEP community while staying true to its goal of supporting future collider designs studies., Comment: Submitted to proceedings of ACAT 2022

Ensuring the precision of channel modeling plays a pivotal role in the development of wireless communication systems, and this requirement remains a persistent challenge within the realm of networks supported by Reconfigurable Intelligent Surfaces (RIS). Achieving a comprehensive and reliable understanding of channel behavior in RIS-aided networks is an ongoing and complex issue that demands further exploration. In this paper, we empirically validate a recently-proposed impedance-based RIS channel model that accounts for the mutual coupling at the antenna array and precisely models the presence of scattering objects within the environment as a discrete array of loaded dipoles. To this end, we exploit real-life channel measurements collected in an office environment to demonstrate the validity of such a model and its applicability in a practical scenario. Finally, we provide numerical results demonstrating that designing the RIS configuration based upon such model leads to superior performance as compared to reference schemes., Comment: Submitted for conference publication

Robot-assisted navigation is a perfect example of a class of applications requiring flexible control approaches. When the human is reliable, the robot should concede space to their initiative. When the human makes inappropriate choices the robot controller should kick-in guiding them towards safer paths. Shared authority control is a way to achieve this behaviour by deciding online how much of the authority should be given to the human and how much should be retained by the robot. An open problem is how to evaluate the appropriateness of the human's choices. One possible way is to consider the deviation from an ideal path computed by the robot. This choice is certainly safe and efficient, but it emphasises the importance of the robot's decision and relegates the human to a secondary role. In this paper, we propose a different paradigm: a human's behaviour is correct if, at every time, it bears a close resemblance to what other humans do in similar situations. This idea is implemented through the combination of machine learning and adaptive control. The map of the environment is decomposed into a grid. In each cell, we classify the possible motions that the human executes. We use a neural network classifier to classify the current motion, and the probability score is used as a hyperparameter in the control to vary the amount of intervention. The experiments collected for the paper show the feasibility of the idea. A qualitative evaluation, done by surveying the users after they have tested the robot, shows that the participants preferred our control method over a state-of-the-art visco-elastic control., Comment: 14 pages, 10 figures. To be published in Intelligent Service Robotics

Reconfigurable Intelligent Surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments, and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localization, and sustainability requirements of next generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardized interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signaling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflectarray measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth and area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future networks., Comment: 43 pages, 21 figures, sumbitted for a journal publication. arXiv admin note: text overlap with arXiv:2203.13478

The reconfigurable intelligent surface (RIS) is an emerging technology that changes how wireless networks are perceived, therefore its potential benefits and applications are currently under intense research and investigation. In this letter, we focus on electromagnetically consistent models for RISs inheriting from a recently proposed model based on mutually coupled loaded wire dipoles. While existing related research focuses on free-space wireless channels thereby ignoring interactions between RIS and scattering objects present in the propagation environment, we introduce an RIS-aided channel model that is applicable to more realistic scenarios, where the scattering objects are modeled as loaded wire dipoles. By adjusting the parameters of the wire dipoles, the properties of general natural and engineered material objects can be modeled. Based on this model, we introduce a provably convergent and efficient iterative algorithm that jointly optimizes the RIS and transmitter configurations to maximize the system sum-rate. Extensive numerical results show the net performance improvement provided by the proposed method compared with existing optimization algorithms., Comment: Submitted for publication

In this paper, we present our experience designing, prototyping, and empirically characterizing RF Switch-based Reconfigurable Intelligent Surfaces (RIS). Our RIS design comprises arrays of patch antennas, delay lines and programmable radio-frequency (RF) switches that enable passive 3D beamforming, i.e., without active RF components. We implement this design using PCB technology and low-cost electronic components, and thoroughly validate our prototype in a controlled environment with high spatial resolution codebooks. Finally, we make available a large dataset with a complete characterization of our RIS and present the costs associated with reproducing our design.

Aerial communication is gradually taking an assertive role within common societal behaviors by means of unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and fixed-wing aircrafts (FWAs). Such devices can assist general operations in a diverse set of heterogeneous applications, such as video-surveillance, remote delivery and connectivity provisioning in crowded events and emergency scenarios. Given their increasingly higher technology penetration rate, telco operators started looking at the sky as a new potential direction to enable a three-dimensional (3D) communication paradigm. However, designing flying mobile stations involves addressing a daunting number of challenges, such as an excessive on-board control overhead, variable battery drain and advanced antenna design. To this end, the newly-born Smart Surfaces technology may come to help: reconfigurable intelligent surfaces (RIS) may be flexibly installed on-board to control the terrestrial propagation environment from an elevated viewpoint by involving low-complex and battery-limited solutions. In this paper, we shed light on novel RIS-based use-cases, corresponding requirements, and potential solutions that might be adopted in future aerial communication infrastructures., Comment: Submitted for journal publication

The disruptive reconfigurable intelligent surface (RIS) technology is steadily gaining relevance as a key element in future 6G networks. However, a one-size-fits-all RIS hardware design is yet to be defined due to many practical considerations. A major roadblock for currently available RISs is their inability to concurrently operate at multiple carrier frequencies, which would lead to redundant installations to support multiple radio access technologies (RATs). In this paper, we introduce FABRIS, a novel and practical multi-frequency RIS design. FABRIS is able to dynamically operate across different radio frequencies (RFs) by means of frequency-tunable antennas as unit cells with virtually no performance degradation when conventional approaches to RIS design and optimization fail. Remarkably, our design preserves a sufficiently narrow beamwidth as to avoid generating signal leakage in unwanted directions and a sufficiently high antenna efficiency in terms of scattering parameters. Indeed, FABRIS selects the RIS configuration that maximizes the signal at the intended target user equipment (UE) while minimizing leakage to non-intended neighboring UEs. Numerical results and full-wave simulations validate our proposed approach against a naive implementation that does not consider signal leakage resulting from multi-frequency antenna arrays.

Central America and the Caribbean (CAC) need science development efforts through ambitious projects that require strong regional collaboration. Inspiration can be drawn from initiatives in regions with similar problems. The bottleneck is the scarcity of public research centers and little or no research in private universities. An interesting proposal is the creation of a Dominican "Silicon Beach". The "Central American Science and Technology Fund" should focus on objectives capable of attracting the attention of the non-academic sector, first and foremost policy makers, but also civil society in general. The successful experience of SESAME (" Synchrotron Light for Experimental Science and Applications in Middle East ") offers an interesting basis for reflection, as it allows scientific research and short-term practical and social applications. Only two of the more than 60 existing synchrotrons are in Latin America, both in Brazil. Together with other similar projects in the South, such as the African Light Source (AFLS), and with the support of SESAME, LNLS and other synchrotrons in the South, it could lead to interesting South-South cooperation, which could be supported by the European Union or the NSF.As David Gross reminded, Science drives Technology, Technology drives Innovation, and this ends up in the welfare of society. A regional synchrotron may be the way to make this a reality in the Great Caribbean Region, as a first historical example of a large regional facility there.

The integration of advanced localization techniques in the upcoming next generation networks (B5G/6G) is becoming increasingly important for many use cases comprising contact tracing, natural disasters, terrorist attacks, etc. Therefore, emerging lightweight and passive technologies that allow accurately controlling the propagation environment, such as reconfigurable intelligent surfaces (RISs), may help to develop advance positioning solutions relying on channel statistics and beamforming. In this paper, we devise PAPIR, a practical localization system leveraging on RISs by designing a two-stage solution building upon prior statistical information on the target user equipment (UE) position. PAPIR aims at finely estimating the UE position by performing statistical beamforming, direction-of-arrival (DoA) and time-of-arrival (ToA) estimation on a given three-dimensional search space, which is iteratively updated by exploiting the likelihood of the UE position., Comment: Accepted for presentation at IEEE SPAWC 2021

This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among UEs is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to as MIMO covariance shaping, that exploits multiple antennas at the UEs and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the UE-side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two-UE case, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced UEs., Comment: Accepted for publication in IEEE Transactions on Wireless Communications

Next generation mobile networks need to expand towards uncharted territories in order to enable the digital transformation of society. In this context, aerial devices such as unmanned aerial vehicles (UAVs) are expected to address this gap in hard-to-reach locations. However, limited battery-life is an obstacle for the successful spread of such solutions. Reconfigurable intelligent surfaces (RISs) represent a promising solution addressing this challenge since on-board passive and lightweight controllable devices can efficiently reflect the signal propagation from the ground BSs towards specific target areas. In this paper, we focus on air-to-ground networks where UAVs equipped with RIS can fly over selected areas to provide connectivity. In particular, we study how to optimally compensate flight effects and propose RiFe as well as its practical implementation Fair-RiFe that automatically configure RIS parameters accounting for undesired UAV oscillations due to adverse atmospheric conditions. Our results show that both algorithms provide robustness and reliability while outperforming state-of-the-art solutions in the multiple conditions studied., Comment: Submitted for journal publication

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology., Comment: Accepted at IEEE Access. Received April 30, 2020, accepted May 11, 2020, date of publication May 14, 2020, date of current version May 28, 2020

Multicasting, where a base station (BS) wishes to convey the same message to several user equipments (UEs), represents a common yet highly challenging wireless scenario. In fact, guaranteeing decodability by the whole UE population proves to be a major performance bottleneck since the UEs in poor channel conditions ultimately determine the achievable rate. To overcome this issue, two-phase cooperative multicasting schemes, which use conventional multicasting in a first phase and leverage device-to-device (D2D) communications in a second phase to effectively spread the message, have been extensively studied. However, most works are limited either to the simple case of single-antenna BS or to a specific channel state information at the transmitter (CSIT) setup. This paper proposes a general two-phase framework that is applicable to the cases of perfect, statistical, and topological CSIT in the presence of multiple antennas at the BS. The proposed method exploits the precoding capabilities at the BS, which enable targeting specific UEs that can effectively serve as D2D relays towards the remaining UEs, and maximize the multicast rate under some outage constraint. Numerical results show that our schemes bring substantial gains over traditional single-phase multicasting and overcome the worst-UE bottleneck behavior in all the considered CSIT configurations., Comment: Submitted to IEEE Transactions on Wireless Communications

One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overload of the health services, portable X-ray devices are recommended during the pandemic, preventing the spread of the disease. However, these devices entail different complications (such as capture quality) that, together with the subjectivity of the clinician, make the diagnostic process more difficult and suggest the necessity for computer-aided diagnosis methodologies despite the scarcity of samples available to do so. To solve this problem, we propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of a unrelated pathology and performed two stages of knowledge transfer to obtain a robust system able to segment lung regions from portable X-ray devices despite the scarcity of samples and lesser quality. This way, our methodology obtained a satisfactory accuracy of $0.9761 \pm 0.0100$ for patients with COVID-19, $0.9801 \pm 0.0104$ for normal patients and $0.9769 \pm 0.0111$ for patients with pulmonary diseases with similar characteristics as COVID-19 (such as pneumonia) but not genuine COVID-19.

The TOP-IMPLART project consists of the design and implementation of a linear proton accelerator, its control and monitoring systems for the treatment of superficial and semi-deep tumors. The energy of 150 MeV (corresponding to a penetration in tissue of about 15 cm) is a milestone in design being useful for the proton therapy treatment of almost 50% of tumors based on their position and depth (including ocular melanoma, head-neck tumors, pediatric tumors, and more superficial tumors). The capability to vary the intensity on a pulse-to-pulse basis combined with an electronic feedback system allows to get the required dose uniformity (2.5%) reducing the number of re-paintings. In this paper the state of the art and the objectives of the TOP-IMPLART project are described within the framework of the progress of Protontherapy.

Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)---which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments---is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs). In this paper we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo-RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance., Comment: Accepted and to appear in IEEE Journal on Selected Areas in Communications, Special Issue on Massive Access for 5G and Beyond

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) has posed a serious threat to global health. As no specific therapeutics are yet available to control disease evolution, more in-depth understanding of the pathogenic mechanisms induced by SARS-CoV-2 will help to characterize new targets for the management of COVID-19. The present study identified a specific set of biological pathways altered in primary human lung epithelium upon SARS-CoV-2 infection, and a comparison with SARS-CoV from the 2003 pandemic was studied. The transcriptomic profiles were also exploited as possible novel therapeutic targets, and anti-signature perturbation analysis predicted potential drugs to control disease progression. Among them, Mitogen-activated protein kinase kinase (MEK), serine-threonine kinase (AKT), mammalian target of rapamycin (mTOR) and I kappa B Kinase (IKK) inhibitors emerged as candidate drugs. Finally, sex-specific differences that may underlie the higher COVID-19 mortality in men are proposed.