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The goal of this project was to evaluate various object detection and tracking models to determine the best combination for real-time vehicle monitoring in urban settings, with a focus on parking spot tracking. This study examined multiple algorithms, including different sizes of YOLOv8 for detection and OC-SORT and StrongSORT for tracking. Both public and private datasets were used to provide a more complete evaluation. In the experiments, YOLOv8 emerged as an effective detection model, with varying trade-offs between speed and accuracy depending on the model size. YOLOv8n, the smaller version, provided higher frames per second (FPS) and lower computational overhead, making it suitable for real-time applications. In contrast, YOLOv8l, the larger version, exhibited greater detection accuracy with longer inference times, suggesting its potential use in applications where precision is outstanding. For tracking, the study compared OC-SORT and StrongSORT. StrongSORT consistently demonstrated better tracking accuracy but with a slightly lower FPS. OC-SORT, on the other hand, achieved higher FPS, making it more suitable for real-time scenarios. Additionally, StrongSORT proved more robust against occlusions and non-linear object motion, thanks to its advanced modules, while OC-SORT excelled in simpler tracking tasks. Based on these results, the ideal solution for a traffic monitoring application focusing on parking spot tracking would be combining YOLOv8n for fast and efficient detection with StrongSORT for robust tracking. This combination offers a balance of speed, accuracy, and computational efficiency, allowing for real-time monitoring while ensuring precise tracking. A key aspect for accurate vehicle tracking is high-resolution video, with 4K being the optimal choice for detailed detection and license plate recognition. The experiments also suggested that for applications requiring faster real-time online tracking, YOLOv8n coupled with OC-SORT might be a viable soluti

Data Science and data-driven Artificial Intelligence are here to stay and they are expected to further transform the current global economy. From a technical point of view, there is an overall agreement that disciplines based on data require to combine data engineering and data analysis skills, but the fact is that data engineering is nowadays trailing and catching up with the rapid changes in the data analysis landscape. To unleash the real power of data, data-centric systems must be professionalized, i.e., operationalized and systematized, so that repetitive, time-consuming and error-prone tasks are automated. To such end, we propose our vision on next generation data governance for data-centric systems based on knowledge graphs. We claim that without the knowledge embedded in the data governance layer, Data Science will not unleash its potential., This work is supported by the Horizon Europe Programme under GA.101135513 (CYCLOPS) and GA. 101093164 (ExtremeXP) and the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RBI00 / AEI/10.13039/501100011033 (DOGO4ML)., Peer Reviewed, Postprint (published version)

The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation and application delivery. AI-SPRINT has achieved significant scientific advances, including streamlined processes, improved efficiency, and the ability to operate in real time, as evidenced by three practical use cases. This paper provides an in-depth examination of these applications – Personalized Healthcare, Maintenance and Inspection, and Farming 4.0 – highlighting their practical implementation and the objectives achieved with the integration of AI-SPRINT technologies. We analyze how the proposed toolchain effectively addresses a range of challenges and refines processes, discussing its relevance and impact in multiple domains . After a comprehensive overview of the main AI-SPRINT tools used in these scenarios, the paper summarizes of the findings and key lessons learned., Peer Reviewed, Postprint (published version)

The AMPERE project is developing the next generation of high-performance and energy efficient Cyber-Physical Systems supporting multi-criteria optimization. This paper provides the general overview of the AMPERE approach to analyse and optimize parallel real-time applications in heterogeneous platforms. More specifically, it details how parallel OpenMP programs are generated from AMALTHEA models, and the multi-criteria optimization methodology, considering the fault-tolerance, time and energy properties of the targeted applications., This research has been co-funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871669, in the context of the AMPERE project., Peer Reviewed, Postprint (author's final draft)

En aquests últims anys, la manera de realitzar estudis de mercat ha canviat radicalment. La informació que s'aconseguia abans en una setmana, demanant l'opinió de la gent pel carrer o a través de trucades telefòniques, ara es pot fer en qüestió d'hores a través d'aplicacions de mòbil. A més, permet aconseguir molta més informació valuosa que abans era impossible sense fer una inversió molt gran, com aconseguir opinions de persones d'un altre país. Ara bé, aquest canvi comporta també nous reptes que s'han de superar per mantenir la qualitat dels estudis. Un d'aquests reptes és assegurar que els demogràfics de la mostra utilitzada en l'estudi estan distribuïts correctament. En aquest treball, s'estudia aquest problema en concret, buscant tant la solució òptima fent un seguit d'assumpcions, com una solució per al cas real que pugui ser utilitzada sense alentir el procés ni haver de malgastar molts recursos. Per aconseguir-ho, s'han utilitzat les dades d'una empresa espanyola del sector, Zinklar, qui està interessada a utilitzar els resultats obtinguts per millorar la gestió del servei que ofereixen., In recent years, the way market research is conducted has changed dramatically. The information that used to be obtained in a week, by asking people on the street or through phone calls, can now be gathered in a matter of hours via mobile applications. Furthermore, it allows for the collection of much more valuable information that was previously impossible without making a significant investment, such as obtaining opinions from people in another country. However, this change also brings new challenges that must be overcome to maintain the quality of the studies. One of these challenges is ensuring that the demographics of the sample used in the study are correctly distributed. This TFG is focused on this problem, seeking both the optimal solution making a series of assumptions, and a solution for the real case that can be used without slowing down the process or wasting many resources to achieve it. To achieve this, Zinklar, a Spanish company in the sector who is interested in using the obtained results to improve the management of the service they offer, has provided the needed data.

Parameter estimation is crucial for polymer electrolyte membrane fuel cell monitoring and control. Nonetheless, most parameter estimation algorithms rely on a persistence of excitation condition, which is rarely satisfied and not convenient in fuel cell systems. For this reason, this work presents and compares three algorithms to estimate in real-time some critical PEMFC parameters in the voltage equation: the ohmic resistance, the charge transfer coefficient and the oxygen activity of a proton exchange fuel cell. The first algorithm is a standard gradient descent, while the other two are based on a set of pre-preprocessing dynamics. It is shown that, while the gradient descent requires the persistence of excitation condition, the addition of the pre-processing dynamics ensures reliable estimation under significantly weaker excitation assumptions. Moreover, it is shown that the pre-processing dynamics improves the transient behaviour and noise performance of the estimators. The results are validated through a set of numerical simulations and in an experimental prototype, where sensor noise and unmodelled disturbances are considered., This work has been supported by the Spanish Ministry of Universities funded by the European Union - NextGenerationEU (2022UPC-MSC-93823). This work is part of the Project MAFALDA (PID2021- 126001OB-C31) funded by MCIN/ AEI /10.13039/501100011033 and by "ERDF A way of making Europe" This work is part of the project MASHED (TED2021-129927BI00), funded by MCIN/ AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR. This research has been developed within the CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+) as part of the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094., Peer Reviewed, Preprint

Multicore architectures are currently under adoption in the aerospace domain, however their software remains single-threaded. In this paper we argue about the benefits offered by homogeneous parallel processing, both in terms of performance, which is necessary for the implementation of advanced functionalities, as well as in terms of certification and in particular about mastering multicore interference.We discuss the implementation details of this programming paradigm in avionics and space real-time operating systems. We experimentally evaluate the performance benefits offered by several high-performance multicore platforms which are considered good candidates for next generation flight computers, using homogeneous parallel processing under a qualifiable real-time operating system used in the aerospace domain. Our results indicate near to linear speed-ups compared to traditional sequential processing, showing the benefits of this approach., This work was supported by the European Union’s Horizon Europe programme under the METASAT project (grant agreement 101082622). In addition, it was supported by ESA through the 4000136514/21/NL/GLC/my co-funded PhD activity ”Mixed Software/Hardware-based Fault-tolerance Techniques for Complex COTS System-on-Chip in Radiation Environments” and the GPU4S (GPU for Space) ESAfunded project. It was also partially supported by the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB-C21 and IJC-2020-045931-I ( Spanish State Research Agency / Agencia Española de Investigación (AEI) / http://dx.doi.org/10.13039/501100011033 ) and by the Department of Research and Universities of the Government of Catalonia with a grant to the CAOS Research Group (Code: 2021 SGR 00637)., Peer Reviewed, Postprint (author's final draft)

Nowadays, UWB technology is growing exponentially in the market and more and more companies are including it in their devices every day. The purpose of this thesis is to continue with improvements in the MK UWB Kit RTLS (Real-Time Location System). The kit has four fixed devices, anchors, that through UWB technology, allow a mobile device, tag, to be located in the cell delimited by the first ones. With the aim of creating a more scalable and optimal system for development, this project has generated Linux software capable of replacing the current microcontroller that manages the UWB module in the anchors, as well as collecting and processing the generated data. Furthermore, the application has access to the network through REST-APIs; these have been implemented to allow remote control from a web server. In order to have a complete update of the kit, the firmware installed on the tags has also been modified according to the Linux application. This project is carried out and supervised by MobileKnowledge company., Actualmente la tecnología UWB está creciendo exponencialmente en el mercado y cada vez más empresas la están a añadiendo a sus dispositivos. Esta tesis tiene el propósito de continuar con las mejoras en el kit MK UWB - RTLS (Real-Time Location System). El kit cuenta con cuatro dispositivos fijos, anchors, que mediante la tecnología UWB permite localizar a un dispositivo móvil, tag, en la celda delimitada por los primeros. Con el objetivo de crear un sistema más escalable y óptimo para desarrollo, en este proyecto se ha generado un software Linux capaz de sustituir al actual microcontrolador que gestiona el módulo UWB en los anchors, así como recoger y procesar los datos generados. Por otro lado, la aplicación dispone de acceso a la red mediante REST-APIs; implementadas para permitir el control remoto desde un servidor web. Con el fin de tener una actualización completa del kit, el firmware instalado en los tags también se ha modificado acorde con la aplicación Linux. El proyecto ha sido llevado a cabo y supervisado por la empresa MobileKnowledge., Actualment, la tecnologia UWB està creixent exponencialment en el mercat i cada cop més empreses l'estan afegint als seus dispositius. Aquesta tesi té el propòsit de continuar amb les millores al kit MK UWB RTLS (Real-Time Location System). El kit conté amb quatre dispositius fixos, anchors, que mitjançant la tecnologia UWB permeten localitzar un dispositiu mòbil, tag, a la cel·la delimitada pels primers. Amb l'objectiu de crear un sistema més escalable i òptim pel desenvolupament, en aquest projecte s'ha generat un programari Linux capaç de substituir a l'actual microcontrolador que gestiona el mòdul UWB als anchors i també recollir i processar les dades generades. Per un altre costat, l'aplicació disposa d'accés a la xarxa per mitjà de REST-APIs; implementades per permetre el control remot des d'un servidor web. Amb la finalitat de tenir una actualització completa del kit, el microprogramari instal·lat als tags també s'ha modificat d'acord amb l'aplicació Linux. El projecte s'ha dut a terme i ha estat supervisat per l'empresa MobileKnowledge.

L'Internet of Things produeix grans quantitats de dades a punt per ser analitzades, però les aproximacions tradicionals de Cloud Computing s'estan quedant curtes. L'Edge Computing, reforçada per la tecnologia 5G, ofereix solucions més ràpides processant les dades més a prop de les seves fonts. Per aprofitar realment aquest potencial en la mobilitat intel·ligent, es requereixen estratègies d'assignació sofisticades. Aquest projecte aspira a millorar el rendiment de les aplicacions en temps real incorporant estratègies d'assignació intricades i tècniques de desplegament automatitzades. Aprofitant els marcs existents, l'objectiu és maximitzar el paral·lelisme computacional., Internet of Things produces vast amounts of data ripe for analysis, but traditional cloud computing approaches are increasingly falling short. Edge computing, bolstered by 5G tech- nology, offers swifter solutions by processing data nearer to its sources. To truly harness this potential in smart mobility, sophisticated allocation strategies are imperative. This project as- pires to enhance the performance of real-time applications by incorporating intricate allocation strategies and automated deployment techniques. By leveraging existing frameworks, the goal is to maximize computing parallel.

In general, the availability of an accurate machine learning (ML) model plays a particularly important role in the development of new networking solutions and is one of the main drivers for the development of 5G and beyond networking. Although an option is to update the model once inaccurate data is detected, such approach requires high computational effort, specially once the data history is large. In this paper, we propose an approach that combines a traffic prediction model based on Long Short-Term Memory (LSTM) with an analysis module for dynamic connection capacity allocation. Once the model is generated, re-training can be triggered after inaccuracies are detected by the analysis module. Illustrative numerical results show the benefits from the proposed decision-based re-training approach to reduce the number of re-training rounds while maintaining model accuracy., This work was partially funded by MCIN/AEI/ 10.13039/501100011033 grant PID2021-126431OB-I00 (ANEMONE), and Generalitat de Catalunya grant 2021 SGR 00770. This work also was funded by the European Commission Horizon Europe SNS JU DESIRE6G project (G.A. 101096466), from the Spanish MINECO and the European Union though the NextGeneration UNICO5G TIMING (TSI-063000-2021-145), and the AEI IBON (PID2020-114135RB-I00) projects. The authors also acknowledge the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-19-CE-25-0001-01 (ARTIC project)., Peer Reviewed, Postprint (author's final draft)

A pesar de tots els avantatges que presenta la generació d’energia a partir de l’ús de generadors FVs, l’eficiència de la conversió energètica d’aquests encara és relativament baixa i el cost inicial de les instal·lacions representa una despesa important. Per tant, resulta essencial introduir tècniques per extreure la màxima potència del sistema i aconseguir així la màxima eficiència que permet la instal·lació. En concret, per ajudar al disseny i optimització d’aquest sistemes, en aquesta tesi de màster es proposa la idea d’utilitzar un Single-Board Computer (SBC) per implementar una plataforma Hardware-in-the-loop (HIL) i comprovar les possibilitats de prototipat i validació que ofereixen aquest tipus de dispositius en un entorn de simulació en temps real. Per fer-ho, es proposa una configuració d’un sistema FV per al que es desenvolupen diferents tipus de controladors de seguiment del punt de màxima potència (MPPT), entre els que s’inclou un ampli espectre de solucions tals com les tècniques clàssiques, les basades en intel·ligència artificial o processos d’optimització, tant per un entorn de simulació pura com es MATLAB-SimulinkTM com per la plataforma HIL basada en una Raspberry PiTM. En ambdós casos, s’analitza el comportament dels controladors sota diferents condicions de treball: condicions de test estàndard (STC), variació de la irradiància, variació de la temperatura i condicions d’ombrejat parcial (PSC). Els resultats obtinguts, a pesar de les limitacions entre la comunicació i potència de càlcul de la plataforma HIL respecte l’entorn de simulació, demostren que sí es possible utilitzar l’enfoc proposat per desenvolupar controladors MPPT de baix cost, contribuint al desenvolupament i implementació de solucions més eficients i sostenibles per a la optimització de l’energia energètica proporcionada pels generadors FVs

Main memory is one of the most complex resources to analyze in multicore-based embedded real-time systems, with contention in the memory controller and the timing constraints of the main memory device as the main contributors to that complexity. One of the main challenges in multicore real-time systems is producing the required evidence on the management of contention delay for the certification. This stems from the fact that current MPSoCs barely provide any event monitors on how tasks interact and delay each other in memory. Besides, even if hardware and software mechanisms are in place to mitigate contention in the memory system, it is hard - if at all possible - to provide evidence about their correctness. In this work, we cover this gap by proposing a lightweight hardware mechanism that tightly tracks inter-core contention in memory. The proposed hardware mechanism, which we evaluate in detail, improves the quality of timing-related evidence that must be provided on how contention in main memory of multicore real-time systems is handled in adherence to applicable safety standards., This work has been supported by Grant PID2019-107255GB-C21 funded by MCIN/AEI/ 10.13039/501100011033, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No.772773), and the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors also thank the support of the Department of Research and Universities of the Government of Catalonia to the Smart and Safe Autonomous Systems (SSAS) Research Group, (Code:2021 SGR 00637)., Peer Reviewed, Postprint (author's final draft)

The Project consists of the analysis, programming and simulation of an on-line scheduler for time-sensitive applications, with different variables to respond to the QoS that each user may have. Thanks to the ability of the algorithm to consider the channel quality of each user, the time delay due to queueing and the different delay and rate requirements, the scheduler is able to minimize the users delay while satisfiying the different per-user QoS constraints. It has been possible to conclude, thanks to the designed simulator, that for a given latency constraint (QoS) bounded to an outage probability the real-time scheduler will be able to give optimal response to the requirements, El proyecto consiste en el análisis, programación y simulación de un planificador en línea para aplicaciones sensibles al tiempo, con diferentes variables para responder a la calidad de servicio (QoS) que cada usuario pueda tener. Gracias a la capacidad del algoritmo para considerar la calidad del canal de cada usuario, el retraso debido a la cola y los diferentes requisitos de retraso y tasa de tráfico, el scheduler es capaz de dar una respuesta óptima y justa, minimizando el retraso de los usuarios mientras satisface las diferentes restricciones de QoS por usuario. Se ha podido concluir, gracias al simulador diseñado, que para una determinada restricción de latencia (QoS) vinculada a una probabilidad de outage, el planificador en tiempo real será capaz de dar una respuesta óptima a los requisitos., El projecte consisteix en l'anàlisi, programació i simulació d'un planificador (scheduler) en temps real per aplicacions sensibles al temps, amb diferents variables per a donar resposta a la qualitat de servei (QoS) que cada usuari pugui tenir. Gràcies a la capacitat de l'algorisme de tenir en compte la qualitat del canal de cada usuari, el retard que porten els paquets en cua i els diferents requisits de retard i tràfic. El scheduler és capaç de donar una resposta òptima i justa, minimitzant el retad pels usuaris mentres satisfà les diferents restriccions de QoS per usuari. S'ha pogut concloure, gràcies al simulador disenyat, que per a una restricció temporal (QoS) lligada a una probabilitat d?outage, el scheduler en temps real, donarà una resposta als requeriments i aquesta serà propera als límits de velocitat establerts per la teoria de la informació.

Network monitoring solutions need to cope with increasing network traffic volumes, as a result, sketch-based monitoring methods have been extensively studied to trade accuracy for memory scalability and storage reduction. However, sketches are sensitive to skewness in network flow distributions due to hash collisions, and need complicated performance optimization to adapt to line-rate packet streams. We provide Jellyfish, an efficient sketch method that performs one-pass clustering over the network stream. One-pass clustering is realized by adapting the monitoring granularity from the whole network flow to fragments called subflows, which not only reduces the ingestion rate but also provides an efficient intermediate representation for the input to the sketch. Jellyfish provides the network-flow level query interface by reconstructing the network-flow level counters by merging subflow records from the same network flow. We provide probabilistic analysis of the expected accuracy of both existing sketch methods and Jellyfish. Real-world trace-driven experiments show that Jellyfish reduces the average estimation errors by up to six orders of magnitude for per-flow queries, by six orders of magnitude for entropy queries, and up to ten times for heavy-hitter queries., This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61972409; in part by Hong Kong Research Grants Council (RGC) under Grant TRS T41-603/20-R, Grant GRF-16213621, and Grant ITF ACCESS; in part by the Spanish I+D+i project TRAINER-A, funded by MCIN/AEI/10.13039/501100011033, under Grant PID2020-118011GB-C21; and in part by the Catalan Institution for Research and Advanced Studies (ICREA Academia)., Peer Reviewed, Postprint (author's final draft)

The prevailing use of multicores in Embedded Critical Systems (ECS) is multi-application workloads in which independent applications run in different cores with data sharing restricted to the communication between applications and the real-time operating system. However, thread-level parallelism is increasingly used, e.g., OpenMP, in ECS to improve individual applications' performance. At the hardware level, we are witnessing increased research efforts to master and improve multicore cache coherence that plays a key role enabling efficient data sharing among threads. Despite these efforts, the limited information provided by performance monitoring counters on cache coherence limits the understanding of coherence's impact on tasks execution time and hence, poses severe constraints to estimate tight worst-case execution time bounds. In this line, this work contributes with an analysis of the impact that cache coherence can have on application timing behavior, and a new set of low-overhead performance monitoring counters that can be used to track the coherence-related contention that different threads can cause on each other when sharing data. Our results show that the proposed performance monitoring counters effectively capture all coherence-related contention that tasks can suffer and hence are key for parallel software timing validation and verification in ECS. Furthermore, they help application optimization by providing key information about data sharing among the application threads., The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773). This work has also been partially supported by Grant PID2019-107255GB-C21 funded by MCIN/AEI/ 10.13039/501100011033., Peer Reviewed, Postprint (author's final draft)

New 5G and beyond applications demand strict delay requirements. In this paper, we propose coordination between radio access and optical transport to guarantee such delay while optimizing optical capacity allocation. Illustrative results show near real-time autonomous capacity adaptation benefits based on radio access delay requirements., The research leading to these results has received funding from the HORIZON SEASON (G.A. 101096120), the UNICO5G TIMING (TSI-063000-2021-145), and the MICINN IBON (PID2020-114135RB-I00) projects and from the ICREA institution., Peer Reviewed, Postprint (author's final draft)

En plena cúspide de crecimiento de popularidad en dispositivos móviles, plataformas y herramientas web que no disponen de versiones adaptadas a dichos dispositivos presentan una brecha en su público objetivo. El presente documento describe la solución planteada al problema: una herramienta móvil que permite a los usuarios gestionar proyectos de la plataforma web Taiga desde cualquier lugar y en cualquier momento. Además, se presenta la aplicación como un instrumento útil para el alumnado de las asignaturas de PES y ASW de la Facultad de Informática de Barcelona. Con esta aplicación móvil se pretende mejorar la eficiencia y la flexibilidad en la gestión de proyectos y facilitar el acceso a la información de los proyectos en tiempo real. Mediante un desarrollo protagonizado por las metodologías ágiles, se ha cumplido el objetivo obteniendo un producto final satisfactorio. El favorable resultado de este proyecto concluye que una adopción de esta aplicación como asistencia a la plataforma web de la herramienta Taiga facilitará la gestión de proyectos ágiles en cualquier momento y lugar., At the peak of the growth in popularity of mobile devices, web platforms and tools that do not possess a mobile device adapted version find a gap in their target audience. This document describes the proposed solution to the problem: a mobile tool that allows users to manage projects on the Taiga web platform from anywhere at any time. In addition, the application is presented as a useful tool for the students of the PES and ASW subjects at the Barcelona School of Informatics. This mobile application aims to improve efficiency and flexibility in project management and facilitate access to project information in real time. Through an agile methodology based development, the objective has been achieved, obtaining a satisfactory final product. The favorable outcome of this project concludes that an adoption of this application to support the Taiga tool's web platform will facilitate the management of agile projects anytime, anywhere.

We present a novel proposal for modeling complex dynamic terrains that offers real-time rendering, dynamic updates and physical interaction of entities simultaneously. We can capture any feature from landscapes including tunnels, overhangs and caves, and we can conduct a total destruction of the terrain. Our approach is based on a Constructive Solid Geometry tree, where a set of spheres are subtracted from a base Digital Elevation Model. Erosions on terrain are easily and efficiently carried out with a spherical sculpting tool with pixel-perfect accuracy. Real-time rendering performance is achieved by applying a one-direction CPU–GPU communication strategy and using the standard depth and stencil buffer functionalities provided by any graphics processor., This work has been partially funded by Ministeri de Ciència i Innovació (MICIN), Agencia Estatal de Investigación (AEI) and the Fons Europeu de Desenvolupament Regional (FEDER) (project PID2021-122136OB-C21 funded by MCIN/AEI/10.13039/50110001 1033/FEDER, UE)., Postprint (published version)

The need for increased application performance in high-integrity systems like those in avionics is on the rise as software continues to implement more complex functionalities. The prevalent computing solution for future high-integrity embedded products are multi-processors systems-on-chip (MPSoC) processors. MPSoCs include CPU multicores that enable improving performance via thread-level parallelism. MPSoCs also include generic accelerators (GPUs) and application-specific accelerators. However, the data processing approach (DPA) required to exploit each of these underlying parallel hardware blocks carries several open challenges to enable the safe deployment in high-integrity domains. The main challenges include the qualification of its associated runtime system and the difficulties in analyzing programs deploying the DPA with out-of-the-box timing analysis and code coverage tools. In this work, we perform a thorough analysis of vector extensions (VExt) in current COTS processors for high-integrity systems. We show that VExt prevent many of the challenges arising with parallel programming models and GPUs. Unlike other DPAs, VExt require no runtime support, prevent by design race conditions that might arise with parallel programming models, and have minimum impact on the software ecosystem enabling the use of existing code coverage and timing analysis tools. We develop vectorized versions of neural network kernels and show that the NVIDIA Xavier VExt provide a reasonable increase in guaranteed application performance of up to 2.7x. Our analysis contends that VExt are the DPA approach with arguably the fastest path for adoption in high-integrity systems., This work has received funding from the the European Research Council (ERC) grant agreement No. 772773 (SuPerCom) and the Spanish Ministry of Science and Innovation (AEI/10.13039/501100011033) under grants PID2019-107255GB-C21 and IJC2020-045931-I., Peer Reviewed, Postprint (author's final draft)

Scalability, interoperability, and cost efficiency are key remaining challenges to successfully providing real-time holo-portation (and Metaverse-like) services. This paper, for the first time, presents the design and integration of a Multipoint Control Unit (MCU) in a pioneering real-time holo-portation platform, supporting realistic and volumetric user representations (i.e., 3D holograms), with the aim of overcoming such challenges. The feasibility and implications of adopting such an MCU, in comparison with state-of-the-art architectural approaches, are assessed through experimentation in two different deployment setups, by iteratively increasing the number of concurrent users in shared sessions. The obtained results are promising, as it is empirically proved that the newly adopted stream multiplexing together with the novel per-client and per-frame Volumetric Video (VV) processing optimization features provided by the MCU allow increasing the number of concurrent users, while: (i) significantly reducing resources consumption metrics (e.g., CPU, GPU, bandwidth) and frame rate degradation on the client side; and (ii) keeping the end-to-end latency within acceptable limits., Peer Reviewed, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura::9.1 - Desenvolupar infraestructures fiables, sostenibles, resilients i de qualitat, incloent infraestructures regionals i transfrontereres, per tal de donar suport al desenvolupament econòmic i al benestar humà, amb especial atenció a l’accés assequible i equitatiu per a totes les persones, Postprint (published version)

In Hard Real-time Systems, the execution of tasks must be completed within a certain timeframe, known as deadline. In consequence, when a hard-real time system is designed, it is strictly necessary to assume that its tasks will always take their Worst Case Execution Time (WCET), to make sure they meet their deadlines. For this reason, Worst Case Execution Times defines the maximum performance of processors in these systems. Since the performance requirements of modern Hard Real-time Systems are rapidly increasing, the use of newer and higher performance processors is needed. However, these processors have a much greater internal complexity compared to the ones previously used in these systems. This complexity can be detrimental given that Worst Case Execution Times, which are critical for these systems, are often estimated using traditional timing analysis methods like Static and Measurement-based deterministic techniques. The need for extremely detailed knowledge of the processor's internals, IP restrictions, and the impossibility of modeling certain features leads to extremely pessimistic assumptions and the disabling of features while using these techniques. On the other hand, Measurement-Based Probabilistic Timing Analysis (MBPTA) allows computing probabilistic WCET estimates easily on top of more complex hardware. This can only be performed when Time Randomisation is introduced in the microarchitectural design, e.g. namely through time-randomised cache designs. Despite their better WCET compared to conventional caches, time-randomised caches provide lower average performance. In this work, we perform the following contributions. First, we add MBPTA support in a commercial RISC-V processor for real-time systems. We show how branch prediction can become MBPTA-compliant, and in conjunction with a cache-locking mechanism can increase the front-end performance of an MBPTA compliant processor. Our evaluation demonstrates benefits both in terms of average case as well

The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators to manage their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real-time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then, we present a case study that leverages a ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks., This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020- 118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund., Peer Reviewed, Postprint (author's final draft)

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Edge computing requires the processing of real-time and personalized information with low power consumption. Neuromorphic devices are promising candidates for applications related to edge computing. Rate neurons, which are typically used in neuromorphic hardware, persistently consume power regardless of their outputs. To further reduce the power consumption of neuromorphic devices, spiking neurons are more suitable because they are event-driven, and information is transferred only when the neuron fires. Herein, we propose a two-variable spiking neuron circuit that operates in a fully analog manner by utilizing the physical properties of transistors as analog devices. By operating in the subthreshold region of the MOS transistor, the energy required to produce a spike is approximately tens of fJ/spike. Furthermore, the analog neuron can exhibit complex spike dynamics, such as chattering, as confirmed using post-layout simulations. The simulations indicated that a neural network comprising the proposed neuron circuits operates successfully and exhibits complex nonlinear behavior. These results provide a basis for dedicated hardware spiking neuron circuits, which could be used as ultra-low-power neuromorphic hardware in various applications, such as realizing liquid-state machines for processing time-series signals. © 2022 IEEE., Peer Reviewed, Postprint (published version)

OpenMP can be used in real-time applications to enhance system performance. However, predictability of OpenMP applications is still a challenge. This paper investigates heuristics for the mapping of OpenMP task graphs in underlying threads, for the development of time-predictable OpenMP programs. These approaches are based on a global scheduling queue, as well as per-thread allocation queues. The proposed method is divided into scheduling and allocation phases. In the former phase, OpenMP task-parts are discovered from OpenMP graph and placed in the scheduling queue. Afterwards, an appropriate allocation queue is selected for each task-part using four heuristic algorithms. In the latter phase, the best task-part is selected from the allocation queue to be allocated to and executed by an idle thread. Preliminary simulation results show that the new method overcomes BFS and WFS in terms of scheduling time and idle time., This work has been co-funded by the European commission through the AMPERE (H2020 grant agreement N° 745601) project., Peer Reviewed, Postprint (author's final draft)

The purpose of this project is the development of an intrusion detection framework for Internet of Things (IoT) networks. It consists on three parts. The first one is the development of a state of the art which includes a variety of datasets that were used and/or are used in the field of Deep Learning (DL) to detect cyber-attacks in Internet of Things (IoT) networks. It also covers the development of a Deep Learning (DL)-based IDS model trained with some datasets (varying the data preprocessing). Finally, studying the usage of data augmentation techniques to improve attacks classification (due to IoT datasets are scarce and unbalanced, there are families of attacks that have few registries)., El objetivo de este proyecto es el desarrollo de un framework de detección de intrusiones para redes Internet of Things (IoT). Consiste en tres partes. La primera es el desarrollo de un state of the art que incluye una variedad de conjuntos de datos que se utilizaron y/o se utilizan en el campo del Deep Learning (DL) que detecten ciberataques en redes Internet of Things (IoT). También cubre el desarrollo de un modelo Deep Learning (DL)-based IDS entrenado con algunos conjuntos de datos (variando el preprocesado de los mismos). Finalmente, estudiar el uso de técnicas de aumento de datos para mejorar la clasificación de los ataques (debido a que los conjuntos de datos de IoT son escasos y desequilibrados, hay familias de ataques que tienen pocos registros)., L'objectiu d'aquest projecte és el desenvolupament d'un framework de detecció d'intrusions per a xarxes Internet of Things (IoT). Consisteix en tres parts. La primera és el desenvolupament d'un state of the art que inclou una varietat de conjunts de dades que es van utilitzar i/o s'utilitzen al camp del Deep Learning (DL) que detectin ciberatacs en xarxes Internet of Things (IoT). També cobreix el desenvolupament d'un model Deep Learning (DL)-based IDS entrenat amb alguns conjunts de dades (variant el preprocessament d'aquets). Finalment, estudiar l'ús de tècniques d'augment de dades per millorar la classificació dels atacs (atès que els conjunts de dades d'IoT s'on escassos i desequilibrats, hi ha famílies d'atacs que tenen pocs registres).

Embedded systems in critical domains, such as auto-motive, aviation, space domains, are often required to guarantee both functional and temporal correctness. Considering transient faults, fault analysis and mitigation approaches are implemented at various levels of the system design, in order to maintain the functional correctness. However, transient faults and their mitigation methods have a timing impact, which can affect the temporal correctness of the system. In this work, we expose the functional and the timing implications of transient faults for critical systems. More precisely, we initially highlight the timing effect of transient faults occurring in the combinational and sequential logic of a processor. Furthermore, we propose a full stack vulnerability analysis that drives the design of selective hardware-based mitigation for real-time applications. Last, we study the timing impact of software-based reliability mitigation methods applied in a COTS GPU, using a fault tolerant middleware., This work has been partially funded by ANR-FASY (ANR-21-CE25-0008-01) and received funding by ESA through the 4000136514/21/NL/GLC/my co-funded PhD activity ”Mixed Software/Hardware-based Fault-tolerance Techniques for Complex COTS System-on-Chip in Radiation Environments” and the GPU4S (GPU for Space) project. Moreover, it was partially supported by the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB-C21 and IJC2020-045931-I (Spanish State Research Agency / http://dx.doi.org/10.13039/501100011033), by the European Union’s Horizon 2020 grant agreement No 739551 (KIOS CoE) and from the Government of the Republic of Cyprus through the Cyprus Deputy Ministry of Research, Innovation and Digital Policy., Peer Reviewed, Postprint (author's final draft)

Researchers have shown the limitations of using the single-modal data stream for emotion classification. Multi-modal data streams are therefore deemed necessary to improve the accuracy and performance of online emotion classifiers. An online decision ensemble is a widely used approach to classify emotions in real-time using multi-modal data streams. There is a plethora of online ensemble approaches; these approaches use a fixed parameter(ß) to adjust the weights of each classifier (called penalty) in case of wrong classification and no reward for a good performing classifier. Also, the performance of the ensemble depends on the ß, which is set using trial and error. This paper presents a new Reward Penaltybased Weighted Ensemble (RPWE) for real-time multi-modal emotion classification using multi-modal physiological data streams. The proposed RPWE is thoroughly tested using two prevalent benchmark data sets, DEAP and AMIGOS. The first experiment confirms the impact of the base stream classifier with RPWE for emotion classification in real-time. The RPWE is compared with different popular and widely used online ensemble approaches using multi-modal data streams in the second experiment. The average balanced accuracy, F1-score results showed the usefulness and robustness of RPWE in emotion classification in real-time from the multi-modal data stream., Arijit Nandi is a fellow of Eurecat’s “Vicente López” PhD grant program. This study has been partially funded by ACCIO, Spain (Pla d’Actuació de Centres Tecnològics 2021) under the project TutorIA., Peer Reviewed, Postprint (author's final draft)

Este proyecto ha surgido a través de las prácticas de empresa realizadas con Tecnocim Innova, empresa dedicada a la consultoría de proyectos de Industria 4.0 y de I+D+i en apoyo a las pequeñas y medianas empresas (PYME) españolas. Se trata de un estudio para la implantación de un Gemelo Digital en el que se ha realizado una consultoría a la empresa participante, Sensomec. Este proyecto se ha realizado a raíz de la obtención de una subvención pública de la Generalitat de Catalunya. Un Gemelo Digital es una representación virtual de un producto, servicio o proceso que simula el comportamiento de un sistema homólogo físico, con el objetivo de monitorizarlo, analizar su comportamiento ante determinadas situaciones y mejorar su rendimiento y eficacia. En este documento se plantea el problema detectado junto con las necesidades de la empresa, y los beneficios que pueden aportar la implantación del Gemelo Digital. Se ha realizado una investigación del estado del arte de esta tecnología perteneciente a la Industria 4.0, y se ha hecho una introducción de las PYME del sector industrial. Incluye una serie de análisis realizados en la consultoría ofrecida a Sensomec y se ha complementado con información más detallada por motivos académicos. El análisis organizativo estudia el grado de madurez digital y de innovación que tiene la empresa mediante encuestas y un análisis DAFO. El análisis técnico describe más en detalle las características del Gemelo Digital implantado por el equipo técnico de Software Tècnic Tecnocim, y se realiza una comparativa tanto de las alternativas técnicas que existen como del parque de maquinaria de la empresa. Finalmente se decide hacer uso del software TopSolid para virtualizar la mitad de las máquinas de mecanizado del taller. En el análisis económico se estudia la viabilidad económica del proyecto en base al coste inicial y el ahorro que se obtiene gracias a la implantación del Gemelo Digital. El análisis social pretende asesorar dando herramientas

This paper presents the end-to-end QoS approach to provide performance guarantees followed in the SELENEplatform, a high-performance RISC-V based heterogeneous SoC for safety-related real-time systems. Our QoS approach includes smart interconnect solutions for buses and NoCs, along with multicore interference-aware statistics units to, cooperatively, achieve end-to-end QoS., This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 871467. BSC work has also been partially supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GB-C21/AEI/10.13039/501100011033., Peer Reviewed, Postprint (published version)

Que fa la teva mascota quan no ets a casa? Amb tècniques de l'estat de l'art en detecció d'objectes i reconeixement d'objectes, un sistema de vigilància capaç de detectar mascotes és possible de fer. No tan sols això, sinó que fent servir altres tècniques de visió per computador, també seria possible l'anàlisi del comportament de mascotes. La finalitat d'aquest projecte és el de desenvolupar un sistema de visió per computador capaç de detectar comportaments com menjar, beure, descansar i moure's. A més a més, l'objectiu és poder fer aquesta detecció en temps real., What does your pet do when you are not home? With the state-of-the-art on object detection and object recognition a surveillance system capable of detecting pet would be possible. Furthermore, with several other computer vision techniques, not only pet detection, but also pet behaviour analysis is also possible. The aim of this project is to develop a computer vision system capable of detecting pet behaviour such as eating, drinking, resting and moving. Moreover, the objective is to do this detection in real-time.

Deriving WCET estimates for software programs with probabilistic means (a.k.a. pWCET estimation) has received significant attention during last years as a way to deal with the increased complexity of the processors used in real-time systems. Many works build on Extreme Value Theory (EVT) that is fed with a sample of the collected data (execution times). In its application, EVT carries two sources of uncertainty: the first one that is intrinsic to the EVT model and relates to determining the subset of the sample that belongs to the (upper) tail, and hence, is actually used by EVT for prediction; and the second one that is induced by the sampling process and hence is inherent to all sample-based methods. In this work, we show that Markov’s inequality can be used to obtain provable trustworthy probabilistic bounds to the tail of a distribution without incurring any model-intrinsic uncertainty. Yet, it produces pessimistic estimates that we shave substantially by proposing the use of a power-of-k function instead of the default identity function used by Markov’s inequality. Lastly, we propose a method to deal with sampling uncertainty for Markov’s inequality that consistently improves EVT estimates on synthetic and real data obtained from a railway application., This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant PID2019-110854RB-I00 / AEI / 10.13039/501100011033 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773)., Peer Reviewed, Postprint (published version)

The H2020 EIC-FTI De-RISC project develops a RISC-V space-grade platform to jointly respond to several emerging, as well as longstanding needs in the space domain such as: (1) higher performance than that of monocore and basic multicore space-grade processors in the market; (2) access to an increasingly rich software ecosystem rather than sticking to the slowly fading SPARC and PowerPC-based ones; (3) freedom (or drastic reduction) of export and license restrictions imposed by commercial ISAs such as Arm; and (4) improved support for the design and validation of safety-related real-time applications, (5) being the platform with software qualified and hardware designed per established space industry standards. De-RISC partners have set up the different layers of the platform during the first phases of the project. However, they have recently boosted integration and assessment activities. This paper introduces the De-RISC space platform, presents recent progress such as enabling virtualization and software qualification, new MPSoC features, and use case deployment and evaluation, including a comparison against other commercial platforms. Finally, this paper introduces the ongoing activities that will lead to the hardware and fully qualified software platform at TRL8 on FPGA by September 2022., This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement EIC-FTI 869945. BSC work has also been partially supported by the Spanish Ministry of Science and Innovation under grant PID2019-07255GBC21/AEI/10.13039/501100011033., Peer Reviewed, Postprint (author's final draft)

Emotional and physical health are strongly connected and should be taken care of simultaneously to ensure completely healthy persons. A person’s emotional health can be determined by detecting emotional states from various physiological measurements (EDA, RB, EEG, etc.). Affective Computing has become the field of interest, which uses software and hardware to detect emotional states. In the IoT era, wearable sensor-based real-time multi-modal emotion state classification has become one of the hottest topics. In such setting, a data stream is generated from wearable-sensor devices, data accessibility is restricted to those devices only and usually a high data generation rate should be processed to achieve real-time emotion state responses. Additionally, protecting the users’ data privacy makes the processing of such data even more challenging. Traditional classifiers have limitations to achieve high accuracy of emotional state detection under demanding requirements of decentralized data and protecting users’ privacy of sensitive information as such classifiers need to see all data. Here comes the federated learning, whose main idea is to create a global classifier without accessing the users’ local data. Therefore, we have developed a federated learning framework for real-time emotion state classification using multi-modal physiological data streams from wearable sensors, called Fed-ReMECS. The main findings of our Fed-ReMECS framework are the development of an efficient and scalable real-time emotion classification system from distributed multimodal physiological data streams, where the global classifier is built without accessing (privacy protection) the users’ data in an IoT environment. The experimental study is conducted using the popularly used multi-modal benchmark DEAP dataset for emotion classification. The results show the effectiveness of our developed approach in terms of accuracy, efficiency, scalability and users’ data privacy protection., Arijit Nandi is a fellow of Eurecat’s “Vicente López” PhD grant program. This study has been partially funded by ACCIÓ Spain (Pla d’Actuació de Centres Tecnològics 2021) under the project TutorIA., Peer Reviewed, Postprint (author's final draft)

This article presents a method for determining near-Pole ionization depletion regions and troughs from global navigation satellite system (GNSS) vertical total electron content (VTEC) maps. To define the regions, we use Image processing tools, considering the VTEC maps as gray-level images. In this work, watershed segmentation is applied to determine the edges of regions, based on the flow of areas between the lowest ionization zones (<1%) and the medium ionization regions (>10%). In order to enhance the contrast between low-ionization regions and those with a higher relative ionization, the histogram of the maps is equalized, which increases the contrast of the VTEC maps, which is the distance between low-ionization regions and those with a higher relative ionization are increased. The use of percentile thresholds makes this method independent of hypotheses about the generation of depletions, their morphology, or the number of depletion regions that can occur on a map. We test the performance by comparing the depletion regions identified in eight events reported in the literature under different conditions of the ionosphere. The events used as reference were measured by satellites, radar, and GNSS. The method that we propose allows for real-time processing of GNSS VTEC maps. The morphology of the detected regions matches the shape that is reported in the literature., This work was supported in part by Ministerio de Ciencia e Inovación (MICINN) under Project PID2019-107579RB-I00, and in part by the European Commission (EC)-Funded Activity PolarGIMs2., Peer Reviewed, Postprint (author's final draft)

The number of cameras deployed to the edge of the network increases by the day, while emerging use cases, such as smart cities or autonomous driving, also grow to expect images to be analyzed in real-time by increasingly accurate and complex neural networks. Unfortunately, state-of-the-art accuracy comes at a computational cost rarely available in the edge cloud. At the same time, due to strict latency constraints and the vast amount of bandwidth edge cameras generate, we can no longer rely on offloading the task to a centralized cloud. Consequently, there is a need for a meeting point between the resource-constrained edge cloud and accurate real-time video analytics. If state-of-the-art models are too expensive to run on the edge, and lightweight models are not accurate enough for the use cases in the edge, one solution is to demand less from the lightweight model and specialize it in a narrower scope of the problem, a technique known as model specialization. By specializing a model to the context of a single camera, we can boost its accuracy while keeping its computational cost constant. However, this also involves one training per camera, which quickly becomes unfeasible unless the entire process is fully automated. In this paper, we present and evaluate COVA (Contextually Optimized Video Analytics), a framework to assist in the automatic specialization of models for video analytics in edge cloud cameras. COVA aims to automatically improve the accuracy of lightweight models by specializing them to the context to which they will be deployed. Moreover, we discuss and analyze each step involved in the process to understand the different trade-offs that each one entails. Using COVA, we demonstrate that the whole pipeline can be effectively automated by leveraging large neural networks used as teachers whose predictions are used to train and specialize lightweight neural networks. Results show that COVA can automatically improve pre-trained models by an average of 21%, This work has been partially supported by the Spanish Government (contract PID2019-107255GB) and by Generalitat de Catalunya, Spain (contract 2014-SGR-1051)., Peer Reviewed, Postprint (published version)

The rising interest in extracting value from data has led to a broad proliferation of monitoring infrastructures, most notably composed by sensors, intended to collect this new oil. Thus, gathering data has become fundamental for a great number of applications, such as predictive maintenance techniques or anomaly detection algorithms. However, before data can be refined into insights and knowledge, it has to be efficiently stored and prepared for its later retrieval. As a consequence of this sensor and IoT boom, Time-Series databases (TSDB), designed to manage sensor data, became the fastest-growing database category since 2019. Here we propose a holistic approach intended to improve TSDB’s performance and efficiency. More precisely, we introduce and evaluate a novel polyglot-based approximation, aimed to tailor the data store, not only to time-series data –as it is done conventionally– but also to the data flow itself: From its ingestion, until its retrieval. In order to evaluate the approach, we materialize it in an alternative implementation of NagareDB, a resource-efficient time-series database, based on MongoDB, in turn, the most popular NoSQL storage solution. After implementing our approach into the database, we observe a global speed up, solving queries up to 12 times faster than MongoDB’s recently launched Time-series capability, as well as generally outperforming InfluxDB, the most popular time-series database. Our polyglot-based data-flow aware solution can ingest data more than two times faster than MongoDB, InfluxDB, and NagareDB’s original implementation, while using the same disk space as InfluxDB, and half of the requested by MongoDB., This research was partly supported by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB) and by the Generalitat de Catalunya (contract 2017-SGR-1414)., Peer Reviewed, Postprint (published version)