475 research outputs found
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
Proximity detection protocols for IoT devices
In recent years, we have witnessed the growth of the Internet of Things paradigm, with its increased pervasiveness in our everyday lives. The
possible applications are diverse: from a smartwatch
able to measure heartbeat and communicate it to the cloud, to the device that triggers an event when we approach an exhibit in a museum. Present in many of these applications is the Proximity Detection task: for instance the heartbeat could be measured only when the wearer is near to a well defined location for medical purposes or the touristic attraction must be triggered only if someone is very close to it. Indeed, the ability of an IoT device to sense the presence of other devices nearby and calculate the distance to them can be considered the cornerstone of various applications, motivating research on this fundamental
topic. The energy constraints of the IoT devices are often in contrast with the needs of continuous operations to sense the environment and to achieve high accurate distance measurements from the neighbors, thus making the design of Proximity Detection protocols a challenging task
Analysis and Design of Algorithms for the Improvement of Non-coherent Massive MIMO based on DMPSK for beyond 5G systems
Mención Internacional en el título de doctorNowadays, it is nearly impossible to think of a service that does not rely on wireless communications.
By the end of 2022, mobile internet represented a 60% of the total global online traffic.
There is an increasing trend both in the number of subscribers and in the traffic handled by each
subscriber. Larger data rates, smaller extreme-to-extreme (E2E) delays and greater number of
devices are current interests for the development of mobile communications. Furthermore, it
is foreseen that these demands should also be fulfilled in scenarios with stringent conditions,
such as very fast varying wireless communications channels (either in time or frequency) or
scenarios with power constraints, mainly found when the equipment is battery powered.
Since most of the wireless communications techniques and standards rely on the fact that the
wireless channel is somehow characterized or estimated to be pre or post-compensated in transmission
(TX) or reception (RX), there is a clear problem when the channels vary rapidly or the
available power is constrained. To estimate the wireless channel and obtain the so-called channel
state information (CSI), some of the available resources (either in time, frequency or any
other dimension), are utilized by including known signals in the TX and RX typically known as
pilots, thus avoiding their use for data transmission. If the channels vary rapidly, they must be
estimated many times, which results in a very low data efficiency of the communications link.
Also, in case the power is limited or the wireless link distance is large, the resulting signal-tointerference-
plus-noise ratio (SINR) will be low, which is a parameter that is directly related to
the quality of the channel estimation and the performance of the data reception. This problem
is aggravated in massive multiple-input multiple-output (massive MIMO), which is a promising
technique for future wireless communications since it can increase the data rates, increase the
reliability and cope with a larger number of simultaneous devices. In massive MIMO, the base
station (BS) is typically equipped with a large number of antennas that are coordinated. In these
scenarios, the channels must be estimated for each antenna (or at least for each user), and thus,
the aforementioned problem of channel estimation aggravates. In this context, algorithms and
techniques for massive MIMO without CSI are of interest.
This thesis main topic is non-coherent massive multiple-input multiple-output (NC-mMIMO)
which relies on the use of differential M-ary phase shift keying (DMPSK) and the spatial
diversity of the antenna arrays to be able to detect the useful transmitted data without CSI knowledge. On the one hand, hybrid schemes that combine the coherent and non-coherent
schemes allowing to get the best of both worlds are proposed. These schemes are based on
distributing the resources between non-coherent (NC) and coherent data, utilizing the NC data
to estimate the channel without using pilots and use the estimated channel for the coherent
data. On the other hand, new constellations and user allocation strategies for the multi-user
scenario of NC-mMIMO are proposed. The new constellations are better than the ones in the
literature and obtained using artificial intelligence techniques, more concretely evolutionary
computation.This work has received funding from the European Union Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No.
813391. The PhD student was the Early Stage Researcher (ESR) number 2 of the project.
This work has also received funding from the Spanish National Project IRENE-EARTH
(PID2020-115323RB-C33) (MINECO/AEI/FEDER, UE), which funded the work of some coauthors.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretario: Matilde Pilar Sánchez Fernández.- Vocal: Eva Lagunas Targaron
Mögliche gesundheitliche Auswirkungen verschiedener Frequenzbereiche elektromagnetischer Felder (HF-EMF). Endbericht zum TA-Projekt
Hochfrequente elektromagnetische Felder (HF-EMF) bilden die Grundlage aller digitalen, drahtlosen Kommunikation im gesamten öffentlichen Raum und in den privaten Haushalten. In den kommenden Jahren ist mit einer weiteren Zunahme von EMF-Quellen verschiedener Frequenzbereiche zu rechnen. Hauptgrund hierfür ist die rasant fortschreitende Digitalisierung nahezu aller Arbeits-, Lebens- und Wirtschaftsbereiche, die zugleich eng mit mobil zu nutzenden Technologien verbunden ist.
Vor diesem Hintergrund stellt der vorliegende Bericht den aktuellen Wissensstand zu möglichen gesundheitlichen Risiken elektromagnetischer Felder – insbesondere des Mobilfunks – dar. Dazu wurde die neuere internationale wissenschaftliche Literatur umfassend gesichtet und die Ergebnisse aktueller nationaler und internationaler Forschungsprojekten daraufhin analysiert, ob relevante neue Erkenntnisse vorliegen, die die Diskussionen zu möglichen gesundheitlichen Risiken der HF-EMF substanziell verändern könnten. Ein weiterer Schwerpunkt lag auf Forschungsbemühungen, die einen substanziellen Beitrag zur verbesserten Risikobewertung der Exposition von jungen Menschen leisten wollen.
Darüber hinaus diskutiert der Bericht relevante Aspekte der EMF-Risikogovernance (z.B. Öffentlichkeitsbeteiligung, Interessenkonflikte, Risikoinformation und -kommunikation) und beschreibt Optionen, wie im Kontext des EMF-Diskurses Barrieren für eine offene wechselseitige Kommunikation von Akteursgruppen – insbesondere zwischen Wissenschaft, Zivilgesellschaft und Politik – abgebaut werden können
Localizability Optimization for Multi Robot Systems and Applications to Ultra-Wide Band Positioning
RÉSUMÉ: RÉSUMÉ Les Systèmes Multi-Robots (SMR) permettent d’effectuer des missions de manière efficace et robuste du fait de leur redondance. Cependant, les robots étant des véhicules autonomes, ils nécessitent un positionnement précis en temps réel. Les techniques de localisation qui utilisent des Mesures Relatives (MR) entre les robots, pouvant être des distances ou des angles, sont particulièrement adaptées puisqu’elles peuvent bénéficier d’algorithmes coopératifs au sein du SMR afin d’améliorer la précision pour l’ensemble des robots. Dans cette thèse, nous proposons des stratégies pour améliorer la localisabilité des SMR, qui est fonction de deux facteurs. Premièrement, la géométrie du SMR influence fondamentalement la qualité de son positionnement pour des MR bruitées. Deuxièmement, les erreurs de mesures dépendent fortement de la technologie utilisée. Dans nos expériences, nous nous focalisons sur la technologie UWB (Ultra-Wide Band), qui est populaire pour le positionnement des robots en environnement intérieur en raison de son coût modéré et sa haute précision. Par conséquent, une partie de notre travail est consacrée à la correction des erreurs de mesure UWB afin de fournir un système de navigation opérationnel. En particulier, nous proposons une méthode de calibration des biais systématiques et un algorithme d’atténuation des trajets multiples pour les mesures de distance en milieu intérieur. Ensuite, nous proposons des Fonctions de Coût de Localisabilité (FCL) pour caractériser la géométrie du SMR, et sa capacité à se localiser. Pour cela, nous utilisons la Borne Inférieure de Cramér-Rao (BICR) en vue de quantifier les incertitudes de positionnement. Par la suite, nous fournissons des schémas d’optimisation décentralisés pour les FCL sous l’hypothèse de MR gaussiennes ou log-normales. En effet, puisque le SMR peut se déplacer, certains de ses robots peuvent être déployés afin de minimiser la FCL. Cependant, l’optimisation de la localisabilité doit être décentralisée pour être adaptée à des SMRs à grande échelle. Nous proposons également des extensions des FCL à des scénarios où les robots embarquent plusieurs capteurs, où les mesures se dégradent avec la distance, ou encore où des informations préalables sur la localisation des robots sont disponibles, permettant d’utiliser la BICR bayésienne. Ce dernier résultat est appliqué au placement d’ancres statiques connaissant la distribution statistique des MR et au maintien de la localisabilité des robots qui se localisent par filtrage de Kalman. Les contributions théoriques de notre travail ont été validées à la fois par des simulations à grande échelle et des expériences utilisant des SMR terrestres. Ce manuscrit est rédigé par publication, il est constitué de quatre articles évalués par des pairs et d’un chapitre supplémentaire. ABSTRACT: ABSTRACT Multi-Robot Systems (MRS) are increasingly interesting to perform tasks eÿciently and robustly. However, since the robots are autonomous vehicles, they require accurate real-time positioning. Localization techniques that use relative measurements (RMs), i.e., distances or angles, between the robots are particularly suitable because they can take advantage of cooperative schemes within the MRS in order to enhance the precision of its positioning. In this thesis, we propose strategies to improve the localizability of the SMR, which is a function of two factors. First, the geometry of the MRS fundamentally influences the quality of its positioning under noisy RMs. Second, the measurement errors are strongly influenced by the technology chosen to gather the RMs. In our experiments, we focus on the Ultra-Wide Band (UWB) technology, which is popular for indoor robot positioning because of its mod-erate cost and high accuracy. Therefore, one part of our work is dedicated to correcting the UWB measurement errors in order to provide an operable navigation system. In particular, we propose a calibration method for systematic biases and a multi-path mitigation algorithm for indoor distance measurements. Then, we propose Localizability Cost Functions (LCF) to characterize the MRS’s geometry, using the Cramér-Rao Lower Bound (CRLB) as a proxy to quantify the positioning uncertainties. Subsequently, we provide decentralized optimization schemes for the LCF under an assumption of Gaussian or Log-Normal RMs. Indeed, since the MRS can move, some of its robots can be deployed in order to decrease the LCF. However, the optimization of the localizability must be decentralized for large-scale MRS. We also propose extensions of LCFs to scenarios where robots carry multiple sensors, where the RMs deteriorate with distance, and finally, where prior information on the robots’ localization is available, allowing the use of the Bayesian CRLB. The latter result is applied to static anchor placement knowing the statistical distribution of the MRS and localizability maintenance of robots using Kalman filtering. The theoretical contributions of our work have been validated both through large-scale simulations and experiments using ground MRS. This manuscript is written by publication, it contains four peer-reviewed articles and an additional chapter
Cooperative Vehicle Perception and Localization Using Infrastructure-based Sensor Nodes
Reliable and accurate Perception and Localization (PL) are necessary for safe intelligent transportation
systems. The current vehicle-based PL techniques in autonomous vehicles are vulnerable to occlusion
and cluttering, especially in busy urban driving causing safety concerns. In order to avoid such safety
issues, researchers study infrastructure-based PL techniques to augment vehicle sensory systems.
Infrastructure-based PL methods rely on sensor nodes that each could include camera(s), Lidar(s),
radar(s), and computation and communication units for processing and transmitting the data. Vehicle
to Infrastructure (V2I) communication is used to access the sensor node processed data to be fused with
the onboard sensor data.
In infrastructure-based PL, signal-based techniques- in which sensors like Lidar are used- can provide
accurate positioning information while vision-based techniques can be used for classification.
Therefore, in order to take advantage of both approaches, cameras are cooperatively used with Lidar in
the infrastructure sensor node (ISN) in this thesis. ISNs have a wider field of view (FOV) and are less
likely to suffer from occlusion. Besides, they can provide more accurate measurements since they are
fixed at a known location. As such, the fusion of both onboard and ISN data has the potential to improve
the overall PL accuracy and reliability.
This thesis presents a framework for cooperative PL in autonomous vehicles (AVs) by fusing ISN
data with onboard sensor data. The ISN includes cameras and Lidar sensors, and the proposed camera Lidar fusion method combines the sensor node information with vehicle motion models and kinematic
constraints to improve the performance of PL. One of the main goals of this thesis is to develop a wind induced motion compensation module to address the problem of time-varying extrinsic parameters of
the ISNs. The proposed module compensates for the effect of the motion of ISN posts due to wind or
other external disturbances. To address this issue, an unknown input observer is developed that uses
the motion model of the light post as well as the sensor data.
The outputs of the ISN, the positions of all objects in the FOV, are then broadcast so that autonomous
vehicles can access the information via V2I connectivity to fuse with their onboard sensory data through
the proposed cooperative PL framework. In the developed framework, a KCF is implemented as a
distributed fusion method to fuse ISN data with onboard data. The introduced cooperative PL
incorporates the range-dependent accuracy of the ISN measurements into fusion to improve the overall
PL accuracy and reliability in different scenarios. The results show that using ISN data in addition to onboard sensor data improves the performance and reliability of PL in different scenarios, specifically
in occlusion cases
Využití softwarově definovaného rádia v oblasti SMART technologii
Modern telecommunication systems are rapidly evolving. This rapid development requires constant research and fast prototyping. This dissertation thesis focusses on deployment of software defined radio (SDR) in multiple application areas, including SMART technologies. SDR itself is a tool behind many breakthroughs in modern telecommunications, due to its major adaptability. It offers a comprehensive way of fast prototyping, which rely on suitable software platform. The field of telecommunications is ever-changing, due to the constant pressure on innovation. For this reason, it is desirable to test some of the alternative communication technologies. Visible light communication (VLC) system based on combination of virtual instrumentation and software defined radios was chosen for experimentation. This dissertation focusses on multiple versions of VLC system that were developed over the years. Each version is further discussed, and their advantages and disadvantages are presented. A draft of fourth and newest version is mentioned along with possible directions of the research. Results from multiple application areas are presented, which show the adaptability of the whole platform to different use cases including but not limited to: SMART technologies, automotive, nuclear waste disposal sites, or industry. It is demonstrated that the newest version of the system, which is based on OFDM modulation, can communicate up to 50 meters in closed environments and up to 35 meters in outdoor scenarios. This opens further research directions such as truck platooning or underwater communications.Moderní komunikační systémy jsou jednou z nejrychleji se rozvíjejících oblastí. Takového markantního posunu lze dosáhnout pouze skrze nový vývoj a aplikaci metodiky fast prototypingu. Tato disertace se zaměřuje na nasazení technologie softwarově definovaného rádia (SDR) v různých aplikačních oblastech. Samotné SDR je díky své adaptabilitě nástrojem, který stál na pozadí rozvoje mnoha moderních telekomunikačních systémů. Jedná se o ucelenou platformu pro fast prototyping, která se opírá o robustní softwarovou základnu. Právě telekomunikace jsou oblastí, kde je takové zařízení nedocenitelné, právě kvůli neustálému tlaku na inovace. Právě to je důvodem, proč je vhodné také testovat různé alternativní technologie pro přenos dat. Jednou z takových je komunikace viditelným spektrem světla (VLC), která je náplní této práce. Součástí praktické části je vývoj a popis několika verzí VLC systému založených na virtuální instrumentaci a SDR, které vznikly během autorova studia. Každá verze je samostatně popsána včetně výhod a nevýhod, které poskytují. Součástí je též první náčrt čtvrté verze, která bude součástí budoucího výzkumu. Prezentované výsledky z různých aplikačních oblastí jasně ukazují, že je celou platformu možné použít v různých aplikačních oblastech, včetně SMART technologií, automotive, úložišti jaderného odpadu anebo Průmyslu 4.0. Součástí jsou též výsledky z poslední verze, které dokazují, že je systém ve vnitřních prostorech komunikovat až na vzdálenost 50 metrů, zatímco ve venkovních podmínkách je to 35 metrů. Díky tomu je možné vytyčit nové oblasti výzkumu jako je například platooning (tandemová jízda) anebo podvodní komunikace.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II
The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above
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