Multi-technology RF fingerprinting with leaky-feeder in underground tunnels

Techniques using RSS fingerprinting for localization have been studied over a number of ifferent technologies in many different scenarios. In the case of underground tunnels localization can be quite challenging, yet it is extremely important for safety reasons. In the specific case of the CERN tunnels, accurate and automatized localization methods would additionally allow the orkflow of some activities to become substantially faster. In a radiation area this would also have the added benefit of reducing the exposure time of personnel conducting so called radiation surveys which have to be carried out before access can be granted. In this paper Fingerprinting techniques for GSM and Wireless LAN are studied and enhanced to ake advantage of both network technologies simultaneously as well as the channels RSS differential and an observed effect in the radiated power in the leaky-feeder cables. Besides the higher accuracy achieved for a single technology, this methodology looks promising for scenarios where several types of wireless networks are available or expected to be installed at a later stage

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Deep Learning Methods for Fingerprint-Based Indoor and Outdoor Positioning

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold: First, a Convolutional Neural Network (CNN)-based method for localizing a smartwatch indoors using geomagnetic field measurements is presented. The proposed method was tested on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.14 meter (m). Second, a method that makes use of cellular signals emitting from a serving eNodeB to provide symbolic indoor positioning is presented. The proposed method utilizes Denoising Autoencoders (DAEs) to mitigate the effects of cellular signal loss. The proposed method was evaluated using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. Third, an investigation is conducted to determine whether Variational Autoencoders (VAEs) and Conditional Variational Autoencoders (CVAEs) are able to learn the distribution of the minority symbolic spaces, for a highly imbalanced fingerprinting dataset, so as to generate synthetic fingerprints that promote enhancements in a classifier\u27s performance. Experimental results show that this is indeed the case. By using various performance evaluation metrics, the achieved results are compared to those obtained by two state-of-the-art oversampling methods known as Synthetic Minority Oversampling TEchnique (SMOTE) and ADAptive SYNthetic (ADASYN) sampling. Fourth, a novel dataset of outdoor location fingerprints is presented. The proposed dataset, named OutFin, addresses the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions which can constitute a high entry barrier for studies. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 Reference Points (RPs). Before OutFin was made available to the public, several experiments were conducted to validate its technical quality

The always best positioned paradigm for mobile indoor applications

In this dissertation, methods for personal positioning in outdoor and indoor environments are investigated. The Always Best Positioned paradigm, which has the goal of providing a preferably consistent self-positioning, will be defined. Furthermore, the localization toolkit LOCATO will be presented, which allows to easily realize positioning systems that follow the paradigm. New algorithms were developed, which particularly address the robustness of positioning systems with respect to the Always Best Positioned paradigm. With the help of this toolkit, three example positioning-systems were implemented, each designed for different applications and requirements: a low-cost system, which can be used in conjunction with user-adaptive public displays, a so-called opportunistic system, which enables positioning with room-level accuracy in any building that provides a WiFi infrastructure, and a high-accuracy system for instrumented environments, which works with active RFID tags and infrared beacons. Furthermore, a new and unique evaluation-method for positioning systems is presented, which uses step-accurate natural walking-traces as ground truth. Finally, six location based services will be presented, which were realized either with the tools provided by LOCATO or with one of the example positioning-systems.In dieser Doktorarbeit werden Methoden zur Personenpositionierung im Innen- und Außenbereich von Gebäuden untersucht. Es wird das ,,Always Best Positioned” Paradigma definiert, welches eine möglichst lückenlose Selbstpositionierung zum Ziel hat. Weiterhin wird die Lokalisierungsplattform LOCATO vorgestellt, welche eine einfache Umsetzung von Positionierungssystemen ermöglicht. Hierzu wurden neue Algorithmen entwickelt, welche gezielt die Robustheit von Positionierungssystemen unter Berücksichtigung des ,,Always Best Positioned” Paradigmas angehen. Mit Hilfe dieser Plattform wurden drei Beispiel Positionierungssysteme entwickelt, welche unterschiedliche Einsatzgebiete berücksichtigen: Ein kostengünstiges System, das im Zusammenhang mit benutzeradaptiven öffentlichen Bildschirmen benutzt werden kann; ein sogenanntes opportunistisches Positionierungssystem, welches eine raumgenaue Positionierung in allen Gebäuden mit WLAN-Infrastruktur ermöglicht, sowie ein metergenaues Positionierungssystem, welches mit Hilfe einer Instrumentierung aus aktiven RFID-Tags und Infrarot-Baken arbeitet. Weiterhin wird erstmalig eine Positionierungsevaluation vorgestellt, welche schrittgenaue, natürliche Bewegungspfade als Referenzsystem einsetzt. Im Abschluss werden 6 lokationsbasierte Dienste vorgestellt, welche entweder mit Hilfe von LOCATO oder mit Hilfe einer der drei Beispiel-Positionierungssysteme entwickelt wurden

Planification visuelle et interactive d'interventions dans des environnements d'accélérateur de particules émettant des rayonnements ionisants

Les radiations sont omniprésentes. Elles ont de nombreuses applications dans des domaines variés: en médecine, elles permettent de réaliser des diagnostiques et de guérir des patients; en communication, tous les systèmes modernes utilisent des formes de rayonnements électromagnétiques; et en science, les chercheurs les utilisent pour découvrir la composition et la structure des matériaux, pour n'en nommer que quelques-unes. Concrètement, la radiation est un processus au cours duquel des particules ou des ondes voyagent à travers différents types de matériaux. La radiation peut être très énergétique, et aller jusqu'à casser les atomes de la matière ordinaire. Dans ce cas, on parlera de radiation ionisante. Il est communément admis que la radiation ionisante peut être bien plus nocif pour les êtres vivants que la radiation non ionisante. Dans cette dissertation, nous traiterons de la radiation ionisante. La radioactivité est le processus d'émission des radiations ionisantes. Elle existe sous forme naturelle, et est présente dans les sols, dans l'air et notre planète entière est bombardée en permanence de rayonnements cosmiques énergétiques. Depuis le début du XXe siècle, les chercheurs sont capables de créer artificiellement de la matière radioactive. Cette découverte a offert de multiples avancées technologiques, mais a eu également de lourdes conséquences pour l'humanité comme l'ont démontrés les évènements de Tchernobyl et de Fukushima ou d'autres accidents dans le monde médical. Cette dangerosité a conduit à l'élaboration d'un système de radioprotection. Dans la pratique, la radioprotection est principalement mise en œuvre en utilisant la méthode ALARA. Cette méthodologie consiste à justifier, optimiser et limiter les doses reçues. Elle est utilisée conjointement avec les limites légales. Le facteur d'optimisation est contraint par le fait que l'exposition volontaire d'un travailleur aux radiations lors d'une opération doit être plus bénéfique que si aucune intervention humaine n'était conduite dans une situation donnée. Dans le monde industriel et scientifique, il existe des infrastructures qui émettent des rayonnements ionisants. La plupart d'entre elles nécessitent des opérations de maintenance. Dans l'esprit du principe ALARA, ces interventions doivent être optimisées pour réduire l'exposition des travailleurs aux rayonnements ionisants. Cette optimisation ne peut pas être réalisée de manière automatique car la faisabilité des interventions nécessite dans tous les cas une évaluation humaine. La planification des interventions peut cependant être facilitée par des moyens techniques et scientifiques comme par exemple un outil informatique. Dans le contexte décrit ci-dessus, cette thèse regroupe des considérations techniques et scientifiques, et présente la méthodologie utilisée pour développer des outils logiciels pour la mise en œuvre de la radioprotection.Radiation is omnipresent. It has many interesting applications: in medicine, where it allows curing and diagnosing patients; in communication, where modern communication systems make use of electromagnetic radiation; and in science, where it is used to discover the structure of materials; to name a few. Physically, radiation is a process in which particles or waves travel through any kind of material, usually air. Radiation can be very energetic, in which case it can break the atoms of ordinary matter (ionization). If this is the case, radiation is called ionizing. It is known that ionizing radiation can be far more harmful to living beings than non-ionizing radiation. In this dissertation, we are concerned with ionizing radiation. Naturally occurring ionizing radiation in the form of radioactivity is a most natural phenomenon. Almost everything is radioactive: there is radiation emerging from the soil, it is in the air, and the whole planet is constantly undergoing streams of energetic cosmic radiation. Since the beginning of the twentieth century, we are also able to artificially create radio-active matter. This has opened a lot of interesting technological opportunities, but has also given a tremendous responsibility to humanity, as the nuclear accidents in Chernobyl and Fukushima, and various accidents in the medical world have made clear. This has led to the elaboration of a radiological protection system. In practice, the radiological protection system is mostly implemented using a methodology that is indicated with the acronym ALARA: As Low As Reasonably Achievable. This methodology consists of justifying, optimizing and limiting the radiation dose received. This methodology is applied in conjunction with the legal limits. The word "reasonably" means that the optimization of radiation exposure has to be seen in context. The optimization is constrained by the fact that the positive effects of an operation might surpass the negative effects caused by the radiation. Several industrial and scientific procedures give rise to facilities with ionizing radiation. Most technical and scientific facilities also need maintenance operations. In the spirit of ALARA, these interventions need to be optimized in terms of the exposure of the maintenace workers to ionizing radiation. This optimization cannot be automated since the feasibility of the intervention tasks requires human assessment. The intervention planning could however be facilitated by technical-scientific means, e.g. software tools. In the context sketched above, this thesis provides technical-scientific considerations and the development of technical-scientific methodologies and software tools for the implementation of radiation protection.In particular, this thesis addresses the need for an interactive visual intervention planning tool in the context of high energy particle accelerator facilities.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav