Target Tracking Using Optical Markers for Remote Handling in ITER

The thesis focuses on the development of a vision system to be used in the remote handling systems of the International Thermonuclear Experimental Rector - ITER. It presents and discusses a realistic solution to estimate the pose of key operational targets, while taking into account the specific needs and restrictions of the application. The contributions to the state of the art are in two main fronts: 1) the development of optical markers that can withstand the extreme conditions in the environment; 2) the development of a robust marker detection and identification framework that can be effectively applied to different use cases. The markers’ locations and labels are used in computing the pose. In the first part of the work, a retro reflective marker made up ITER compliant materials, particularly, fused silica and stainless steel, is designed. A methodology is proposed to optimize the markers’ performance. Highly distinguishable markers are manufactured and tested. In the second part, a hybrid pipeline is proposed that detects uncoded markers in low resolution images using classical methods and identifies them using a machine learning approach. It is demonstrated that the proposed methodology effectively generalizes to different marker constellations and can successfully detect both retro reflective markers and laser engravings. Lastly, a methodology is developed to evaluate the end-to-end accuracy of the proposed solution using the feedback provided by an industrial robotic arm. Results are evaluated in a realistic test setup for two significantly different use cases. Results show that marker based tracking is a viable solution for the problem at hand and can provide superior performance to the earlier stereo matching based approaches. The developed solutions could be applied to other use cases and applications

Wearable sensors for human–robot walking together

Thanks to recent technological improvements that enable novel applications beyond the industrial context, there is growing interest in the use of robots in everyday life situations. To improve the acceptability of personal service robots, they should seamlessly interact with the users, understand their social signals and cues and respond appropriately. In this context, a few proposals were presented to make robots and humans navigate together naturally without explicit user control, but no final solution has been achieved yet. To make an advance toward this end, this paper proposes the use of wearable Inertial Measurement Units to improve the interaction between human and robot while walking together without physical links and with no restriction on the relative position between the human and the robot. We built a prototype system, experimented with 19 human participants in two different tasks, to provide real-time evaluation of gait parameters for a mobile robot moving together with a human, and studied the feasibility and the perceived usability by the participants. The results show the feasibility of the system, which obtained positive feedback from the users, giving valuable information for the development of a natural interaction system where the robot perceives human movements by means of wearable sensors

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Design, Fabrication and Testing of Tunable RF Meta-atoms

Metamaterials are engineered structures designed to alter the propagation of electromagnetic waves incident upon the structure. The focus of this research was the effect of metamaterials on electromagnetic signals at radio frequencies. RF meta-atoms were investigated to further develop the theory, modeling, design and fabrication of metamaterials. Comparing the analytic modeling and experimental testing, the results provide a deeper understanding into metamaterials which could lead to applications for beam steering, invisibility cloaking, negative refraction, super lenses, and hyper lenses. RF meta-atoms integrated with microelectromechanical systems produce tunable meta-atoms in the 10 - 15 GHz and 1 - 4 GHz frequency ranges. RF meta-atoms with structural design changes are developed to show how inductance changes based on structural modifications. RF meta-atoms integrated with gain medium are investigated showing that loss due to material characteristics can be compensated using active elements such as a Low Noise Amplifier. Integrating the amplifier into the split ring resonator causes a deeper null at the resonant frequency. The research results show that the resonant frequency can be tuned using microelectromechanical systems, or by induction with structural designs and reduce loss associated with the material conductivity by compensating with an active gain medium. Proposals that offer future research activities are discussed for inductance and active element meta-atoms. In addition, terahertz (THz), infrared (IR), and optical structures are briefly investigate

Design of an Electromagnetic Coil Array for Wireless Endoscope Capsule Localization

Wireless capsule endoscopy is a technique that visualizes mucosa of gastrointestinal tract. The first wireless capsule endoscope was developed in 2001, and since then, the tech-nology has been in constant development. With its reduced amount of discomfort, ability to visualize the whole gastrointestinal tract and many interesting future prospects, capsule en-doscopy is challenging the conventional procedure, in which a camera module at the end of a tube is inserted to the gastrointestinal tract. In general, the method can be used to diagnose many diseases, such as cancers, Celiac disease and Crohn’s disease. In order to achieve all the future prospects of the technology, for example, active steering of the capsule, biopsy and drug-delivery with the capsule, challenge of detecting the cap-sule’s position and orientation inside human needs to be overcome. Therefore, the localiza-tion challenge is considered in this thesis. The aim is to select an optimal method for localiza-tion based on current research, and to model and develop a prototype that could be utilized in capsule localization. The designed sensor array is evaluated with the help of finite element method -based modelling, sensitivity analysis and practical experiments. Based on the studied literature, an active magnetic field strength -based localization tech-nique was selected for further analysis. According to the literature, the method provides high accuracy and could also be utilized in other purposes, for example, wireless charging and ac-tive locomotion of the capsule. In addition, the method does not suffer from attenuation of the fields within a human body. Therefore, theoretical basis of the magnetic field strength -based localization was presented, and four electromagnetic coil arrays were designed for sensitivity analysis. Contrary to many developed arrays seen in the literature review, all the designed systems were planar, in order to develop a system that could be fitted, for instance, inside a hospital bed. Based on the sensitivity analysis, an array with relatively large sensitivity and optimal number of measurement channels was selected for practical study. In addition, pos-sible markers that could be fitted inside a regular sized endoscopic capsule were numerically modelled. It was found that a resonated solenoid marker with ferrite core causes the largest voltage compared to other modelled targets, and therefore it was constructed for experi-ments. The whole array was built and equipped with electronics and measurement devices to be able to perform testing. Two channels of the array were selected for example measurements, and the results were analysed and compared with modelled values and sensitivity patterns. The system was tested at multiple different heights and positions, and the effect of changing the marker’s orientation with respect to the array was analysed. It was found that the system gives a reasonable response when the marker is oriented along the excitation magnetic field. With this orientation, it was possible to measure significant voltages caused by the marker even at distance of 25 cm from the array. However, when the marker was oriented so that the excitation field could not properly excite it, the measured voltages got smaller. In addition, at certain orientation, the measured voltages did not seem reliable because voltage caused by the marker could not been discriminated from the noise of the system. The study indicates that the method is suitable for localization of resonated solenoid sample with ferrite core, but further improvements are needed to make the system work at each position and orientation of the marker. In addition, an inversion algorithm that estimates marker’s position and orien-tation based on the measured voltage needs to be integrated to the system

Satellite Communications

This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking

Grasp-sensitive surfaces

Grasping objects with our hands allows us to skillfully move and manipulate them. Hand-held tools further extend our capabilities by adapting precision, power, and shape of our hands to the task at hand. Some of these tools, such as mobile phones or computer mice, already incorporate information processing capabilities. Many other tools may be augmented with small, energy-efficient digital sensors and processors. This allows for graspable objects to learn about the user grasping them - and supporting the user's goals. For example, the way we grasp a mobile phone might indicate whether we want to take a photo or call a friend with it - and thus serve as a shortcut to that action. A power drill might sense whether the user is grasping it firmly enough and refuse to turn on if this is not the case. And a computer mouse could distinguish between intentional and unintentional movement and ignore the latter. This dissertation gives an overview of grasp sensing for human-computer interaction, focusing on technologies for building grasp-sensitive surfaces and challenges in designing grasp-sensitive user interfaces. It comprises three major contributions: a comprehensive review of existing research on human grasping and grasp sensing, a detailed description of three novel prototyping tools for grasp-sensitive surfaces, and a framework for analyzing and designing grasp interaction: For nearly a century, scientists have analyzed human grasping. My literature review gives an overview of definitions, classifications, and models of human grasping. A small number of studies have investigated grasping in everyday situations. They found a much greater diversity of grasps than described by existing taxonomies. This diversity makes it difficult to directly associate certain grasps with users' goals. In order to structure related work and own research, I formalize a generic workflow for grasp sensing. It comprises *capturing* of sensor values, *identifying* the associated grasp, and *interpreting* the meaning of the grasp. A comprehensive overview of related work shows that implementation of grasp-sensitive surfaces is still hard, researchers often are not aware of related work from other disciplines, and intuitive grasp interaction has not yet received much attention. In order to address the first issue, I developed three novel sensor technologies designed for grasp-sensitive surfaces. These mitigate one or more limitations of traditional sensing techniques: **HandSense** uses four strategically positioned capacitive sensors for detecting and classifying grasp patterns on mobile phones. The use of custom-built high-resolution sensors allows detecting proximity and avoids the need to cover the whole device surface with sensors. User tests showed a recognition rate of 81%, comparable to that of a system with 72 binary sensors. **FlyEye** uses optical fiber bundles connected to a camera for detecting touch and proximity on arbitrarily shaped surfaces. It allows rapid prototyping of touch- and grasp-sensitive objects and requires only very limited electronics knowledge. For FlyEye I developed a *relative calibration* algorithm that allows determining the locations of groups of sensors whose arrangement is not known. **TDRtouch** extends Time Domain Reflectometry (TDR), a technique traditionally used for inspecting cable faults, for touch and grasp sensing. TDRtouch is able to locate touches along a wire, allowing designers to rapidly prototype and implement modular, extremely thin, and flexible grasp-sensitive surfaces. I summarize how these technologies cater to different requirements and significantly expand the design space for grasp-sensitive objects. Furthermore, I discuss challenges for making sense of raw grasp information and categorize interactions. Traditional application scenarios for grasp sensing use only the grasp sensor's data, and only for mode-switching. I argue that data from grasp sensors is part of the general usage context and should be only used in combination with other context information. For analyzing and discussing the possible meanings of grasp types, I created the GRASP model. It describes five categories of influencing factors that determine how we grasp an object: *Goal* -- what we want to do with the object, *Relationship* -- what we know and feel about the object we want to grasp, *Anatomy* -- hand shape and learned movement patterns, *Setting* -- surrounding and environmental conditions, and *Properties* -- texture, shape, weight, and other intrinsics of the object I conclude the dissertation with a discussion of upcoming challenges in grasp sensing and grasp interaction, and provide suggestions for implementing robust and usable grasp interaction.Die Fähigkeit, Gegenstände mit unseren Händen zu greifen, erlaubt uns, diese vielfältig zu manipulieren. Werkzeuge erweitern unsere Fähigkeiten noch, indem sie Genauigkeit, Kraft und Form unserer Hände an die Aufgabe anpassen. Digitale Werkzeuge, beispielsweise Mobiltelefone oder Computermäuse, erlauben uns auch, die Fähigkeiten unseres Gehirns und unserer Sinnesorgane zu erweitern. Diese Geräte verfügen bereits über Sensoren und Recheneinheiten. Aber auch viele andere Werkzeuge und Objekte lassen sich mit winzigen, effizienten Sensoren und Recheneinheiten erweitern. Dies erlaubt greifbaren Objekten, mehr über den Benutzer zu erfahren, der sie greift - und ermöglicht es, ihn bei der Erreichung seines Ziels zu unterstützen. Zum Beispiel könnte die Art und Weise, in der wir ein Mobiltelefon halten, verraten, ob wir ein Foto aufnehmen oder einen Freund anrufen wollen - und damit als Shortcut für diese Aktionen dienen. Eine Bohrmaschine könnte erkennen, ob der Benutzer sie auch wirklich sicher hält und den Dienst verweigern, falls dem nicht so ist. Und eine Computermaus könnte zwischen absichtlichen und unabsichtlichen Mausbewegungen unterscheiden und letztere ignorieren. Diese Dissertation gibt einen Überblick über Grifferkennung (*grasp sensing*) für die Mensch-Maschine-Interaktion, mit einem Fokus auf Technologien zur Implementierung griffempfindlicher Oberflächen und auf Herausforderungen beim Design griffempfindlicher Benutzerschnittstellen. Sie umfasst drei primäre Beiträge zum wissenschaftlichen Forschungsstand: einen umfassenden Überblick über die bisherige Forschung zu menschlichem Greifen und Grifferkennung, eine detaillierte Beschreibung dreier neuer Prototyping-Werkzeuge für griffempfindliche Oberflächen und ein Framework für Analyse und Design von griff-basierter Interaktion (*grasp interaction*). Seit nahezu einem Jahrhundert erforschen Wissenschaftler menschliches Greifen. Mein Überblick über den Forschungsstand beschreibt Definitionen, Klassifikationen und Modelle menschlichen Greifens. In einigen wenigen Studien wurde bisher Greifen in alltäglichen Situationen untersucht. Diese fanden eine deutlich größere Diversität in den Griffmuster als in existierenden Taxonomien beschreibbar. Diese Diversität erschwert es, bestimmten Griffmustern eine Absicht des Benutzers zuzuordnen. Um verwandte Arbeiten und eigene Forschungsergebnisse zu strukturieren, formalisiere ich einen allgemeinen Ablauf der Grifferkennung. Dieser besteht aus dem *Erfassen* von Sensorwerten, der *Identifizierung* der damit verknüpften Griffe und der *Interpretation* der Bedeutung des Griffes. In einem umfassenden Überblick über verwandte Arbeiten zeige ich, dass die Implementierung von griffempfindlichen Oberflächen immer noch ein herausforderndes Problem ist, dass Forscher regelmäßig keine Ahnung von verwandten Arbeiten in benachbarten Forschungsfeldern haben, und dass intuitive Griffinteraktion bislang wenig Aufmerksamkeit erhalten hat. Um das erstgenannte Problem zu lösen, habe ich drei neuartige Sensortechniken für griffempfindliche Oberflächen entwickelt. Diese mindern jeweils eine oder mehrere Schwächen traditioneller Sensortechniken: **HandSense** verwendet vier strategisch positionierte kapazitive Sensoren um Griffmuster zu erkennen. Durch die Verwendung von selbst entwickelten, hochauflösenden Sensoren ist es möglich, schon die Annäherung an das Objekt zu erkennen. Außerdem muss nicht die komplette Oberfläche des Objekts mit Sensoren bedeckt werden. Benutzertests ergaben eine Erkennungsrate, die vergleichbar mit einem System mit 72 binären Sensoren ist. **FlyEye** verwendet Lichtwellenleiterbündel, die an eine Kamera angeschlossen werden, um Annäherung und Berührung auf beliebig geformten Oberflächen zu erkennen. Es ermöglicht auch Designern mit begrenzter Elektronikerfahrung das Rapid Prototyping von berührungs- und griffempfindlichen Objekten. Für FlyEye entwickelte ich einen *relative-calibration*-Algorithmus, der verwendet werden kann um Gruppen von Sensoren, deren Anordnung unbekannt ist, semi-automatisch anzuordnen. **TDRtouch** erweitert Time Domain Reflectometry (TDR), eine Technik die üblicherweise zur Analyse von Kabelbeschädigungen eingesetzt wird. TDRtouch erlaubt es, Berührungen entlang eines Drahtes zu lokalisieren. Dies ermöglicht es, schnell modulare, extrem dünne und flexible griffempfindliche Oberflächen zu entwickeln. Ich beschreibe, wie diese Techniken verschiedene Anforderungen erfüllen und den *design space* für griffempfindliche Objekte deutlich erweitern. Desweiteren bespreche ich die Herausforderungen beim Verstehen von Griffinformationen und stelle eine Einteilung von Interaktionsmöglichkeiten vor. Bisherige Anwendungsbeispiele für die Grifferkennung nutzen nur Daten der Griffsensoren und beschränken sich auf Moduswechsel. Ich argumentiere, dass diese Sensordaten Teil des allgemeinen Benutzungskontexts sind und nur in Kombination mit anderer Kontextinformation verwendet werden sollten. Um die möglichen Bedeutungen von Griffarten analysieren und diskutieren zu können, entwickelte ich das GRASP-Modell. Dieses beschreibt fünf Kategorien von Einflussfaktoren, die bestimmen wie wir ein Objekt greifen: *Goal* -- das Ziel, das wir mit dem Griff erreichen wollen, *Relationship* -- das Verhältnis zum Objekt, *Anatomy* -- Handform und Bewegungsmuster, *Setting* -- Umgebungsfaktoren und *Properties* -- Eigenschaften des Objekts, wie Oberflächenbeschaffenheit, Form oder Gewicht. Ich schließe mit einer Besprechung neuer Herausforderungen bei der Grifferkennung und Griffinteraktion und mache Vorschläge zur Entwicklung von zuverlässiger und benutzbarer Griffinteraktion

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System