Im Rahmen von UPNS4D+ wird von mir der Teilbereich der automatisierten untertägigen Detektion von Gangerzlagerstätten und seltenen Erden bearbeitet. Dies erfolgt mittels eines Multisensoransatzes, der aus einer Hyperspektralkamera, einer RGB-Kamera und einem Laserscanner besteht. Die Grundlagen für die Kombination von hyperspektraler Bildverarbeitung und einer RGB-Kamera sind in der Industrie im Bereich von automatisierten Sortieranlagen zu finden. Im Bereich der Fernerkundung ist der Einsatz hyperspektraler Bilder für die Detektion geologischer Merkmale seit einigen Jahrzehnten üblich. Hier kann im Rahmen meiner Forschung gezeigt werden, dass mittels hyperspektraler Bilder Pyrit unter Tage detektiert werden kann. / In my research I work on a system which detects automatically the ore and rare earth element in a mine. This is part of UPNS4D+. For the detection I use a multi sensor system which consists of a hyperspectral camera, a RGB camera and a Laser scanner. Basics of this combination can be found in the industry. The combination of a RGB camera and a hyperspectral camera enables an automatic sorting of for example waste materials. Landsat satellites in the 1970 uses spectral information in order to detect the geology of the surface. I have tested the hyperspectral imaging in the Reiche Zeche and I can now show that Pyrite can be detected.

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Im Rahmen von UPNS4D+ wird von mir der Teilbereich der automatisierten untertägigen Detektion von Gangerzlagerstätten und seltenen Erden bearbeitet. Dies erfolgt mittels eines Multisensoransatzes, der aus einer Hyperspektralkamera, einer RGB-Kamera und einem Laserscanner besteht. Die Grundlagen für die Kombination von hyperspektraler Bildverarbeitung und einer RGB-Kamera sind in der Industrie im Bereich von automatisierten Sortieranlagen zu finden. Im Bereich der Fernerkundung ist der Einsatz hyperspektraler Bilder für die Detektion geologischer Merkmale seit einigen Jahrzehnten üblich. Hier kann im Rahmen meiner Forschung gezeigt werden, dass mittels hyperspektraler Bilder Pyrit unter Tage detektiert werden kann. / In my research I work on a system which detects automatically the ore and rare earth element in a mine. This is part of UPNS4D+. For the detection I use a multi sensor system which consists of a hyperspectral camera, a RGB camera and a Laser scanner. Basics of this combination can be found in the industry. The combination of a RGB camera and a hyperspectral camera enables an automatic sorting of for example waste materials. Landsat satellites in the 1970 uses spectral information in order to detect the geology of the surface. I have tested the hyperspectral imaging in the Reiche Zeche and I can now show that Pyrite can be detected.

In our society with its aging population, the design and implementation of a highperformance distributed multi-sensor and information system for autonomous physical services become more and more important. In line with this, this thesis proposes an Intelligent Multi-Sensor System, IMSS, that surveys a human activities space to detect and identify a target for a specific service. The subject of this thesis covers three main aspects related to the set-up of an IMSS: an improved depth measurement and reconstruction method and its related uncertainty, a surveillance and tracking algorithm and finally a way to validate and evaluate the proposed methods and algorithms. The thesis discusses how a model of the depth spatial quantisation uncertainty can be implemented to optimize the configuration of a sensor system to capture information of the target objects and their environment with required specifications. The thesis introduces the dithering algorithm which significantly reduces the depth reconstruction uncertainty. Furthermore, the dithering algorithm is implemented on a sensor-shifted stereo camera, thus simplifying depth reconstruction without compromising the common stereo field of view. To track multiple targets continuously, the Gaussian Mixture Probability Hypothesis Density, GM-PHD, algorithm is implemented with the help of vision and Radio Frequency Identification, RFID, technologies. The performance of the tracking algorithm in a vision system is evaluated by a circular motion test signal. The thesis introduces constraints to the target space, the stereo pair characteristics and the depth reconstruction accuracy to optimize the vision system and to control the performance of surveillance and 3D reconstruction through integer linear programming. The human being within the activity space is modelled as a tetrahedron, and a field of view in spherical coordinates are used in the control algorithms. In order to integrate human behaviour and perception into a technical system, the proposed adaptive measurement method makes use of the Fuzzily Defined Variable, FDV. The FDV approach enables an estimation of the quality index based on qualitative and quantitative factors for image quality evaluation using a neural network. The thesis consists of two parts, where Part I gives an overview of the applied theory and research methods used, and Part II comprises the eight papers included in the thesis.

Le risque de chute provoqué par le phénomène épisodique de ‘Freeze of Gait’ (FoG) est un symptôme commun de la maladie de Parkinson. Cette étude concerne la détection et le diagnostic des épisodes de FoG à l'aide d'un prototype multi-capteurs. La première contribution est l'introduction de nouveaux capteurs (télémètres et goniomètres) dans le dispositif de mesure pour la détection des épisodes de FoG. Nous montrons que l'information supplémentaire obtenue avec ces capteurs améliore les performances de la détection. La seconde contribution met œuvre un algorithme de détection basé sur des réseaux de neurones gaussiens. Les performance de cet algorithme sont discutées et comparées à l'état de l'art. La troisième contribution est développement d'une approche de modélisation probabiliste basée sur les réseaux bayésiens pour diagnostiquer le changement du comportement de marche des patients avant, pendant et après un épisode de FoG. La dernière contribution est l'utilisation de réseaux bayésiens arborescents pour construire un modèle global qui lie plusieurs symptômes de la maladie de Parkinson : les épisodes de FoG, la déformation de l'écriture et de la parole. Pour tester et valider cette étude, des données cliniques ont été obtenues pour des patients atteints de Parkinson. Les performances en détection, classification et diagnostic sont soigneusement étudiées et évaluées. / Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of Parkinson's disease (PD). This research is headed toward implementing a detection, diagnosis and correction system that prevents FoG episodes using a multi-sensor device. This particular study aims to detect/diagnose FoG using different machine learning approaches. In this study we validate the choice of integrating multiple sensors to detect FoG with better performance. Our first level of contribution is introducing new types of sensors for the detection of FoG (telemeter and goniometer). An advantage in our work is that due to the inconsistency of FoG events, the extracted features from all sensors are combined using the Principal Component Analysis technique. The second level of contribution is implementing a new detection algorithm in the field of FoG detection, which is the Gaussian Neural Network algorithm. The third level of contribution is developing a probabilistic modeling approach based on Bayesian Belief Networks that is able to diagnosis the behavioral walking change of patients before, during and after a freezing event. Our final level of contribution is utilizing tree-structured Bayesian Networks to build a global model that links and diagnoses multiple Parkinson's disease symptoms such as FoG, handwriting, and speech. To achieve our goals, clinical data are acquired from patients diagnosed with PD. The acquired data are subjected to effective time and frequency feature extraction then introduced to the different detection/diagnosis approaches. The used detection methods are able to detect 100% of the present appearances of FoG episodes. The classification performances of our approaches are studied thoroughly and the accuracy of all methodologies is considered carefully and evaluated

Die Erweiterung experimenteller Infrastrukturen um neuartige Sensor eröffnen die Möglichkeit, qualitativ neuartige Erkenntnisse zu gewinnen. Um diese Informationen vollständig zu erschließen ist ein Abdecken der gesamten Verarbeitungskette von der Datenauslese bis zu anwendungsbezogenen Auswertung erforderlich. Eine Erweiterung bestehender wissenschaftlicher Instrumente beinhaltet die strukturelle und zeitbezogene Integration der neuen Sensordaten in das Bestandssystem. Das hier vorgestellte Framework bietet durch seinen flexiblen Ansatz das Potenzial, unterschiedliche Sensortypen in unterschiedliche, leistungsfähige Plattformen zu integrieren. Zwei unterschiedliche Integrationsansätze zeigen die Flexibilität dieses Ansatzes, wobei einer auf die Steigerung der Sensitivität einer Anlage zur Sekundärionenmassenspektroskopie und der andere auf die Bereitstellung eines Prototypen zur Untersuchung von Rezyklaten ausgerichtet ist. Die sehr unterschiedlichen Hardwarevoraussetzungen und Anforderungen der Anwendung bildeten die Basis zur Entwicklung eines flexiblen Softwareframeworks. Um komplexe und leistungsfähige Applikationsbausteine bereitzustellen wurde eine Softwaretechnologie entwickelt, die modulare Pipelinestrukturen mit Sensor- und Ausgabeschnittstellen sowie einer Wissensbasis mit entsprechenden Konfigurations- und Verarbeitungsmodulen kombiniert.:1. Introduction 2. Hardware Architecture and Application Background 3. Software Concept 4. Experimental Results 5. Conclusion and Outlook / Novel sensors with the ability to collect qualitatively new information offer the potential to improve experimental infrastructure and methods in the field of research technology. In order to get full access to this information, the entire range from detector readout data transfer over proper data and knowledge models up to complex application functions has to be covered. The extension of existing scientific instruments comprises the integration of diverse sensor information into existing hardware, based on the expansion of pivotal event schemes and data models. Due to its flexible approach, the proposed framework has the potential to integrate additional sensor types and offers migration capabilities to high-performance computing platforms. Two different implementation setups prove the flexibility of this approach, one extending the material analyzing capabilities of a secondary ion mass spectrometry device, the other implementing a functional prototype setup for the online analysis of recyclate. Both setups can be regarded as two complementary parts of a highly topical and ground-breaking unique scientific application field. The requirements and possibilities resulting from different hardware concepts on one hand and diverse application fields on the other hand are the basis for the development of a versatile software framework. In order to support complex and efficient application functions under heterogeneous and flexible technical conditions, a software technology is proposed that offers modular processing pipeline structures with internal and external data interfaces backed by a knowledge base with respective configuration and conclusion mechanisms.:1. Introduction 2. Hardware Architecture and Application Background 3. Software Concept 4. Experimental Results 5. Conclusion and Outlook

The high stiffness to weight ratio of glass fibre-reinforced polymers (GFRP) makes them an attractive material for rotors e.g. in the aerospace industry. We report on recent developments towards non-contact, in-situ deformation measurements with temporal resolution up to 200 µs and micron measurement uncertainty. We determine the starting point of damage evolution inside the rotor material through radial expansion measurements. This leads to a better understanding of dynamic material behaviour regarding damage evolution and the prediction of damage initiation and propagation. The measurements are conducted using a novel multi-sensor system consisting of four laser Doppler distance (LDD) sensors. The LDD sensor, a two-wavelength Mach-Zehnder interferometer was already successfully applied for dynamic deformation measurements at metallic rotors. While translucency of the GFRP rotor material limits the applicability of most optical measurement techniques due to speckles from both surface and volume of the rotor, the LDD profits from speckles and is not disturbed by backscattered laser light from the rotor volume. The LDD sensor evaluates only signals from the rotor surface. The anisotropic glass fibre-reinforcement results in a rotationally asymmetric dynamic deformation. A novel signal processing algorithm is applied for the combination of the single sensor signals to obtain the shape of the investigated rotors. In conclusion, the applied multi-sensor system allows high temporal resolution dynamic deformation measurements. First investigations regarding damage evolution inside GFRP are presented as an important step towards a fundamental understanding of the material behaviour and the prediction of damage initiation and propagation.

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