Haptography: Capturing and Recreating the Rich Feel of Real Surfaces

Haptic interfaces, which allow a user to touch virtual and remote environments through a hand-held tool, have opened up exciting new possibilities for applications such as computer-aided design and robot-assisted surgery. Unfortunately, the haptic renderings produced by these systems seldom feel like authentic re-creations of the richly varied surfaces one encounters in the real world. We have thus envisioned the new approach of haptography, or haptic photography, in which an individual quickly records a physical interaction with a real surface and then recreates that experience for a user at a different time and/or place. This paper presents an overview of the goals and methods of haptography, emphasizing the importance of accurately capturing and recreating the high frequency accelerations that occur during tool-mediated interactions. In the capturing domain, we introduce a new texture modeling and synthesis method based on linear prediction applied to acceleration signals recorded from real tool interactions. For recreating, we show a new haptography handle prototype that enables the user of a Phantom Omni to feel fine surface features and textures

Capturing tactile properties of real surfaces for haptic reproduction

Tactile feedback of an object’s surface enables us to discern its material properties and affordances. This understanding is used in digital fabrication processes by creating objects with high-resolution surface variations to influence a user’s tactile perception. As the design of such surface haptics commonly relies on knowledge from real-life experiences, it is unclear how to adapt this information for digital design methods. In this work, we investigate replicating the haptics of real materials. Using an existing process for capturing an object’s microgeometry, we digitize and reproduce the stable surface information of a set of 15 fabric samples. In a psychophysical experiment, we evaluate the tactile qualities of our set of original samples and their replicas. From our results, we see that direct reproduction of surface variations is able to influence different psychophysical dimensions of the tactile perception of surface textures. While the fabrication process did not preserve all properties, our approach underlines that replication of surface microgeometries benefits fabrication methods in terms of haptic perception by covering a large range of tactile variations. Moreover, by changing the surface structure of a single fabricated material, its material perception can be influenced. We conclude by proposing strategies for capturing and reproducing digitized textures to better resemble the perceived haptics of the originals

Signal and Information Processing Methods for Embedded Robotic Tactile Sensing Systems

The human skin has several sensors with different properties and responses that are able to detect stimuli resulting from mechanical stimulations. Pressure sensors are the most important type of receptors for the exploration and manipulation of objects. In the last decades, smart tactile sensing based on different sensing techniques have been developed as their application in robotics and prosthetics is considered of huge interest, mainly driven by the prospect of autonomous and intelligent robots that can interact with the environment. However, regarding object properties estimation on robots, hardness detection is still a major limitation due to the lack of techniques to estimate it. Furthermore, finding processing methods that can interpret the measured information from multiple sensors and extract relevant information is a Challenging task. Moreover, embedding processing methods and machine learning algorithms in robotic applications to extract meaningful information such as object properties from tactile data is an ongoing challenge, which is controlled by the device constraints (power constraint, memory constraints, etc.), the computational complexity of the processing and machine learning algorithms, the application requirements (real-time operations, high prediction performance). In this dissertation, we focus on the design and implementation of pre-processing methods and machine learning algorithms to handle the aforementioned challenges for a tactile sensing system in robotic application. First, we propose a tactile sensing system for robotic application. Then we present efficient preprocessing and feature extraction methods for our tactile sensors. Then we propose a learning strategy to reduce the computational cost of our processing unit in object classification using sensorized Baxter robot. Finally, we present a real-time robotic tactile sensing system for hardness classification on a resource-constrained devices. The first study represents a further assessment of the sensing system that is based on the PVDF sensors and the interface electronics developed in our lab. In particular, first, it presents the development of a skin patch (multilayer structure) that allows us to use the sensors in several applications such as robotic hand/grippers. Second, it shows the characterization of the developed skin patch. Third, it validates the sensing system. Moreover, we designed a filter to remove noise and detect touch. The experimental assessment demonstrated that the developed skin patch and the interface electronics indeed can detect different touch patterns and stimulus waveforms. Moreover, the results of the experiments defined the frequency range of interest and the response of the system to realistic interactions with the sensing system to grasp and release events. In the next study, we presented an easy integration of our tactile sensing system into Baxter gripper. Computationally efficient pre-processing techniques were designed to filter the signal and extract relevant information from multiple sensor signals, in addition to feature extraction methods. These processing methods aim in turn to reduce also the computational complexity of machine learning algorithms utilized for object classification. The proposed system and processing strategy were evaluated on object classification application by integrating our system into the gripper and we collected data by grasping multiple objects. We further proposed a learning strategy to accomplish a trade-off between the generalization accuracy and the computational cost of the whole processing unit. The proposed pre-processing and feature extraction techniques together with the learning strategy have led to models with extremely low complexity and very high generalization accuracy. Moreover, the support vector machine achieved the best trade-off between accuracy and computational cost on tactile data from our sensors. Finally, we presented the development and implementation on the edge of a real–time tactile sensing system for hardness classification on Baxter robot based on machine and deep learning algorithms. We developed and implemented in plain C a set of functions that provide the fundamental layer functionalities of the Machine learning and Deep Learning models (ML and DL), along with the pre–processing methods to extract the features and normalize the data. The models can be deployed to any device that supports C code since it does not rely on any of the existing libraries. Shallow ML/DL algorithms for the deployment on resource–constrained devices are designed. To evaluate our work, we collected data by grasping objects of different hardness and shape. Two classification problems were addressed: 5 levels of hardness classified on the same objects’ shape, and 5 levels of hardness classified on two different objects’ shape. Furthermore, optimization techniques were employed. The models and pre–processing were implemented on a resource constrained device, where we assessed the performance of the system in terms of accuracy, memory footprint, time latency, and energy consumption. We achieved for both classification problems a real-time inference (< 0.08 ms), low power consumption (i.e., 3.35 μJ), extremely small models (i.e., 1576 Byte), and high accuracy (above 98%)

Diagnosis of Bearing Fault Using Morphological Features Extraction and Entropy Deconvolution Method

It is observed that the bearing failure of rotating machinery is a pulse in the vibration signal, but it is mostly immersed in noise. In order to effectively eliminate this noise and detect pulses, a novel an image fusion technology based on morphological operators inference is proposed. The correctness of morphological operators lies in the correct selection of structural elements (SE). This report presents an effective algorithm for SE selection based on kurtosis, which makes the analysis free empirical method. When analyzing three different groups of faults, the results show that this method effectively and robustly generates impulse. It enables the algorithm to detect early faults too. Recently, minimum entropy deconvolution (MED) was introduced to the machine in the field of condition monitoring, to enhance the detection of rolling bearing and gear failures. MED analysis helps to extract these pulses and diagnose their source, namely defects bearing components. In this research, MED will be reviewed and reintroduced, Application in fault detection and diagnosis of rolling bearings. MED parameters are selected and its combination with pre-whitening. Test cases are presented to illustrate benefits of MED technology. The simulation has been done on MATLAB and a graphical user interface has been created for analysis of bearing and detection of bearing faults using morphological features

Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Efficient Acoustic Simulation for Immersive Media and Digital Fabrication

Sound is a crucial part of our life. Well-designed acoustic behaviors can lead to significant improvement in both physical and virtual interactions. In computer graphics, most existing methods focused primarily on improving the accuracy. It remained underexplored on how to develop efficient acoustic simulation algorithms for interactive practical applications. The challenges arise from the dilemma between expensive accurate simulations and fast feedback demanded by intuitive user interaction: traditional physics-based acoustic simulations are computationally expensive; yet, for end users to benefit from the simulations, it is crucial to give prompt feedback during interactions. In this thesis, I investigate how to develop efficient acoustic simulations for real-world applications such as immersive media and digital fabrication. To address the above-mentioned challenges, I leverage precomputation and optimization to significantly improve the speed while preserving the accuracy of complex acoustic phenomena. This work discusses three efforts along this research direction: First, to ease sound designer's workflow, we developed a fast keypoint-based precomputation algorithm to enable interactive acoustic transfer values in virtual sound simulations. Second, for realistic audio editing in 360° videos, we proposed an inverse material optimization based on fast sound simulation and a hybrid ambisonic audio synthesis that exploits the directional isotropy in spatial audios. Third, we devised a modular approach to efficiently simulate and optimize fabrication-ready acoustic filters, achieving orders of magnitudes speedup while maintaining the simulation accuracy. Through this series of projects, I demonstrate a wide range of applications made possible by efficient acoustic simulations

Perceptual Model-Driven Authoring of Plausible Vibrations from User Expectations for Virtual Environments

One of the central goals of design is the creation of experiences that are rated favorably in the intended application context. User expectations play an integral role in tactile product quality and tactile plausibility judgments alike. In the vibrotactile authoring process for virtual environments, vibra-tion is created to match the user’s expectations of the presented situational context. Currently, inefficient trial and error approaches attempt to match expectations implicitly. A more efficient, model-driven procedure based explicitly on tactile user expectations would thus be beneficial for author-ing vibrations. In everyday life, we are frequently exposed to various whole-body vibrations. Depending on their temporal and spectral proper-ties we intuitively associate specific perceptual properties such as “tin-gling”. This suggests a systematic relationship between physical parame-ters and perceptual properties. To communicate with potential users about such elicited or expected tactile properties, a standardized design language is proposed. It contains a set of sensory tactile perceptual attributes, which are sufficient to characterize the perceptual space of vibration encountered in everyday life. This design language enables the assessment of quantita-tive tactile perceptual specifications by laypersons that are elicited in situational contexts such as auditory-visual-tactile vehicle scenes. Howev-er, such specifications can also be assessed by providing only verbal de-scriptions of the content of these scenes. Quasi identical ratings observed for both presentation modes suggest that tactile user expectations can be quantified even before any vibration is presented. Such expected perceptu-al specifications are the prerequisite for a subsequent translation into phys-ical vibration parameters. Plausibility can be understood as a similarity judgment between elicited features and expected features. Thus, plausible vibration can be synthesized by maximizing the similarity of the elicited perceptual properties to the expected perceptual properties. Based on the observed relationships between vibration parameters and sensory tactile perceptual attributes, a 1-nearest-neighbor model and a regression model were built. The plausibility of the vibrations synthesized by these models in the context of virtual auditory-visual-tactile vehicle scenes was validat-ed in a perceptual study. The results demonstrated that the perceptual spec-ifications obtained with the design language are sufficient to synthesize vibrations, which are perceived as equally plausible as recorded vibrations in a given situational context. Overall, the demonstrated design method can be a new, more efficient tool for designers authoring vibrations for virtual environments or creating tactile feedback. The method enables further automation of the design process and thus potential time and cost reductions.:Preface III Abstract V Zusammenfassung VII List of Abbreviations XV 1 Introduction 1 1.1 General Introduction 1 1.1 Objectives of the Thesis 4 1.2 Structure of the Thesis 4 2. Tactile Perception in Real and Virtual Environments 7 2.1 Tactile Perception as a Multilayered Process 7 2.1.1 Physical Layer 8 2.1.2 Mechanoreceptor Layer 9 2.1.3 Sensory Layer 19 2.1.4 Affective Layer 26 2.2 Perception of Virtual Environments 29 2.2.1 The Place Illusion 29 2.2.2 The Plausibility Illusion 31 2.3 Approaches for the Authoring of Vibrations 38 2.3.1 Approaches on the Physical Layer 38 2.3.2 Approaches on the Mechanoreceptor Layer 40 2.3.3 Approaches on the Sensory Layer 40 2.3.4 Approaches on the Affective Layer 43 2.4 Summary 43 3. Research Concept 47 3.1 Research Questions 47 3.1.1 Foundations of the Research Concept 47 3.1.2 Research Concept 49 3.2 Limitations 50 4. Development of the Experimental Setup 53 4.1 Hardware 53 4.1.1 Optical Reproduction System 53 4.1.2 Acoustical Reproduction System 54 4.1.3 Whole-Body Vibration Reproduction System 56 4.2 Software 64 4.2.1 Combination of Reproduction Systems for Unimodal and Multimodal Presentation 64 4.2.2 Conducting Perceptual Studies 65 5. Assessment of a Sensory Tactile Design Language for Characterizing Vibration 67 5.1.1 Design Language Requirements 67 5.1.2 Method to Assess the Design Language 69 5.1.3 Goals of this Chapter 70 5.2 Tactile Stimuli 72 5.2.1 Generalization into Excitation Patterns 72 5.2.2 Definition of Parameter Values of the Excitation Patterns 75 5.2.3 Generation of the Stimuli 85 5.2.4 Summary 86 5.3 Assessment of the most relevant Sensory Tactile Perceptual Attributes 86 5.3.1 Experimental Design 87 5.3.2 Participants 88 5.3.3 Results 88 5.3.4 Aggregation and Prioritization 89 5.3.5 Summary 91 5.4 Identification of the Attributes forming the Design Language 92 5.4.1 Experimental Design 93 5.4.2 Participants 95 5.4.3 Results 95 5.4.4 Selecting the Elements of the Sensory Tactile Design Language 106 5.4.5 Summary 109 5.5 Summary and Discussion 109 5.5.1 Summary 109 5.5.2 Discussion 111 6. Quantification of Expected Properties with the Sensory Tactile Design Language 115 6.1 Multimodal Stimuli 116 6.1.1 Selection of the Scenes 116 6.1.2 Recording of the Scenes 117 6.1.3 Recorded Stimuli 119 6.2 Qualitative Communication in the Presence of Vibration 123 6.2.1 Experimental Design 123 6.2.2 Participants 124 6.2.3 Results 124 6.2.4 Summary 126 6.3 Quantitative Communication in the Presence of Vibration 126 6.3.1 Experimental Design 127 6.3.2 Participants 127 6.3.3 Results 127 6.3.4 Summary 129 6.4 Quantitative Communication in the Absence of Vibration 129 6.4.1 Experimental Design 130 6.4.2 Participants 132 6.4.3 Results 132 6.4.4 Summary 134 6.5 Summary and Discussion 135 7. Synthesis Models for the Translation of Sensory Tactile Properties into Vibration 137 7.1 Formalization of the Tactile Plausibility Illusion for Models 139 7.1.1 Formalization of Plausibility 139 7.1.2 Model Boundaries 143 7.2 Investigation of the Influence of Vibration Level on Attribute Ratings 144 7.2.1 Stimuli 145 7.2.2 Experimental Design 145 7.2.3 Participants 146 7.2.4 Results 146 7.2.5 Summary 148 7.3 Comparison of Modulated Vibration to Successive Impulse-like Vibration 148 7.3.1 Stimuli 149 7.3.2 Experimental Design 151 7.3.3 Participants 151 7.3.4 Results 151 7.3.5 Summary 153 7.4 Synthesis Based on the Discrete Estimates of a k-Nearest-Neighbor Classifier 153 7.4.1 Definition of the K-Nearest-Neighbor Classifier 154 7.4.2 Analysis Model 155 7.4.3 Synthesis Model 156 7.4.4 Interpolation of acceleration level for the vibration attribute profile pairs 158 7.4.5 Implementation of the Synthesis 159 7.4.6 Advantages and Disadvantages 164 7.5 Synthesis Based on the Quasi-Continuous Estimates of Regression Models 166 7.5.1 Overall Model Structure 168 7.5.2 Classification of the Excitation Pattern with a Support Vector Machine 171 7.5.3 General Approach to the Regression Models of each Excitation Pattern 178 7.5.4 Synthesis for the Impulse-like Excitation Pattern 181 7.5.5 Synthesis for the Bandlimited White Gaussian Noise Excitation Pattern 187 7.5.6 Synthesis for the Amplitude Modulated Sinusoidal Excitation Pattern 193 7.5.7 Synthesis for the Sinusoidal Excitation Pattern 199 7.5.8 Implementation of the Synthesis 205 7.5.9 Advantages and Disadvantages of the Approach 208 7.6 Validation of the Synthesis Models 210 7.6.1 Stimuli 212 7.6.2 Experimental Design 212 7.6.3 Participants 214 7.6.4 Results 214 7.6.5 Summary 219 7.7 Summary and Discussion 219 7.7.1 Summary 219 7.7.2 Discussion 222 8. General Discussion and Outlook 227 Acknowledgment 237 References 237Eines der zentralen Ziele des Designs von Produkten oder virtuellen Um-gebungen ist die Schaffung von Erfahrungen, die im beabsichtigten An-wendungskontext die Erwartungen der Benutzer erfüllen. Gegenwärtig versucht man im vibrotaktilen Authoring-Prozess mit ineffizienten Trial-and-Error-Verfahren, die Erwartungen an den dargestellten, virtuellen Situationskontext implizit zu erfüllen. Ein effizienteres, modellgetriebenes Verfahren, das explizit auf den taktilen Benutzererwartungen basiert, wäre daher von Vorteil. Im Alltag sind wir häufig verschiedenen Ganzkörper-schwingungen ausgesetzt. Abhängig von ihren zeitlichen und spektralen Eigenschaften assoziieren wir intuitiv bestimmte Wahrnehmungsmerkmale wie z.B. “kribbeln”. Dies legt eine systematische Beziehung zwischen physikalischen Parametern und Wahrnehmungsmerkmalen nahe. Um mit potentiellen Nutzern über hervorgerufene oder erwartete taktile Eigen-schaften zu kommunizieren, wird eine standardisierte Designsprache vor-geschlagen. Sie enthält eine Menge von sensorisch-taktilen Wahrneh-mungsmerkmalen, die hinreichend den Wahrnehmungsraum der im Alltag auftretenden Vibrationen charakterisieren. Diese Entwurfssprache ermög-licht die quantitative Beurteilung taktiler Wahrnehmungsmerkmale, die in Situationskontexten wie z.B. auditiv-visuell-taktilen Fahrzeugszenen her-vorgerufen werden. Solche Wahrnehmungsspezifikationen können jedoch auch bewertet werden, indem der Inhalt dieser Szenen verbal beschrieben wird. Quasi identische Bewertungen für beide Präsentationsmodi deuten darauf hin, dass die taktilen Benutzererwartungen quantifiziert werden können, noch bevor eine Vibration präsentiert wird. Die erwarteten Wahr-nehmungsspezifikationen sind die Voraussetzung für eine anschließende Übersetzung in physikalische Schwingungsparameter. Plausible Vibratio-nen können synthetisiert werden, indem die erwarteten Wahrnehmungs-merkmale hervorgerufen werden. Auf der Grundlage der beobachteten Beziehungen zwischen Schwingungs¬parametern und sensorisch-taktilen Wahrnehmungsmerkmalen wurden ein 1-Nearest-Neighbor-Modell und ein Regressionsmodell erstellt. Die Plausibilität der von diesen Modellen synthetisierten Schwingungen im Kontext virtueller, auditorisch-visuell-taktiler Fahrzeugszenen wurde in einer Wahrnehmungsstudie validiert. Die Ergebnisse zeigten, dass die mit der Designsprache gewonnenen Wahr-nehmungsspezifikationen ausreichen, um Schwingungen zu synthetisieren, die in einem gegebenen Situationskontext als ebenso plausibel empfunden werden wie aufgezeichnete Schwingungen. Die demonstrierte Entwurfsme-thode stellt ein neues, effizienteres Werkzeug für Designer dar, die Schwingungen für virtuelle Umgebungen erstellen oder taktiles Feedback für Produkte erzeugen.:Preface III Abstract V Zusammenfassung VII List of Abbreviations XV 1 Introduction 1 1.1 General Introduction 1 1.1 Objectives of the Thesis 4 1.2 Structure of the Thesis 4 2. Tactile Perception in Real and Virtual Environments 7 2.1 Tactile Perception as a Multilayered Process 7 2.1.1 Physical Layer 8 2.1.2 Mechanoreceptor Layer 9 2.1.3 Sensory Layer 19 2.1.4 Affective Layer 26 2.2 Perception of Virtual Environments 29 2.2.1 The Place Illusion 29 2.2.2 The Plausibility Illusion 31 2.3 Approaches for the Authoring of Vibrations 38 2.3.1 Approaches on the Physical Layer 38 2.3.2 Approaches on the Mechanoreceptor Layer 40 2.3.3 Approaches on the Sensory Layer 40 2.3.4 Approaches on the Affective Layer 43 2.4 Summary 43 3. Research Concept 47 3.1 Research Questions 47 3.1.1 Foundations of the Research Concept 47 3.1.2 Research Concept 49 3.2 Limitations 50 4. Development of the Experimental Setup 53 4.1 Hardware 53 4.1.1 Optical Reproduction System 53 4.1.2 Acoustical Reproduction System 54 4.1.3 Whole-Body Vibration Reproduction System 56 4.2 Software 64 4.2.1 Combination of Reproduction Systems for Unimodal and Multimodal Presentation 64 4.2.2 Conducting Perceptual Studies 65 5. Assessment of a Sensory Tactile Design Language for Characterizing Vibration 67 5.1.1 Design Language Requirements 67 5.1.2 Method to Assess the Design Language 69 5.1.3 Goals of this Chapter 70 5.2 Tactile Stimuli 72 5.2.1 Generalization into Excitation Patterns 72 5.2.2 Definition of Parameter Values of the Excitation Patterns 75 5.2.3 Generation of the Stimuli 85 5.2.4 Summary 86 5.3 Assessment of the most relevant Sensory Tactile Perceptual Attributes 86 5.3.1 Experimental Design 87 5.3.2 Participants 88 5.3.3 Results 88 5.3.4 Aggregation and Prioritization 89 5.3.5 Summary 91 5.4 Identification of the Attributes forming the Design Language 92 5.4.1 Experimental Design 93 5.4.2 Participants 95 5.4.3 Results 95 5.4.4 Selecting the Elements of the Sensory Tactile Design Language 106 5.4.5 Summary 109 5.5 Summary and Discussion 109 5.5.1 Summary 109 5.5.2 Discussion 111 6. Quantification of Expected Properties with the Sensory Tactile Design Language 115 6.1 Multimodal Stimuli 116 6.1.1 Selection of the Scenes 116 6.1.2 Recording of the Scenes 117 6.1.3 Recorded Stimuli 119 6.2 Qualitative Communication in the Presence of Vibration 123 6.2.1 Experimental Design 123 6.2.2 Participants 124 6.2.3 Results 124 6.2.4 Summary 126 6.3 Quantitative Communication in the Presence of Vibration 126 6.3.1 Experimental Design 127 6.3.2 Participants 127 6.3.3 Results 127 6.3.4 Summary 129 6.4 Quantitative Communication in the Absence of Vibration 129 6.4.1 Experimental Design 130 6.4.2 Participants 132 6.4.3 Results 132 6.4.4 Summary 134 6.5 Summary and Discussion 135 7. Synthesis Models for the Translation of Sensory Tactile Properties into Vibration 137 7.1 Formalization of the Tactile Plausibility Illusion for Models 139 7.1.1 Formalization of Plausibility 139 7.1.2 Model Boundaries 143 7.2 Investigation of the Influence of Vibration Level on Attribute Ratings 144 7.2.1 Stimuli 145 7.2.2 Experimental Design 145 7.2.3 Participants 146 7.2.4 Results 146 7.2.5 Summary 148 7.3 Comparison of Modulated Vibration to Successive Impulse-like Vibration 148 7.3.1 Stimuli 149 7.3.2 Experimental Design 151 7.3.3 Participants 151 7.3.4 Results 151 7.3.5 Summary 153 7.4 Synthesis Based on the Discrete Estimates of a k-Nearest-Neighbor Classifier 153 7.4.1 Definition of the K-Nearest-Neighbor Classifier 154 7.4.2 Analysis Model 155 7.4.3 Synthesis Model 156 7.4.4 Interpolation of acceleration level for the vibration attribute profile pairs 158 7.4.5 Implementation of the Synthesis 159 7.4.6 Advantages and Disadvantages 164 7.5 Synthesis Based on the Quasi-Continuous Estimates of Regression Models 166 7.5.1 Overall Model Structure 168 7.5.2 Classification of the Excitation Pattern with a Support Vector Machine 171 7.5.3 General Approach to the Regression Models of each Excitation Pattern 178 7.5.4 Synthesis for the Impulse-like Excitation Pattern 181 7.5.5 Synthesis for the Bandlimited White Gaussian Noise Excitation Pattern 187 7.5.6 Synthesis for the Amplitude Modulated Sinusoidal Excitation Pattern 193 7.5.7 Synthesis for the Sinusoidal Excitation Pattern 199 7.5.8 Implementation of the Synthesis 205 7.5.9 Advantages and Disadvantages of the Approach 208 7.6 Validation of the Synthesis Models 210 7.6.1 Stimuli 212 7.6.2 Experimental Design 212 7.6.3 Participants 214 7.6.4 Results 214 7.6.5 Summary 219 7.7 Summary and Discussion 219 7.7.1 Summary 219 7.7.2 Discussion 222 8. General Discussion and Outlook 227 Acknowledgment 237 References 23