The analysis of the immensity of data in space and time is a challenging task. For this thesis, the time-dependent data has been explored in various directions. The studies focused on data visualization, feature extraction, and data classification. The data that has been used in the studies comes from various well-recognized archives and has been the basis of numerous researches. The data characteristics ranged from the univariate time series to multivariate time series, from hand gestures to unconstrained views of general human movements. The experiments covered more than one hundred datasets. In addition, we also discussed the applications of visual analytics to video data. Two approaches were proposed to create a feature vector for time-dependent data classification. One is designed especially for a bio-inspired model for human motion recognition and the other is a subspace-based approach for arbitrary data characteristics. The extracted feature vectors of the proposed approaches can be easily visualized in two-dimensional space. For the classification, we experimented with various known models and offered a simple model using data in subspaces for light-weight computation. Furthermore, this method allows a data analyst to inspect feature vectors and detect an anomaly from a large collection of data simultaneously. Various classification techniques were compared and the findings were summarized. Hence, the studies can assist a researcher in picking an appropriate technique when setting up a corresponding model for a given characteristic of temporal data, and offer a new perspective for analyzing the time series data. This thesis is comprised of two parts. The first part gives an overview of time-dependent data and of this thesis with its focus on classification; the second part covers the collection of seven publications.

Die vorliegende Arbeit beschreibt ein wissensbasiertes Decision Support System (DSS) zur Analyse von Wirkungsbeziehungen in einem betriebswirtschaftlichen Umfeld, um die Ursachen von beobachteten Wirkungen zu verstehen und dadurch eine Unterstützung in Entscheidungssituationen zu ermöglichen. Die Umweltbedingungen einer Entscheidungssituation werden während der Analyse berücksichtigt, sodass die Unterstützung auf die jeweilige Situation angepasst ist. Den Kern des Konzeptes stellen eine Wissensbasis sowie eine Ablaufsteuerung zur situativen Auswertung der Wissensbasis dar. Die Wissensbasis speichert Wirkungsbeziehungen zwischen Elementen in Form eines semantischen Netzes. Ein Element besitzt zudem Metainformationen über interne und externe, beliebig strukturierte Daten zur situativen Validierung der Wirkungsbeziehungen. Ebenfalls können Zeitverzögerungen bei Wirkungsbeziehungen berücksichtigt werden. Die Ablaufsteuerung verarbeitet das gespeicherte Wissen aus der Wissensbasis zur situativen Entscheidungsunterstützung in drei aufeinander aufbauenden Phasen. Die Initialisierungsphase dient der Eingabe einer Entscheidungssituation ins System sowie dem Matching der Entscheidungssituation mit Elementen der Wissensbasis. Die Explorationsphase durchsucht die Wissensbasis anhand der Wirkungsbeziehungen nach potenziell relevanten Elementen für die Entscheidungssituation und validiert diese mit statistischen Methoden, indem die Daten der potenziell relevanten Elemente anhand der Metainformationen abgerufen und mit geeigneten Methoden ausgewertet werden. Die Evaluationsphase ermöglicht es dem Anwender, die Ergebnisse zu überprüfen und optional die Wissensbasis anzupassen. Zur Evaluation des Konzeptes erfolgt eine prototypische Implementierung sowie deren empirische Evaluation mit einem Laborexperiment. Auf Basis des Aufgabe-Technologie-Fit-Modells wird eine bessere Eignung des Prototyps im Vergleich zu einer am Markt etablierten DSS-Software ohne automatisierte Analysen von Wirkungsbeziehungen nachgewiesen.

Applicable image-based reconstruction of three-dimensional (3D) objects offers many interesting industrial as well as private use cases, such as augmented reality, reverse engineering, 3D printing and simulation tasks. Unfortunately, image-based 3D reconstruction is not yet applicable to these quite complex tasks, since the resulting 3D models are single, monolithic objects without any division into logical or functional subparts. This thesis aims at making image-based 3D reconstruction feasible such that captures of standard cameras can be used for creating functional 3D models. The research presented in the following does not focus on the fine-tuning of algorithms to achieve minor improvements, but evaluates the entire processing pipeline of image-based 3D reconstruction and tries to contribute at four critical points, where significant improvement can be achieved by advanced human-computer interaction: (i) As the starting point of any 3D reconstruction process, the object of interest (OOI) that should be reconstructed needs to be annotated. For this task, novel pixel-accurate OOI annotation as an interactive process is presented, and an appropriate software solution is released. (ii) To improve the interactive annotation process, traditional interface devices, like mouse and keyboard, are supplemented with human sensory data to achieve closer user interaction. (iii) In practice, a major obstacle is the so far missing standard for file formats for annotation, which leads to numerous proprietary solutions. Therefore, a uniform standard file format is implemented and used for prototyping the first gaze-improved computer vision algorithms. As a sideline of this research, analogies between the close interaction of humans and computer vision systems and 3D perception are identified and evaluated. (iv) Finally, to reduce the processing time of the underlying algorithms used for 3D reconstruction, the ability of artificial neural networks to reconstruct 3D models of unknown OOIs is investigated. Summarizing, the gained improvements show that applicable image-based 3D reconstruction is within reach but nowadays only feasible by supporting human-computer interaction. Two software solutions, one for visual video analytics and one for spare part reconstruction are implemented. In the future, automated 3D reconstruction that produces functional 3D models can be reached only when algorithms become capable of acquiring semantic knowledge. Until then, the world knowledge provided to the 3D reconstruction pipeline by human computer interaction is indispensable.

In the context of evolutionary robotics and neurorobotics, artificial neural networks, used as controllers for animats, are examined to identify principles of neuro-control, network organization, the interaction between body and control, and other likewise properties. Before such an examination can take place, suitable neuro-controllers have to be identified. A promising and widely used technique to search for such networks are evolutionary algorithms specifically adapted for neural networks. These allow the search for neuro-controllers with various network topologies directly on physically grounded (simulated) animats. This neuro-evolution approach works well for small neuro-controllers and has lead to interesting results. However, due to the exponentially increasing search space with respect to the number of involved neurons, this approach does not scale well with larger networks. This scaling problem makes it difficult to find non-trivial, larger networks, that show interesting properties. In the context of this thesis, networks of this class are called mid-scale networks, having between 50 and 500 neurons. Searching for networks of this class involves very large search spaces, including all possible synaptic connections between the neurons, the bias terms of the neurons and (optionally) parameters of the neuron model, such as the transfer function, activation function or parameters of learning rules. In this domain, most evolutionary algorithms are not able to find suitable, non-trivial neuro-controllers in feasible time. To cope with this problem and to shift the frontier for evolvable network topologies a bit further, a novel evolutionary method has been developed in this thesis: the Interactively Constrained Neuro-Evolution method (ICONE). A way to approach the problem of increasing search spaces is the introduction of measures that reduce and restrict the search space back to a feasible domain. With ICONE, this restriction is realized with a unified, extensible and highly adaptable concept: Instead of evolving networks freely, networks are evolved within specifically designed constraint masks, that define mandatory properties of the evolving networks. These constraint masks are defined primarily using so called functional constraints, that actively modify a neural network to enforce the adherence of all required limitations and assumptions. Consequently, independently of the mutations taking place during evolution, the constraint masks repair and readjust the networks so that constraint violations are not able to evolve. Such functional constraints can be very specific and can enforce various network properties, such as symmetries, structure reuse, connectivity patterns, connectivity density heuristics, synaptic pathways, local processing assemblies, and much more. Constraint masks therefore describe a narrow, user defined subset of the parameter space -- based on domain knowledge and user experience -- that focuses the search on a smaller search space leading to a higher success rate for the evolution. Due to the involved domain knowledge, such evolutions are strongly biased towards specific classes of networks, because only networks within the defined search space can evolve. This, surely, can also be actively used to lead the evolution towards specific solution approaches, allowing the experimenter not only to search for any upcoming solution, but also to confirm assumptions about possible solutions. This makes it easier to investigate specific neuro-control principles, because the experimenter can systematically search for networks implementing the desired principles, simply by using suitable constraints to enforce them. Constraint masks in ICONE are built up by functional constraints working on so called neuro-modules. These modules are used to structure the networks, to define the scope for constraints and to simplify the reuse of (evolved) neural structures. The concept of functional, constrained neuro-modules allows a simple and flexible way to construct constraint masks and to inherit constraints when neuro-modules are reused or shared. A final cornerstone of the ICONE method is the interactive control of the evolution process, that allows the adaptation of the evolution parameters and the constraint masks to guide evolution towards promising domains and to counteract undesired developments. Due to the constraint masks, this interactive guidance is more effective than the adaptation of the evolution parameters alone, so that the identification of promising search space regions becomes easier. This thesis describes the ICONE method in detail and shows several applications of the method and the involved features. The examples demonstrate that the method can be used effectively for problems in the domain of mid-scale networks. Hereby, as effects of the constraint masks and the herewith reduced complexity of the networks, the results are -- despite their size -- often easy to comprehend, well analyzable and easy to reuse. Another benefit of constraint masks is the ability to deliberately search for very specific network configurations, which allows the effective and systematic exploration of distinct variations for an evolution experiment, simply by changing the constraint masks over the course of multiple evolution runs. The ICONE method therefore is a promising novel evolution method to tackle the problem of evolving mid-scale networks, pushing the frontier of evolvable networks a bit further. This allows for novel evolution experiments in the domain of neurorobotics and evolutionary robotics and may possibly lead to new insights into neuro-dynamical principles of animat control.

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.

Mobile robots need to move in the real world for the majority of tasks. Their control is often intertwined with the tasks they have to solve. Unforeseen events must have an adequate and prompt reaction, in order to solve the corresponding task satisfactorily. A robust system must be able to respond to a variety of events with specific solutions and strategies to keep the system running. Robot navigation control systems are essential for this. In this thesis we present a robot navigation control system that fulfills these requirements: Move Base Flex. Furthermore, the map representation used to model the environment is essential for path planning. Depending on the representation of the map, path planners can solve problems like simple 2D indoor navigation, but also complex rough terrain outdoor navigation with multiple levels and varying slopes, if the corresponding representation can model them accurately. With Move Base Flex, we present a middle layer navigation framework for navigation control, that is map independent at its core. Based on this, we present the Mesh Navigation Stack to master path planning in complex outdoor environments using a developed mesh map to model surfaces in 3D. Finally, to solve path planning in complex outdoor environments, we have developed and integrated the Continuous Vector Field Planner with the aforementioned solutions and evaluated it on five challenging and complex outdoor datasets in simulation and in the real-world. Beyond that, the corresponding developed software packages are open source available and have been released to easily reproduce the provided scientific results.