For many years real-time task models have focused the timing constraints on execution windows defined by earliest start times and deadlines for feasibility. However, the utility of some application may vary among scenarios which yield correct behavior, and maximizing this utility improves the resource utilization. For example, target sensitive applications have a target point where execution results in maximized utility, and an execution window for feasibility. Execution around this point and within the execution window is allowed, albeit at lower utility. The intensity of the utility decay accounts for the importance of the application. Examples of such applications include multimedia and control; multimedia application are very popular nowadays and control applications are present in every automated system. In this thesis, we present a novel real-time task model which provides for easy abstractions to express the timing constraints of target sensitive RT applications: the gravitational task model. This model uses a simple gravity pendulum (or bob pendulum) system as a visualization model for trade-offs among target sensitive RT applications. We consider jobs as objects in a pendulum system, and the target points as the central point. Then, the equilibrium state of the physical problem is equivalent to the best compromise among jobs with conflicting targets. Analogies with well-known systems are helpful to fill in the gap between application requirements and theoretical abstractions used in task models. For instance, the so-called nature algorithms use key elements of physical processes to form the basis of an optimization algorithm. Examples include the knapsack problem, traveling salesman problem, ant colony optimization, and simulated annealing. We also present a few scheduling algorithms designed for the gravitational task model which fulfill the requirements for on-line adaptivity. The scheduling of target sensitive RT applications must account for timing constraints, and the trade-off among tasks with conflicting targets. Our proposed scheduling algorithms use the equilibrium state concept to order the execution sequence of jobs, and compute the deviation of jobs from their target points for increased system utility. The execution sequence of jobs in the schedule has a significant impact on the equilibrium of jobs, and dominates the complexity of the problem --- the optimum solution is NP-hard. We show the efficacy of our approach through simulations results and 3 target sensitive RT applications enhanced with the gravitational task model.

At present the standardization of third generation (3G) mobile radio systems is the subject of worldwide research activities. These systems will cope with the market demand for high data rate services and the system requirement for exibility concerning the offered services and the transmission qualities. However, there will be de ciencies with respect to high capacity, if 3G mobile radio systems exclusively use single antennas. Very promising technique developed for increasing the capacity of 3G mobile radio systems the application is adaptive antennas. In this thesis, the benefits of using adaptive antennas are investigated for 3G mobile radio systems based on Time Division CDMA (TD-CDMA), which forms part of the European 3G mobile radio air interface standard adopted by the ETSI, and is intensively studied within the standardization activities towards a worldwide 3G air interface standard directed by the 3GPP (3rd Generation Partnership Project). One of the most important issues related to adaptive antennas is the analysis of the benefits of using adaptive antennas compared to single antennas. In this thesis, these bene ts are explained theoretically and illustrated by computer simulation results for both data detection, which is performed according to the joint detection principle, and channel estimation, which is applied according to the Steiner estimator, in the TD-CDMA uplink. The theoretical explanations are based on well-known solved mathematical problems. The simulation results illustrating the benefits of adaptive antennas are produced by employing a novel simulation concept, which offers a considerable reduction of the simulation time and complexity, as well as increased exibility concerning the use of different system parameters, compared to the existing simulation concepts for TD-CDMA. Furthermore, three novel techniques are presented which can be used in systems with adaptive antennas for additionally improving the system performance compared to single antennas. These techniques concern the problems of code-channel mismatch, of user separation in the spatial domain, and of intercell interference, which, as it is shown in the thesis, play a critical role on the performance of TD-CDMA with adaptive antennas. Finally, a novel approach for illustrating the performance differences between the uplink and downlink of TD-CDMA based mobile radio systems in a straightforward manner is presented. Since a cellular mobile radio system with adaptive antennas is considered, the ultimate goal is the investigation of the overall system efficiency rather than the efficiency of a single link. In this thesis, the efficiency of TD-CDMA is evaluated through its spectrum efficiency and capacity, which are two closely related performance measures for cellular mobile radio systems. Compared to the use of single antennas, the use of adaptive antennas allows impressive improvements of both spectrum efficiency and capacity. Depending on the mobile radio channel model and the user velocity, improvement factors range from six to 10.7 for the spectrum efficiency, and from 6.7 to 12.6 for the spectrum capacity of TD-CDMA. Thus, adaptive antennas constitute a promising technique for capacity increase of future mobile communications systems.

The recently established technologies in the areas of distributed measurement and intelligent information processing systems, e.g., Cyber Physical Systems (CPS), Ambient Intelligence/Ambient Assisted Living systems (AmI/AAL), the Internet of Things (IoT), and Industry 4.0 have increased the demand for the development of intelligent integrated multi-sensory systems as to serve rapid growing markets [1, 2]. These increase the significance of complex measurement systems, that incorporate numerous advanced methodological implementations including electronics circuit, signal processing, and multi-sensory information fusion. In particular, in multi-sensory cognition applications, to design such systems, the skill-required tasks, e.g., method selection, parameterization, model analysis, and processing chain construction are elaborated with immense effort, which conventionally are done manually by the expert designer. Moreover, the strong technological competition imposes even more complicated design problems with multiple constraints, e.g., cost, speed, power consumption, exibility, and reliability. Thus, the conventional human expert based design approach may not be able to cope with the increasing demand in numbers, complexity, and diversity. To alleviate the issue, the design automation approach has been the topic for numerous research works [3-14] and has been commercialized to several products [15-18]. Additionally, the dynamic adaptation of intelligent multi-sensor systems is the potential solution for developing dependable and robust systems. Intrinsic evolution approach and self-x properties [19], which include self-monitoring, -calibrating/trimming, and -healing/repairing, are among the best candidates for the issue. Motivated from the ongoing research trends and based on the background of our research work [12, 13] among the pioneers in this topic, the research work of the thesis contributes to the design automation of intelligent integrated multi-sensor systems. In this research work, the Design Automation for Intelligent COgnitive system with self- X properties, the DAICOX, architecture is presented with the aim of tackling the design effort and to providing high quality and robust solutions for multi-sensor intelligent systems. Therefore, the DAICOX architecture is conceived with the defined goals as listed below. Perform front to back complete processing chain design with automated method selection and parameterization, Provide a rich choice of pattern recognition methods to the design method pool, Associate design information via interactive user interface and visualization along with intuitive visual programming, Deliver high quality solutions outperforming conventional approaches by using multi-objective optimization, Gain the adaptability, reliability and robustness of designed solutions with self-x properties, Derived from the goals, several scientific methodological developments and implementations, particularly in the areas of pattern recognition and computational intelligence, will be pursued as part of the DAICOX architecture in the research work of this thesis. The method pool is aimed to contain a rich choice of methods and algorithms covering data acquisition and sensor configuration, signal processing and feature computation, dimensionality reduction, and classification. These methods will be selected and parameterized automatically by the DAICOX design optimization to construct a multi-sensory cognition processing chain. A collection of non-parametric feature quality assessment functions for the purpose of Dimensionality Reduction (DR) process will be presented. In addition, to standard DR methods, the variations of feature selection method, in particular, feature weighting will be proposed. Three different classification categories shall be incorporated in the method pool. Hierarchical classification approach will be proposed and developed to serve as a multi-sensor fusion architecture at the decision level. Beside multi-class classification, one-class classification methods, e.g., One-Class SVM and NOVCLASS will be presented to extend functionality of the solutions, in particular, anomaly and novelty detection. DAICOX is conceived to effectively handle the problem of method selection and parameter setting for a particular application yielding high performance solutions. The processing chain construction tasks will be carried out by meta-heuristic optimization methods, e.g., Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), with multi-objective optimization approach and model analysis for robust solutions. In addition, to the automated system design mechanisms, DAICOX will facilitate the design tasks with intuitive visual programming and various options of visualization. Design database concept of DAICOX is aimed to allow the reusability and extensibility of the designed solutions gained from previous knowledge. Thus, the cooperative design of machine and knowledge from the design expert can also be utilized for obtaining fully enhanced solutions. In particular, the integration of self-x properties as well as intrinsic optimization into the system is proposed to gain enduring reliability and robustness. Hence, DAICOX will allow the inclusion of dynamically reconfigurable hardware instances to the designed solutions in order to realize intrinsic optimization and self-x properties. As a result from the research work in this thesis, a comprehensive intelligent multisensor system design architecture with automated method selection, parameterization, and model analysis is developed with compliance to open-source multi-platform software.It is integrated with an intuitive design environment, which includes visual programming concept and design information visualizations. Thus, the design effort is minimized as investigated in three case studies of different application background, e.g., food analysis (LoX), driving assistance (DeCaDrive), and magnetic localization. Moreover, DAICOX achieved better quality of the solutions compared to the manual approach in all cases, where the classification rate was increased by 5.4%, 0.06%, and 11.4% in the LoX, DeCaDrive, and magnetic localization case, respectively. The design time was reduced by 81.87% compared to the conventional approach by using DAICOX in the LoX case study. At the current state of development, a number of novel contributions of the thesis are outlined below. Automated processing chain construction and parameterization for the design of signal processing and feature computation. Novel dimensionality reduction methods, e.g., GA and PSO based feature selection and feature weighting with multi-objective feature quality assessment. A modification of non-parametric compactness measure for feature space quality assessment. Decision level sensor fusion architecture based on proposed hierarchical classification approach using, i.e., H-SVM. A collection of one-class classification methods and a novel variation, i.e., NOVCLASS-R. Automated design toolboxes supporting front to back design with automated model selection and information visualization. In this research work, due to the complexity of the task, neither all of the identified goals have been comprehensively reached yet nor has the complete architecture definition been fully implemented. Based on the currently implemented tools and frameworks, ongoing development of DAICOX is pursuing towards the complete architecture. The potential future improvements are the extension of method pool with a richer choice of methods and algorithms, processing chain breeding via graph based evolution approach, incorporation of intrinsic optimization, and the integration of self-x properties. According to these features, DAICOX will improve its aptness in designing advanced systems to serve the increasingly growing technologies of distributed intelligent measurement systems, in particular, CPS and Industrie 4.0.

Photonic crystals are inhomogeneous dielectric media with periodic variation of the refractive index. A photonic crystal gives us new tools for the manipulation of photons and thus has received great interests in a variety of fields. Photonic crystals are expected to be used in novel optical devices such as thresholdless laser diodes, single-mode light emitting diodes, small waveguides with low-loss sharp bends, small prisms, and small integrated optical circuits. They can be operated in some aspects as "left handed materials" which are capable of focusing transmitted waves into a sub-wavelength spot due to negative refraction. The thesis is focused on the applications of photonic crystals in communications and optical imaging: • Photonic crystal structures for potential dispersion management in optical telecommunication systems • 2D non-uniform photonic crystal waveguides with a square lattice for wide-angle beam refocusing using negative refraction • 2D non-uniform photonic crystal slabs with triangular lattice for all-angle beam refocusing • Compact phase-shifted band-pass transmission filter based on photonic crystals

Um die in der Automatisierung zunehmenden Anforderungen an Vorschubachsen hinsichtlich Dynamik, Präzision und Wartungsaufwand bei niedriger Bauhöhe und kleiner werdendem Bauvolumen gerecht zu werden, kommen immer mehr Synchron-Linearmotoren in Zahnspulentechnik mit Permanentmagneterregung in Werkzeugmaschinen zum Einsatz. Als hauptsächlicher Vorteil gegenüber der rotierenden Antriebslösung mit Getriebeübersetzung und Kugelrollspindel wird die direkte Kraftübertragung ohne Bewegungswandler genannt. Der Übergang vom konventionellen linearen Antriebssystem zum Direktantriebssystem eröffnet dem Werkzeugmaschinenherstellern und den Industrieanwendungen eine Vielzahl neuer Möglichkeiten durch beeindruckende Verfahrgeschwindigkeit und hohes Beschleunigungsvermögen sowie Positionier- und Wiederholgenauigkeit und bietet darüber hinaus die Chance zu einer weiteren Produktivitäts- und Qualitätssteigerung. Um alle dieser Vorteile ausnutzen zu können, muss der Antrieb zuerst hinsichtlich der für Linearmotoren typisch Kraftwelligkeit optimiert werden. Die Suche nach wirtschaftlichen und praxistauglichen Gegenmaßnahmen ist ein aktuelles Forschungsthema in der Antriebstechnik. In der vorliegenden Arbeit werden die Kraftschwankungen infolge Nutung, Endeffekt und elektrischer Durchflutung in PM-Synchron-Linearmotor rechnerisch und messtechnisch untersucht. Ursachen und Eigenschaften der Kraftwelligkeit werden beschrieben und Einflussparameter aufgezeigt. Es besteht die Möglichkeit, die Kraftwelligkeit durch bestimmte Maßnahmen zu beeinflussen, z. B. mit Hilfe des Kraftwelligkeitsausgleichs bestehend aus ferromagnetischem Material oder durch gegenseitigen Ausgleich mehrerer zusammengekoppelter Primärteile. Wie die Untersuchungen gezeigt haben, ist eine Abstimmung der Einflussparameter auf analytischem Weg kaum möglich, in der Praxis führt das auf eine experimentell-iterative Optimierung mit FEM-Unterstützung. Die gute Übereinstimmung zwischen Messung und Simulation bietet einen klaren Hinweis, dass die hier vorgestellten Maßnahmen als geeignet angesehen werden können, sie ermöglichen eine Kraftwelligkeitsreduzierung von ursprünglichen 3-5% bis auf 1%, wobei eine leichte Herabsetzung der Kraftdichte in Kauf genommen werden muss. Beim Maschinenentwurf muss rechtzeitig ermittelt werden, welches Kompensationsverfahren günstig ist bezüglich der vorgesehenen Anwendungen.