For circuit simulation and device optimisation it is necessary to have one consistent model which describes not only the DC-behaviour of the device correcty but also the the small-signal and noise behaviour. This work presents a rigorous modell-description of an InP based heterojunction bipolartransitor taking into account material specific effects for all bias conditions.

The effect of the group-V termination of GaAsSb(100) surfaces on the sharpness of InP/GaAsSb heterointerfaces was investigated. Lattice matched GaAsSb/InP(100)-layers were grown by metalorganic vapor phase epitaxy (MOVPE). Contamination free sample transfer from the MOVPE reactor into ultrahigh vacuum (UHV) allowed for the correlation of in-situ reflectance anisotropy/difference (RA/RD) spectra with low energy electron diffraction (LEED) and photoelectron spectra (XPS/UPS). The in-situ RA spectra indicated that the GaAsSb surface was Sb-rich during growth and turned preferably into an As-rich surface after growth. With LEED and XPS the Sb-rich and As-rich surfaces were correlated with (4×3) and c(4×4) symmetries, respectively. These are well known reconstructions from the related binaries GaAs and GaSb. The study of the InP/GaAsSb interfaces compared the two ordered GaAsSb(100) surfaces as templates for InP growth. XPS measurements of InP/GaAsSb interfaces taken in UHV and I-V curves of InP/GaAsSb resonant tunneling diodes indicated that Sb segregation into a subsequent InP layer was significantly lower when the InP film was grown on the c(4×4) reconstructed GaAsSb surface compared to the (4×3) reconstructed surface.

Modern driver assistance systems simultaneously increase the safety and the comfort of driving. These systems are designed to fulfil routine tasks and thus aid the driver. Critical situations are detected early and are prevented by direct intervention, i.e. active braking and in the future also active steering. With the complexity of the assistance systems the information demand increases as well. It must be ensured, that the information used, in particular the signals measured by the sensors, are proper. As redundant sensors are generally not acceptable for cost reasons, the sensors must be supervised reliably. In this work the application of model-based sensor-fault diagnosis is examined at the example of the sensors for lateral vehicle dynamics. The aim is to achieve a fast, model-based detection of sensor-faults while avoiding false alarms in any driving situation. This includes driving situations, where the models used are imprecise, and under the influence of an unknown input, the road bank angle. The basis for model-based fault diagnosis are suitable models of the vehicle with adequate accuracy. The process of modelling is thus revisited carefully leading to the two-track model with non-linear modelling of the tyre forces according to the HSRI-model. From this model the one-track model with consideration of vehicle rolling motion is deducted. The essential part of this work are two methods, that are developed. The first method is the normbased estimation of the effect of modelling errors on the state-variables, which are estimated with a model or an observer. The modelling errors of the one-track model in comparison to the two-track model with nonlinear modelling of tyre forces are described as a funcion of measured sensor signals. The method provides an estimate of the faults of the system variables, which can be utilized as well for the definition of a confidence interval as for the generation of adaptive thresholds for fault diagnosis. Subsequently a novel concept for fault diagnosis is presented, which is based on the evaluation of two residuals fro each sensor to be supervised. A detection residual is sensitive to the fault to be detected while a checking residual does not react on this fault but only on any other fault as well as on model uncertainties and unknown input variables. The checking residual is used to define a threshold for the detection residual. This threshold is based on the definition of fault areas in the vector-space spanned by the residuals. To avoid false alarms, the threshold is automatically increased in driving situations with restricted model accuracy. For the realisation of the concept the residuals are generated using Kalman-Filters. The validation of the method is accomplished using measurements from real driving maneuvers.

This thesis reports on the development of waveguide electroabsorption modulators based on multiple quantum well structures. The requirements to the modulator are given by the application in a fiberoptic E-field sensor system. The theoretical calculation of a quantum well structure which is tailored to the application, of the waveguide and of the electrical structure of the device is treated first. Following the technological realization of the developed concept and the characterization of the fabricated devices is described. The achieved performance of the modulators is discussed regarding the influence on the sensorsystem. This results in a minimum detectable field of 20 mV/m, a dynamic range of 105 dB and a frequency range from 100 MHz up to 8 GHz. In a final comparison between the performance of the sensorsystem which is dominated by the modulator and that of commercially available products based on lithiumniobate modulators the advantages of the developed concept are described. In addition to a high sensitivity and a broad frequency range also the operation of the modulator in optical reflection and the low polarization dependence of the modulator which is necessary for the use of conventional non polarization maintaining fibers are worth mentioning.

This paper concerns the optimization and coordination of the conventional FACTS (Flexible AC Transmission Systems) damping controllers in multi-machine power system. Firstly, the parameters ofFACTS controller are optimized. Then, a hybrid fuzzy logic controller for the coordination of FACTS controllers is presented. This coordination method is well suitable to series connected FACTS devices like UPFC, TCSC etc. in damping multi-modal oscillations in multi-machine power systems. Digital simulations of a multi-machine power system subjected to a wide variety of disturbances and different structures validate the efficiency of the new approach.

Competitive markets play a key role in modern industrial In many European countries, politicians set the stage for an open electricity market to facilitate competition with full access to grids. This has led to changes in load-flow patterns in transmission networks. The grid access is accompanied by large power transits even over long distances and unpredictable short-term changes of system conditions.

This paper deals with suitable approaches for fast assessment of oscillatory stability of large interconnected power systems within power system operation. Beside voltage stability and transient stability, oscillatory stability has to be assessed, too. It can be shown, that parallel eigenvalue computation using a parallel variant of Arnoldi method can reduce the time necessary for eigenvalue computation of large power systems.

This paper introduces several feature extraction and selection techniques.Previous studies showed good results when neural networks are applied to the small-signal stability assessment. However the use of reduction techniques can decrease the number of features and thus the number of quantities, which need to be observed, measured and transmitted, respectively.

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In this thesis new methods for the automatic recognition of the content of image sequences are presented. Solutions to the following video sequences analysis tasks are developed: temporal decomposition of an image sequence into scenes and classification of the scenes, and the recognition of people and their movements in the image sequence. The temporal segmentation of a image sequence and the classification of the segments can be used for image sequences with a given content structure, like broadcast news. The image sequences have a defined chronology of scenes, which belong to certain content classes. The content classes and their chronology are represented by nested Hidden Markov models during the recognition. Another application of the Hidden Markov Modells are the classification of movements of objects in the image sequence. The recognition of human gestures for the application of human-computer-interaction is investigated. The recognition system is capable of recognizing a set of pre-defined gestures that are performed in the viewing area of a camera. The system is able to identify undefined movements and can distinguish them from the gestures. The final task is recognizing people visible in image sequences. The recognition of the people is done by recognizing their faces. The indexing of the faces is composed of the two sub-tasks: detection of the faces and recognition of the faces. It is shown that the face-based video indexing can be used to find known persons in the image sequence as well as to group the people in the sequence unsupervised.