Abstract not available

In this thesis a new approach for characterisation of digital mobile radio channels is investigated. The new approach is based on recognition of the fact that while the fading which is characteristic of the mobile radio channel is very rapid, the processes underlying this fading may vary much more slowly. The comparative stability of these underlying processes has not been exploited in system designs to date. Channel models are proposed which take account of the stability of the channel. Estimators for the parameters of the models are proposed, and their performance is analysed theoretically and by simulation and measurement. Bounds are derived for the extent to which the mobile channel can be predicted, and the critical factors which define these bounds are identified. Two main applications arise for these channel models. The first is the possibility of prediction of the overall system performance. This may be used to avoid channel fading (for instance by change of frequency), or compensate for it (by change of the signal rate or by power control). The second application is in channel equalisation. An equaliser based on a model which has parameters varying only very slowly can offer improved performance especially in the case of channels which appear to be varying so rapidly that the convergence rate of an equaliser based on the conventional model is not adequate. The first of these applications is explored, and a relationship is derived between the channel impulse response and the performance of a broadband system.

<p>Artificial intelligence (AI) is already a fundamental component of computer games. In this context is emotions a growing part in simulating real life. The proposed emotional state module, provides a way for the game agents to select an action in real-time virtual environments. The modules function has been tested with the open-source strategy game ORTS. This thesis proposes a new approach for the design of an interacting network, similar to a spreading activation system, of emotional states that keeps track of emotion intensities changing and interacting over time. The network of emotions can represent any number of persisting states, such as moods, emotions and drives. Any emotional signal can affect every state positively or negatively. The states' response to emotional signals are influenced by the other states represented in the network. The network is contained within an emotional state module. This interactions between emotions are not the focus of much research, neither is the representation model. The focus tend to be on the mechanisms eliciting emotions and on how to express the emotions.</p>

This thesis addresses a number of problems in computer vision, image processing, and geometry processing, and presents novel solutions to these problems. The overarching theme of the techniques presented here is a multi-scale approach, leveraging mathematical tools to represent images and surfaces at different scales, and methods that can be adapted from one type of domain (eg., the plane) to another (eg., the sphere). The main problem addressed in this thesis is known as stereo reconstruction: reconstructing the geometry of a scene or object from two or more images of that scene. We develop novel algorithms to do this, which work for both planar and spherical images. By developing a novel way to formulate the notion of disparity for spherical images, we are able effectively adapt our algorithms from planar to spherical images. Our stereo reconstruction algorithm is based on a novel application of distance transforms to multi-scale matching. We use matching information aggregated over multiple scales, and enforce consistency between these scales using distance transforms. We then show how multiple spherical disparity maps can be efficiently and robustly fused using visibility and other geometric constraints. We then show how the reconstructed point clouds can be used to synthesize a realistic sequence of novel views, images from points of view not captured in the input images, in real-time. Along the way to this result, we address some related problems. For example, multi-scale features can be detected in spherical images by convolving those images with a filterbank, generating an overcomplete spherical wavelet representation of the image from which the multiscale features can be extracted. Convolution of spherical images is much more efficient in the spherical harmonic domain than in the spatial domain. Thus, we develop a GPU implementation for fast spherical harmonic transforms and frequency domain convolutions of spherical images. This tool can also be used to detect multi-scale features on geometric surfaces. When we have a point cloud of a surface of a particular class of object, whether generated by stereo reconstruction or by some other modality, we can use statistics and machine learning to more robustly estimate the surface. If we have at our disposal a database of surfaces of a particular type of object, such as the human face, we can compute statistics over this database to constrain the possible shape a new surface of this type can take. We show how a statistical spherical wavelet shape prior can be used to efficiently and robustly reconstruct a face shape from noisy point cloud data, including stereo data.

At present, the quality of image taken from infrared cameras is low compared to the other cameras because of manufacturing technology. So, resolution enhancement processes are becoming more important for these cameras. Super resolution is a good approach to solve this resolution problem. In general, the systems that infrared cameras used require video processing to perform in real time. So, a suitable approach should be selected and implemented to work in real time. The computational load and processing time are big issues in this case. FPGAs are proven to be suitable hardware devices for these types of works. Super resolution involves two parts as global motion estimation and high resolution image reconstruction. In this study, one suitable algorithm, namely as PM, for global motion estimation in the literature is selected to be implemented in real time. On the other hand, for high resolution image reconstruction part, FPGA structures of some well known algorithms in the literature, namely as POCS, MLE, MAP and LMS are proposed and their performance, resource requirements and timing considerations are discussed. Most efficient one is selected and implemented in FPGA.

Time-Triggered Controller Area Network (TTCAN) is a hybrid communication paradigm with combining both time-triggered and event-triggered traffic scheduling. Different from the standard Controller Area Network (CAN), communication in TTCAN is performed according to a pre-computed, fixed (during system run) schedule that is called as TTCAN System Matrix. Thus, communication performance of TTCAN network is directly related to structure of the system matrix, which makes the design of system matrix a crucial process. The study in this thesis consists of the extended work on the development of a systematic approach for system matrix construction. Methods for periodic message scheduling and an approach for aperiodic message scheduling are proposed with the aim of constructing a feasible system matrix, combining three important aspects: message properties, protocol constraints and system performance requirements in terms of designated performance metrics. Also, system matrix design, analyses and performance evaluation are performed on example message sets with the help of two developed software tools.

Timing of ordering of inventory items is of very great importance in Materials Requirement Planning. Uncertainties in timing can have an adverse effect on the system performance. Most often the lead time variation contribute to timing uncertainties; and their effects are reflected in added costs. Lead time error effects are investigated in this thesis. The study attempts to estimate the effects through some relevant costs, and their variations across the lot sizing rules. The hypotheses for this study are 1) Between any two lot sizing rules, there will be a significant difference in error coats due to combined effect of purchased lead time error and manufacturing lead time errors; 2) Relative cost performance of lot sizing rules in MRP is influenced by the lead time errors; 3) There will be a difference in error cost between lot for l o t rule and least total cost rule even with single source of lead time variation. To carry out the study a MRP programme was developed, in FORTRAN 77 with provisions to include the lot sizing rules while exploding the structure. The lot sizing rules used in the study are Lot for Lot, Silver and Meal heuristics, Wagner-Whitin algorithm, Least total cost, Least unit cost and Part Period balancing. A simulation model is developed using GPSS/PC, to test the hypotheses. An hypothetical production situation with three end items, each with a different product structure is designed. In addition, a master production schedule and a job shop are also structured. Appropriate distributions are assumed for both manufacturing lead times and purchase lead times. These provide the stochastic variables in the simulation experiments. A series of experiments were carried out with the model to investigate into the variations of costs amongst lot sizing rules. Results of the simulation experiments prove that there are costs associated with lead time errors in MRP. These error costs vary significantly with different lot sizing rules. It is also found that the resultant error costs vary significantly even with a single source of lead time variation. Least unit cost rule gives the beat performance having least error costs. Lot for Lot rule has shown the worst performance amongst the lot sizing rules considered. Other interesting results have emerged out of the study.

Die Kryptosporidiose stellt aufgrund ihres zoonotischen Charakters und der Entwicklung chronischer Durchfälle bei Immunsupprimierten ein hohes Gesundheitsrisiko für den Menschen, aber ebenso für Tiere dar. Derzeit verfügbare Therapeutika ermöglichen keine zuverlässige Bekämpfung klinischer Symptome oder eine Erregerelimination, daher ist die Erforschung neuer Therapieansätze dringend notwendig. CDPK stellen in diesem Zusammenhang interessante Zielmoleküle dar, da sie zwar in Pflanzen und Protisten einschließlich Apikomplexa, jedoch nicht in Pilzen und Säugetieren vorkommen. Trotz der Entdeckung vielversprechender neuer Wirkstoffe gegen CpCDPK1 in den letzten Jahren ist zur Lokalisation und Funktion von CDPK in C. parvum wenig bekannt.Diese Arbeit belegt die Transkription von sechs CpCDPK in vitro und beschreibt erstmals die Länge der 3’UTR von CpCDPK. Die Translation wurde durch den Nachweis spezifischen Proteins in Sporozoiten im Immunoblot sowie die Lokalisation von CpCDPK1 mit Hilfe der Immunfluoreszenz belegt. Möglicherweise wird die CpCDPK1 durch N-Myristoylierung an Membranen gebunden, an die Oberfläche von Zoiten gebracht und sezerniert. Eine Rolle des Enzyms im Invasions- und Egressmechanismus des Parasiten wird diskutiert.

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This thesis deals with automatic localization of thin surgical tools such as needles or electrodes in 3D ultrasound images. The precise and reliable localization is important for medical interventions such as needle biopsy or electrode insertion into tissue. The reader is introduced to basics of medical ultrasound (US) imaging. The state of the art localization methods are reviewed in the work. Many methods such as Hough transform (HT) or Parallel Integral Projection (PIP) are based on projections. As the existing PIP implementations are relatively slow, we suggest an acceleration by using a multiresolution approach. We propose to use model fitting approach which uses randomized sample consensus (RANSAC) and local optimization. It is a fast method suitable for real-time use and it is robust with respect to the presence of other high-intensity structures in the background. We propose two new shape and appearance models of tool in 3D US images. Tool localization can be improved by exploiting its tubularity. We propose a tool model which uses line filtering and we incorporated it into the model fitting scheme. The robustness of such localization algorithm is improved at the expense of additional time for pre-processing. The real-time localization using the shape model is demonstrated by implementation on the 3D US scanner Ultrasonix RP. All proposed methods were tested on simulated data, phantom US data (a replacement for a tissue) and real tissue US data of breast with biopsy needle. The proposed methods had comparable accuracy and the lower number of failures than the state of the art projection based methods.