Universiẗat, Diss., 2004--Mannheim.

This thesis deals with applying methods on object detection in image. Separation of objects off the background is often needed during the image processing. It isolates the region of interest that can be worked with. The main purpose of this paper is the explanation of principles of pre-processing and segmentation of image, resulting in object detection using the wavelet transformation. This wavelet transformation is described more in detail, because it is the base of the primary used method. In the practical part of this thesis the main method was implemented to MATLAB environment and tested on set of images. The method was tested for robustness against noise and blur of image. It was compared with commonly used methods, using the edge detectors and thresholding. A simulation program was created for comparison of methods efficiency, including user interface.

Modernized and advanced electricity transmission and distribution infrastructure ensures reliable, efficient, and affordable delivery of electric power. The complexity of fault location problem increases with the proliferation of unusual topologies and with the advent of renewable energy-based power generation in the smart grid environment. The proliferation of new Intelligent Electronic Devices (IEDs) provides a venue for the implementation of more accurate and intelligent fault location methods. This dissertation focuses on intelligent fault location methods for smart power grids and it aims at improving fault location accuracies and decreasing the cost and the mean time to repair damaged equipment in major power outages subsequently increasing the reliability of the grid. The developed methods utilize wavelet transformation to extract the traveling wave information in the very fast voltage and current transients which are initiated immediately after a fault occurs, support vector machines to classify the fault type and identify the faulted branches and finally Bewley diagrams to precisely locate the fault. The approach utilizes discrete wavelet transformation (DWT) for analysis of transient voltage and current measurements. The transient wavelet energies are calculated and utilized as the input for support vector machine (SVM) classifiers. SVM learns the mapping between inputs (i.e. transient voltages and/or currents wavelet energies) and desired outputs (i.e. faulty phase and/or faulty section) through processing a set of training cases. This dissertation presents the proposed methodologies applied to three complex power transmission systems. The first transmission system is a three-terminal (teed) three-phase AC transmission network, a common topology in high- and extra high-voltage networks. It is used to connect three substations that are wide apart from each other through long transmission lines with a tee-point, which is not supported by a substation nor equipped with a measuring device. The developed method overcomes the difficulties introduced by the discontinuity: the tee point. The second topology is a hybrid high voltage alternative current (HVAC) transmission line composed of an overhead line combined with an underground cable. The proposed fault location method is utilized to overcome the difficulties introduced by the discontinuity at the transition point from the overhead line to the underground cable and the different traveling wave velocities along the line and the cable. The third topology is a segmented high voltage direct current (HVDC) transmission line including an overhead line combined with an underground cable. This topology is widely utilized to transmit renewable energy-based electrical power from remote locations to the load centers such as from off-shore wind farms to on-shore grids. This dissertation introduces several enhancements to the existing fault type and fault location algorithms: improvement in the concept of fault type classification and faulty section identification by using SVMs with smaller inputs and improvements in the fault location in the complex configurations by utilizing less measurements from the terminals. / Ph. D.

During vibration of engineering structures, fatigue cracks may exhibit repetitive crack open-close breathing like phenomenon. In this thesis, the concept of entropy is employed to quantify this bi-linearity/irregularity of the vibration response so as to evaluate crack severity. To increase the sensitivity of the entropy calculation to detect the damage severity, entropy is merged with wavelet transformation (WT). A cantilever beam with a breathing crack is studied to asses proposed crack identification method under two vibration conditions: sinusoidal and random excitations. Through numerical simulations and experimental testing, the breathing crack identification under sinusoidal excitation is studied first and proven to be effective. Then, the crack identification sensitivity under lower excitation frequencies is further improved by parametric optimization of sample entropy and WT. Finally, breathing crack identification under general random excitations are experimentally studied and realized using frequency response functions (FRFs) as an add-in tool with the proposed crack identification technique. / October 2016

Advance technology such as various types of automatic data acquisitions, management, and networking systems has created a tremendous capability for managers to access valuable production information to improve their operation quality and efficiency. Signal processing and data mining techniques are more popular than ever in many fields including intelligent manufacturing. As data sets increase in size, their exploration, manipulation, and analysis become more complicated and resource consuming. Timely synthesized information such as functional data is needed for product design, process trouble-shooting, quality/efficiency improvement and resource allocation decisions. A major obstacle in those intelligent manufacturing system is that tools for processing a large volume of information coming from numerous stages on manufacturing operations are not available. Thus, the underlying theme of this thesis is to reduce the size of data in a mathematical rigorous framework, and apply existing or new procedures to the reduced-size data for various decision-making purposes. This thesis, first, proposes {it Wavelet-based Random-effect Model} which can generate multiple functional data signals which have wide fluctuations(between-signal variations) in the time domain. The random-effect wavelet atom position in the model has {it locally focused impact} which can be distinguished from other traditional random-effect models in biological field. For the data-size reduction, in order to deal with heterogeneously selected wavelet coefficients for different single curves, this thesis introduces the newly-defined {it Wavelet Vertical Energy} metric of multiple curves and utilizes it for the efficient data reduction method. The newly proposed method in this thesis will select important positions for the whole set of multiple curves by comparison between every vertical energy metrics and a threshold ({it Vertical Energy Threshold; VET}) which will be optimally decided based on an objective function. The objective function balances the reconstruction error against a data reduction ratio. Based on class membership information of each signal obtained, this thesis proposes the {it Vertical Group-Wise Threshold} method to increase the discriminative capability of the reduced-size data so that the reduced data set retains salient differences between classes as much as possible. A real-life example (Tonnage data) shows our proposed method is promising.

Serving geospatial data collected from remote sensing methods (satellite images, areal photos, etc.) have become crutial in many geographic information system (GIS) applications such as disaster management, municipality applications, climatology, environmental observations, military applications, etc. Even in today&rsquo / s highly developed information systems, geospatial image data requies huge amount of physical storage spaces and such characteristics of geospatial image data make its usage limited in above mentioned applications. For this reason, web-based GIS applications can benefit from geospatial image streaming through web-based architectures. Progressive transmission of geospatial image and map data on web-based architectures is implemented with the developed image streaming methodology. The software developed allows user interaction in such a way that the users will visualize the images according to their level of detail. In this way geospatial data is served to the users in an efficient way. The main methods used to transmit geospatial images are serving tiled image pyramids and serving wavelet based compressed bitstreams. Generally, in GIS applications, tiled image pyramids that contain copies of raster datasets at different resolutions are used rather than differences between resolutions. Thus, redundant data is transmitted from GIS server with different resolutions of a region while using tiled image pyramids. Wavelet based methods decreases redundancy. On the other hand methods that use wavelet compressed bitsreams requires to transform the whole dataset before the transmission. A hybrid streaming methodology is developed to decrease the redundancy of tiled image pyramids integrated with wavelets which does not require transforming and encoding whole dataset. Tile parts&rsquo / coefficients produced with the methodlogy are encoded with JPEG 2000, which is an efficient technology to compress images at wavelet domain.

This master’s thesis deals with the wavelet transform and its use in processing and removing noise from images acquired by nuclear magnetic resonance. It defines fundamental terms for this work as mother wavelet or thresholding. Above all, it describes the principle of wavelet transform, thresholding techniques and criteria for evaluating the effectiveness of filtration. It describes the relation between wavelet transforms and digital filter banks. The experimental section describes the designed filtering method for removing noise from an image captured by the technique of nuclear magnetic resonance. We applied to different kinds of mother wavelets. Evaluation of the effectiveness of filtering was performed using the signal to noise ratio, relative contrast and the steepness of the intensity changes in signal intensity. It also discusses the comparison of properties of the image and selecting the mother wavelets based on image characteristics. Images were compared in terms of a histogram, cumulative histogram, k-space and the difference image.

Within the dissertation thesis “Study of Biological Material Attributes by Using Image Analysis Methods”, attention is focused on monitoring of the application of image analysis methods, mostly a fractal analysis, in studying the properties of various yeast species. The thesis includes determining the number of yeast cells and vegetative propagation of yeast using fractal parameters – fractal measure D and fractal dimension K. Attention is also paid not only to the application of the existing image analysis methods, but also to their renovation. The obtained images were evaluated using the box counting method specified by implementation of wavelet transformation. To monitor yeast cells for a longer time, it was first necessary to prepare a suitable microscopic preparation. To distinguish live and dead cells, the following fluorescent dyes were used: acridine orange, fluorescein diacetate, FUN-1, and Calcofluor White M2R. The images of yeast cells were recorded using a still camera or a CCD camera and microscope. Clips of the same size were obtained from the acquired digital photographs and processed by the HarFA program developed at the Faculty of Chemistry, Brno University of Technology. On the results it is possible to see a change in the fractal dimension depending on time, i.e. on the change of a budding cell structure, or to determine the number and radius of yeast cells upon predefined conditions.

The thesis analyses co-movement of 10Y sovereign bond yields of 11 EU mem- bers (Greece, Spain, Portugal, Italy, France, Germany, Netherlands, Great Britain, Belgium, Sweden and Denmark) divided into the three groups (the Core of the Eurozone, the Periphery of the Eurozone, the states outside the Eurozone). In the center of attention are changes of co-movement in the crisis period, especially near the two significant dates - the fall of Lehman Brothers (15.9.2008) and the day, when increase of Greek public deficit was announced (20.10.2009). Main contribution of the thesis is usage of alternative methodol- ogy - wavelet transformation. It allows to research how co-movement changes across scales (frequencies) and through time. Wavelet coherence is used as well as wavelet bivariate and multiple correlation. The thesis brings three main findings: (1) co-movement significantly decreased in the crisis period, but the results differ in the groups, (2) co-movement significantly differs across scales, but its heterogeneity decreased in the crisis period, (3) near to the examined dates sharp and significant decrease of wavelet correlation was observable across lower scales in some states. JEL Classification C32, C49, C58, H63 Keywords Co-movement, Wavelet Transformation, Sovereign Debt Crisis, Sovereign Bond Yields,...

Im Rahmen des TERA-Projektes (Turbo Evaluation and Rapid Algorithms) wurde ein neuartiger, hochgradig effizienter probabilistischer Algorithmus zum Lösen polynomialer Gleichungssysteme entwickelt und für den komplexen Fall implementiert. Die Geometrie polarer Varietäten gestattet es, diesen Algorithmus zu einem Verfahren zur Charakterisierung der reellen Lösungsmengen polynomialer Gleichungssysteme zu erweitern. Ziel dieser Arbeit ist es, eine Implementierung dieses Verfahrens zur Bestimmung reeller Lösungen auf eine Klasse von Beispielproblemen anzuwenden. Dabei wurde Wert darauf gelegt, dass diese Beispiele reale, praxisbezogene Anwendungen besitzen. Diese Anforderung ist z.B. für polynomiale Gleichungssysteme erfüllt, die sich aus dem Entwurf von schnellen Wavelet-Transformationen ergeben. Die hier betrachteten Wavelet-Transformationen sollen die praktisch wichtigen Eigenschaften der Orthogonalität und Symmetrie besitzen. Die Konstruktion einer solchen Wavelet-Transformation hängt von endlich vielen reellen Parametern ab. Diese Parameter müssen gewisse polynomiale Gleichungen erfüllen. In der veröffentlichten Literatur zu diesem Thema wurden bisher ausschließlich Beispiele mit endlichen Lösungsmengen behandelt. Zur Berechnung dieser Beispiele war es dabei ausreichend, quadratische Gleichungen in einer oder zwei Variablen zu lösen. Zur Charakterisierung der reellen Lösungsmenge eines polynomialen Gleichungssystems ist es ein erster Schritt, in jeder reellen Zusammenhangskomponente mindestens einen Punkt aufzufinden. Schon dies ist ein intrinsisch schweres Problem. Es stellt sich heraus, dass der Algorithmus des TERA-Projektes zur Lösung dieser Aufgabe bestens geeignet ist und daher eine größere Anzahl von Beispielproblemen lösen kann als die besten kommerziell erhältlichen Lösungsverfahren. / As a result of the TERA-project on Turbo Evaluation and Rapid Algorithms a new type, highly efficient probabilistic algorithm for the solution of systems of polynomial equations was developed and implemented for the complex case. The geometry of polar varieties allows to extend this algorithm to a method for the characterization of the real solution set of systems of polynomial equations. The aim of this work is to apply an implementation of this method for the determination of real solutions to a class of example problems. Special emphasis was placed on the fact that those example problems possess real-life, practical applications. This requirement is satisfied for the systems of polynomial equations that result from the design of fast wavelet transforms. The wavelet transforms considered here shall possess the practical important properties of symmetry and orthogonality. The specification of such a wavelet transform depends on a finite number of real parameters. Those parameters have to obey certain polynomial equations. In the literature published on this topic, only example problems with a finite solution set were presented. For the computation of those examples it was sufficient to solve quadratic equations in one or two variables. To characterize the set of real solutions of a system of polynomial equations it is a first step to find at least one point in each connected component. Already this is an intrinsically hard problem. It turns out that the algorithm of the TERA-project performes very well with this task and is able to solve a larger number of examples than the best known commercial polynomial solvers.