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API migration refers to the change of a used API to a different API in a program. A special case is called wrapper-based API migration. The API change is done without touching the program but the old API is reimplemented by means of the from now on used one. This so called wrapper has the interface of the reimplemented API but uses the implementation of the new one.
This is an interesting approach because the wrapper can be used in each program, which uses the old API.
To make this approach reproducible we study a ranking-based method for implementing a wrapper, where we always implement the method with the highest priority depending on failing test cases. Thus, we can reconstruct each implementation step.
We first develop an infrastructure to run and log test suites of Java projects that use an API, which we want to change.
We then build a wrapper for a given API using the ranking-based approach.
In this thesis we exercise a wide variety of libraries, frameworks and other technologies that are available for the Haskell programming language. We show various applications of Haskell in real-world scenarios and contribute implementations and taxonomy entities to the 101companies system. That is, we cover a broad range of the 101companies feature model and define related terms and technologies. The implementations illustrate how different language concepts of Haskell, such as a very strong typing system, polymorphism, higher-order functions and monads, can be effectively used in the development of information systems. In this context we demonstrate both advantages and limitations of different Haskell technologies.
Im Rahmen dieser Bachelorarbeit wurde ein Back-Office für die elektronische Version des Europäischen Schadensberichtes erstellt. Es wurde bereits in anderen Arbeiten ein mobiler Client, welcher auf einem Windows Mobile Handy läuft, sowie ein Polizei Client erstellt. Diese greifen auf das Back-Office zu, um Daten, wie z.B. die Autodaten (Automarke, der Typ, das Baujahr und Bilder eines 3D-Modells des Autos) zu einem bestimmten Kennzeichen oder die Personendaten des jeweiligen Autobesitzers zu erhalten. Der mobile Client sendet zudem die Unfallakte an das Back-Office, damit die Daten über einen Unfall in diesem abgespeichert und weiter bearbeitet werden können. Ziel der Arbeit war es ein erweiterbares, modulares System zu entwickeln, welches später um weitere Module ergänzt werden kann, um neue Funktionen bereitstellen zu können. Diese Module können jeweils beliebige Daten in einer Datenbank abspeichern und diese von der Datenbank auch wieder abfragen, sowie verändern, ohne dass das relationale Schema der Datenbank verändert werden muss.
Texture-based text detection in digital images using wavelet features and support vector machines
(2010)
In this bachelor thesis a new texture-based approach for the detection of text in digital images is presented. The procedure can be essentially divided into two main tasks, in detection of text blocks and detection of individual words, whereby the individual words are extracted from the detected text blocks. Roughly, the developed method acts with multiple support vector machines, which classify possible text regions of an image into real text regions, using wavelet-based features. In the process the possible text regions are defifined by edge projections with diσerent orientations. The results of the approach are X/Y coordinates, width and height of rectangular regions of an image, which contains individual words. This knowledge can be further processed, for example by an optical character recognition software to get the important and useful text information.
Colonoscopy is the gold standard for the detection of colorectal polyps that can progress into cancer. In such an examination, physicians search for polyps in endoscopic images. Thereby polyps can be removed. To support experts with a computer-aided diagnosis system, the University of Koblenz-Landau currently makes some efforts in research different methods for automatic detection. Comparable to traditional pattern recognition systems, features are initially extracted and a classifier is trained on such data. Afterwards, unknown endoscopic images can be classified with the previously trained classifier. This thesis concentrates on the extension of the feature extraction module in the existing system. New detection methods are compared to existing techniques. Several features are implemented, incorporating Graylevel Co-occurrence Matrices, Local Binary Patterns and Discrte Wavelet Transform. Different modifications on those features are applied and evaaluated.