An important task of information retrieval is to induce classifiers capable of categorizing text documents. The fact that the same document can simultaneously belong to two or more categories is referred by the term multi-label classification (or categorization). Domains of this kind have been encountered in diverse fields even outside information retrieval. This dissertation discusses one challenging aspect of text categorization: the documents (i.e., training examples) are characterized by an extremely large number of features. As a result, many existing machine learning techniques are in such domains prohibitively expensive. This dissertation seeks to reduce these costs significantly. The proposed scheme consists of two steps. The first runs a so-called baseline induction algorithm (BIA) separately on different versions of the data, each time inducing a different subclassifier---more specifically, BIA is run always on the same training documents that are each time described by a different subset of the features. The second step then combines the subclassifiers by a fusion algorithm: when a document is to be classified, each subclassifier outputs a set of class labels accompanied by its confidence in these labels; these outputs are then combined into a single multi-label recommendation. The dissertation investigates a few alternative fusion techniques, including an original one, inspired by the Dempster-Shafer Theory. The main contribution is a mechanism for assigning the mass function to individual labels from subclassifiers. The system's behavior is illustrated on two real-world data sets. As indicated, in each of them the examples are described by thousands of features, and each example is labeled with a subset of classes. Experimental evidence indicates that the method can scale up well and achieves impressive computational savings in exchange for only a modest loss in the classification performance. The fusion method proposed is also shown to be more accurate than other more traditional fusion mechanisms. For a very large multi-label data set, the proposed mechanism not only speeds up the total induction time, but also facilitates the execution of the task on a small computer. The fact that subclassifiers can be constructed independently and more conveniently from small subsets of features provides an avenue for parallel processing that might offer further increase in computational efficiency.

Attitude and position determination systems in satellites are absolutely necessary to keep the desired trajectory. A very accurate, reliable and most used sensor for attitude determination is the star tracker, which orient itself in space by observing and comparing star constellations with known star patterns. For on earth tests of movements and docking maneuvers of spacecrafts, the new Spacecraft Maneuvering and Docking (SMD) facility at the chair of Aerospace Information Technology at the University of Würzburg has been built. Air bearing systems on the space ve- hicles help to create micro gravity environment on a smooth surface and simulate an artificial space-like surrounding. A new star tracker based optical sensor for indoor application need to be developed in order to get the attitude and position of the vehicles. The main objective of this thesis is to research on feasible star tracking algorithms for the SMD facility first and later to implement a star detection software framework with new developed voting methods to give the star tracker system its fully autonomous function of attitude determination and position tracking. Furthermore, together with image processing techniques, the software framework is embedded into a controller board. This thesis proposes also a wireless network system for the facility, where all the devices on the vehicles can uniquely communicate within the same network and a devel- opment of a ground station to monitor the star tracker process has also been introduced. Multiple test results with different scenarios on position tracking and attitude determination, discussions and suggestions on improvements complete the entire thesis work.