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Darmstadt, Germany

Debes C.,AGT Group RandD GmbH | Zoubir A.M.,TU Darmstadt | Amin M.G.,Villanova University
IEEE Transactions on Geoscience and Remote Sensing

We consider the problem of through-the-wall radar imaging (TWRI), in which polarimetric imaging is used for automatic target detection. Two generalized statistical detectors are proposed which perform joint detection and fusion of a set of multipolarization radar images. The first detector is an extension of a previously proposed iterative target detector for multiview TWRI. This extension allows the detector to automatically adapt to statistics that may vary, depending on target locations and electromagnetic-wave polarizations. The second detector is based on Bayes' test and is of interest when target pixel occupancies are known from, e.g., secondary data. Properties of the proposed detectors are delineated and demonstrated by real data measurements using wideband sum-and-delay beamforming, acquired in a semicontrolled lab environment. We examine the performance of the proposed detectors when imaging both metal objects and humans. © 2012 IEEE. Source

Deisenroth M.P.,TU Darmstadt | Deisenroth M.P.,University of Washington | Turner R.D.,Winton Capital | Turner R.D.,University of Cambridge | And 3 more authors.
IEEE Transactions on Automatic Control

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail. © 2011 IEEE. Source

Walther M.,AGT Group RandD GmbH
CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

Product information search has become one of the most important application areas of the Web. Especially considering pricey technical products, consumers tend to carry out intensive research activities previous to an actual acquisition. However, the vast amount of available data about such products and its various representations may easily overstrain potential customers. In this paper, we develop a comprehensive technique for extracting product specifications about arbitrary technical products from web pages in a widely unsupervised manner. The technique is based on a clustering approach that uses structural and visual features of web page elements. The resulting detailed information sets allow a potential consumer to effectively compare products while saving the manual extraction work. © 2012 IEEE. Source

Ros S.P.,University of Murcia | Lischka M.,AGT Group RandD GmbH | Marmol F.G.,NEC Europe Ltd.
Proceedings of ACM Symposium on Access Control Models and Technologies, SACMAT

The amount of private information in the Internet is constantly increasing with the explosive growth of cloud computing and social networks. XACML is one of the most important standards for specifying access control policies for web services. The number of XACML policies grows really fast and evaluation processing time becomes longer. The XEngine approach proposes to rearrange the matching tree according to the attributes used in the target sections, but for speed reasons they only support equality of attribute values. For a fast termination the combining algorithms are transformed into a first applicable policy, which does not support obligations correctly. In our approach all comparison functions defined in XACML as well as obligations are supported. In this paper we propose an optimization for XACML policies evaluation based on two tree structures. The first one, called Matching Tree, is created for a fast searching of applicable rules. The second one, called Combining Tree, is used for the evaluation of the applicable rules. Finally, we propose an exploring method for the Matching Tree based on the binary search algorithm. The experimental results show that our approach is orders of magnitude better than Sun PDP. Copyright 2012 ACM. Source

Hahn J.,TU Darmstadt | Debes C.,AGT Group RandD GmbH | Leigsnering M.,TU Darmstadt | Zoubir A.M.,TU Darmstadt
Digital Signal Processing: A Review Journal

Hyperspectral imaging (HSI) is an emerging technique, which provides the continuous acquisition of electro-magnetic waves, usually covering the visible as well as the infrared light range. Many materials can be easily discriminated by means of their spectra rendering HSI an interesting method for the reliable classification of contents in a scene. Due to the high amount of data generated by HSI, effective compression algorithms are required. The computational complexity as well as the potentially high number of sensors render HSI an expensive technology. It is thus of practical interest to reduce the number of required sensor elements as well as computational complexity - either for cost or for energy reasons. In this paper, we present two different systems that acquire hyperspectral images with less samples than the actual number of pixels, i.e. in a low dimensional representation. First, a design based on compressive sensing (CS) is explained. Second, adaptive direct sampling (ADS) is utilized to obtain coefficients of hyperspectral images in the 3D (Haar) wavelet domain, simplifying the reconstruction process significantly. Both approaches are compared with conventionally captured images with respect to image quality and classification accuracy. Our results based on real data show that in most cases only 40% of the samples suffice to obtain high quality images. Using ADS, the rate can be reduced even to a greater extent. Further results confirm that, although the number of acquired samples is dramatically reduced, we can still obtain high classification rates. © 2013 Elsevier Inc. Source

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