Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Porous Silicon-Based Photonic Biosensors: Current Status and Emerging Applications

Abstract: Since its first demonstration as a promising material for molecular detection two decades ago, porous silicon (PSi) has become a commercially viable optical biosensor platform attracting sustained research interest. Progress in both fundamental understanding and diverse application areas has occurred. In particular, new approaches for biosensor design, new implementations of PSi as a host matrix for synergistic materials that enable alternate biosensor readout approaches and signal enhancement, new methods to reliably achieve higher detection sensitivity, and new emphases on detection of molecules in complex media, integration with microfluidics for sample handling, and multiplexed detection capabilities have been reported. In all cases, the extremely high internal surface area of PSi, the ease in modifying the surface chemistry of PSi, and the straightforward fabrication of PSi films are key advantages for PSi biosensors. This review focuses on advances in PSi optical biosensors achieved over the past three years.

Coarse orientation of terrestrial laser scans in urban environments

Abstract: The use of terrestrial laser scanners is becoming increasingly popular. For the acquisition of larger scenes, it is usually necessary to align all scans to a common reference frame. While there are methods using direct measurement of the orientation, due to simplicity and costs, mostly artificial targets are used. This works reliably, but usually adds a substantial amount of time to the acquisition process. Methods to align scans using the scan data itself have been known for a long time, however, being iterative, they need good initial values. In this paper, we investigate two different methods targeted at the determination of suitable initial values. The first one is based on a symbolic approach, using corresponding features to compute the orientation. The second one is based on an iterative alignment scheme originally proposed in the robotics domain. To assess the performance of both methods, a set of 20 scans has been acquired systematically along a trajectory in a downtown area. Reference orientations were obtained by a standard procedure using artificial targets. We present the results of both methods regarding convergence and accuracy, and compare their performance.

Naive Bayes Super-Resolution Forest

Abstract: This paper presents a fast, high-performance method for super resolution with external learning. The first contribution leading to the excellent performance is a bimodal tree for clustering, which successfully exploits the antipodal invariance of the coarse-to-high-res mapping of natural image patches and provides scalability to finer partitions of the underlying coarse patch space. During training an ensemble of such bimodal trees is computed, providing different linearizations of the mapping. The second and main contribution is a fast inference algorithm, which selects the most suitable mapping function within the tree ensemble for each patch by adopting a Local Naive Bayes formulation. The experimental validation shows promising scalability properties that reflect the suitability of the proposed model, which may also be generalized to other tasks. The resulting method is beyond one order of magnitude faster and performs objectively and subjectively better than the current state of the art.