Graph separators: a parameterized view

Graph separation is a well-known tool to make (hard) graph problems accessible to a divide-and-conquer approach. We show how to use graph separator theorems in combination with (linear) problem kernels in order to develop fixed parameter algorithms for many well-known NP-hard (planar) graph problems. We coin the key notion of glueable select&verify graph problems and derive from that a prospective way to easily check whether a planar graph problem will allow for a fixed parameter algorithm of running time for constant c. One of the main contributions of the paper is to exactly compute the base c of the exponential term and its dependence on the various parameters specified by the employed separator theorem and the underlying graph problem. We discuss several strategies to improve on the involved constant c.     

Substrate Modes of (Al,In)GaN Semiconductor Laser Diodes on SiC and GaN Substrates

In semiconductor laser diodes layers with high refractive index can act as parasitic waveguides and cause severe losses to the optical mode propagating in the longitudinal direction. For (Al,In)GaN laser diodes, the parasitic modes are typically caused by the SiC or GaN substrate or buffer layers, hence the name substrate modes. A set of four different experiments shows the effect of substrate modes in the near-field (the most direct evidence of substrate modes), as side lobes in far-field, oscillations of the optical gain spectra, and as dependency of threshold current on n-cladding thickness. We derive several basic properties of the substrate modes by simple estimates. For a quantitative analysis we employ a 2-D finite element electromagnetic simulation tool. We simulate periodic variations in the cavity gain spectrum that explain the measurements in terms of absolute value and oscillation amplitude. We show that it is necessary to include the refractive index dispersion in order to get the correct period of the gain oscillations. Furthermore, we use the simulations to optimize the laser diode design with respect to substrate mode losses within the constraints given, e.g., by growth conditions     

A stereophonic acoustic signal extraction scheme for noisy and reverberant environments

In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation (‘directional BSS’), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions.     

Rotation Averaging

This paper is conceived as a tutorial on rotation averaging, summarizing the research that has been carried out in this area; it discusses methods for single-view and multiple-view rotation averaging, as well as providing proofs of convergence and convexity in many cases. However, at the same time it contains many new results, which were developed to fill gaps in knowledge, answering fundamental questions such as radius of convergence of the algorithms, and existence of local minima. These matters, or even proofs of correctness have in many cases not been considered in the Computer Vision literature. We consider three main problems: single rotation averaging, in which a single rotation is computed starting from several measurements; multiple-rotation averaging, in which absolute orientations are computed from several relative orientation measurements; and conjugate rotation averaging, which relates a pair of coordinate frames. This last is related to the hand-eye coordination problem and to multiple-camera calibration.     

Consumer credit risk: Individual probability estimates using machine learning

Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the creditworthiness of consumers, and they are usually estimated by logistic regression. Here, we present a general framework for estimating individual consumer credit risks by use of machine learning methods. Since a probability is an expected value, all nonparametric regression approaches which are consistent for the mean are consistent for the probability estimation problem. Among others, random forests (RF), k-nearest neighbors (kNN), and bagged k-nearest neighbors (bNN) belong to this class of consistent nonparametric regression approaches. We apply the machine learning methods and an optimized logistic regression to a large dataset of complete payment histories of short-termed installment credits. We demonstrate probability estimation in Random Jungle, an RF package written in C++ with a generalized framework for fast tree growing, probability estimation, and classification. We also describe an algorithm for tuning the terminal node size for probability estimation. We demonstrate that regression RF outperforms the optimized logistic regression model, kNN, and bNN on the test data of the short-term installment credits.     

Application of a new algorithm using Doppler information to retrieve complex wind fields over the Black Sea from ENVISAT SAR images

Several algorithms have been proposed to retrieve near-surface wind fields from C-band synthetic aperture radar (SAR) images acquired over the ocean. They mainly differ in the way they retrieve the wind direction. Conventionally, the wind direction is taken from atmospheric models or is extracted from the linear features sometimes visible in SAR images. Recently, a new wind retrieval algorithm has been proposed, which also includes the Doppler shift induced by motions of the sea surface. In this article, we apply three wind retrieval algorithms, including the one using Doppler information, to three complex wind events encountered over the Black Sea and compare the SAR-derived wind fields with model wind fields calculated using the high-resolution weather research and forecasting (WRF) model. It is shown that the new algorithm is very efficient in resolving the 180° ambiguity in the wind direction, which is often a problem in the streak-based wind retrieval algorithms. However, the Doppler-based algorithm only yields good results for wind directions that have a significant component in the look direction of the SAR antenna. Furthermore, it is dependent on good separation of the contributions to the Doppler shift induced by surface currents and wind-related effects (wind drift and wind-sea components of the ocean wave spectrum). We conclude that an optimum wind retrieval algorithm should consist of a combination of the algorithms based on linear features and Doppler information.     

Direct comparison between hot pressing and electric field-assisted sintering of submicron alumina

This study compares hot pressing (HP) and the electric field-assisted sintering technique (FAST) of two different electrically insulating Al₂O₃ submicron powders with median particle sizes of 150 and 500 nm. Sample geometry, heating schedule, applied pressure and atmosphere were identical for both sintering methods. The densification behavior and characterization of the microstructure revealed that FAST sintered samples reached a higher density compared with HP, in particular for the finer powder. It was found that an increase in dwell time was required to reach the same final density by HP. However, analysis of the sintering curves showed that the densification mechanism for both sintering methods was grain boundary diffusion. Increasing the heating rate up to 150 K min^?1 did not modify the densification mechanism. The sintering trajectory showed that the grain size was only dependent on density and was insensitive to the sintering method, in addition to showing a lack of preferential grain orientation.     

Effect of cathodic protection on macrocell currents: A viable method to monitor and control the efficiency of CP?

Corrosion protection of steel reinforcement in concrete structures by cathodic protection (CP) is a cost effective, reliable, and widely accepted method to stop and prevent the corrosion of the steel reinforcement. The efficiency of CP is usually monitored by the “24 h, 100 mV depolarization criterion,” a purely empirical criterion whose implementation is cost and labor intensive and that does not allow online control of CP. Within an extended research project on CP applied to concrete members of a highway bridge exposed to penetrating moisture, three sets of macrocells (MC), each composed of five MC sensors, were installed in conjunction with concrete resistance sensors and silver/silver chloride reference cells. Chloride profiles were determined from the cored or drilled powder originating from the installation of sensors and from drilling cores. Corrosion currents, steel potentials, and concrete resistance were monitored over a period of 1 year before, after installing the CP systems (which remained switched off for half a year for evaluating the effect of the conductive coating), and after start-up of the CP systems. The CP systems applied consist of a moisture resistant conductive coating. As expected CP has a pronounced effect on local MC currents: Anodic MC currents were reduced or changed into cathodic currents, whereas cathodic currents were only weakly influenced. By adjusting the applied protection current all anodic MC currents may be changed into cathodic MC currents. Results indicate that the 100 mV depolarization criterion is a conservative criterion in atmospherically exposed concrete; it is not reliable in strongly wetted concrete. There was no consistent correlation between the CP induced changes in the local MC currents and 24 h depolarization values indicating that large potential shifts induced by CP do not necessarily imply overprotection. Results show that monitoring MC currents before, during, and after CP operation allows to demonstrate in a transparent way the effect of CP on the corrosion of the steel reinforcement. Online monitoring of MC currents is proposed as a viable and comprehensible method to monitor and control the efficiency of CP.