Speech and noise power estimation using Gamma modeling

In speech enhancement, having an accurate estimation of the power of the speech and noise signals forming the noisy observation is critical, as it can highly affect the performance of the enhancement algorithm. A method is introduced in which the distributions of the power of the speech and noise periodograms are modeled using the Gamma distribution to extract their shape parameters. These shape parameters are later used in the observed noisy speech to estimate the power when forming speech and noise periodograms. This method results in more accurate and faster power estimation with respect to the well‐known minimum statistics power estimation algorithm and together with the maximum a posteriori speech enhancement algorithm exhibits good speech enhancement performance.     

Gram-Negative Flagella Glycosylation

Protein glycosylation had been considered as an eccentricity of a few bacteria. However, through advances in analytical methods and genome sequencing, it is now established that bacteria possess both N-linked and O-linked glycosylation pathways. Both glycosylation pathways can modify multiple proteins, flagellins from Archaea and Eubacteria being one of these. Flagella O-glycosylation has been demonstrated in many polar flagellins from Gram-negative bacteria and in only the Gram-positive genera Clostridium and Listeria. Furthermore, O-glycosylation has also been demonstrated in a limited number of lateral flagellins. In this work, we revised the current advances in flagellar glycosylation from Gram-negative bacteria, focusing on the structural diversity of glycans, the O-linked pathway and the biological function of flagella glycosylation.     

Analyzing probabilistic pushdown automata

The paper gives a summary of the existing results about algorithmic analysis of probabilistic pushdown automata and their subclasses.     

Automated resolution of connector architectures using constraint solving (ARCAS method)

In current software systems, connectors play an important role by encapsulating the communication and coordination logic. Since they share common patterns (elements) depending on characteristics of the connections, the elements can be predefined and reused. A method of connector implementation based on a composition of predefined elements naturally comprises two steps: resolution of the connector architecture, and creation of the actual connector code based on the architecture. However, manual resolution of a connector architecture is very difficult due to the number of factors to be considered. Thus, the challenge is to come up with an automated method, able to address all the important factors. In this paper, we present a method for automated resolution of connector architectures based on constraint solving techniques. We exploit a propositional logic with relational calculus for defining a connector theory, a constraint specification reflecting both the predefined parts and the important resolution factors, and employ a constraint solver to find a suitable connector architecture as a model of the theory. As a proof of the concept, we show how the theory can be captured in the Alloy language and resolved via the Alloy Analyzer.     

A framework for computing asset-sighting distance

Safety critical railway assets need to be visible from prescribed distances, ensuring safety. Traditional methods for measuring these sighting distances involve manual labour and disruption of rail services. Drawing parallels with other areas, efficiency and cost of such a task can be improved with automation. Hence, in this work, we describe a framework providing such automation, using image frames captured from video equipment as input. Any such framework needs to meet a set of associated challenges, including: determining the image position of decreasing sized assets as they appear farther from the observer, extracting three-dimensional positioning information (relative to motion) from two-dimensional video information and determining when an asset can no longer be seen (i.e. the sighting distance). Results show that the methods included in this framework perform better than a traditional method and information regarding asset-sighting distance is accurately computed.     

Investigation on PEM water electrolysis cell design and components for a HyCon solar hydrogen generator

Hydrogen as a secondary energy carrier promises a large potential as a long term storage for fluctuating renewable energies. In this sense a highly efficient solar hydrogen generation is of great interest especially in southern countries having high solar irradiation. The patented Hydrogen Concentrator (HyCon) concept yields high efficiencies combining multi-junction solar cells with proton exchange (PEM) membrane water electrolysis. In this work, a special PEM electrolysis cell for the HyCon concept was developed and investigated. It is shown that the purpose-made PEM cell shows a high performance using a titanium hybrid fiber sinter function both as a porous transport layer and flow field. The electrolysis cell shows a high performance with 1.83 V at 1 A/cm² and 24 °C working under natural convection with a commercially available catalyst coated membrane. A theoretical examination predicts a total efficiency for the HyCon module from sunlight to hydrogen of approximately 19.5% according to the higher heating value.     

Temperature and pump power dependent photoluminescence characterization of MBE grown GaAsBi on GaAs

Bulk and quantum well GaAs_1?xBi_x/GaAs layers with Bi mole fractions from 0.02 to 0.10 are grown by molecular-beam epitaxy at temperatures ranging from 280 to 320?°C. The samples are characterized using temperature and pump-power dependent photoluminescence measurements covering 8–300?K and 1–250?mW (7–1,800?W/cm²), respectively. The results indicate that there is strong reduction in bandgap energy with the incorporation of small amounts of Bi and that GaAsBi most likely forms a weak type-I band alignment with GaAs.     

Hierarchical Correlation Clustering in Multiple 2D Scalar Fields

Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.