Integrated npn/pnp GaAs/AlGaAs HBTs grown by selective MBE

npn and pnp GaAs/AlGaAs heterojunction bipolar transistors have been successfully fabricated on the same GaAs substrate using selective molecular beam epitaxy and a new merged HBT processing technology. The DC and microwave characteristics of the transistors are equivalent to those of similar HBTs grown by conventional MBE on separate GaAs substrates.<>     

10 kV IGCTs

This article describes the requirements, semiconductor design, and the characteristics of the first 10-kV integrated gate commutated thyristors (IGCT) for 6- to 7.2 kV, three-level NPC voltage source converters. It is shown that the use of 10-kV IGCTs enables the reduction of the total number of the main power components and also the reliability of the converter is increased. Finally the tradeoff curve and concept specifications of 10-kV IGCTs are presented.     

Importance Driven Halftoning

Most halftoning techniques have been primarily concerned with achieving an accurate reproduction of local gray-scale intensities while avoiding the introduction of artifacts. A secondary concern in halftoning has been the preservation of edges in the halftoned image. In this paper, we will introduce a new halftoning technique that utilizes a bandpass pyramid to achieve an accurate reproduction of important attributes in the image. Ink is distributed through the bandpass pyramid primarily according to a user defined importance function. This technique has three main characteristics. First, our technique can produce results similar to many other halftoning techniques by allowing a generic importance function to be specified. If the chosen importance function is average intensity we obtain results similar to traditional halftoning. We also show how the importance function can be changed to highlight areas with high variance. Second, in addition to changing the importance function, the drawing primitives can also be changed. By using line segments instead of single pixels as drawing primitives we illustrate how edge enhancement can be achieved. Third, this technique allows the user to easily limit the number drawing primitives used. This is useful in limited resource rendering.
In addition to providing a tailorable halftoning technique our method can easily be adapted to produce two tone non-photorealistic (NPR) images. We illustrate this by showing how sketched effects can be achieved by aligning the drawing primitives according to different image attributes.     

Towards biodegradable wireless implants

A new generation of partially or even fully biodegradable implants is emerging. The idea of using temporary devices is to avoid a second surgery to remove the implant after its period of use, thereby improving considerably the patient's comfort and safety. This paper provides a state-of-the-art overview and an experimental section that describes the key technological challenges for making biodegradable devices. The general considerations for the design and synthesis of biodegradable components are illustrated with radiofrequency-driven resistor-inductor-capacitor (RLC) resonators made of biodegradable metals (Mg, Mg alloy, Fe, Fe alloys) and biodegradable conductive polymer composites (polycaprolactone-polypyrrole, polylactide-polypyrrole). Two concepts for partially/fully biodegradable wireless implants are discussed, the ultimate goal being to obtain a fully biodegradable sensor for in vivo sensing.     

Maximum likelihood training of probabilistic neural networks

A maximum likelihood method is presented for training probabilistic neural networks (PNN's) using a Gaussian kernel, or Parzen window. The proposed training algorithm enables general nonlinear discrimination and is a generalization of Fisher's method for linear discrimination. Important features of maximum likelihood training for PNN's are: 1) it economizes the well known Parzen window estimator while preserving feedforward NN architecture, 2) it utilizes class pooling to generalize classes represented by small training sets, 3) it gives smooth discriminant boundaries that often are "piece-wise flat" for statistical robustness, 4) it is very fast computationally compared to backpropagation, and 5) it is numerically stable. The effectiveness of the proposed maximum likelihood training algorithm is assessed using nonparametric statistical methods to define tolerance intervals on PNN classification performance.     

Analysis of porous features in zirconia based inert matrix, impact on the material qualification

Pore size distribution, porosities and consequently derived densities are key parameters for the qualification of inert matrix fuel (IMF). Porous features must be determined accurately for assessing the in-pile behaviour of the fuel. The analytical methodologies to measure porosities are revisited. The paper discusses in a comprehensive way the results obtained for simulated or plutonium loaded inert matrix fuel i.e. erbia and ceria or plutonia doped yttria stabilised zirconia, by applying thermodynamic or instrumental approaches. The n-D results (open/close porosities) gained by applying invasive/non-invasive, microscopic/macroscopic, neutron/photon and reflection/diffraction/transmission methods with fluid intrusion or not, are given and discussed for the zirconia based cubic solid solution.     

Pseudomonas aeruginosa Biofilm Growth Inhibition on Medical Plastic Materials by Immobilized Esterases and Acylase

Biofilms are matrix‐encapsulated cell aggregates that cause problems in technical and health‐related areas; for example, 65 % of all human infections are biofilm associated. This is mainly due to their ameliorated resistance against antimicrobials and immune systems. Pseudomonas aeruginosa, a biofilm‐forming organism, is commonly responsible for nosocomial infections. Biofilm development is partly mediated by signal molecules, such as acyl‐homoserine lactones (AHLs) in Gram‐negative bacteria. We applied horse liver esterase, porcine kidney acylase, and porcine liver esterase; these can hydrolyze AHLs, thereby inhibiting biofilm formation. As biofilm infections are often related to foreign material introduced into the human body, we immobilized the enzymes on medical plastic materials. Biofilm formation was quantified by Crystal Violet staining and confocal laser scanning microscopy, revealing up to 97 % (on silicone), 54 % (on polyvinyl chloride), and 77 % (on polyurethane) reduced biomass after 68 h growth.     

A security framework in G-Hadoop for big data computing across distributed Cloud data centres

MapReduce is regarded as an adequate programming model for large-scale data-intensive applications. The Hadoop framework is a well-known MapReduce implementation that runs the MapReduce tasks on a cluster system. G-Hadoop is an extension of the Hadoop MapReduce framework with the functionality of allowing the MapReduce tasks to run on multiple clusters. However, G-Hadoop simply reuses the user authentication and job submission mechanism of Hadoop, which is designed for a single cluster. This work proposes a new security model for G-Hadoop. The security model is based on several security solutions such as public key cryptography and the SSL protocol, and is dedicatedly designed for distributed environments. This security framework simplifies the users authentication and job submission process of the current G-Hadoop implementation with a single-sign-on approach. In addition, the designed security framework provides a number of different security mechanisms to protect the G-Hadoop system from traditional attacks.     

Pathfinder: Visual Analysis of Paths in Graphs

The analysis of paths in graphs is highly relevant in many domains. Typically, path‐related tasks are performed in node‐link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path‐related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder, a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node‐link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node‐link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.     

An agent-based simulation model for analyzing the governance of the Brazilian Financial System

Regulation can play an important role in effectively managing systemic risk while providing accountability to all affected governments. IMF points out weak governance structures as one of the main causes for financial/economical crisis. However, research in this area is still limited. One of the reasons is the inherent complexity of the public sector governance notion. In this research, the regulatory governance of the financial sector is conceived as a complex system, in which governance is perceived as a phenomenon resulting from the interactions among all the actors that influence or are influenced by regulatory activities within the financial sector. An agent-based simulation was developed to analyze and evaluate the emergent behaviors from the governance in the Brazilian finance sector under different macroeconomics variables and different attitudes, perceptions and desires of economic and political actors. The agent-based model is combined with an econometric model, which is intended to characterize the macroeconomic environment. The regulatory environment is modeled by computational agents using BDI (beliefs–desires–intentions) architecture. The agents have beliefs about their environment and desires they want to satisfy, thus leading them to create intentions to act. The agents’ behavior was modeled using fuzzy rules built by means of content analysis of newspapers and in-depth interviews with experts from the financial area. Computational experiments demonstrate the potential of the agent-based model simulation in the study of complex environments involving regulatory governance.