Vanadia doped tungsten-titania SCR catalysts as functional materials for exhaust gas sensor applications

Urea-SCR systems (selective catalytic reduction) are required to meet future NO_x emission standards of heavy-duty and light-duty vehicles. It is a key factor to control the SCR systems and to monitor the catalysts’ functionalities to achieve low emissions. The novel idea of this study is to apply commercially available SCR catalyst materials based on vanadia-doped tungsten-titania as gas sensing films for impedimetric thick-film exhaust gas sensor devices. The dependence of the impedance on the surrounding gas atmosphere, especially on the concentrations of NH₃ and NO₂, is investigated, as well as cross interferences from other components of the exhaust. The sensors provide a good NH₃ sensitivity at 500 °C. The sensor behavior is explained in light of the literature combining the fields of catalysts and semiconducting gas sensors.     

Physically Guided Animation of Trees

This paper presents a new method to animate the interaction of a tree with wind both realistically and in real time. The main idea is to combine statistical observations with physical properties in two major parts of tree animation. First, the interaction of a single branch with the forces applied to it is approximated by a novel efficient two step nonlinear deformation method, allowing arbitrary continuous deformations and circumventing the need to segment a branch to model its deformation behavior. Second, the interaction of wind with the dynamic system representing a tree is statistically modeled. By precomputing the response function of branches to turbulent wind in frequency space, the motion of a branch can be synthesized efficiently by sampling a 2D motion texture.
Using a hierarchical form of vertex displacement, both methods can be combined in a single vertex shader, fully leveraging the power of modern GPUs to realistically animate thousands of branches and ten thousands of leaves at practically no cost.     

Distributed top-<Emphasis Type="Italic">k</Emphasis> aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.     

Geno-mathematical identification of the multi-layer perceptron

In this paper, we will focus on the use of the three-layer backpropagation network in vector-valued time series estimation problems. The neural network provides a framework for noncomplex calculations to solve the estimation problem, yet the search for optimal or even feasible neural networks for stochastic processes is both time consuming and uncertain. The backpropagation algorithm—written in strict ANSI C—has been implemented as a standalone support library for the genetic hybrid algorithm (GHA) running on any sequential or parallel main frame computer. In order to cope with ill-conditioned time series problems, we extended the original backpropagation algorithm to a K nearest neighbors algorithm (K-NARX), where the number K is determined genetically along with a set of key parameters. In the K-NARX algorithm, the terminal solution at instant t can be used as a starting point for the next t, which tends to stabilize the optimization process when dealing with autocorrelated time series vectors. This possibility has proved to be especially useful in difficult time series problems. Following the prevailing research directions, we use a genetic algorithm to determine optimal parameterizations for the network, including the lag structure for the nonlinear vector time series system, the net structure with one or two hidden layers and the corresponding number of nodes, type of activation function (currently the standard logistic sigmoid, a bipolar transformation, the hyperbolic tangent, an exponential function and the sine function), the type of minimization algorithm, the number K of nearest neighbors in the K-NARX procedure, the initial value of the Levenberg–Marquardt damping parameter and the value of the neural learning (stabilization) coefficient α. We have focused on a flexible structure allowing addition of, e.g., new minimization algorithms and activation functions in the future. We demonstrate the power of the genetically trimmed K-NARX algorithm on a representative data set.     

Reduced Aggregate Scattering Operators for Path Tracing

Aggregate scattering operators (ASOs) describe the overall scattering behavior of an asset (i.e., an object or volume, or collection thereof) accounting for all orders of its internal scattering. We propose a practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing. Our approach is modular avoiding costly and inflexible scene‐dependent precomputation. This is achieved by decoupling light transport within and outside of each asset, and precomputing on a per‐asset level. We store the internal transport in a reduced‐dimensional subspace tailored to the structure of the asset geometry, its scattering behavior, and typical illumination conditions, allowing the ASOs to maintain good accuracy with modest memory requirements. The precomputed ASO can be reused across all instances of the asset and across multiple scenes. We augment ASOs with functionality enabling multi‐bounce importance sampling, fast short‐circuiting of complex light paths, and compact caching, while retaining rapid progressive preview rendering. We demonstrate the benefits of our ASOs by efficiently path tracing scenes containing many instances of objects with complex inter‐reflections or multiple scattering.     

A Parallelization Approach for Hard Real-Time Systems and Its Application on Two Industrial Programs

Applications in industry often have grown and improved over many years. Since their performance demands increase, they also need to benefit from the availability of multi-core processors. However, a reimplementation from scratch and even a restructuring of these industrial applications is very expensive, often due to high certification efforts. Therefore, a strategy for a systematic parallelization of legacy code is needed. We present a parallelization approach for hard real-time systems, which ensures a high reusage of legacy code and preserves timing analysability. To show its applicability, we apply it on the core algorithm of an avionics application as well as on the control program of a large construction machine. We create models of the legacy programs showing the potential of parallelism, optimize them and change the source codes accordingly. The parallelized applications are placed on a predictable multi-core processor with up to 18 cores. For evaluation, we compare the worst case execution times and their speedups. Furthermore, we analyse limitations coming up at the parallelization process.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

Computational performance of a parallelized three-dimensional high-order spectral element toolbox

In this paper, a comprehensive performance review of an MPI-based high-order three-dimensional spectral element method C++ toolbox is presented. The focus is put on the performance evaluation of several aspects with a particular emphasis on the parallel efficiency. The performance evaluation is analyzed with the help of a time prediction model based on a parameterization of the application and the hardware resources. Two tailor-made benchmark cases in computational fluid dynamics (CFD) are introduced and used to carry out this review, stressing the particular interest for clusters with up to thousands of cores. Some problems in the parallel implementation have been detected and corrected. The theoretical complexities with respect to the number of elements, to the polynomial degree, and to communication needs are correctly reproduced. It is concluded that this type of code has a nearly perfect speedup on machines with thousands of cores, and is ready to make the step to next-generation petaFLOP machines.     

Simulations, models, and testbeds: A mutual catalysis

Recent developments in the area of decentralized and infrastructureless systems opened avenues for novel applications. Along with these new technologies, new questions regarding their operational bounds in terms of e.g. scalability and security arose. Due to the sparse presence of real-world decentralized and infrastructureless systems, new protocols and applications have to be scrutinized by means of simulation, in (small-scale) testbeds, and by analytical models. In this paper, we discuss challenges of evaluating security mechanisms for mobile ad hoc networks and peer-to-peer systems. We focus on harmonizing predictions of analytical models and results obtained from simulation studies and testbed experiments.     

Reducing Protocol Analysis with XOR to the XOR-Free Case in the Horn Theory Based Approach

In the Horn theory based approach for cryptographic protocol analysis, cryptographic protocols and (Dolev?CYao) intruders are modeled by Horn theories and security analysis boils down to solving the derivation problem for Horn theories. This approach and the tools based on this approach, including ProVerif, have been very successful in the automatic analysis of cryptographic protocols. However, dealing with the algebraic properties of operators, such as the exclusive OR (XOR), which are frequently used in cryptographic protocols has been problematic. In particular, ProVerif cannot deal with XOR. In this paper, we show how to reduce the derivation problem for Horn theories with XOR to the XOR-free case. Our reduction works for an expressive class of Horn theories. A large class of intruder capabilities and protocols that employ the XOR operator can be modeled by these theories. Our reduction allows us to carry out protocol analysis using tools, such as ProVerif, that cannot deal with XOR, but are very efficient in the XOR-free case. We implemented our reduction and, in combination with ProVerif, used it for the fully automatic analysis of several protocols that employ the XOR operator. Among others, our analysis revealed a new attack on an IBM security module.