A robotics wheelchair for crowded public environment

Describes the robotic wheelchair MAid (Mobility Aid for Elderly and Disabled People). MAid's task is to support and transport people with limited motion skills. It is based on a commercial wheelchair that has been equipped with an intelligent control and navigation system. Conversations with disabled and elderly people and with their physicians indicate that the automatic functions desired in a robotic wheelchair do not include following walls or passing doorways, but do include navigation in narrow, cluttered environments and through wide, crowded areas. MAid performs these functions     

Symbolic Cache Analysis for Real-Time Systems

Caches impose a major problem for predicting execution times of real-time systems since the cache behavior depends on the history of previous memory references. Too pessimistic assumptions on cache hits can obtain worst-case execution time estimates that are prohibitive for real-time systems. This paper presents a novel approach for deriving a highly accurate analytical cache hit function for C-programs at compile-time based on the assumption that no external cache interference (e.g. process dispatching or DMA activity) occurs. First, a symbolic tracefile of an instrumented C-program is generated based on symbolic evaluation, which is a static technique to determine the dynamic behavior of programs. All memory references of a program are described by symbolic expressions and recurrences and stored in chronological order in the symbolic tracefile. Second, a cache hit function for several cache architectures is computed based on a cache evaluation technique. Our approach goes beyond previous work by precisely modelling program control flow and program unknowns, modelling large classes of cache architectures, and providing very accurate cache hit predictions. Examples for the SPARC architecture are used to illustrate the accuracy and effectiveness of our symbolic cache prediction.     

Efficient Sequence Alignment with Side-Constraints by Cluster Tree Elimination

Aligning DNA and protein sequences is a core technique in molecular biology. Often, it is desirable to include partial prior knowledge and conditions in an alignment. Going beyond prior work, we aim at the integration of such side constraints in free combination into alignment algorithms. The most common and successful technique for efficient alignment algorithms is dynamic programming (DP). However, a weakness of DP is that one cannot include additional constraints without specifically tailoring a new DP algorithm. Here, we discuss a declarative approach that is based on constraint techniques and show how it can be extended by formulating additional knowledge as constraints. We take special care to obtain the efficiency of DP for sequence alignment. This is achieved by careful modeling and applying proper solving strategies. Finally, we apply our method to the scanning for RNA motifs in large sequences. This case study demonstrates how the new approach can be used in real biological problems. A prototypic implementation of the method is available at .     

Vascular proteomics

The characterization of patients with acute coronary syndromes (ACS) at the molecular and cellular levels provides a novel vision for understanding the pathological and clinical expression of the disease. Recent advances in proteomic technologies permit the evaluation of systematic changes in protein expression in many biological systems and have been extensively applied to cardiovascular diseases (CVD). The cardiovascular system is in permanent intimate contact with blood, making blood-based biomarker discovery a particularly worthwhile approach. Thus, proteomics can potentially yield novel biomarkers reflecting CVD, establish earlier detection strategies, and monitor response to therapy. Here we review the different proteomic strategies used in the study of atherosclerosis and the novel proteins differentially expressed and secreted by atherosclerotic lesions which constitute novel potential biomarkers (HSP-27, Cathepsin D). Special attention is paid to MS-Imaging of atheroma plaque and the generation, for the first time, of 2-D images of lipids, showing the distribution of these molecules in the different areas of the atherosclerotic lesions. In addition new potential biomarkers have been identified in plasma (amyloid A1α, transtherytin), circulating cells (protein profile in monocytes from ACS patients) and individual cells constituents of atheroma plaques (endothelial, VSMC, macrophages) which provide novel insights into vascular pathophysiology.     

Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability

The variability of fresh water availability in arid and semi-arid countries poses a serious challenge to farmers to cope with when depending on irrigation for crop growing. This has shifted the focus onto improving irrigation management and water productivity (WP) through controlled deficit irrigation (DI). DI can be conceived as a strategy to deal with these challenges but more knowledge on risks and chances of this strategy is urgently needed. The availability of simulation models that can reliably predict crop yield under the influence of soil, atmosphere, irrigation, and agricultural management practices is a prerequisite for deriving reliable and effective deficit irrigation strategies. In this context, this article discusses the performance of the crop models CropWat, PILOTE, Daisy, and APSIM when being part of a stochastic simulation-based approach to improve WP by focusing primarily on the impact of climate variability. The stochastic framework consists of: (i) a weather generator for simulating regional impacts of climate variability; (ii) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop growth in a sound manner. The results present stochastic crop water production functions (SCWPFs) that can be used as basic tools for assessing the impact on the risk for the potential yield due to water stress and climate variability. Example simulations from India, Malawi, France and Oman are presented and the suitability of these crop models to be employed in a framework for optimizing WP is evaluated.     

Improving metastable impact electron spectroscopy and ultraviolet photoelectron spectroscopy signals by means of a modified time-of-flight separation

The separation of ultraviolet photoelectron spectroscopy (UPS) and metastable impact electron spectroscopy (MIES) is usually performed by a time-of-flight (ToF) separation using pre-set ToF for both types of signal. In this work, we present a new, improved ex situ signal separation method for the separation of MIES and UPS for every single measurement. Signal separation issues due to changes of system parameters can be overcome by changing the ToF separation and therefore allowing for the application of a wider range of measuring conditions. The method also enables to identify and achieve separation of the two signals without any time consuming calibration and the use of any special material for the calibration. Furthermore, changes made to the discharge source are described that enable to operate an existing MIES/UPS source over a broader range of conditions. This allows for tuning of the yield of UV photons and metastable rare gas atoms leading to an improved signal to noise ratio. First results of this improved setup are well in agreement with spectra reported in literature and show increased resolution and higher signal intensities for both MIE and UP spectra compared to the previous, non-optimized setup.     

A symbolic summation approach to Feynman integral calculus

Given a Feynman parameter integral, depending on a single discrete variable N$N$ and a real parameter ε$\epsilon$, we discuss a new algorithmic framework to compute the first coefficients of its Laurent series expansion in ε$\epsilon$. In a first step, the integrals are expressed by hypergeometric multi-sums by means of symbolic transformations. Given this sum format, we develop new summation tools to extract the first coefficients of its series expansion whenever they are expressible in terms of indefinite nested product–sum expressions. In particular, we enhance the known multi-sum algorithms to derive recurrences for sums with complicated boundary conditions, and we present new algorithms to find formal Laurent series solutions of a given recurrence relation.     

Autonomous landing at unprepared sites by a full-scale helicopter

Helicopters are valuable since they can land at unprepared sites; however, current unmanned helicopters are unable to select or validate landing zones (LZs) and approach paths. For operation in unknown terrain it is necessary to assess the safety of a LZ. In this paper, we describe a lidar-based perception system that enables a full-scale autonomous helicopter to identify and land in previously unmapped terrain with no human input.We describe the problem, real-time algorithms, perception hardware, and results. Our approach has extended the state of the art in terrain assessment by incorporating not only plane fitting, but by also considering factors such as terrain/skid interaction, rotor and tail clearance, wind direction, clear approach/abort paths, and ground paths.In results from urban and natural environments we were able to successfully classify LZs from point cloud maps. We also present results from 8 successful landing experiments with varying ground clutter and approach directions. The helicopter selected its own landing site, approaches, and then proceeds to land. To our knowledge, these experiments were the first demonstration of a full-scale autonomous helicopter that selected its own landing zones and landed.     

Maximum margin Bayesian network classifiers

We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.     

Investigations of ferritic/martensitic super heat resistant 11‐12 %Cr steels for 650 °C power plants

The investigations of advanced ferritic/martensitic 11–12 %Cr steels for 650 °C power plant components focus on the improvement of high‐temperature creep properties with respect to chemical composition. The claim of the DFG research work was the development of new heat‐resistant 12 %Cr ferritic‐martensitic steels with sufficient creep and oxidation resistance for a 650 °C application by using basic principles and concepts of physical metallurgy on the basis of the state of art and to overcome the usual trial and error industrial alloy development. Efforts are focussed on a 100,000h creep strength of 100MPa at 650 °C in combination with a sufficient corrosion resistance by a Cr content of 12 % with contents 4‐5 %W, 3.4‐5,5 %Co, V, B and 1 %Cu as well as the choice of Ta or Ti instead of Nb. The results demonstrate that the aim is not to realize with the used alloying concept. In the long term range all 12 %Cr melts have a lower creep rupture strength than the advanced 9 %Cr piping steel P92. A high creep strength could be reached with a 0.06 % Ta alloyed 11 %Cr melt, which is in addition alloyed with a higher C and B content and as well as with lower W and Co portions. The results indicate in accordance with the finding of other steel researcher that a lower Cr content allows more effectiveness for the alloying partners respectively for the generation of more stable precipitates.