Informatik

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Multiscale phenomena: Green's functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods
(Amsterdam [u.a.] : Elsevier Science, 1995) Hughes, Thomas J. R.
An approach is developed for deriving variational methods capable of representing multiscale phenomena. The ideas are first illustrated on the exterior problem for the Helmholtz equation. This leads to the well-known Dirichlet-to-Neumann formulation. Next, a class of subgrid scale models is developed and the relationships to 'bubble function' methods and stabilized methods are established. It is shown that both the latter methods are approximate subgrid scale models. The identification for stabilized methods leads to an analytical formula for τ, the 'intrinsic time scale', whose origins have been a mystery heretofore. © 1995.
GALS for Bursty Data Transfer based on Clock Coupling
(Amsterdam : Elsevier, 2009) Krstić, M.; Fan, X.; Grass, E.; Gürkaynak, F.K.
In this paper we introduce a novel burst-mode GALS technique. The goal of this technique is improving the performance of the GALS approach for systems with predominantly bursty data transfer. This new technique has been used to implement a GALS-based version of a hardware accelerator of a 60 GHz OFDM baseband processor. The simulation results show a significant performance improvement in comparison with a classical implementation of GALS using pausible clocking. © 2009 Elsevier B.V. All rights reserved.
The finite volume-complete flux scheme for advection-diffusion-reaction equations
(New York, NY [u.a.] : Springer Science + Business Media B.V., 2010) ten Thije Boonkkamp, J. H. M.; Anthonissen, M. J. H.
We present a new finite volume scheme for the advection-diffusion-reaction equation. The scheme is second order accurate in the grid size, both for dominant diffusion and dominant advection, and has only a three-point coupling in each spatial direction. Our scheme is based on a new integral representation for the flux of the one-dimensional advection-diffusion-reaction equation, which is derived from the solution of a local boundary value problem for the entire equation, including the source term. The flux therefore consists of two parts, corresponding to the homogeneous and particular solution of the boundary value problem. Applying suitable quadrature rules to the integral representation gives the complete flux scheme. Extensions of the complete flux scheme to two-dimensional and time-dependent problems are derived, containing the cross flux term or the time derivative in the inhomogeneous flux, respectively. The resulting finite volume-complete flux scheme is validated for several test problems. © 2010 The Author(s).
Statistical parametric maps for functional MRI experiments in R: The package fmri
(Los Angeles : UCLA, 2011) Tabelow, K.; Polzehl, J.
The purpose of the package fmri is the analysis of single subject functional magnetic resonance imaging (fMRI) data. It provides fMRI analysis from time series modeling by a linear model to signal detection and publication quality images. Specifically, it implements structural adaptive smoothing methods with signal detection for adaptive noise reduction which avoids blurring of activation areas. Within this paper we describe the complete pipeline for fMRI analysis using fmri. We describe data reading from various medical imaging formats and the linear modeling used to create the statistical parametric maps. We review the rationale behind the structural adaptive smoothing algorithms and explain their usage from the package fmri. We demonstrate the results of such analysis using two experimental datasets. Finally, we report on the usage of a graphical user interface for some of the package functions.
Efficient retrieval of 3D building models using embeddings of attributed subgraphs
(Institut für Informatik II, Universität Bonn, 2011) Wessel, R.; Ochmann, S.; Vock, R.; Blümel, Ina; Klein, R.
We present a novel method for retrieval and classification of 3D building models that is tailored to the specific requirements of architects. In contrast to common approaches our algorithm relies on the interior spatial arrangement of rooms instead of exterior geometric shape. We first represent the internal topological building structure by a Room Connectivity Graph (RCG). Each room is characterized by a node. Connections between rooms like e.g. doors are represented by edges. Nodes and edges are additionally assigned attributes reflecting room and edge properties like e.g area or window size. To enable fast and efficient retrieval and classification with RCGs, we transform the structured graph representation into a vector-based one. We first decompose the RCG into a set of subgraphs. For each subgraph, we compute the similarity to a set of codebook graphs. Aggregating all similarity values finally provides us with a single vector for each RCG which enables fast retrieval and classification. For evaluation, we introduce a classification scheme that was carefully developed following common guidelines in architecture.We finally provide comprehensive experiments showing that the introduced subgraph embeddings yield superior performance compared to state-of-the-art graph retrieval approaches.
Supermodeling by combining imperfect models
(Amsterdam : Elsevier B.V., 2011) Selten, F.M.; Duane, G.S.; Wiegerinck, W.; Keenlyside, N.; Kurths, J.; Kocarev, L.
SUMO (Supermodeling by combining imperfect models) is a three-year project funded under FET Open program with a starting date October, 1, 2010. We review some basic facts and findings of the SUMO project.
ISense: A portable ultracold-atom-based gravimeter
(Amsterdam : Elsevier, 2011) de Angelis, M.; Angonin, M.C.; Beaufils, Q.; Becker, Ch.; Bertoldi, A.; Bongs, K.; Bourdel, T.; Bouyer, P.; Boyer, V.; Dörscher, S.; Duncker, H.; Ertmer, W.; Fernholz, T.; Fromhold, T.M.; Herr, W.; Krüger, P.; Kürbis, Ch.; Mellor, C.J.; Pereira Dos Santos, F.; Peters, A.; Poli, N.; Popp, M.; Prevedelli, M.; Rasel, E.M.; Rudolph, J.; Schreck, F.; Sengstock, K.; Sorrentino, F.; Stellmer, S.; Tino, G.M.; Valenzuela, T.; Wendrich, T.J.; Wicht, A.; Windpassinger, P.; Wolf, P.
We present iSense, a recently initiated FET project aiming to use Information and Communication Technologies (ICT) to develop a platform for portable quantum sensors based on cold atoms. A prototype of backpack-size highprecision force sensor will be built to demonstrate the concept.
How people interact in evolving online affiliation networks
(College Park, MD : American Physical Society, 2012) Gallos, L.K.; Rybski, D.; Liljeros, F.; Havlin, S.; Makse, H.A.
The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks.
Synchronization of Time-Delay Chaotic System in Presence of Noise
(Milton Park : Taylor and Francis Ltd., 2012) Lei, M.; Peng, H.-P.; Yang, C.-Y.; Li, L.-X.
Chaotic synchronization, as a key technique of chaotic secure communication, has received much attention in recent years. This paper proposes a nonlinear synchronization scheme for the time-delay chaotic system in the presence of noise. In this scheme, an integrator is introduced to suppress the influence of channel noise in the synchronization process. The experimental results demonstrate the effectiveness and feasibility of the proposed scheme which is strongly robust against noises, especially the high-frequency noises.
Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size
(San Francisco, CA : Public Library of Science (PLoS), 2012) Zhu, W.; Fang, J.-A.; Tang, Y.; Zhang, W.; Du, W.
Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.
Identifying controlling nodes in neuronal networks in different scales
(San Francisco, CA : Public Library of Science (PLoS), 2012) Tang, Y.; Gao, H.; Zou, W.; Kurths, J.
Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats' brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats' brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks.
Order patterns networks (orpan) - A method to estimate time-evolving functional connectivity from multivariate time series
(Lausanne : Frontiers Research Foundation, 2012) Schinkel, S.; Zamora-López, G.; Dimigen, O.; Sommer, W.; Kurths, J.
Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.
Communication activity in a social network: Relation between long-term correlations and inter-event clustering
(London : Nature Publishing Group, 2012) Rybski, D.; Buldyrev, S.V.; Havlin, S.; Liljeros, F.; Makse, H.A.
Human communication in social networks is dominated by emergent statistical laws such as non-trivial correlations and temporal clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study the collective behavior of the whole community with the goal of understanding the origin of clustering and long-term persistence. At the individual level, we find that the correlations in activity are a byproduct of the clustering expressed in the power-law distribution of inter-event times of single users, i.e. short periods of many events are separated by long periods of no events. On the contrary, the activity of the whole community presents long-term correlations that are a true emergent property of the system, i.e. they are not related to the distribution of inter-event times. This result suggests the existence of collective behavior, possibly arising from nontrivial communication patterns through the embedding social network.
Magnetofluidic platform for multidimensional magnetic and optical barcoding of droplets
(Cambridge : RSC, 2014) Lin, Gungun; Makarov, Denys; Medina-Sánchez, Mariana; Guix, Maria; Baraban, Larysa; Cuniberti, Gianaurelio; Schmidt, Oliver G.
We present a concept of multidimensional magnetic and optical barcoding of droplets based on a magnetofluidic platform. The platform comprises multiple functional areas, such as an encoding area, an encoded droplet pool and a magnetic decoding area with integrated giant magnetoresistive (GMR) sensors. To prove this concept, penicillin functionalized with fluorescent dyes is coencapsulated with magnetic nanoparticles into droplets. While fluorescent dyes are used as conventional optical barcodes which are decoded with an optical decoding setup, an additional dimensionality of barcodes is created by using magnetic nanoparticles as magnetic barcodes for individual droplets and integrated micro-patterned GMR sensors as the corresponding magnetic decoding devices. The strategy of incorporating a magnetic encoding scheme provides a dynamic range of ~40 dB in addition to that of the optical method. When combined with magnetic barcodes, the encoding capacity can be increased by more than 1 order of magnitude compared with using only optical barcodes, that is, the magnetic platform provides more than 10 unique magnetic codes in addition to each optical barcode. Besides being a unique magnetic functional element for droplet microfluidics, the platform is capable of on-demand facile magnetic encoding and real-time decoding of droplets which paves the way for the development of novel non-optical encoding schemes for highly multiplexed droplet-based biological assays.
The quest for research information
(Amsterdam : Elsevier, 2014) Blümel, Ina; Dietze, Stefan; Heller, Lambert; Jäschke, Robert; Mehlberg, Martin
Research information, i.e., data about research projects, organisations, researchers or research outputs such as publications or patents, is spread across the web, usually residing in institutional and personal web pages or in semi-open databases and information systems. While there exists a wealth of unstructured information, structured data is limited and often exposed following proprietary or less-established schemas and interfaces. Therefore, a holistic and consistent view on research information across organisational and national boundaries is not feasible. On the other hand, web crawling and information extraction techniques have matured throughout the last decade, allowing for automated approaches of harvesting, extracting and consolidating research information into a more coherent knowledge graph. In this work, we give an overview of the current state of the art in research information sharing on the web and present initial ideas towards a more holistic approach for boot-strapping research information from available web sources.
Web technologies for environmental Big Data
(Amsterdam [u.a.] : Elsevier Science, 2014) Vitolo, Claudia; Elkhatib, Yehia; Reusser, Dominik; Macleod, Christopher J.A.; Buytaert, Wouter
Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources.
SCSlib: Transparently Accessing Protected Sensor Data in the Cloud
(Amsterdam [u.a.] : Elsevier, 2014) Henze, Martin; Bereda, Sebastian; Hummen, René; Wehrle, Klaus
As sensor networks get increasingly deployed in real-world scenarios such as home and industrial automation, there is a similarly growing demand in analyzing, consolidating, and storing the data collected by these networks. The dynamic, on-demand resources offered by today’s cloud computing environments promise to satisfy this demand. However, prevalent security concerns still hinder the integration of sensor networks and cloud computing. In this paper, we show how recent progress in standardization can provide the basis for protecting data from diverse sensor devices when outsourcing data processing and storage to the cloud. To this end, we present our Sensor Cloud Security Library (SCSlib) that enables cloud service developers to transparently access cryptographically protected sensor data in the cloud. SCSlib specifically allows domain specialists who are not security experts to build secure cloud services. Our evaluation proves the feasibility and applicability of SCSlib for commodity cloud computing environments.
Implementation of an adaptive BDF2 formula and comparison with the MATLAB Ode15s
(Amsterdam [u.a.] : Elsevier, 2014) Celaya, E. Alberdi; Aguirrezabala, J. J. Anza; Chatzipantelidis, P.
After applying the Finite Element Method (FEM) to the diffusion-type and wave-type Partial Differential Equations (PDEs), a first order and a second order Ordinary Differential Equation (ODE) systems are obtained respectively. These ODE systems usually present high stiffness, so numerical methods with good stability properties are required in their resolution. MATLAB offers a set of open source adaptive step functions for solving ODEs. One of these functions is the ode15s recommended to solve stiff problems and which is based on the Backward Differentiation Formulae (BDF). We describe the error estimation and the step size control implemented in this function. The ode15s is a variable order algorithm, and even though it has an adaptive step size implementation, the advancing formula and the local error estimation that uses correspond to the constant step size formula. We have focused on the second order accurate and unconditionally stable BDF (BDF2) and we have implemented a real adaptive step size BDF2 algorithm using the same strategy as the BDF2 implemented in the ode15s, resulting the new algorithm more efficient than the one implemented in MATLAB. © The Authors. Published by Elsevier B.V.
Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera
(Basel : MDPI, 2015) Dworak, Volker; Huebner, Michael; Selbeck, Joern
Most of the relevant technology related to precision agriculture is currently controlled by Global Positioning Systems (GPS) and uploaded map data; however, in sensitive areas with young or expensive plants, small robots are becoming more widely used in exclusive work. These robots must follow the plant lines with centimeter precision to protect plant growth. For cases in which GPS fails, a camera-based solution is often used for navigation because of the system cost and simplicity. The low-cost plant camera presented here generates images in which plants are contrasted against the soil, thus enabling the use of simple cross-correlation functions to establish high-resolution navigation control in the centimeter range. Based on the foresight provided by images from in front of the vehicle, robust vehicle control can be established without any dead time; as a result, off-loading the main robot control and overshooting can be avoided.
Analysis, simulation and prediction of multivariate random fields with package randomfields
(Los Angeles, Calif. : UCLA, Dept. of Statistics, 2015) Schlather, Martin; Malinowski, Alexander; Menck, Peter J.; Oesting, Marco; Strokorb, Kirstin
Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with crosscovariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matérn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction.

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