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- ItemUltrashort optical pulse propagation in terms of analytic signal(New York, NY : Hindawi, 2011) Amiranashvili, Sh.; Demircan, A.We demonstrate that ultrashort optical pulses propagating in a nonlinear dispersive medium are naturally described through incorporation of analytic signal for the electric field. To this end a second-order nonlinear wave equation is first simplified using a unidirectional approximation. Then the analytic signal is introduced, and all nonresonant nonlinear terms are eliminated. The derived propagation equation accounts for arbitrary dispersion, resonant four-wave mixing processes, weak absorption, and arbitrary pulse duration. The model applies to the complex electric field and is independent of the slowly varying envelope approximation. Still the derived propagation equation posses universal structure of the generalized nonlinear Schrdinger equation (NSE). In particular, it can be solved numerically with only small changes of the standard split-step solver or more complicated spectral algorithms for NSE. We present exemplary numerical solutions describing supercontinuum generation with an ultrashort optical pulse.
- ItemSimulation of microwave circuits and laser structures including PML by means of FIT(München : European Geopyhsical Union, 2004) Hebermehl, G.; Schefter, J.; Schlundt, R.; Tischler, Th.; Zscheile, H.; Heinrich, W.Field-oriented methods which describe the physical properties of microwave circuits and optical structures are an indispensable tool to avoid costly and time-consuming redesign cycles. Commonly the electromagnetic characteristics of the structures are described by the scattering matrix which is extracted from the orthogonal decomposition of the electric field. The electric field is the solution of an eigenvalue and a boundary value problem for Maxwell’s equations in the frequency domain. We discretize the equations with staggered orthogonal grids using the Finite Integration Technique (FIT). Maxwellian grid equations are formulated for staggered nonequidistant rectangular grids and for tetrahedral nets with corresponding dual Voronoi cells. The interesting modes of smallest attenuation are found solving a sequence of eigenvalue problems of modified matrices. To reduce the execution time for high-dimensional problems a coarse and a fine grid is used. The calculations are carried out, using two levels of parallelization. The discretized boundary value problem, a large-scale system of linear algebraic equations with different right-hand sides, is solved by a block Krylov subspace method with various preconditioning techniques. Special attention is paid to the Perfectly Matched Layer boundary condition (PML) which causes non physical modes and a significantly increased number of iterations in the iterative methods.
- ItemAdaptive smoothing of digital images: The R package adimpro(Los Angeles, Calif. : UCLA, Dept. of Statistics, 2007) Polzehl, J.; Tabelow, K.Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the propagationseparation approach by (Polzehl arid Spokoiriy 2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.
- ItemStatistical 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.
- ItemModification of Newton's law of gravity at very large distances(Amsterdam : Elsevier, 2002) Kirillov, A.A.; Turaev, D.We discuss a Modified Field Theory (MOFT) in which the number of fields can vary. It is shown that when the number of fields is conserved MOFT reduces to the standard field theory but interaction constants undergo an additional renormalization and acquire a dependence on spatial scales. In particular, the renormalization of the gravitational constant leads to the deviation of the law of gravity from the Newton's law in some range of scales rmin < r < rmax, in which the gravitational potential shows essentially logarithmic ∼ ln r (instead of 1/r) behavior. In this range, the renormalized value of the gravitational constant G increases and at scales r > rmax acquires a new constant value G′ ∼ Grmax/rmin. From the dynamical standpoint this looks as if every point source is surrounded with a halo of dark matter. It is also shown that if the maximal scale rmax is absent, the homogeneity of the dark matter in the Universe is consistent with a fractal distribution of baryons in space, in which the luminous matter is located on thin two-dimensional surfaces separated by empty regions of ever growing size.
- ItemA propagation-separation approach to estimate the autocorrelation in a time-series(Göttingen : Copernicus, 2008) Divine, D.V.; Polzehl, J.; Godtliebsen, F.The paper presents an approach to estimate parameters of a local stationary AR(1) time series model by maximization of a local likelihood function. The method is based on a propagation-separation procedure that leads to data dependent weights defining the local model. Using free propagation of weights under homogeneity, the method is capable of separating the time series into intervals of approximate local stationarity. Parameters in different regions will be significantly different. Therefore the method also serves as a test for a stationary AR(1) model. The performance of the method is illustrated by applications to both synthetic data and real time-series of reconstructed NAO and ENSO indices and GRIP stable isotopes.
- ItemClassification and clustering: models, software and applications(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2009) Mucha, Hans-Joachim; Ritter, GunterWe are pleased to present the report on the 30th Fall Meeting of the working group ``Data Analysis and Numerical Classification'' (AG-DANK) of the German Classification Society. The meeting took place at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, from Friday Nov. 14 till Saturday Nov. 15, 2008. Already 12 years ago, WIAS had hosted a traditional Fall Meeting with special focus on classification and multivariate graphics (Mucha and Bock, 1996). This time, the special topics were stability of clustering and classification, mixture decomposition, visualization, and statistical software.
- ItemWPM package manager version 1.0 : software documentation(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2010) Streckenbach, TimoWPM is a command-line tool designed to support build and installation facilities. It is implemented as a collection of script files, written in Bourne shell syntax. For the sake of portability the code takes care of the common pitfalls of shell programming.
- ItemTetGen : a 3D Delaunay tetrahedral mesh generator version 1.2 user's manual(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2002) Si, HangThis technical report describes the main features and the using of TetGen, a 3D Delaunay tetrahedral mesh generator. Based on the most recent developments in mesh generation algorithms, this program has been specifically designed to fulfill the task of automatically generating high quality tetrahedral meshes, which are suitable for scientific computing using numerical methods such as finite element and finite volume methods. In this document, the user will learn how to create 3D tetrahedral meshes using TetGen's input files and command line switches. Various examples were given for better understanding. This document describes the features of the version 1.2.
- ItemModel-based cluster analysis applied to flow cytometry data of phytoplankton(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2002) Mucha, H.-J.; Simon, U.; Brüggemann, R.Starting from well-known model-based clustering models equivalent formulations for some special models based on pairwise distances are presented. Moreover, these models can be generalized in order to taking into account both weights of observations and weights of variables. Well-known cluster analysis techniques like the iterative partitional K-means method or the agglomerative hierarchical Ward method are useful for discovering partitions or hierarchies in the underlying data. Here these methods are generalised in two ways, firstly by using weighted observations and secondly by allowing different volumes of clusters. Then a more general K-means approach based on pair-wise distances is recommended. Simulation studies are carried out in order to compare the new clustering techniques with the well-known ones. Afterwards a successful application in the field of freshwater ecology is presented. As an example, the cluster analysis of a snapshot from monitoring of phytoplankton (algae) is considered in more detail. Indeed, monitoring by microscope is very time- and work-consuming. Flow cytometry provides the opportunity to investigate algae communities in a semiautomatic way. Statistical data analysis and cluster analysis can at least support the investigations. Here a combination of agglomerative hierarchical clustering and iterative clustering is recommended. In order to give some insight into the data under investigation several univariate, bivariate and multivariate visualizations are proposed.