522 research outputs found
Frequency domain identification of autoregressive models in the presence of additive noise
No full textThis paper describes a new approach for identifying autoregressive models from a finite number of measurements,
in presence of additive and uncorrelated white noise. As a major novelty, the proposed approach deals with
frequency domain data. In particular, two different frequency domain algorithms are proposed.
The first algorithm is based on some theoretical results concerning the so-called dynamic Frisch Scheme.
The second algorithm maps the AR identification problem into a quadratic eigenvalue problem.
Both methods resemble in many aspects some other identification algorithms, originally
developed in the time domain. The features of the proposed methods are compared each other
and with those of other time domain algorithms by means of Monte Carlo simulations
Frequency domain EIV identification combining the Frisch scheme and Yule-Walker equations
No full textThe paper proposes a new frequency domain method for identifying linear dynamic errors-in-variables (EIV) models. The noise-free input is an arbitrary signal, not necessarily periodic and the input and output noises are additive and uncorrelated white processes. The method combines, in a frequency domain context, the characteristics of the Frisch scheme and the properties of the Yule-Walker equations. The features of the method are illustrated by means of numerical examples
Errors in Variables Identification using maximum likelihood estimation in the Frequency Domain
No full textThis report deals with the identification of errors–in–variables (EIV) models
corrupted by additive and uncorrelated white Gaussian noises when the noise–free
input is an arbitrary signal, not required to be periodic. In particular, a frequency
domain maximum likelihood (ML) estimator is proposed and analyzed in some
detail. As some other EIV estimators, this method assumes that the ratio of the
noise variances is known. The estimation problem is formulated in the frequency
domain. It is shown that the parameter estimates are consistent. An explicit algorithm
for computing the asymptotic covariance matrix of the parameter estimates
is derived. The possibility to effectively use lowpass filtered data by using only
part of the frequency domain is discussed, analyzed and illustrated
When Are Errors-in-Variables Aspects Important to Consider in System Identification?
No full textWhen recorded signals are corrupted by noise on both input and output sides, standard identification methods give biased parameter estimates, due to the presence of input noise. This paper discusses in what situations such a bias is large and, consequently, when errors-in-variables identification methods should preferably be used
Entwicklung einer schnellen Pulsformanalyse für asymmetrische AGATA-Germanium-Detektoren
No full textOnTEAM metadata: GDSID: DOC-2007-May-32; Attribute ID: LIBRARY-thesis_diss-2007-005; Title: [GSI Diss 2007-05] Entwicklung einer schnellen Pulsformanalyse für asymmetrische AGATA-Germanium-Detektoren; Author(s): Beck, Torsten; Corporate author(s): ; Publication date: 20070501; Creator: manton; Creation date: 15.05.2007 16:02:12; Change date: 29.10.2008 16:29:34; Access: nur berechtigte Gruppen; Attribute type: Text.Thesis.Diss; Directory path: ['GSI Publications', 'GSI as Publisher']; Attribute path: ['Infrastructure', 'Library and Documentation', 'thesis_diss', 'Added in 2007']; File name(s): ['DOC-2007-May-32-1.pdf']; File title(s): ['']; File access: ['nur berechtigte Gruppen'
Manifolds, sheaves, and cohomology
No full textThis book explains techniques that are essential in almost all branches of modern geometry such as algebraic geometry, complex geometry, or non-archimedian geometry. It uses the most accessible case, real and complex manifolds, as a model. The author especially emphasizes the difference between local and global questions. Cohomology theory of sheaves is introduced and its usage is illustrated by many examples. Content Topological Preliminaries - Algebraic Topological Preliminaries - Sheaves - Manifolds - Local Theory of Manifolds - Lie Groups - Torsors and Non-abelian Cech Cohomology - Bundles - Soft Sheaves - Cohomology of Complexes of Sheaves - Cohomology of Sheaves of Locally Constant Functions - Appendix: Basic Topology, The Language of Categories, Basic Algebra, Homological Algebra, Local Analysis Readership Graduate Students in Mathematics / Master of Science in Mathematics About the Author Prof. Dr. Torsten Wedhorn, Department of Mathematics, Technische Universität Darmstadt, Germany
James Watson, Maclyn McCarty, and Torsten Wiesel
No full textTorsten Wiesel (right) with Professor Emeritus Maclyn McCarty (center), co-author of the paper with Oswald Avery and Colin MacLeod, and James D. Watson, director of Cold Spring Harbor Laboratory, 1994
Photo by Leif Carlsson
To commemorate the fiftieth anniversary of the discovery at The Rockefeller University that genes are made of DNA - considered by many to be the single most important biological discovery of the twentieth century - the university has kicked off a year-long series of events that were running through May 1994. The celebration was formally inaugurated in November 1993 with a lecture by Nobel laureate James D. Watson, best known for discovering the double-helical structure of DNA.
See also Search Winter 1994, vol. 4, no. 1https://digitalcommons.rockefeller.edu/group-portraits/1013/thumbnail.jp
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