نتایج جستجو برای: spectral space
تعداد نتایج: 644588 فیلتر نتایج به سال:
1. Local coefficient systems 2 1.1. The fundamental groupoid 2 1.2. Coefficient systems 3 1.3. Path connected based spaces 4 1.4. Singular chains with local coefficients 4 1.5. Homology with local coefficients 5 1.6. Universal covers 7 2. Exact couples and spectral sequences 9 2.1. Spectral sequences 9 2.2. Exact couples 9 2.3. The spectral sequence associated to an exact couple 10 2.4. Example...
This work aims to extract the mineralogical constituents of the Lahroud Hyperion scene situated in the NW of Iran. Like the other push-broom sensors, Hyperion images suffer from spectral distortions, namely the smile effect. The corresponding spectral curvature is defined as an across-track wavelength shift from the nominal central wavelength, and alters the pixel spectra. The common “column me...
On the Lyapunov Equation, Coinvariant Subspaces and Some Problems Related to Spectral Factorizations
A geometric approach to stochastic realization theory, and hence to spectral factorization problems, has been developed by Lindquist and Picci [1985,1991], Lindquist, Michaletzky and Picci [1995]. Most of this work was done abstractly. Fuhrmann and Gombani [1998] adopted an entirely Hardy space approach to this set of problems, studying the set of rectangular spectral factors of given size for ...
Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the M...
in this paper power system model is represented in a new domain that relates to multi-resolution analysis (mra) space. by developing mathematical model of elements in this space using galerkin method, a new alternative method for power system simulation in nonsinusoidal and periodic conditions is developed. the mathematical formulation and characteristics of new proposed space is expressed. als...
To test for the white noise null hypothesis, we study the Cramér–von Mises test statistic that is based on the sample spectral distribution function. Since the critical values of the test statistic are difficult to obtain, we propose a blockwise wild bootstrap procedure to approximate its asymptotic null distribution. Using a Hilbert space approach, we establish the weak convergence of the diff...
Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Olavi Nevanlinna Name of the publication Multicentric representation and von Neumann spectral sets Publisher School of Science Unit Department of Mathematics and Systems Analysis Series Aalto University publication series SCIENCE + TECHNOLOGY 11/2011 Field of research Mathematics Abstract We show how multicentric representatio...
The transductive SVM is a semi-supervised learning algorithm that searches for a large margin hyperplane in feature space. By withholding the training labels and adding a constraint that favors balanced clusters, it can be turned into a clustering algorithm. The Normalized Cuts clustering algorithm of Shi and Malik, although originally presented as spectral relaxation of a graph cut problem, ca...
Spectral unraveling by space-selective Hadamard spectroscopy (SUSHY) enables recording of NMR spectra of multiple samples loaded in multiple sample tubes in a modified spinner turbine and a regular 5mm liquids NMR probe equipped with a tri-axis pulsed field gradient coil. The individual spectrum from each sample is extracted by adding and subtracting data that are simultaneously obtained from a...
let h be an infinite--dimensional hilbert space and k(h) be the set of all compact operators on h. we will adopt spectral theorem for compact self-adjoint operators, to investigate of higher derivation and higher jordan derivation on k(h) associated with the following cauchy-jencen type functional equation 2f(frac{t+s}{2}+r)=f(t)+f(s)+2f(r) for all t,s,rin k(h).
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