Hyperspectral Blind Reconstruction From Random Spectral Projections

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2016.2541541 ◽

2016 ◽

Vol 9 (6) ◽

pp. 2390-2399 ◽

Author(s):

Gabriel Martin ◽

Jose M. Bioucas-Dias

Keyword(s):

Spectral Projections ◽

Blind Reconstruction

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Blind Reconstruction of Multiframe Imagery Based on Fusion and Classification

Blind Image Deconvolution ◽

10.1201/9781420007299.ch9 ◽

2007 ◽

pp. 349-375

Author(s):

Alexia Giannoula ◽

Jianxin Han ◽

Dimitrios Hatzinakos

Keyword(s):

Blind Reconstruction

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Spectral projections for the twisted Laplacian

Studia Mathematica ◽

10.4064/sm180-2-1 ◽

2007 ◽

Vol 180 (2) ◽

pp. 103-110 ◽

Author(s):

Herbert Koch ◽

Fulvio Ricci

Keyword(s):

Twisted Laplacian ◽

Spectral Projections

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Spectral projections, semigroups of operators, and the Laplace transform

Banach Center Publications ◽

10.4064/-38-1-193-204 ◽

1997 ◽

Vol 38 (1) ◽

pp. 193-204

Author(s):

Ralph deLaubenfels

Keyword(s):

Laplace Transform ◽

Semigroups Of Operators ◽

The Laplace Transform ◽

Spectral Projections

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Blind Reconstruction of Reed-Solomon Encoder and Interleavers Over Noisy Environment

IEEE Transactions on Broadcasting ◽

10.1109/tbc.2018.2795461 ◽

2018 ◽

Vol 64 (4) ◽

pp. 830-845 ◽

Author(s):

R. Swaminathan ◽

A.S. Madhukumar ◽

Guohua Wang ◽

Ting Shang Kee

Keyword(s):

Noisy Environment ◽

Blind Reconstruction

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13. Spectral Projections: Color, Race, and Abstraction in the Moving Image

Abstract Video ◽

10.1525/9780520958135-016 ◽

2019 ◽

pp. 192-204

Keyword(s):

Moving Image ◽

Spectral Projections

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An optimal semiclassical bound on commutators of spectral projections with position and momentum operators

Letters in Mathematical Physics ◽

10.1007/s11005-020-01328-3 ◽

2020 ◽

Vol 110 (12) ◽

pp. 3343-3373

Author(s):

Søren Fournais ◽

Søren Mikkelsen

Keyword(s):

Spectral Projections

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Spectral Projections, Riesz Transforms and H∞-calculus for Bisectorial Operators

Progress in Nonlinear Differential Equations and Their Applications - Nonlinear Elliptic and Parabolic Problems ◽

10.1007/3-7643-7385-7_6 ◽

2006 ◽

pp. 99-112 ◽

Author(s):

Markus Duelli ◽

Lutz Weis

Keyword(s):

Riesz Transforms ◽

Spectral Projections

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Blind reconstruction of x-ray penumbral images based on a heuristic method

Review of Scientific Instruments ◽

10.1063/1.1537862 ◽

2003 ◽

Vol 74 (3) ◽

pp. 2240-2244 ◽

Author(s):

Shinya Nozaki ◽

Yen-Wei Chen ◽

Zensho Nakao ◽

Ryosuke Kodama ◽

Hiroyuki Shiraga

Keyword(s):

Heuristic Method ◽

X Ray ◽

Blind Reconstruction

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Radius of analyticity and exponential convergence for spectral projections of the generalized KdV equation

Communications in Nonlinear Science and Numerical Simulation ◽

10.1016/j.cnsns.2009.05.015 ◽

2010 ◽

Vol 15 (4) ◽

pp. 869-880 ◽

Author(s):

Magnar Bjørkavåg ◽

Henrik Kalisch

Keyword(s):

Exponential Convergence ◽

Kdv Equation ◽

Generalized Kdv Equation ◽

Radius Of Analyticity ◽

Spectral Projections

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A STRONG OPERATOR TOPOLOGY ADIABATIC THEOREM

Reviews in Mathematical Physics ◽

10.1142/s0129055x02001247 ◽

2002 ◽

Vol 14 (06) ◽

pp. 569-584 ◽

Author(s):

ALEXANDER ELGART ◽

JEFFREY H. SCHENKER

Keyword(s):

Spectral Data ◽

Strong Operator Topology ◽

Continuous Functions ◽

Adiabatic Theorem ◽

Strong Operator ◽

Intrinsic Time ◽

Embedded Eigenvalues ◽

Additive Perturbation ◽

Spectral Projections

We prove an adiabatic theorem for the evolution of spectral data under a weak additive perturbation in the context of a system without an intrinsic time scale. For continuous functions of the unperturbed Hamiltonian the convergence is in norm while for a larger class functions, including the spectral projections associated to embedded eigenvalues, the convergence is in the strong operator topology.

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