eigenvalue decomposition
On the existence and uniqueness of the eigenvalue decomposition of a parahermitian matrix
... Abstract—This paper addresses the extension of the factoriza- tion of a Hermitian matrix by an eigenvalue decomposition (EVD) to the case of a parahermitian matrix that is analytic at least on an annulus ...
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Design of FIR paraunitary filter banks for subband coding using a polynomial eigenvalue decomposition
... SUBMITTED TO IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2011 1 Design of FIR Paraunitary Filter Banks for Subband Coding Using a Polynomial Eigenvalue Decomposition Soydan Redif Member, IEE[r] ...
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Enforcing eigenvector smoothness for a compact DFT-based polynomial eigenvalue decomposition
... matrix eigenvalue decomposition (PEVD), which is defined in [8] as an extension of the eigenvalue decomposition to parahermitian matrices, uses finite impulse response (FIR) paraunitary ...
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Joint DOD and DOA Estimation for Bistatic MIMO Radar Without Eigenvalue Decomposition
... In this paper, we present a pairing automatically DODs and DOAs estimation method for bistatic MIMO radar without eigenvalue decomposition. A novel bistatic MIMO radar geometry is proposed to construct CCM ...
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Fast Estimation of Range and Bearing for a Single Near-field Source without Eigenvalue Decomposition
... the eigenvalue decomposition implemen- tation still consume great computational ...hence eigenvalue decomposition is inevitable and increasing the computational ...
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Time-Frequency-Based Speech Regions Characterization and Eigenvalue Decomposition Applied to Speech Watermarking
... Once separated, they could be further combined in various ways to provide an arbitrary time-frequency map used as a support function in watermark modelling. 4.1. Selection of Speech Formants Suitable for Watermarking. ...
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Shortening of paraunitary matrices obtained by polynomial eigenvalue decomposition algorithms
... Abstract —This paper extends the analysis of the recently in- troduced row-shift corrected truncation method for paraunitary matrices to those produced by the state-of-the-art sequential matrix diagonalisation (SMD) ...
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Multichannel spectral factorization algorithm using polynomial matrix eigenvalue decomposition
... Abstract —In this paper, we present a new multichannel spectral factorization algorithm which can be utilized to cal- culate the approximate spectral factor of any para-Hermitian polynomial matrix. The proposed algorithm ...
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Blind Source Separation via Generalized Eigenvalue Decomposition
... generalized eigenvalue decomposition under the assumptions of non-Gaussian, non-stationary, or non-white independent ...generalized eigenvalue decomposition, non-Gaussian, non- white, ...
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Low complexity and high accuracy angle of arrival estimation using eigenvalue decomposition with extension to 2D AOA and power estimation
... the eigenvalue decomposition (EVD) method on the crosscorrelation matrix of the received signals at two sides of the antenna array ...the eigenvalue-decomposition-based AOA (EDBA) ...the ...
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Eigenvalue decomposition as a generalized synchronization cluster analysis
... The eigenvalue decomposition of the equal-time correlation matrix of a set of signals is one of the standard tools of multivariate data analysis ...the eigenvalue decomposition of the ...
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A New DOA Estimation Method Based on Spatial Structure of Array Signals
... the eigenvalue decomposition feature of signal’s auto-correlation and cross- correlation matrix is fully used to construct DOA matrix of array signal, the eigenvector corresponding to the non-zero ...
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Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
... We apply the three methods to approximately compute kernel principal component analysis (KPCA), and contrast with the exact solution. The experiment setting follows Zhang and Kwok (2010). We fix k and vary c. For our ...
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Inversion of Parahermitian matrices
... polynomial eigenvalue decomposition obtained by the sequential best rotation algorithm to decompose a parahermitian matrix into a product of two parau- nitary, ...
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A multilevel approach for computing the limited-memory Hessian and its inverse in variational data assimilation
... multilevel eigenvalue decomposition of a given symmetric ...spectral decomposition, an operator A, say, can be approximated by a finite number of its eigenvalues and ...value decomposition ...
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Relevance of polynomial matrix decompositions to broadband blind signal separation
... the eigenvalue decomposition (EVD) to the realm of polynomial matrices have had a growing impact on signal processing theory and practice, mainly because they can be used to solve generalisations of ...
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Numerical Methods for Large Eigenvalue Problems - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... This chapter introduces some elementary spectral theory for linear operators on finite dimensional spaces as well as some elements of perturbation analysis. The main question that perturbation theory addresses is: how ...
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Systematic Link of Modal Identification with Model Updating
... The modal identification extracting the dynamic characteristics of the structure from the measured data can be done in both frequency domain and time domain. FDD (Frequency Domain Decomposition) [1] is a modal ...
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Evaluation of drought resistance in Iris germanica L. based on subordination function and principal component analysis
... Last, the proportion of the eigenvalue of each principal component in the total eigenvalue of extracted principal components was used as the weight to calculate the comprehensive e[r] ...
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Inverse eigenvalue problem for matrices whose graph is a banana tree
... In this paper, we have solved the IEP for construction of matrices whose graphs are banana trees. This is performed by using the minimal and maximal eigenvalues of all leading principal submatrices of the required ...
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