AbstractWe study the Singular Value Decomposition (SVD) of the three building blocks of the matrix representation of two-channel orthogonal filter banks. We show the highly structured decomposition of these blocks and derive their mutual relations. We give examples to exploit the SVD properties in the implementation and design of orthogonal filter banks and wavelets
Considers the construction of orthogonal time-varying filter banks. By examining the time domain des...
Filter banks and wavelets have found applications in signal compression, noise removal, and in many ...
Filter Banks plays crucial role in signal processing and image processing as subband processing give...
AbstractWe study the Singular Value Decomposition (SVD) of the three building blocks of the matrix r...
Wavelets are used in many applications, including image processing, signal analysis and seismology. ...
It is a challenging task to design orthogonal filter banks, especially multidimensional (MD) ones. I...
The design and analysis of one-dimensional(1D) nearly-orthogonal symmetric wavelet filter banks has ...
The design and analysis of two-channel two-dimensional (2D) nonseparable nearly-orthogonal symmetric...
The design and analysis of one-dimensional (1D) nearly-orthogonal symmetric wavelet filter banks has...
The design and analysis of nearly-orthogonal symmetric wavelet filter banks has been studied. Method...
Methods for spatially diagonalizing wideband multiple-input multiple-output channels using linear fi...
This paper presents an algebraic approach to construct M-band orthogonal wavelet bases. A system of ...
Abstract The singular value decomposition (SVD) is a very important tool for narrowband adaptive sen...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering...
Perfect reconstruction oversampled filter banks are equivalent to a particular class of frames in t(...
Considers the construction of orthogonal time-varying filter banks. By examining the time domain des...
Filter banks and wavelets have found applications in signal compression, noise removal, and in many ...
Filter Banks plays crucial role in signal processing and image processing as subband processing give...
AbstractWe study the Singular Value Decomposition (SVD) of the three building blocks of the matrix r...
Wavelets are used in many applications, including image processing, signal analysis and seismology. ...
It is a challenging task to design orthogonal filter banks, especially multidimensional (MD) ones. I...
The design and analysis of one-dimensional(1D) nearly-orthogonal symmetric wavelet filter banks has ...
The design and analysis of two-channel two-dimensional (2D) nonseparable nearly-orthogonal symmetric...
The design and analysis of one-dimensional (1D) nearly-orthogonal symmetric wavelet filter banks has...
The design and analysis of nearly-orthogonal symmetric wavelet filter banks has been studied. Method...
Methods for spatially diagonalizing wideband multiple-input multiple-output channels using linear fi...
This paper presents an algebraic approach to construct M-band orthogonal wavelet bases. A system of ...
Abstract The singular value decomposition (SVD) is a very important tool for narrowband adaptive sen...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering...
Perfect reconstruction oversampled filter banks are equivalent to a particular class of frames in t(...
Considers the construction of orthogonal time-varying filter banks. By examining the time domain des...
Filter banks and wavelets have found applications in signal compression, noise removal, and in many ...
Filter Banks plays crucial role in signal processing and image processing as subband processing give...
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