The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform
Steven Johnson - One of the best experts on this subject based on the ideXlab platform.
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The Relationship Between the Matched-Filter Operator and the Target Signature Space-Orthogonal
2000Co-Authors: Steven JohnsonAbstract:An Operator called the target signature space-orthog- onal projection classifier (TSC) was recently introduced. In this paper, we will show that the TSC is actually a scaling of the fa- miliar matched-Filter Operator. We will also prove that scaling the matched-Filter Operator does not alter its receiver operating char- acteristic (ROC) curve. Therefore, the TSC has the same perfor- mance as the matched-Filter Operator but requires more computa- tions.
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the relationship between the matched Filter Operator and the target signature space orthogonal projection classifier
IEEE Transactions on Geoscience and Remote Sensing, 2000Co-Authors: Steven JohnsonAbstract:An Operator called the target signature space-orthogonal projection classifier (TSC) was recently introduced. The author shows that the TSC is actually a scaling of the familiar matched-Filter Operator. It is also proved that scaling the matched-Filter Operator does not alter its receiver operating characteristic (ROC) curve. Therefore, the TSC has the same performance as the matched-Filter Operator but requires more computations.
L S Cederbaum - One of the best experts on this subject based on the ideXlab platform.
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parallel Filter diagonalization a novel method to resolve quantum states in dense spectral regions
Journal of Chemical Physics, 2000Co-Authors: Robin Santra, J Breidbach, J Zobeley, L S CederbaumAbstract:A parallel version of D. Neuhauser’s Filter diagonalization algorithm is presented. In contrast to the usual procedure of acting with a set of narrow Filter Operators on a single or just a few initial vectors, parallelizability is achieved by working with a single, broad Filter Operator and a correspondingly large number of initial vectors. Apart from the obvious speedup in computation time, there is no need for communication between the processors involved in the computation. Furthermore, because a significantly reduced number of matrix vector multiplications is needed per initial vector, parallel Filter diagonalization is numerically more stable than the single processor approach. It is argued that this method is particularly attractive for calculating eigenvectors of the large-scale secular matrices arising in quantum chemistry, especially in dense spectral regions. An application to dense state distributions of a cationic molecular cluster serves as an illustrative example. This is the first time filt...
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Parallel Filter diagonalization: A novel method to resolve quantum states in dense spectral regions
The Journal of Chemical Physics, 2000Co-Authors: Robin Santra, J Breidbach, J Zobeley, L S CederbaumAbstract:A parallel version of D. Neuhauser’s Filter diagonalization algorithm is presented. In contrast to the usual procedure of acting with a set of narrow Filter Operators on a single or just a few initial vectors, parallelizability is achieved by working with a single, broad Filter Operator and a correspondingly large number of initial vectors. Apart from the obvious speedup in computation time, there is no need for communication between the processors involved in the computation. Furthermore, because a significantly reduced number of matrix vector multiplications is needed per initial vector, parallel Filter diagonalization is numerically more stable than the single processor approach. It is argued that this method is particularly attractive for calculating eigenvectors of the large-scale secular matrices arising in quantum chemistry, especially in dense spectral regions. An application to dense state distributions of a cationic molecular cluster serves as an illustrative example. This is the first time Filter diagonalization is used as a tool for ab initio electronic structure calculations.
Rongqing Chen - One of the best experts on this subject based on the ideXlab platform.
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a general and efficient Filter diagonalization method without time propagation
Journal of Chemical Physics, 1996Co-Authors: Rongqing ChenAbstract:A general and efficient Filter‐diagonalization method is proposed to solve the quantum mechanical eigenvalue/eigenstate problem in a pre‐specified energy range. This method is in similar spirit to the original Filter‐diagonalization approach, but it eliminates the time propagation by expanding the Filter Operator directly in the energy space in terms of Chebychev polynomials. Our approach is more efficient than the existing methods because neither time propagation nor time‐to‐energy transformation is needed. It also allows the choice of the most efficient Filter Operator for the system of interest. This method is tested for a one‐dimensional Morse oscillator and for the two‐dimensional Henon–Heiles system. The results show excellent convergence and accuracy.
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A general and efficient Filter‐diagonalization method without time propagation
Journal of Chemical Physics, 1996Co-Authors: Rongqing ChenAbstract:A general and efficient Filter‐diagonalization method is proposed to solve the quantum mechanical eigenvalue/eigenstate problem in a pre‐specified energy range. This method is in similar spirit to the original Filter‐diagonalization approach, but it eliminates the time propagation by expanding the Filter Operator directly in the energy space in terms of Chebychev polynomials. Our approach is more efficient than the existing methods because neither time propagation nor time‐to‐energy transformation is needed. It also allows the choice of the most efficient Filter Operator for the system of interest. This method is tested for a one‐dimensional Morse oscillator and for the two‐dimensional Henon–Heiles system. The results show excellent convergence and accuracy.
Ross A Black - One of the best experts on this subject based on the ideXlab platform.
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simple time variant band pass Filtering by Operator scaling
Geophysics, 1995Co-Authors: Choon B Park, Ross A BlackAbstract:A convolutional method of time-variant, band-pass Filtering presented shows that a change of Filter cutoff frequencies with time is achieved by frequency scaling the amplitude spectrum of a reference Operator. According to the scaling property of the Fourier transform, this frequency scaling is actually accomplished by a simple time-domain scaling of the reference Operator in which the Filter Operator at a sample point on a seismogram is obtained by compressing the reference Operator after multiplication by a constant value. Therefore, the length of Filter Operator changes as the cutoff frequencies and the pass band change with time; the higher the cutoff frequencies and the broader the passband, the shorter the Operator length. The algorithm does not involve any complex-valued arithmetic that may significantly reduce the computational efficiency if a small computer is used. Because the time-variant convolution formula is exact, the change of cutoff frequencies is not limited to slowly varying or monotonic variations used in other algorithms.The way of changing cutoff frequencies restricts the passband of the Filter to a constant value in terms of octaves. However, this restriction can be relaxed significantly in practical usage by a cascaded implementation if the Nyquist frequency is well above the passband of the Filter. Computational efficiency of the method is quite comparable to that of the time-invariant, band-pass Filtering. Tests of the method on both real and synthetic data sets confirm the effectiveness of the Filter.
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Simple time‐variant, band‐pass Filtering by Operator scaling
GEOPHYSICS, 1995Co-Authors: Choon B Park, Ross A BlackAbstract:A convolutional method of time-variant, band-pass Filtering presented shows that a change of Filter cutoff frequencies with time is achieved by frequency scaling the amplitude spectrum of a reference Operator. According to the scaling property of the Fourier transform, this frequency scaling is actually accomplished by a simple time-domain scaling of the reference Operator in which the Filter Operator at a sample point on a seismogram is obtained by compressing the reference Operator after multiplication by a constant value. Therefore, the length of Filter Operator changes as the cutoff frequencies and the pass band change with time; the higher the cutoff frequencies and the broader the passband, the shorter the Operator length. The algorithm does not involve any complex-valued arithmetic that may significantly reduce the computational efficiency if a small computer is used. Because the time-variant convolution formula is exact, the change of cutoff frequencies is not limited to slowly varying or monotonic variations used in other algorithms.The way of changing cutoff frequencies restricts the passband of the Filter to a constant value in terms of octaves. However, this restriction can be relaxed significantly in practical usage by a cascaded implementation if the Nyquist frequency is well above the passband of the Filter. Computational efficiency of the method is quite comparable to that of the time-invariant, band-pass Filtering. Tests of the method on both real and synthetic data sets confirm the effectiveness of the Filter.
Robin Santra - One of the best experts on this subject based on the ideXlab platform.
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parallel Filter diagonalization a novel method to resolve quantum states in dense spectral regions
Journal of Chemical Physics, 2000Co-Authors: Robin Santra, J Breidbach, J Zobeley, L S CederbaumAbstract:A parallel version of D. Neuhauser’s Filter diagonalization algorithm is presented. In contrast to the usual procedure of acting with a set of narrow Filter Operators on a single or just a few initial vectors, parallelizability is achieved by working with a single, broad Filter Operator and a correspondingly large number of initial vectors. Apart from the obvious speedup in computation time, there is no need for communication between the processors involved in the computation. Furthermore, because a significantly reduced number of matrix vector multiplications is needed per initial vector, parallel Filter diagonalization is numerically more stable than the single processor approach. It is argued that this method is particularly attractive for calculating eigenvectors of the large-scale secular matrices arising in quantum chemistry, especially in dense spectral regions. An application to dense state distributions of a cationic molecular cluster serves as an illustrative example. This is the first time filt...
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Parallel Filter diagonalization: A novel method to resolve quantum states in dense spectral regions
The Journal of Chemical Physics, 2000Co-Authors: Robin Santra, J Breidbach, J Zobeley, L S CederbaumAbstract:A parallel version of D. Neuhauser’s Filter diagonalization algorithm is presented. In contrast to the usual procedure of acting with a set of narrow Filter Operators on a single or just a few initial vectors, parallelizability is achieved by working with a single, broad Filter Operator and a correspondingly large number of initial vectors. Apart from the obvious speedup in computation time, there is no need for communication between the processors involved in the computation. Furthermore, because a significantly reduced number of matrix vector multiplications is needed per initial vector, parallel Filter diagonalization is numerically more stable than the single processor approach. It is argued that this method is particularly attractive for calculating eigenvectors of the large-scale secular matrices arising in quantum chemistry, especially in dense spectral regions. An application to dense state distributions of a cationic molecular cluster serves as an illustrative example. This is the first time Filter diagonalization is used as a tool for ab initio electronic structure calculations.