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# S. Haykin, Nonlinear Methods of Spectral Analysis, 2nd Ed. frequency, and the vector of frequency points F. to handle the "matrix must be positive definite" error in linalg.cholesky.
Two methods of spectral data analysis are employed, namely difference spectroscopy (DS) and least-squares curve fitting (LSCF).
We connect both with methods of spectral representation of stochastic problems, and introduce a computational procedure without any sampling which works completely deterministically, and is fast and reliable.
For this purpose we apply ideas which were used for the Witten Laplacian in the real case and various methods of spectral theory of these operators.
We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides.
We gain the advantage of being able to use methods of spectral theory, albeit in the murkier non-normal setting.
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This was done with the method of spectral tempering.
The formulation is based on the extension of stochastic decomposition technique to wavelet domain via the method of spectral factorization.
We report a novel multichannel surface plasmon resonance (SPR) sensor based on the combination of parallel sensing channel architecture and the method of spectral discrimination of sensing channels.
Our method of spectral analysis of the data reveals that plasma detected during the event consists of ions both solar wind and ionosphere origin.
In this paper a novel analytical method of spectral analysis for acoustic Gaussian noise signals propagated in lossless fluids is presented.
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