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Wavelet orthogonal signal correction
Journal of Chemometrics, 2005AbstractIn this paper a novel signal‐preprocessing technique that combines the local and multiscale properties of the wavelet prism with the global filtering capability of orthogonal signal correction (OSC) is presented for the pretreatment of spectroscopic data. In this hybrid method, referred to as wavelet OSC (WOSC), a separate OSC filter is applied
Robert N. Feudale +4 more
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Piecewise orthogonal signal correction
Chemometrics and Intelligent Laboratory Systems, 2002Abstract A novel signal-processing method that performs orthogonal signal correction (OSC) in a piecewise manner, namely piecewise OSC (POSC), is developed and applied to two near-infrared (NIR) data sets of multivariate calibration. Partial least squares (PLS) regression models were constructed for the POSC-corrected spectra, and the results were ...
Robert N Feudale +2 more
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Orthogonal Signals and the Orthogonalization Procedure
2021Abstract Chapter 2 is dedicated to the principle of signal orthogonalization, because orthogonal signals are widely used in telecommunication theory and practice, like the carriers of baseband signals, subcarriers in orthogonal frequency division multiplexing systems, and the spreading sequences in spread-spectrum and code division ...
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2021
In this chapter we learn about orthogonal signal decomposition into a sum of weighted orthogonal functions (decomposition basis) like in Fourier series analysis. The decomposition weights are found by orthogonal transformation of signal samples, i.e. by multiplying them by rectangular orthogonal matrix having complex-conjugated decomposition functions ...
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In this chapter we learn about orthogonal signal decomposition into a sum of weighted orthogonal functions (decomposition basis) like in Fourier series analysis. The decomposition weights are found by orthogonal transformation of signal samples, i.e. by multiplying them by rectangular orthogonal matrix having complex-conjugated decomposition functions ...
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Adaptive orthogonalization of opponent-color signals
Biological Cybernetics, 1993This paper concerns the processing of the outputs of the two opponent-color mechanisms in the human visual system. We present experimental evidence that opponent-color signals interact after joint modulation even though they are essentially independent under neutral steady adaptation and after exclusive modulation of each mechanism.
Zaidi, Qasim, Shapiro, Arthur G.
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Improved Piecewise Orthogonal Signal Correction Algorithm
Applied Spectroscopy, 2003Piecewise orthogonal signal correction (POSC), an algorithm that performs local orthogonal filtering, was recently developed to process spectral signals. POSC was shown to improve partial least-squares regression models over models built with conventional OSC.
Robert N, Feudale +2 more
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Nonstationary local signal-and-noise orthogonalization
GEOPHYSICS, 2021The local signal-and-noise orthogonalization method has been widely used in the seismic processing and imaging community. This method uses a fixed triangle smoother for regularizing the local orthogonalization weight, which is based on the assumption that the energy is homogeneously distributed across the whole seismic profile.
Yangkang Chen, Sergey Fomel
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Signal identification by orthogonal transforms
IEE Proceedings - Radar, Sonar and Navigation, 1998The phase of a signal contains most of the essential characteristics for its identification. A technique is proposed to process the phase for noise reduction and signal identification. The identifier models the phase as a linear combination of orthogonal vectors, computes the transform coefficients, discards the insignificant coefficients to reduce ...
K.C. Ho +3 more
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'Complementary' Signals and Orthogonalized Exponentials
IRE Transactions on Circuit Theory, 1962When a signal is approximated by a finite set of component signals which span a subspace \Phi , the least-square approximation may be interpreted geometrically in signal space as the projection of the true signal vector upon this finite dimensional subspace.
T. Young, W. Huggins
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The Journal of the Acoustical Society of America, 2006
A least-squares approach was used to construct near-orthogonal signals that can be useful in sonar applications. For the matched-filter case the solution can be written with Lagrange multiplier terms, which reduces to a simple eigenvector problem. Decoding filters are also examined, and these results are compared to the matched-filter case.
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A least-squares approach was used to construct near-orthogonal signals that can be useful in sonar applications. For the matched-filter case the solution can be written with Lagrange multiplier terms, which reduces to a simple eigenvector problem. Decoding filters are also examined, and these results are compared to the matched-filter case.
openaire +1 more source