Results 211 to 220 of about 20,190 (261)
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Multicomponent signal denoising with synchrosqueezing
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012In this paper, we develop a new technique based on the synchrosqueezing method to denoise multicomponent signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method ...
Stephen Mclaughlin, Thomas Oberlin
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On the decomposition of multichannel nonstationary multicomponent signals
Signal Processing, 2020Abstract With their ability to cater for simultaneously for multifaceted information, multichannel (multivariate) signals have been used to solve problems that are normally not solvable with signals obtained from a single source. One such problem is the decomposition of signals which comprise several components for which the domains of support ...
Ljubisa Stankovic +3 more
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Multicomponent signals and financial constraints
Technology Analysis & Strategic Management, 2019The inventive process creates knowledge asymmetries between research-intensive firms and external investors, making it difficult for firms to obtain funding for inventive activities.
Edward Levitas, M. Ann McFadyen
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Instantaneous frequency of multicomponent signals
IEEE Signal Processing Letters, 1999The failure of the traditional definition of instantaneous frequency (IF/sub t/) in the multicomponent case has been often reported. We determine the reasons for the failure of this definition. This enables us to understand and integrate all previously reported cases in a simple unified theory.
Paulo M. Oliveira, Victor A. N. Barroso
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Comodulation masking release for multicomponent signals
The Journal of the Acoustical Society of America, 1988Detection of signals composed of one, two, or three pure-tone components was examined in comodulated and noncomodulated masking noises. The masking noise was either a single 30-Hz-wide narrow band of noise, two narrow bands of noise, or three narrow bands of noise.
J W, Hall, J H, Grose, M P, Haggard
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Signal-Adaptive Decomposition of Multicomponent Signals
The Digital Signal Processing workshop, 2005Abstract In this paper we present a new method for adaptively decomposing a multicomponent signal into its components. This method is based on fitting an autoregressive (AR) model to the short-time spectra ofthe signal. The AR parameters represent the coefficients of the linear predictive (LP) polynomial. Theroots of this polynomial constitute a set of
K.T. Assaleh, R.J. Mammone
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Multicomponent analysis of electrochemical signals in the wavelet domain
Talanta, 2003Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN).
COCCHI, Marina +5 more
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Time-frequency decomposition of multivariate multicomponent signals
Signal Processing, 2018Abstract A solution of the notoriously difficult problem of characterization and decomposition of multicomponent multivariate signals which partially overlap in the joint time-frequency domain is presented. This is achieved based on the eigenvectors of the signal autocorrelation matrix.
Ljubisa Stankovic +3 more
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What is a multicomponent signal?
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992Multicomponent signals, which produce delineated concentrations in the time-frequency plane, are common in nature, human speech being a prime example. An explanation of what types of signals are multicomponent is presented. The idea is generalized to time-scale. The recognition of multicomponent signals is considered. >
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Detection and classification of multicomponent signals
[1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers, 2002The multicomponent nature of many naturally occurring signals, such as speech, is exploited to provide a new means of detection and classification. A component of a multicomponent signal is defined in terms of the local bandwidth about the instantaneous frequency in the time-frequency distribution.
A.B. Fineberg, R.J. Mammone
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