Results 221 to 230 of about 136,308 (262)

Mono‐components for decomposition of signals

Mathematical Methods in the Applied Sciences, 2006
AbstractThis note further carries on the study of the eigenfunction problem: Find f(t)=ρ(t)eiθ(t) such that Hf=−if, ρ(t)⩾0 and θ′(t)⩾0, a.e. where H is Hilbert transform. Functions satisfying the above conditions are called mono‐components, that have been sought in time‐frequency analysis.
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A new matrix decomposition for signal processing

Automatica, 1993
We extend the generalized singular value decomposition to a new decomposition that can be updated at a low cost. In addition, we show how a forgetting factor can be incorporated in our decomposition.
Franklin T. Luk, Sanzheng Qiao
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Decomposition of mechanical signals

2009 35th Annual Conference of IEEE Industrial Electronics, 2009
Time frequency transformations have gained increasing attention for the characterization of non-stationary signals in a broad spectrum of science and engineering applications. Signals encountered in rotary machine systems can be broadly classified as being either stationary or nonstationary.
Cao Jun, Wang Xingsong
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Composite signal decomposition

IEEE Transactions on Audio and Electroacoustics, 1970
A technique for decomposing a composite signal, which consists of the superposition of known multiple signals overlapping in time, is described. Decomposition includes determining the number of signals present, their epochs (arrival times), and amplitudes. The procedure is investigated for the noise-free and noisy situation.
D. Childers, R. Varga, N. Perry
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Signal-Adaptive Decomposition of Multicomponent Signals

The Digital Signal Processing workshop, 2005
Abstract 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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On the decomposition of multichannel nonstationary multicomponent signals

Signal Processing, 2020
Abstract 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, Milos Brajovic, Milos Dakovic, Danilo P. Mandic   +3 more
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New signal processing techniques for the decomposition of EMG signals

Medical & Biological Engineering & Computing, 1992
This paper relates to the use of knowledge-based signal processing techniques in the decomposition of EMG signals. The aim of the research is to automatically decompose EMG signals recorded at force levels up to 20 per cent maximum voluntary contraction (MVC) into their constituent motor unit action potentials (MUAPS), and to display the MUAP shapes ...
G H, Loudon, N B, Jones, A S, Sehmi
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Morphological modeling of cardiac signals based on signal decomposition

Computers in Biology and Medicine, 2013
In this paper a general framework is presented for morphological modeling of cardiac signals from a signal decomposition perspective. General properties of a desired morphological model are presented and special cases of the model are studied in detail.
Ebadollah Kheirati Roonizi, Reza Sameni
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An axiomatic approach to multiresolution signal decomposition

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), 2002
We have been developing general multiresolution signal decomposition schemes that unify traditional (linear) approaches and allow use of nonlinear filtering techniques in the decomposition. This paper summarizes our approach and provides several simple examples.
John Goutsias, Henk J. A. M. Heijmans
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Automated decomposition of intramuscular electromyographic signals

IEEE Transactions on Biomedical Engineering, 2006
We present a novel method for extracting and classifying motor unit action potentials (MUAPs) from one-channel electromyographic recordings. The extraction of MUAP templates is carried out using a symbolic representation of waveforms, a common technique in signature verification applications. The assignment of MUAPs to their specific trains is achieved
Joël R. Florestal, Pierre A. Mathieu, Armando Malanda   +2 more
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