Results 251 to 260 of about 590,374 (288)
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2015
Signal decomposition is in large part concerned with recovering the underlying structure of the signal of interest. This subject has been studied extensively in the literature and holds an important place in the biomedical signal processing field as most signal processing problems in this arena defy the simple signal plus AWGN model.
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Signal decomposition is in large part concerned with recovering the underlying structure of the signal of interest. This subject has been studied extensively in the literature and holds an important place in the biomedical signal processing field as most signal processing problems in this arena defy the simple signal plus AWGN model.
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Morphological modeling of cardiac signals based on signal decomposition
Computers in Biology and Medicine, 2013In 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.
A. Kheirati Roonizi, R. Sameni
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Zerocross Density Decomposition: A Novel Signal Decomposition Method
2020We developed the Zerocross Density Decomposition (ZCD) method for decomposition of nonstationary signals into subcomponents (intrinsic modes). The method is based on the histogram of zero-crosses of a signal across different scales. We discuss the main properties of ZCD and parameters of ZCD modes (statistical characteristics, principal frequencies and
Tatjana Sidekerskienė +2 more
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On-signal decomposition techniques
Optical Engineering, 1991Well-known block transforms and perfect reconstruction orthonormal filter banks are evaluated based on their frequency behavior and energy compaction. The filter banks outperform the block transforms for the signal sources considered. Although the latter are simpler to implement and already the choice of the existing video coding standards, filter ...
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Mono‐components for decomposition of signals
Mathematical Methods in the Applied Sciences, 2006AbstractThis 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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Decompositions using maximum signal factors
Journal of Chemometrics, 2014Maximum autocorrelation factors (MAF) and whitened principal components analysis are gaining popularity as tools for exploratory analysis of hyperspectral images. This paper shows that the two approaches are mathematically identical when signal and noise (clutter) are defined similarly.
Neal B. Gallagher +4 more
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Logic Synthesis by Signal-Driven Decomposition
2010This chapter investigates some restructuring techniques based on decomposition and factorization, with the objective to move critical signals toward the output while minimizing area. A specific application is synthesis for minimum switching activity (or high performance), with minimum area penalty, where decompositions with respect to specific critical
A. Bernasconi +3 more
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Assisted signals based mode decomposition
2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2017This paper proposes the bi-dimensional assisted signals based mode decomposition (ASMD). First, we investigate how ASMD behaves in images out of the aspects: the frequencies, the amplitudes and the crossed-angles of the assisted signals and the images.
Guanlei Xu +3 more
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Data-driven signal decomposition method
2005 IEEE International Conference on Information Acquisition, 2006This paper introduces the data-driven signal decomposition method based on the empirical mode decomposition (EMD) technique. The decomposition process uses the data themselves to derive the base function in order to decompose the one-dimensional signal into a finite set of intrinsic mode signals.
P. Chanyagorn +2 more
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Ecg signal watermarking using QR decomposition
Physical and Engineering Sciences in MedicineThis study introduces a novel watermarking technique for electrocardiogram (ECG) signals. Watermarking embeds critical information within the ECG signal, enabling data origin authentication, ownership verification, and ensuring the integrity of research data in domains like telemedicine, medical databases, insurance, and legal proceedings.
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