Results 261 to 270 of about 3,600,645 (297)
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Resolution limits in signal recovery
IEEE Transactions on Signal Processing, 1996Resolution analysis for the problem of signal recovery from finitely many linear measurements is the subject of this paper. The classical Rayleigh limit serves only as a lower bound on resolution since it does not assume any recovery strategy and is based only on observed data.
Satya Dharanipragada, K. S. Arun
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Signal recovery in imaging photoplethysmography
Physiological Measurement, 2013Imaging photoplethysmography is an emerging technique for the extraction of biometric information from people using video recordings. The focus is on extracting the cardiac heart rate of the subject by analysing the luminance of the colour video signal and identifying periodic components.
Benjamin D, Holton +3 more
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2009
The literature on the recovery of signals and images is vast (e.g., [23, 110, 112, 257, 391, 439, 791, 795, 933, 934, 937, 945, 956, 1104, 1324, 1494, 1495, 1551]). In this Chapter, the specific problem of recovering lost signal intervals from the remaining known portion of the signal is considered. Signal recovery is also a topic of Chapter 11 on POCS.
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The literature on the recovery of signals and images is vast (e.g., [23, 110, 112, 257, 391, 439, 791, 795, 933, 934, 937, 945, 956, 1104, 1324, 1494, 1495, 1551]). In this Chapter, the specific problem of recovering lost signal intervals from the remaining known portion of the signal is considered. Signal recovery is also a topic of Chapter 11 on POCS.
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Modeling and Recovery of Graph Signals and Difference-Based Signals
2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019In this paper, we consider the problem of representing and recovering graph signals with a nonlinear measurement model. We propose a two-stage graph signal processing (GSP) framework. First, a GSP representation is obtained by finding the graph filter that best approximates the known measurement function.
Ariel Kroizer +2 more
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Sublinear Recovery of Sparse Wavelet Signals
Data Compression Conference (dcc 2008), 2008There are two main classes of decoding algorithms for "compressed sensing," those which run in time polynomial in the signal length and those which use sublinear resources. Most of the sublinear algorithms focus on signals which are compressible in either the Euclidean domain or the Fourier domain.
Ray Maleh, Anna C. Gilbert
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Physics Bulletin, 1973
Suppose that one were given the task of measuring the magnetism of a rock sample in the presence of the unavoidable and much larger magnetic field of the earth, or of measuring a weak infrared signal in a warm laboratory. In both cases the signal of interest would be obscured by a large background signal which will be referred to as 'interference' and ...
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Suppose that one were given the task of measuring the magnetism of a rock sample in the presence of the unavoidable and much larger magnetic field of the earth, or of measuring a weak infrared signal in a warm laboratory. In both cases the signal of interest would be obscured by a large background signal which will be referred to as 'interference' and ...
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Cancer risk among World Trade Center rescue and recovery workers: A review
Ca-A Cancer Journal for Clinicians, 2022Andrew Todd +2 more
exaly
Investigation of Kronecker-Based Recovery of Compressed ECG Signal
IEEE Transactions on Instrumentation and Measurement, 2020Hadi Zand +2 more
exaly
Uncertainty Principles and Signal Recovery
SIAM Journal on Applied Mathematics, 1989David Donoho
exaly