Sparse sampling: theory, methods and an application in neuroscience
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Publication:309637
DOI10.1007/s00422-014-0639-xzbMath1344.92094OpenAlexW2129184388WikidataQ38276182 ScholiaQ38276182MaRDI QIDQ309637
Jon Oñativia, Pier Luigi Dragotti
Publication date: 7 September 2016
Published in: Biological Cybernetics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00422-014-0639-x
Biomedical imaging and signal processing (92C55) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Cites Work
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- Exponential splines and minimal-support bases for curve representation
- Reconstruction of aperiodic FRI signals and estimation of the rate of innovation based on the state space method
- Compressive sampling of EEG signals with finite rate of innovation
- Analytic sensing for multi-layer spherical models with application to EEG source imaging
- Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise
- Signal enhancement-a composite property mapping algorithm
- Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
- Estimating Signals With Finite Rate of Innovation From Noisy Samples: A Stochastic Algorithm
- Sampling Piecewise Sinusoidal Signals With Finite Rate of Innovation Methods
- Innovation Rate Sampling of Pulse Streams With Application to Ultrasound Imaging
- 2D Finite Rate of Innovation Reconstruction Method for Step Edge and Polygon Signals in the Presence of Noise
- Sampling-50 years after Shannon
- Sampling signals with finite rate of innovation
- Cardinal exponential splines: part I - theory and filtering algorithms
- Sampling and reconstruction of signals with finite rate of innovation in the presence of noise
- Exact Feature Extraction Using Finite Rate of Innovation Principles With an Application to Image Super-Resolution
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