The DOI matches its Crossref record; 29 of the 32 references it cites were found in a registry or an index.
An Introduction To Compressive Sampling
Abstract
Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.
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“This and other non-equilibrium exchanges between system and environment can have considerable influence on the system dynamics [28-35], influence the direction of energy transfer [28] and may even lead to the generation of long-lived quantum coherence in the system [28, 36-38].”
From Vibrations, quanta and biology (2013) · cites this paper as [36]
In the citing paper
“In the following we will show that the inclusion of significant coupling of electronic motion to long-lived vibrational modes [28, 34] are capable of explaining the observations [36, 37]”
From Vibrations, quanta and biology (2013) · cites this paper as [36]
In the citing paper
“The physical picture presented here illustrates a key point: electronic coherence may emerge from transiently exciting robust, weakly dephasing vibrational coherences which then transfer back coherence to those exciton transitions that are well matched to the mode [28, 36, 37].”
From Vibrations, quanta and biology (2013) · cites this paper as [36]
Citations
Cites
Regression shrinkage and selection via the lasso
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Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,715
Decoding by Linear Programming
Candès, E. J., & Tao, T. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.858979 · Cited by 7,134
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candès, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,809
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,734
The Dantzig selector: Statistical estimation when p is much larger than n
Candès, E. J., & Tao, T. (2007) · The Annals of Statistics · doi:10.1214/009053606000001523 · Cited by 3,202
Flagged first, then by citations
3 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- cand�s, 2007, Enhancing sparsity by reweighting l
- cand�s, 2007, Journal of Institute of Mathematics and its Applications
- taubman, 2001, JPEG 2000 Image Compression Fundamentals Standards and Practice
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517384113 · Cited by 4,796
A tutorial on synthetic aperture radar
Moreira, A., Prats, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2248301 · Cited by 2,866
Kuybeda, O., Frank, G. A., Bartesaghi, A., Borgnia, M. J., Subramaniam, S., & Sapiro, G. (2012) · Journal of Structural Biology · doi:10.1016/j.jsb.2012.10.010 · Cited by 2,200
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
Inverse problems: A Bayesian perspective
Stuart, A. M. (2010) · Acta Numerica · doi:10.1017/s0962492910000061 · Cited by 1,941
Metadata
- Authors
- Candes, E. J., & Wakin, M. B.
- Affiliations
- École Polytechnique (FR)
- ORCID
- Wakin, M.B.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2008-03
- Volume
- 25
- Issue
- 2
- Pages
- 21-30
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2119667497
- Cited by
- 10,017
- References
- 32
- Citations by year
- 2026: 233 · 2025: 356 · 2024: 359 · 2023: 437 · 2022: 449 · 2021: 561 · 2020: 545 · 2019: 727 · 2018: 818 · 2017: 792 · 2016: 823 · 2015: 899 · 2014: 803 · 2013: 709 · 2012: 614
- FWCI
- 310.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
- Keywords
- compressed sensing, Nyquist rate, undersampling, signal reconstruction, image reconstruction, analog-to-digital conversion, data acquisition
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