Cooperative greedy pursuit strategies for sparse signal representation by partitioning
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21541849
- Type
- D - Journal article
- DOI
-
10.1016/j.sigpro.2016.02.008
- Title of journal
- Signal processing
- Article number
- -
- First page
- 365
- Volume
- 125
- Issue
- -
- ISSN
- 0165-1684
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper develops the foundations of a number of mathematical methods for achieving high-quality representation of large signals. The methods, designed to assist other signal procession techniques, have been shown in subsequent publications to be relevant in the contexts of: weak signal processing of ground penetrating radar detection, https://link.springer.com/article/10.1007/s11771-019-4236-y, electrical signals processing applications https://ieeexplore.ieee.org/abstract/document/8378818, granular signal processing https://doi.org/10.1016/j.sigpro.2017.05.026 and compression of melodic music DOI: 10.1049/el.2017.3908 A library of routines implementing the techniques has been made available on a dedicated website http://www.nonlinear-approx.info/examples/node01.html (620 downloads)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
