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Multistep sampling for recovering sparse signals from noise

总结

Rui Manuel Castro, Ph.D.

技术优势

Systematically determine how and where measurements should be acquired to efficiently use resources Improvements in signal-to-noise ratioReduced acquisition timeReduced power or energy expenditure Decreases use of acquisition resourcesIncreased fidelity for signal estimationImproved reliability for signal detection Patent information:Patent Issued (US 8,521,473)Tech Ventures Reference: IR M09-079

技术应用

Imaging Applications (Conventional imaging, medical imaging (such as MRI/PET), astronomical and other scientific imaging, etc.)Biomedical Applications (e.g. gene expression microarray experiments)Communications (Cognitive radio, spectrum estimation, etc.)Signal Intelligence

详细技术说明

Rui Manuel Castro, Ph.D.

*Abstract

None

*Inquiry

Jay HickeyColumbia Technology VenturesTel: (212) 854-8444Email: TechTransfer@columbia.edu

*IR

m09-079

*Principal Investigation

*Publications

J. Haupt, R. Castro, and R. Nowak. “Distilled sensing: selective sampling for sparse signal recovery”, in D. van Dyk and M. Welling (Eds.), Proceedings of The 12th International Conference on Artificial Intelligence and Statistics (AISTATS) 2009 Apr.Tajer A, Castro R, Wang X. "Adaptive spectrum sensing for agile cognitive radios" 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). 2010 Mar 14-19

国家/地区

美国