Charles J. Garfinkle

ORCID: 0000-0003-0513-4393
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About
Contact & Profiles
Research Areas
  • Sparse and Compressive Sensing Techniques
  • Blind Source Separation Techniques
  • Image and Signal Denoising Methods
  • Fish biology, ecology, and behavior
  • Marine animal studies overview
  • Neural dynamics and brain function
  • Mathematical Analysis and Transform Methods
  • Ultrasound Imaging and Elastography

Center for Theoretical Biological Physics
2016-2019

University of Ottawa
2012

Interactions among animals can result in complex sensory signals containing a variety of socially relevant information, including the number, identity, and relative motion conspecifics. How spatiotemporal properties such evolving naturalistic are encoded is key question neuroscience. Here, we present results from experiments modeling that address this issue context electric sense, which combines spatial aspects vision touch, with temporal audition. Wave-type fish, as brown ghost knifefish,...

10.1371/journal.pcbi.1002564 article EN cc-by PLoS Computational Biology 2012-07-12

Learning optimal dictionaries for sparse coding has exposed characteristic features of many natural signals. However, universal guarantees the stability such in presence noise are lacking. Here, we provide very general conditions guaranteeing when yielding sparsest encodings unique and stable with respect to measurement or modeling error. We demonstrate that some all original dictionary elements recoverable from noisy data even if fails satisfy spark condition, its size is overestimated,...

10.1109/tsp.2019.2935914 article EN cc-by IEEE Transactions on Signal Processing 2019-08-22

Learning optimal dictionaries for sparse coding has exposed characteristic features of many natural signals. However, universal guarantees the stability such in presence noise are lacking. Here, we provide very general conditions guaranteeing when yielding sparsest encodings unique and stable with respect to measurement or modeling error. We demonstrate that some all original dictionary elements recoverable from noisy data even if fails satisfy spark condition, its size is overestimated,...

10.48550/arxiv.1606.06997 preprint EN other-oa arXiv (Cornell University) 2016-01-01
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