2016 - IEEE Kiyo Tomiyasu Award “For development of the theory and implementation of sub-Nyquist sampling with applications to radar, communications, and ultrasound.”
2013 - IEEE Fellow For contributions to compressed sampling, generalized sampling, and convex optimization
Her scientific interests lie mostly in Algorithm, Compressed sensing, Mathematical optimization, Signal processing and Signal reconstruction. Yonina C. Eldar has included themes like Subspace topology, Sampling, Signal, Phase retrieval and Convex optimization in her Algorithm study. Her Compressed sensing research is multidisciplinary, incorporating perspectives in Sampling, Sparse matrix, Sparse approximation, Bandwidth and Iterative reconstruction.
Her Bandwidth research includes elements of Nyquist rate, Wideband and Nyquist–Shannon sampling theorem. She studied Mathematical optimization and Mean squared error that intersect with Estimation of covariance matrices. Yonina C. Eldar has researched Signal processing in several fields, including Continuous signal, Analog signal, Robustness, Artificial intelligence and Pattern recognition.
Her primary areas of investigation include Algorithm, Compressed sensing, Mathematical optimization, Artificial intelligence and Electronic engineering. While the research belongs to areas of Algorithm, Yonina C. Eldar spends her time largely on the problem of Sampling, intersecting her research to questions surrounding Nyquist–Shannon sampling theorem. The concepts of her Compressed sensing study are interwoven with issues in Matrix, Beamforming, Sparse approximation and Analog signal.
Her biological study spans a wide range of topics, including Mean squared error, Estimator, Applied mathematics and Convex optimization. Her Artificial intelligence study integrates concerns from other disciplines, such as Ultrasound, Computer vision and Pattern recognition. Her research integrates issues of Radar and MIMO in her study of Electronic engineering.
Her scientific interests lie mostly in Algorithm, Artificial intelligence, Deep learning, Communication channel and Radar. Her Algorithm research focuses on Compressed sensing in particular. Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Beamforming, Computer vision and Pattern recognition.
Her Deep learning study also includes fields such as
Yonina C. Eldar spends much of her time researching Algorithm, Artificial intelligence, MIMO, Deep learning and Communication channel. Her Algorithm study combines topics from a wide range of disciplines, such as Graphical model, Graph, Graph and Signal processing. Her Artificial intelligence research is multidisciplinary, relying on both Ultrasound and Ultrasound imaging.
Her studies deal with areas such as Electronic circuit, Analog signal, Reduction, Baseband and Electronic engineering as well as MIMO. Her Electronic engineering research includes elements of Radar, Wireless network, Spectral bands and Compressed sensing. Her Compressed sensing research is multidisciplinary, incorporating elements of Waveform, Narrowband and Antenna.
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Compressed sensing : theory and applications
Yonina C. Eldar;Gitta Kutyniok.
Published in <b>2012</b> in Cambridge New York by Cambridge University Press (2012)
Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
Yonina C Eldar;Patrick Kuppinger;Helmut Bolcskei.
IEEE Transactions on Signal Processing (2010)
From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals
M. Mishali;Y.C. Eldar.
IEEE Journal of Selected Topics in Signal Processing (2010)
Structured Compressed Sensing: From Theory to Applications
M. F. Duarte;Y. C. Eldar.
IEEE Transactions on Signal Processing (2011)
Robust Recovery of Signals From a Structured Union of Subspaces
Y.C. Eldar;M. Mishali.
IEEE Transactions on Information Theory (2009)
Compressed Sensing: List of contributors
Yonina C. Eldar;Gitta Kutyniok.
(2012)
Linear precoding via conic optimization for fixed MIMO receivers
A. Wiesel;Y.C. Eldar;S. Shamai.
IEEE Transactions on Signal Processing (2006)
Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals
M. Mishali;Y.C. Eldar.
IEEE Transactions on Signal Processing (2009)
Compressed sensing with coherent and redundant dictionaries
Emmanuel J. Candès;Yonina C. Eldar;Deanna Needell;Paige Randall.
Applied and Computational Harmonic Analysis (2011)
A probabilistic Hough transform
N. Kiryati;Y. Eldar;A. M. Bruckstein.
Pattern Recognition (1991)
Princeton University
Tel Aviv University
Technion – Israel Institute of Technology
New Jersey Institute of Technology
New York University
Duke University
Technion – Israel Institute of Technology
TU Darmstadt
California Institute of Technology
Profile was last updated on December 6th, 2021.
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