Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Zvika Ben-Haim"'
Autor:
Zvika Ben-Haim, Zach Moshe, Moriah Royz, Yuval Levin, Nofar Peled Levi, Frederik Kratzert, Niv Giladi, Hila Noga, Dana Weitzner, Dafi Voloshin, Gregory Begelman, Gideon Dror, Sella Nevo, Shahar Timnat, Yotam Gigi, Liora Yuklea, Ofir Reich, Oren Gilon, Asher Metzger, Guy Shalev, Yossi Matias, Chen Barshai, Adi Gerzi Rosenthal, Vladimir Anisimov, Efrat Morin, Tal Shechter, Grey Nearing, Avinatan Hassidim, Ira Shavitt, Gal Elidan, Ronnie Maor
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71a4149b2b83102b65c7ecaeeed7d573
https://doi.org/10.5194/hess-2021-554
https://doi.org/10.5194/hess-2021-554
Autor:
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, Yossi Matias
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a9ffa6c756998128c621dc3244cd2e1
https://doi.org/10.5194/hess-2021-554-supplement
https://doi.org/10.5194/hess-2021-554-supplement
Autor:
Efrat Morin, Sella Nevo, Asher Metzger, Vova Anisimov, Gal Elidan, Zach Moshe, Ran El-Yaniv, Zvika Ben-Haim, Ofir Reich, Guy Shalev
One of the major natural disasters is flooding, which causes thousands of fatalities, affects the lives of hundreds of millions, and results in huge economic damages annually. Google’s Flood Forecasting Initiative aims at providing high-resolution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb4fd030ab7acd272a17f7e4af430f1b
https://doi.org/10.5194/egusphere-egu2020-4134
https://doi.org/10.5194/egusphere-egu2020-4134
Publikováno v:
IEEE Transactions on Information Theory. 57:7856-7876
The problem studied in this paper is unbiased estimation of a sparse nonrandom vector corrupted by additive white Gaussian noise. It is shown that while there are infinitely many unbiased estimators for this problem, none of them has uniformly minimu
Autor:
Yonina C. Eldar, Zvika Ben-Haim
Publikováno v:
IEEE Transactions on Signal Processing. 58:3384-3389
The goal of this contribution is to characterize the best achievable mean-squared error (MSE) in estimating a sparse deterministic parameter from measurements corrupted by Gaussian noise. To this end, an appropriate definition of bias in the sparse s
Autor:
Zvika Ben-Haim, Yonina C. Eldar
Publikováno v:
IEEE Signal Processing Letters. 16:453-456
The problem considered in this letter is to bound the performance of estimators of a deterministic parameter which satisfies given constraints. Specifically, previous work on the constrained Cramer-Rao bound (CRB) is generalized to include singular F
Autor:
Yonina C. Eldar, Zvika Ben-Haim
Publikováno v:
IEEE Transactions on Signal Processing. 53:3172-3182
We consider the linear regression problem of estimating a deterministic parameter vector x from observations y = Hx + w, where H is known, and w is additive noise. We seek an estimator whose estimation error does not exceed a given maximum error for
Autor:
Yonina C. Eldar, Zvika Ben-Haim
Publikováno v:
IEEE Transactions on Information Theory. 54:4682-4684
The Weiss-Weinstein bound (WWB) provides a lower limit on the mean-squared error (MSE) achievable by an estimator of an unknown random parameter. In this correspondence, it is shown that some previously proposed simplified versions of the bound do no
We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3e57037b4c64a3ba0bef66651c8ffcb
http://arxiv.org/abs/1304.3886
http://arxiv.org/abs/1304.3886
Publikováno v:
Compressed Sensing
Parametric signals, such as streams of short pulses, appear in many applications including bio-imaging, radar, and spread-spectrum communication. The recently developed finite rate of innovation (FRI) framework, has paved the way to low rate sampling
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfa34a5a102e865943ca7191ca4f7503
https://doi.org/10.1017/cbo9780511794308.005
https://doi.org/10.1017/cbo9780511794308.005