Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Dave Zachariah"'
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2021, Iss 1, Pp 1-18 (2021)
Abstract We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by non-ideal propagation conditions. While timing-based techniques can enable accurate localization, they are sensitive t
Externí odkaz:
https://doaj.org/article/e9fbbd5a32ec4635a5af5c54332277c9
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 1, Pp 287-294 (2020)
We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method minimizes a ri
Externí odkaz:
https://doaj.org/article/71f0630898ab4b20a0e624c31a8c46eb
Publikováno v:
IEEE Access, Vol 7, Pp 59788-59796 (2019)
In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modeling assumptions. The contribution in th
Externí odkaz:
https://doaj.org/article/893cb5462d96484dbc55655189d1b899
Analysis of the Minimum-Norm Least-Squares Estimator and Its Double-Descent Behavior [Lecture Notes]
Publikováno v:
IEEE Signal Processing Magazine. 40:39-75
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a191aa2b90c0c6add1c12ad3f7d46541
https://doi.org/10.1109/msp.2023.3242083
https://doi.org/10.1109/msp.2023.3242083
Publikováno v:
IEEE Transactions on Signal Processing. 71:1175-1183
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fbe8b05c3fd6c82fae52480031d5da1
https://doi.org/10.1109/tsp.2023.3263363
https://doi.org/10.1109/tsp.2023.3263363
Air pollution is one of the major concerns in global urbanization. Data science can help to understand the dynamics of air pollution and build reliable statistical models to forecast air pollution levels. To achieve these goals, one needs to learn th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edc7908c7f0396e215cb6544942852fd
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-492802
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-492802
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2021, Iss 1, Pp 1-18 (2021)
We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by non-ideal propagation conditions. While timing-based techniques can enable accurate localization, they are sensitive to corrupt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71c5033c36308bfc701b61aa4a0bbc38
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-456481
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-456481
Publikováno v:
MLSP
Nonparametric regression using Gaussian Process (GP) models is a powerful but computationally demanding method. While various approximation methods have been developed to mitigate its computation complexity, few works have addressed the quality of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8723d549e296bf4bb898ae757b8b45e1
https://doi.org/10.1109/mlsp49062.2020.9231601
https://doi.org/10.1109/mlsp49062.2020.9231601
Publikováno v:
ECC
In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe13a653328effad41850ef87a237875
https://doi.org/10.23919/ecc51009.2020.9143860
https://doi.org/10.23919/ecc51009.2020.9143860
Publikováno v:
IEEE Journal of Oceanic Engineering. 43:725-734
In this paper, we review and compare the performance of two recently introduced hyperparameter-free sparse signal processing methods namely, the sparse iterative covariance-based estimation method and the sparse Bayesian learning-based relevance vect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9924fa46a20dc39337bca1195fe81c6
https://doi.org/10.1109/joe.2017.2706100
https://doi.org/10.1109/joe.2017.2706100