Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Janne M. J. Huttunen"'
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 8, p e1007259 (2019)
Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of virtual subjects, with sizes limited only by computational resources. In this work, we study i
Externí odkaz:
https://doaj.org/article/a87a605bbbc34951a5493b1985c000cc
Autor:
Jaakko Pihlajasalo, Dani Korpi, Mikko Honkala, Janne M. J. Huttunen, Taneli Riihonen, Jukka Talvitie, Mikko A. Uusitalo, Mikko Valkama
Publikováno v:
2021 55th Asilomar Conference on Signals, Systems, and Computers.
In this paper, we propose a machine learning (ML) aided physical layer receiver technique for demodulating OFDM signals that are subject to very high Doppler effects and the corresponding distortion in the received signal. Specifically, we develop a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8ecd6ffc4136dd9bce3eb75fc7ddb83
https://doi.org/10.1109/ieeeconf53345.2021.9723316
https://doi.org/10.1109/ieeeconf53345.2021.9723316
Autor:
Jaakko Pihlajasalo, Jukka Talvitie, Taneli Riihonen, Mikko A. Uusitalo, Alberto Brihuega, Mikko Valkama, Mikko Honkala, Janne M. J. Huttunen, Dani Korpi
Publikováno v:
PIMRC
In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ed36c14eaae2215ccb83349c30a0d30
https://doi.org/10.1109/pimrc50174.2021.9569393
https://doi.org/10.1109/pimrc50174.2021.9569393
Autor:
Jan S. Hesthaven, Leo Kärkkäinen, Janne M. J. Huttunen, Antti Pasanen, Timo Lähivaara, Alireza Malehmir
Publikováno v:
Geophysical Prospecting. 67:2115-2126
Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components such as the amount of groundwater stored in an aquifer and delineate water-table level, from active-so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74495d69de814be17d6be00c3b95f4d0
https://doi.org/10.1111/1365-2478.12831
https://doi.org/10.1111/1365-2478.12831
Publikováno v:
Journal of Inverse and Ill-posed Problems. 27:225-240
We consider inverse problems in which the unknown target includes sharp edges, for example interfaces between different materials. Such problems are typical in image reconstruction, tomography, and other inverse problems algorithms. A common solution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42703c64b33484a2515e0e16847e3771
https://doi.org/10.1515/jiip-2017-0048
https://doi.org/10.1515/jiip-2017-0048
Deep learning has solved many problems that are out of reach of heuristic algorithms. It has also been successfully applied in wireless communications, even though the current radio systems are well-understood and optimal algorithms exist for many ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bef69df1fa1a106b4d75b9796fdf6f15
http://arxiv.org/abs/2005.01494
http://arxiv.org/abs/2005.01494
Publikováno v:
International Journal for Numerical Methods in Biomedical Engineering. 36
Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f22405b1a88f2645fcdd697810a2544c
https://doi.org/10.1002/cnm.3303
https://doi.org/10.1002/cnm.3303
Publikováno v:
ICC
Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the context of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ff8ad0d8ff840f252be4abe4f142394
Publikováno v:
Computational Statistics & Data Analysis. 123:13-31
Model reduction, parameter uncertainties and state estimation in spatiotemporal problems induced by chaotic partial differential equations is considered. The model reduction and parameter uncertainties induce a specific structure for the state noise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::040960d739392806980f2df65bca7e9b
https://doi.org/10.1016/j.csda.2018.01.015
https://doi.org/10.1016/j.csda.2018.01.015
Publikováno v:
The Journal of the Acoustical Society of America. 143:1148-1158
The feasibility of data based machine learning applied to ultrasound tomography is studied to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5aae30347a04eca3ce0d5ea55a9b8c03
https://doi.org/10.1121/1.5024341
https://doi.org/10.1121/1.5024341