Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Omar DeGuchy"'
Publikováno v:
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f2f0337a39d1b70efe98adb62b07acc
https://doi.org/10.1109/icmla55696.2022.00078
https://doi.org/10.1109/icmla55696.2022.00078
Publikováno v:
2021 55th Asilomar Conference on Signals, Systems, and Computers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20e491d21aa40fbc14e5ca107f4440d8
https://doi.org/10.1109/ieeeconf53345.2021.9723266
https://doi.org/10.1109/ieeeconf53345.2021.9723266
Publikováno v:
2021 29th European Signal Processing Conference (EUSIPCO).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::858c9ac680a7d02ecd1d63c2ea99012e
https://doi.org/10.23919/eusipco54536.2021.9616149
https://doi.org/10.23919/eusipco54536.2021.9616149
Publikováno v:
EUSIPCO
The human genome, composed of nucleotides, is represented by a long sequence of the letters A,C,G,T. Typically, organisms in the same species have similar genomes that differ by only a few sequences of varying lengths at varying positions. These diff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47f726f2b293c1499559956d9f0c690e
https://doi.org/10.23919/eusipco47968.2020.9287597
https://doi.org/10.23919/eusipco47968.2020.9287597
Publikováno v:
IGARSS
Synthetic aperture radar (SAR) is a remote sensing technique used to obtain high-resolution images, where image classification is a primary application. However, reconstructing SAR data is difficult which makes the classification of these images even
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81d15a93404986d135d1f721a2e3f393
https://doi.org/10.1109/igarss39084.2020.9323529
https://doi.org/10.1109/igarss39084.2020.9323529
Publikováno v:
Applications of Machine Learning 2020.
We present a study that uses machine learning to solve the forward and inverse scattering problems for synthetic aperture radar (SAR). Using a training set of known reflectivities as inputs and the resulting SAR measurements as outputs, the machine l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e512065ef3fb23eabaa93de8fa119626
https://doi.org/10.1117/12.2568302
https://doi.org/10.1117/12.2568302
Autor:
Omar DeGuchy, Roummel F. Marcia
Publikováno v:
Wavelets and Sparsity XVIII.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::72b7beb0955e375e2dea6f27031ac6ea
https://doi.org/10.1117/12.2530229
https://doi.org/10.1117/12.2530229
Publikováno v:
Applications of Machine Learning.
In many signal recovery applications, measurement data is comprised of multiple signals observed concurrently. For instance, in multiplexed imaging, several scene subimages are sensed simultaneously using a single detector. This technique allows for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05af97d6d9093cdcacb53ec8b6573189
https://doi.org/10.1117/12.2530230
https://doi.org/10.1117/12.2530230
Publikováno v:
Algorithms for Synthetic Aperture Radar Imagery XXVI.
While many aspects of the image recognition problem have been largely solved by presenting large datasets to convolutional neural networks, there is still much work to do when data is sparse. For synthetic aperture radar (SAR), there is a lack of dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8ad2f5e562122441c949db735fa490d
https://doi.org/10.1117/12.2518452
https://doi.org/10.1117/12.2518452
Publikováno v:
ICASSP
In this paper, we implement deep learning methods to recover downsampled noisy signals often present in compressed sensing applications. As an alternative to relying on previously established optimization based algorithms, we implement stacked denois
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c684874243c006b7044c9262b01e0a29
https://doi.org/10.1109/icassp.2019.8682767
https://doi.org/10.1109/icassp.2019.8682767