Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Fadi N. Karameh"'
Autor:
Mahmoud K Madi, Fadi N Karameh
Publikováno v:
PLoS ONE, Vol 12, Iss 7, p e0181513 (2017)
Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended
Externí odkaz:
https://doaj.org/article/5b259c4536824bb2b10ef6aa2064ebc7
Autor:
Ziad Nahas, Fadi N. Karameh
Publikováno v:
Brain Topography. 32:28-65
Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bdf4b3a6bb87de3e6173c1354e7df33
https://doi.org/10.1007/s10548-018-0666-3
https://doi.org/10.1007/s10548-018-0666-3
Publikováno v:
Biological Cybernetics. 110:435-454
The cochlea is an indispensable preliminary processing stage in auditory perception that employs mechanical frequency-tuning and electrical transduction of incoming sound waves. Cochlear mechanical responses are shown to exhibit active nonlinear spat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5107b09d1cdb4fad0c756fe6e5151773
https://doi.org/10.1007/s00422-016-0703-9
https://doi.org/10.1007/s00422-016-0703-9
Publikováno v:
Applied Soft Computing. 96:106586
There have been significant advances in machine learning due to the profusion in data collection and computing resources. However, the need for large annotated datasets to train machine learning models remains a problematic constraint. To address the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96f96208ab18b043d1a033ca61389068
https://doi.org/10.1016/j.asoc.2020.106586
https://doi.org/10.1016/j.asoc.2020.106586
Autor:
Fadi N. Karameh, Mahmoud K. Madi
Publikováno v:
Journal of neural engineering. 15(4)
OBJECTIVE Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using expe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e96f6138540b4db0a9e7063d46bc0ee
https://pubmed.ncbi.nlm.nih.gov/29749350
https://pubmed.ncbi.nlm.nih.gov/29749350
Autor:
Fadi N. Karameh, Ursula C. Eid
Publikováno v:
2016 3rd Middle East Conference on Biomedical Engineering (MECBME).
the noisy and complex nature of many biological signals such as the electroencephalogram (EEG) has long constituted a major challenge in terms of analysis and prediction for single and multivariate problems. Nonlinear signal modeling, despite its wid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b0af29664d5ab1794d11d58eeb46885
https://doi.org/10.1109/mecbme.2016.7745409
https://doi.org/10.1109/mecbme.2016.7745409
Autor:
Fadi N. Karameh, Mahmoud K. Madi
Publikováno v:
2016 3rd Middle East Conference on Biomedical Engineering (MECBME).
The Kalman Filter (KF) is a powerful state estimation technique developed for linear time-varying systems and has recently extended for estimating nonlinear time varying dynamical systems. However, a major challenge for this technique is the choice o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08e6506b91c9edd8c8066068d604aee5
https://doi.org/10.1109/mecbme.2016.7745400
https://doi.org/10.1109/mecbme.2016.7745400
Publikováno v:
2016 3rd Middle East Conference on Biomedical Engineering (MECBME).
Human activity can serve as an identifier of subject health, behavioral patterns, and personal preferences. With the sudden splurge in mobile and wearable devices, activity data has become more readily available to design useful applications that enh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb08e8bb4ef4ccc00aff0f5dbf7ab000
https://doi.org/10.1109/mecbme.2016.7745395
https://doi.org/10.1109/mecbme.2016.7745395
Autor:
Fadi N. Karameh, S.G. Massaquoi
Publikováno v:
Journal of Neurophysiology. 101:207-233
Augmenting responses (ARs) are characteristic recruitment phenomena that can be generated in target neural populations by repetitive intracortical or thalamic stimulation and that may facilitate activity transmission from thalamic nuclei to the corte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be8bcd39ad58507d85dd5d6b7587fb37
https://doi.org/10.1152/jn.01280.2007
https://doi.org/10.1152/jn.01280.2007
Publikováno v:
2015 International Conference on Advances in Biomedical Engineering (ICABME)
2015 International Conference on Advances in Biomedical Engineering (ICABME), Sep 2015, Beirut, Lebanon. ⟨10.1109/ICABME.2015.7323270⟩
2015 International Conference on Advances in Biomedical Engineering (ICABME), Sep 2015, Beirut, Lebanon. ⟨10.1109/ICABME.2015.7323270⟩
International audience; —Epileptic seizures reflect runaway excitation that severely hinders normal brain functions. With EEG recordings reflecting real-time brain activity, it is essential to both predict seizures and improve the classification of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37e22a6c8b2e3543b205c5d5a0100bf1
https://www.hal.inserm.fr/inserm-01238586/document
https://www.hal.inserm.fr/inserm-01238586/document
