Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Elahe Rahimian"'
Autor:
Seyed Esmaeil Ahmadi, Elahe Rahimian, Samira Rahimi, Bahman Zarandi, Mehran Bahraini, Maral Soleymani, Seyed Mehrab Safdari, Ashkan Shabannezhad, Niloofar Jaafari, Majid Safa
Publikováno v:
Biomarker Research, Vol 12, Iss 1, Pp 1-60 (2024)
Abstract The p53 protein, encoded by the TP53 gene, serves as a critical tumor suppressor, playing a vital role in maintaining genomic stability and regulating cellular responses to stress. Dysregulation of p53 is frequently observed in hematological
Externí odkaz:
https://doaj.org/article/edd454dcab5842c794282c615d36013f
Autor:
Mansooreh Montazerin, Elahe Rahimian, Farnoosh Naderkhani, S. Farokh Atashzar, Svetlana Yanushkevich, Arash Mohammadi
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-23 (2023)
Abstract Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a Compact Transformer-based H
Externí odkaz:
https://doaj.org/article/d2a5a596fa464b5592a0437f884b9d09
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4211-4224 (2023)
Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals have recently shown considerable potential for development of advanced myoelectric-controlled prosthesis. Although deep learning techniques can improve HGR a
Externí odkaz:
https://doaj.org/article/f6024e7e4976438b9315bb57347b82e8
Publikováno v:
Experimental Hematology & Oncology, Vol 10, Iss 1, Pp 1-26 (2021)
Abstract Chromosomal translocations are the main etiological factor of hematologic malignancies. These translocations are generally the consequence of aberrant DNA double-strand break (DSB) repair. DSBs arise either exogenously or endogenously in cel
Externí odkaz:
https://doaj.org/article/f65821dddac94eee87c69283e2a72504
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::329ff6c4c99ee61e8a5446a9a31a3f91
https://doi.org/10.1109/icassp49357.2023.10096292
https://doi.org/10.1109/icassp49357.2023.10096292
Autor:
Mansooreh, Montazerin, Soheil, Zabihi, Elahe, Rahimian, Arash, Mohammadi, Farnoosh, Naderkhani
Publikováno v:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
Recently, there has been a surge of significant interest on application of Deep Learning (DL) models to autonomously perform hand gesture recognition using surface Electromyogram (sEMG) signals. Many of the existing DL models are, however, designed t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e19ca9658388b84a985e09346b3a7b05
https://doi.org/10.1109/embc48229.2022.9871489
https://doi.org/10.1109/embc48229.2022.9871489
Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78d3d03e0ac56de7c4431e469357ecad
http://arxiv.org/abs/2110.08717
http://arxiv.org/abs/2110.08717
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d10682309d05ca2439c127fbe1b45e4
https://doi.org/10.1109/icip42928.2021.9506285
https://doi.org/10.1109/icip42928.2021.9506285
Publikováno v:
Biomedical Signal Processing and Control. 78:103850
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d12db51626d0d2fa1b2e54e09129dd34
https://doi.org/10.1016/j.bspc.2022.103850
https://doi.org/10.1016/j.bspc.2022.103850
Publikováno v:
2021 IEEE International Conference on Autonomous Systems (ICAS).
This work is motivated by potentials of Deep Neural Networks (DNNs)-based solutions in improving myoelectric control for trustworthy Human-Machine Interfacing (HMI). In this context, we propose the Trustworthy Few Shot-Hand Gesture Recognition (TFS-H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::267bb37d2e55c3eec9540c5849da6d1e
https://doi.org/10.1109/icas49788.2021.9551144
https://doi.org/10.1109/icas49788.2021.9551144