Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Mohamed Abouzahir"'
Autor:
Fatima Zahra Guerrouj, Sergio Rodríguez Flórez, Mohamed Abouzahir, Abdelhafid El Ouardi, Mustapha Ramzi
Publikováno v:
Journal of Low Power Electronics and Applications, Vol 13, Iss 2, p 40 (2023)
Convolutional Neural Networks (CNNs) have been incredibly effective for object detection tasks. YOLOv4 is a state-of-the-art object detection algorithm designed for embedded systems. It is based on YOLOv3 and has improved accuracy, speed, and robustn
Externí odkaz:
https://doaj.org/article/4e0584af1f3f4846b780e383c4c00e65
Autor:
Monsef Boufettal, Mohamed Azouz, Abdelkarim Rhanim, Mohamed Abouzahir, Mustapha Mahfoud, Ahmed El Bardouni, Mohamed S. Berrada, Moradh El Yaacoubi
Publikováno v:
Clinical Medicine Insights: Case Reports, Vol 2014, Iss 7, Pp 71-73 (2014)
Externí odkaz:
https://doaj.org/article/0a3b5ea9454f45f28cb7c6241849aa56
Autor:
Ramzi, Fatima Zahra Guerrouj, Sergio Rodríguez Flórez, Mohamed Abouzahir, Abdelhafid El Ouardi, Mustapha
Publikováno v:
Journal of Low Power Electronics and Applications; Volume 13; Issue 2; Pages: 40
Convolutional Neural Networks (CNNs) have been incredibly effective for object detection tasks. YOLOv4 is a state-of-the-art object detection algorithm designed for embedded systems. It is based on YOLOv3 and has improved accuracy, speed, and robustn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::824f8cb9fc009123c8a12466e3df2db6
Publikováno v:
International Journal of Advanced Computer Science and Applications. 13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e9cc27bee11a96e6eb68e9461245858
https://doi.org/10.14569/ijacsa.2022.01301101
https://doi.org/10.14569/ijacsa.2022.01301101
Publikováno v:
2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7e614a7f14f36976b545751b4f5b2ad
https://doi.org/10.1109/isaect53699.2021.9668607
https://doi.org/10.1109/isaect53699.2021.9668607
Publikováno v:
E3S Web of Conferences, Vol 229, p 01055 (2021)
SLAM algorithm permits the robot to cartography the desired environment while positioning it in space. It is a more efficient system and more accredited by autonomous vehicle navigation and robotic application in the ongoing research. Except it did n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24c8ae975fcf40168108943f79c77de9
https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/05/e3sconf_iccsre2021_01055.pdf
https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/05/e3sconf_iccsre2021_01055.pdf
Publikováno v:
Digital Technologies and Applications ISBN: 9783030738815
Simultaneous Localization And Mapping (SLAM) algorithm allows the robot to map the environment while locating it in the space. SLAM algorithm is the more efficient and more accredited system by autonomous vehicle navigation and robotic application in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29ac9955ddc14a79b1c710f7a90c7b9c
https://doi.org/10.1007/978-3-030-73882-2_37
https://doi.org/10.1007/978-3-030-73882-2_37
Publikováno v:
ITM Web of Conferences. 46:04001
Simultaneous Localization And Mapping (SLAM) algorithms are being used in many robotic applications and autonomous navigation systems. The FastSLAM2.0 addresses an issue of the SLAM problem and allows a robot to navigate in an unknown environment. Se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d9af5a8ced9f0e6669711a04b23f97d
https://doi.org/10.1051/itmconf/20224604001
https://doi.org/10.1051/itmconf/20224604001
Publikováno v:
Robotics and Autonomous Systems
Robotics and Autonomous Systems, Elsevier, 2018, 100, pp.14-26. ⟨10.1016/j.robot.2017.10.019⟩
Robotics and Autonomous Systems, Elsevier, 2018, 100, pp.14-26. ⟨10.1016/j.robot.2017.10.019⟩
Development of Simultaneous Localization and Mapping (SLAM) systems in the era of autonomous navigation and the growing demand for autonomous robots have put into question how to reduce the computational complexity and make use of SLAM algorithms to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2409abdd1ab5ca120e6da438be3aee0b
https://hal.archives-ouvertes.fr/hal-01943158
https://hal.archives-ouvertes.fr/hal-01943158
Publikováno v:
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2016, 2016 (1), ⟨10.1186/s13634-016-0386-3⟩
EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2016, 2016 (1), ⟨10.1186/s13634-016-0386-3⟩
Simultaneous localization and mapping (SLAM) is widely used in many robotic applications and autonomous navigation. This paper presents a study of FastSLAM2.0 computational complexity based on a monocular vision system. The algorithm is intended to o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::890345a74ab97213a707a1c60ee82647
https://hal.archives-ouvertes.fr/hal-01684809
https://hal.archives-ouvertes.fr/hal-01684809