Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Mohammad Loni"'
Autor:
Mehdi Asadi, Fatemeh Poursalim, Mohammad Loni, Masoud Daneshtalab, Mikael Sjödin, Arash Gharehbaghi
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract This paper presents a novel machine learning framework for detecting PxAF, a pathological characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involv
Externí odkaz:
https://doaj.org/article/3b05984cff9840a4b27d9c39663f831e
Publikováno v:
Drones, Vol 4, Iss 3, p 48 (2020)
Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to gua
Externí odkaz:
https://doaj.org/article/173d084e2e704e27a1b97f83bddef0ad
Publikováno v:
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b39198922d3fff2784c8294cdc5018a
https://doi.org/10.23919/date54114.2022.9774615
https://doi.org/10.23919/date54114.2022.9774615
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031159183
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19cbf9f9d2415099de67a3d6d258827c
https://doi.org/10.1007/978-3-031-15919-0_34
https://doi.org/10.1007/978-3-031-15919-0_34
Autor:
Mohammad Loni, Reza Akbari, Ali Zoljodi, Mikael Sjödin, Ben Juurlink, Masoud Daneshtalab, Amin Majd, Daniel Maier
Publikováno v:
CEC
Stereo vision cameras are flexible sensors due to providing heterogeneous information such as color, luminance, disparity map (depth), and shape of the objects. Today, Convolutional Neural Networks (CNNs) present the highest accuracy for the disparit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0669d28c16ebe6c9dae5530a39266af
https://doi.org/10.1109/cec48606.2020.9185611
https://doi.org/10.1109/cec48606.2020.9185611
Autor:
Tiberiu Seceleanu, Elena Troubitsyna, Marjan Sirjani, Mohammad Loni, Masoud Daneshtalab, Maghsood Salimi, Cristina Seceleanu, Amin Majd
Publikováno v:
ECBS
Real-world applications are composed of multiple tasks which usually have intricate data dependencies. To exploit distributed processing platforms, task allocation and scheduling, that is assigning tasks to processing units and ordering inter-process
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52fee9613939d54eaac614a4979eb791
https://doi.org/10.1145/3352700.3352713
https://doi.org/10.1145/3352700.3352713
Publikováno v:
DSD
High computation demands and big memory resources are the major implementation challenges of Convolutional Neural Networks (CNNs) especially for low-power and resource-limited embedded devices. Many binarized neural networks are recently proposed to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25d4b2c309b6f74ab865ea32b4a2d908
https://doi.org/10.1109/dsd.2019.00052
https://doi.org/10.1109/dsd.2019.00052
Publikováno v:
IECON
Fog computing offers a wide range of service levels including low bandwidth usage, low response time, support of heterogeneous applications, and high energy efficiency. Therefore, real-time embedded applications could potentially benefit from Fog inf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8feffb56b65fdac473ef75dd6945d0a8
http://hdl.handle.net/11577/3340673
http://hdl.handle.net/11577/3340673
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation ISBN: 9783030304867
ICANN (1)
ICANN (1)
Convolutional Neural Networks (CNNs) suffer from energy-hungry implementation due to their computation and memory intensive processing patterns. This problem is even more significant by the proliferation of CNNs on embedded platforms. To overcome thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32eef9bcbe96393aba7bcd889a030d80
https://doi.org/10.1007/978-3-030-30487-4_17
https://doi.org/10.1007/978-3-030-30487-4_17