Zobrazeno 1 - 10
of 224
pro vyhledávání: '"Masoud Daneshtalab"'
Publikováno v:
IEEE Access, Vol 12, Pp 148230-148239 (2024)
Federated Learning (FL) presents a decentralized approach to machine learning, allowing multiple clients to jointly train neural networks while maintaining the privacy of their local data. However, FL faces challenges due to data heterogeneity, leadi
Externí odkaz:
https://doaj.org/article/9d2288dd756348c88834db29209982c9
Publikováno v:
Sensors, Vol 24, Iss 17, p 5696 (2024)
Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide
Externí odkaz:
https://doaj.org/article/9f9ee72ff9eb4c9bb94ad159df8e2670
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
Autor:
Rajendra Singh, Manoj Kumar Bohra, Prashant Hemrajani, Anshuman Kalla, Devershi Pallavi Bhatt, Nitin Purohit, Masoud Daneshtalab
Publikováno v:
IEEE Access, Vol 10, Pp 129245-129268 (2022)
Recent advances in very-large-scale integration (VLSI) technologies have offered the capability of integrating thousands of processing elements onto a single silicon microchip. Multiprocessor systems-on-chips (MPSoCs) are the latest creation of this
Externí odkaz:
https://doaj.org/article/c8fe2e5784714af0aad196efd71ea0b8
Publikováno v:
IEEE Access, Vol 7, Pp 142843-142854 (2019)
The ever-shrinking size of a transistor has made Network on Chip (NoC) susceptible to faults. A single error in the NoC can disrupt the entire communication. In this paper, we introduce Defender, a fault-tolerant router architecture, that is capable
Externí odkaz:
https://doaj.org/article/8ddf7532c54645d09552d47eb6fec728
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:
IEEE Design & Test. 39:45-53
Long Short-Term Memory (LSTM) achieved great success in healthcare applications. However, its extensive computation cost and massive model size have become the major obstacles for the deployment of such a powerful algorithm in resource-limited embedd
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
Autor:
Mahdi Taheri, Mohammad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab, Jaan Raik, Mikael Sjödin, Björn Lisper
While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. It raises the necessity of improving the reliability of DNN accelerator
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a0344dac8b5f872b997b4aaa1159c22
http://arxiv.org/abs/2303.08226
http://arxiv.org/abs/2303.08226
Publikováno v:
Computing. 105:1121-1139
Solving Integer Linear Programming (ILP) models generally lies in the category of NP-hard problems and finding the optimal answer for large models is a computational challenge. Genetic algorithms are a family of metaheuristic algorithms capable of ad
Publikováno v:
2022 IEEE Nordic Circuits and Systems Conference (NorCAS).