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pro vyhledávání: '"Siami, A."'
As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep
Externí odkaz:
http://arxiv.org/abs/2411.18731
We introduce a novel dataset for multi-robot activity recognition (MRAR) using two robotic arms integrating WiFi channel state information (CSI), video, and audio data. This multimodal dataset utilizes signals of opportunity, leveraging existing WiFi
Externí odkaz:
http://arxiv.org/abs/2408.16703
Motivated by the growing use of Artificial Intelligence (AI) tools in control design, this paper takes the first steps towards bridging the gap between results from Direct Gradient methods for the Linear Quadratic Regulator (LQR), and neural networks
Externí odkaz:
http://arxiv.org/abs/2408.15456
Vision-based methods are commonly used in robotic arm activity recognition. These approaches typically rely on line-of-sight (LoS) and raise privacy concerns, particularly in smart home applications. Passive Wi-Fi sensing represents a new paradigm fo
Externí odkaz:
http://arxiv.org/abs/2407.06154
Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human inputs. LLMs, can not only compose textual scripts such as emails and e
Externí odkaz:
http://arxiv.org/abs/2405.19578
This paper introduces a novel method for the stability analysis of positive feedback systems with a class of fully connected feedforward neural networks (FFNN) controllers. By establishing sector bounds for fully connected FFNNs without biases, we pr
Externí odkaz:
http://arxiv.org/abs/2406.12744
Numerical ``direct'' approaches to time-optimal control often fail to find solutions that are singular in the sense of the Pontryagin Maximum Principle, performing better when searching for saturated (bang-bang) solutions. In previous work by one of
Externí odkaz:
http://arxiv.org/abs/2406.07644
Autor:
Wafi, Moh. Kamalul, Siami, Milad
This paper addresses the challenge of network synchronization under limited communication, involving heterogeneous agents with different dynamics and various network topologies, to achieve consensus. We investigate the distributed adaptive control fo
Externí odkaz:
http://arxiv.org/abs/2405.15178
Autor:
SeyedAlinaghi, SeyedAhmad, Yarmohammadi, Soudabeh, Farahani Rad, Farid, Rasheed, Muhammad Ali, Javaherian, Mohammad, Afsahi, Amir Masoud, Siami, Haleh, Bagheri, AmirBehzad, Zand, Ali, Dadras, Omid, Mehraeen, Esmaeil
Publikováno v:
International Journal of Prison Health, 2024, Vol. 20, Issue 4, pp. 393-409.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJOPH-01-2024-0005
Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms' activities often re
Externí odkaz:
http://arxiv.org/abs/2312.15345