Zobrazeno 1 - 10
of 503
pro vyhledávání: '"Poncino, Massimo"'
Autor:
Risso, Matteo, Goffi, Alessia, Motetti, Beatrice Alessandra, Burrello, Alessio, Bove, Jean Baptiste, Macii, Enrico, Poncino, Massimo, Pagliari, Daniele Jahier, Maffeis, Giuseppe
Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large a
Externí odkaz:
http://arxiv.org/abs/2410.04802
Autor:
Burrello, Alessio, Carlucci, Francesco, Pollo, Giovanni, Wang, Xiaying, Poncino, Massimo, Macii, Enrico, Benini, Luca, Pagliari, Daniele Jahier
PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained for this task implement either a PPG-to-BP signal-to-signal reconstructi
Externí odkaz:
http://arxiv.org/abs/2409.07485
Autor:
Motetti, Beatrice Alessandra, Risso, Matteo, Burrello, Alessio, Macii, Enrico, Poncino, Massimo, Pagliari, Daniele Jahier
The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization, which lead to latency and memory occupatio
Externí odkaz:
http://arxiv.org/abs/2407.01054
Autor:
Daghero, Francesco, Burrello, Alessio, Poncino, Massimo, Macii, Enrico, Pagliari, Daniele Jahier
Depthwise separable convolutions are a fundamental component in efficient Deep Neural Networks, as they reduce the number of parameters and operations compared to traditional convolutions while maintaining comparable accuracy. However, their low data
Externí odkaz:
http://arxiv.org/abs/2406.12478
Autor:
Benfenati, Luca, Pagliari, Daniele Jahier, Zanatta, Luca, Velez, Yhorman Alexander Bedoya, Acquaviva, Andrea, Poncino, Massimo, Macii, Enrico, Benini, Luca, Burrello, Alessio
Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use
Externí odkaz:
http://arxiv.org/abs/2404.02944
Autor:
Hamdi, Mohamed Amine, Pollo, Giovanni, Risso, Matteo, Haugou, Germain, Burrello, Alessio, Macii, Enrico, Poncino, Massimo, Vinco, Sara, Pagliari, Daniele Jahier
RISC-V cores have gained a lot of popularity over the last few years. However, being quite a recent and novel technology, there is still a gap in the availability of comprehensive simulation frameworks for RISC-V that cover both the functional and ex
Externí odkaz:
http://arxiv.org/abs/2404.01861
Autor:
Risso, Matteo, Daghero, Francesco, Motetti, Beatrice Alessandra, Pagliari, Daniele Jahier, Macii, Enrico, Poncino, Massimo, Burrello, Alessio
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonetheless, their size and simple electronics pose severe challenges in implem
Externí odkaz:
http://arxiv.org/abs/2402.15273
Autor:
Risso, Matteo, Xie, Chen, Daghero, Francesco, Burrello, Alessio, Mollaei, Seyedmorteza, Castellano, Marco, Macii, Enrico, Poncino, Massimo, Pagliari, Daniele Jahier
Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows while preserving privacy and minimizing energy consumption. Deep Neural Networks (DNNs) have been shown to be w
Externí odkaz:
http://arxiv.org/abs/2402.01226
Autor:
Alamin, Khaled Sidahmed Sidahmed, Daghero, Francesco, Pollo, Giovanni, Pagliari, Daniele Jahier, Chen, Yukai, Macii, Enrico, Poncino, Massimo, Vinco, Sara
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it at run time, a model is required for its estimation. Recently, several
Externí odkaz:
http://arxiv.org/abs/2401.05474
Autor:
Daghero, Francesco, Burrello, Alessio, Macii, Enrico, Montuschi, Paolo, Poncino, Massimo, Pagliari, Daniele Jahier
With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes. Decision tree ensembles, such as Random Forests (RFs) and Gradient B
Externí odkaz:
http://arxiv.org/abs/2306.09789