Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Rusci, Manuele"'
Autor:
Bompani, Luca, Crupi, Luca, Palossi, Daniele, Baldoni, Olmo, Brunelli, Davide, Conti, Francesco, Rusci, Manuele, Benini, Luca
The codling moth pest poses a significant threat to global crop production, with potential losses of up to 80% in apple orchards. Special camera-based sensor nodes are deployed in the field to record and transmit images of trapped insects to monitor
Externí odkaz:
http://arxiv.org/abs/2408.15911
This paper proposes a self-learning framework to incrementally train (fine-tune) a personalized Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio sensors. We address the fundamental problem of the absence of labeled tra
Externí odkaz:
http://arxiv.org/abs/2408.12481
This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors. This method reduces the average compute load of an off-the-shelf Deep Neural Network (DNN) b
Externí odkaz:
http://arxiv.org/abs/2404.11488
Keyword spotting accuracy degrades when neural networks are exposed to noisy environments. On-site adaptation to previously unseen noise is crucial to recovering accuracy loss, and on-device learning is required to ensure that the adaptation process
Externí odkaz:
http://arxiv.org/abs/2403.10549
Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (\ie sub-\SI{100}{\milli\watt} processor). W
Externí odkaz:
http://arxiv.org/abs/2403.04071
Autor:
Pourjabar, Mahyar, Rusci, Manuele, Bompani, Luca, Lamberti, Lorenzo, Niculescu, Vlad, Palossi, Daniele, Benini, Luca
This work presents a multi-sensory anti-collision system design to achieve robust autonomous exploration capabilities for a swarm of 10 cm-side nano-drones operating on object detection missions. We combine lightweight single-beam laser ranging to av
Externí odkaz:
http://arxiv.org/abs/2312.13086
Autor:
Pourjabar, Mahyar, AlKatheeri, Ahmed, Rusci, Manuele, Barcis, Agata, Niculescu, Vlad, Ferrante, Eliseo, Palossi, Daniele, Benini, Luca
Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope.
Externí odkaz:
http://arxiv.org/abs/2307.10255
Autor:
Rusci, Manuele, Tuytelaars, Tinne
A personalized KeyWord Spotting (KWS) pipeline typically requires the training of a Deep Learning model on a large set of user-defined speech utterances, preventing fast customization directly applied on-device. To fill this gap, this paper investiga
Externí odkaz:
http://arxiv.org/abs/2306.02161
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models in future TinyML applications. This paper tackles this challenge by i
Externí odkaz:
http://arxiv.org/abs/2305.19167
Autor:
Lamberti, Lorenzo, Bompani, Luca, Kartsch, Victor Javier, Rusci, Manuele, Palossi, Daniele, Benini, Luca
Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions (e.g., safe flight, exploration, object detection) is extremely challe
Externí odkaz:
http://arxiv.org/abs/2301.12175