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- W4309156963 abstract "The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Ex-ploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents Wide Vision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multi-radio platform, including a LoRa interface, enabling medium-and long-range communication, with a novel parallel RISC- V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC- V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter." @default.
- W4309156963 created "2022-11-24" @default.
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- W4309156963 date "2022-10-10" @default.
- W4309156963 modified "2023-09-26" @default.
- W4309156963 title "WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for Distributed Surveillance" @default.
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- W4309156963 doi "https://doi.org/10.1109/wimob55322.2022.9941670" @default.
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