{"title":"一种无电池远程无线智能人脸检测相机","authors":"Marco Giordano, Philipp Mayer, M. Magno","doi":"10.1145/3417308.3430273","DOIUrl":null,"url":null,"abstract":"This paper presents a battery-free smart camera that combines tiny machine learning, long-range communication, power management, and energy harvesting. The smart sensor node has been implemented and evaluated in the field, showing both battery-less capabilities with a small-size photovoltaic panel and the energy efficiency of the proposed solution. We evaluated two different ARM Cortex-M4F microcontrollers, the Ambiq Apollo 3 that is an energy-efficient microcontroller, and a Microchip SAMD51 able to work in high radiation environments but with a higher power in active mode. Finally, a low power LoRa module provides the long-range wireless transmission capability. The tiny machine learning algorithm for face recognition has been optimized in terms of accuracy versus energy, achieving up to 97% accuracy recognizing five different faces. Experimental results demonstrated the capability of the developed sensor node to start from the cold start after 1 minute at a very low luminosity of 350 lux using a cm size flexible photovoltaic panels and work perpetually after the cold start.","PeriodicalId":386523,"journal":{"name":"Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"A Battery-Free Long-Range Wireless Smart Camera for Face Detection\",\"authors\":\"Marco Giordano, Philipp Mayer, M. Magno\",\"doi\":\"10.1145/3417308.3430273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a battery-free smart camera that combines tiny machine learning, long-range communication, power management, and energy harvesting. The smart sensor node has been implemented and evaluated in the field, showing both battery-less capabilities with a small-size photovoltaic panel and the energy efficiency of the proposed solution. We evaluated two different ARM Cortex-M4F microcontrollers, the Ambiq Apollo 3 that is an energy-efficient microcontroller, and a Microchip SAMD51 able to work in high radiation environments but with a higher power in active mode. Finally, a low power LoRa module provides the long-range wireless transmission capability. The tiny machine learning algorithm for face recognition has been optimized in terms of accuracy versus energy, achieving up to 97% accuracy recognizing five different faces. Experimental results demonstrated the capability of the developed sensor node to start from the cold start after 1 minute at a very low luminosity of 350 lux using a cm size flexible photovoltaic panels and work perpetually after the cold start.\",\"PeriodicalId\":386523,\"journal\":{\"name\":\"Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3417308.3430273\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3417308.3430273","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
摘要
本文介绍了一种无电池智能相机,它结合了微型机器学习、远程通信、电源管理和能量收集。智能传感器节点已经在现场实施和评估,显示了小尺寸光伏板的无电池能力和提出的解决方案的能源效率。我们评估了两种不同的ARM Cortex-M4F微控制器,Ambiq Apollo 3是一种节能微控制器,而Microchip SAMD51能够在高辐射环境中工作,但在主动模式下具有更高的功率。最后,低功耗LoRa模块提供了远程无线传输能力。用于人脸识别的微型机器学习算法在准确性和能量方面进行了优化,识别五种不同面孔的准确率高达97%。实验结果表明,所开发的传感器节点能够在350勒克斯的极低亮度下,使用厘米大小的柔性光伏板,在1分钟后从冷启动开始,并在冷启动后永久工作。
A Battery-Free Long-Range Wireless Smart Camera for Face Detection
This paper presents a battery-free smart camera that combines tiny machine learning, long-range communication, power management, and energy harvesting. The smart sensor node has been implemented and evaluated in the field, showing both battery-less capabilities with a small-size photovoltaic panel and the energy efficiency of the proposed solution. We evaluated two different ARM Cortex-M4F microcontrollers, the Ambiq Apollo 3 that is an energy-efficient microcontroller, and a Microchip SAMD51 able to work in high radiation environments but with a higher power in active mode. Finally, a low power LoRa module provides the long-range wireless transmission capability. The tiny machine learning algorithm for face recognition has been optimized in terms of accuracy versus energy, achieving up to 97% accuracy recognizing five different faces. Experimental results demonstrated the capability of the developed sensor node to start from the cold start after 1 minute at a very low luminosity of 350 lux using a cm size flexible photovoltaic panels and work perpetually after the cold start.