{"title":"摘要:基于深度神经网络自编码的BLE图像存储与广播","authors":"Chong Shao, S. Nirjon","doi":"10.1109/IoTDI.2018.00050","DOIUrl":null,"url":null,"abstract":"This demo in an implementation of a new Deep Image Beacon system that is capable of broadcasting color images over a very long period (years, as opposed to days or weeks) using a set of cheap, low-power, memory-constrained Bluetooth Low Energy (BLE) beacon devices. We adopt a deep neural network image encoder to encode the given input image and generates a compact representation of the image. The representation can be as short as 10 bytes. On the receiver end, we adopt a deep neural network decoder running on a mobile device. When the mobile device receives the BLE broadcasted image data, it decodes the original image. We develop a pair of smartphone applications. One application takes an image and user-requirements as inputs, shows previews of different quality output images, writes the encoded image into a set of beacons. The second application reads the broadcasted image back.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Demo Abstract: Image Storage and Broadcast over BLE with Deep Neural Network Autoencoding\",\"authors\":\"Chong Shao, S. Nirjon\",\"doi\":\"10.1109/IoTDI.2018.00050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This demo in an implementation of a new Deep Image Beacon system that is capable of broadcasting color images over a very long period (years, as opposed to days or weeks) using a set of cheap, low-power, memory-constrained Bluetooth Low Energy (BLE) beacon devices. We adopt a deep neural network image encoder to encode the given input image and generates a compact representation of the image. The representation can be as short as 10 bytes. On the receiver end, we adopt a deep neural network decoder running on a mobile device. When the mobile device receives the BLE broadcasted image data, it decodes the original image. We develop a pair of smartphone applications. One application takes an image and user-requirements as inputs, shows previews of different quality output images, writes the encoded image into a set of beacons. The second application reads the broadcasted image back.\",\"PeriodicalId\":149725,\"journal\":{\"name\":\"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IoTDI.2018.00050\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IoTDI.2018.00050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Demo Abstract: Image Storage and Broadcast over BLE with Deep Neural Network Autoencoding
This demo in an implementation of a new Deep Image Beacon system that is capable of broadcasting color images over a very long period (years, as opposed to days or weeks) using a set of cheap, low-power, memory-constrained Bluetooth Low Energy (BLE) beacon devices. We adopt a deep neural network image encoder to encode the given input image and generates a compact representation of the image. The representation can be as short as 10 bytes. On the receiver end, we adopt a deep neural network decoder running on a mobile device. When the mobile device receives the BLE broadcasted image data, it decodes the original image. We develop a pair of smartphone applications. One application takes an image and user-requirements as inputs, shows previews of different quality output images, writes the encoded image into a set of beacons. The second application reads the broadcasted image back.