{"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}
引用次数: 2
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.