A Flexible Graphene-Based Fabric Ultrasound Source for Machine Learning Enhanced Information Encryption

Abstract

Information encryption has become an essential trend in the era of big data. Ultrasound can be used as an excellent encrypted transmission medium carrying information due to its strong concealment and small interference. And, better wearable needs cannot be ignored. Here, we propose a flexible graphene-based fabric ultrasound source (GUS) for machine learning enhanced information encryption. GUS is prepared by one-step laser method to generate laser-induced graphene on NOMEX fabric substrate. The easily captured low-frequency sound signals are modulated to high frequency to become ultrasound signals and emit through GUS. The key to success of this work is excellent sound pressure level (SPL) output of GUS in high frequency. Besides, the Mel Frequency Cestrum Coefficient (MFCC) feature of ultrasound is extracted by convolutional neural network, and the accuracy of speech recognition is 98.2 %.