一种新的物质使用数据流实时机器学习仿真平台

Stefan A. Bruendl, Hua Fang, H. Ngo, E. Boyer, Honggang Wang
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引用次数: 1

摘要

随着5G网络的兴起,为研究人员提供能够在5G传输领域进行开发和实验的工具变得越来越重要。医疗保健可以从这些发展中受益匪浅。本文描述并测试了一种实时传输技术,如果实现,可穿戴设备可以在不同频率上传输多个数据流。这些测试将用于解释所提出的平台如何工作,拟议方案存在哪些缺点和优点,以及如何进一步开发以更高频率实时传输敏感数据(如物质使用数据)的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Emulation Platform for Real-time Machine Learning in Substance Use Data Streams
With 5G networks on the rise, it becomes more and more important to grant researchers access to tools that allow for development and experimentation in the field of 5G transmission. Healthcare can benefit greatly from these developments. In this paper a real-time transmission technique is described and tested that, if implemented, allows wearable devices to transmit multiple streams of data on various frequencies. These tests will be used to explain how this presented platform works, what drawbacks and benefits exist with the proposed scheme, and how to further develop the solution of real-time transmission of sensitive data, such as substance-use data, at higher frequencies.
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