面向可穿戴设备的压缩感知关键技术研究

项目名称： 面向可穿戴设备的压缩感知关键技术研究

项目编号： No.61501096

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 叶娅兰

作者单位： 电子科技大学

项目金额： 23万元

中文摘要： 可穿戴设备由于较低采样率和噪声干扰，导致采样的生理信号的稀疏性较差。大多数压缩感知算法对不足够稀疏的信号重构效果不理想，而目前能对不足够稀疏信号重构的仅有的少数压缩感知算法，不能有效满足可穿戴远程健康监护系统同时对重构精度和速度的需求。因此，面向可穿戴设备的压缩感知关键技术研究具有一定的挑战性。本项目的主要研究内容：1.针对不足够稀疏的生理信号，设计高效率的字典学习算法，以获得更为有效的稀疏字典，解决生理信号的最佳稀疏表示；2.设计与最佳稀疏字典满足非相干条件的观测矩阵，确保可穿戴设备以高压缩率采样后的数据不丢失微弱的重要生理信息，为精确重构信号提供保障；3.将机器学习领域的变分推断方法与稀疏贝叶斯学习理论相结合，提出基于变分贝叶斯推断的重构恢复算法，以满足可穿戴健康监护系统同时对快速和高精度重构信号的需求。

中文关键词： 可穿戴设备；压缩感知；字典学习；变分推断；稀疏贝叶斯学习

英文摘要： Due to the low sampling rate and a number of strong noise and interference in Wearable Devices, the collected raw physiological signals are non-sparse in time domain or transform domain. It is difficult to recover the physiological signals with high reconstruction quality. Most Compressed Sensing (CS) algorithms fail in directly reconstructing such non-sparse signals. However only several CS algorithms, which can recover non-sparse signals, can’t be efficiently satisfied for high reconstruction quality and speed need of the physiological signals in Wearable Devices. Therefore, it is a challenge for research on research key technologies of CS based on the non-sparse signals in Wearable Device based Health Telemonitoring Systems. The main research works are as follows:(1) the dictionary learning algorithm, which is suitable for the non-sparse physiological signals, is designed so as to obtain the best sparse representation of the signals;(2) based on the best sparse dictionary, an effective sensing matrix is constructed to ensure that the important information is not missed in the compressed signals; (3) a novel Variational Inference (VI) Bayesian learning based CS reconstruction algorithm with better recovery quality and high iterative convergence speed, is proposed for an application need of the Wearable Device based Health Telemonitoring Systems. Because a VI method of Machine Learning field is used into the framework of block Sparse Bayesian Learning.

英文关键词： Wearable Device;Compressed Sensing;Dictionary Learning;Variational Inference;Sparse Bayesian Learning