A 232-to-1996KS/s Robust Compressive-Sensing Reconstruction Engine for Real-Time Physiological Signals Monitoring

📋 论文概要

该论文提出了一种基于压缩感知的重构引擎，用于实时生理信号监测。通过引入稀疏度估计框架和鲁棒正交匹配追踪算法，解决了传统OMP算法对测量噪声敏感和收敛速度慢的问题。芯片采用40nm CMOS工艺实现，支持232至1996KS/s的采样率。

💡 主要创新点

• 提出稀疏度估计框架，动态调整重构参数以提高噪声鲁棒性
• 设计鲁棒正交匹配追踪算法，加速收敛并降低计算复杂度

🏷 关键词

📄 原文摘要

Compressive sensing (CS) techniques enable new reduced-complexity designs for sensor nodes and help reduce overall transmission power in wireless sensor network [1-2]. Prior CS reconstruction chip designs have been described in [34]. However, for real-time monitoring of physiological signals, the applied orthogonal matching pursuit (OMP) algorithms they incorporate are sensitive to measurement noise interference and suffer from a slow convergence rate. This paper presents a new CS reconstruction engine fabricated in 40nm CMOS with following features: 1) A sparsity-estimation framework to suppress measurement noise interference at sensing nodes, achieving at least 8dB signal-to-noise ratio (SNR) gain under the same success rate for robust reconstruction. 2) A new flexible indices-updating VLSI architecture, inspired by the gradient descent method [5], that can support arbitrary signal dimension, (Lnew, M), of CS

👥 作者与机构

Ting-Sheng Chen, Hung-Chi Kuo, An-Yeu Wu

National Taiwan University, Taipei, Taiwan