摘要
基于压缩感知理论,设计了一种用于心电信号的低功耗压缩感知电路。利用心电信号的周期性,通过预先计算压缩处理过程中产生的最大数据,确定了压缩电路中累加器的位数,避免了使用多余的寄存器,有效降低了电路的功耗并提高了数据的压缩比。使用贝叶斯学习算法进行重构验证。结果表明:压缩感知电路的逻辑门数由42 071减少到了25 029,功耗由11.247μW降低到了6.847μW,较优化前减少了39.12%;重构信号的均方根误差百分比达到了1.14%。
A low power consumption compressed sensing circuit for electrocardiogram（ ECG） signals is designed based on compressed sensing（ CS） theory. Making use of the periodicity of ECG signals,the bits of accumulator is determined by precalculating and compressed processing the maximum value of data in advance,avoid using redundant registers in the circuit,thus the power consumption is reduced effectively and data compression ratio is improved. Reconstruction verification is carried out by using the Bayesian learning algorithm. Verification results show that the logic gate number of CS circuit decreases from 42 071 gates to 25 029 ones and the power consumption is lowered from 11. 247 μW to 6. 847 μW,achieving a power reduction of 39. 12 %. The percentage of root-mean-squared error of reconstructed signals reaches 1. 14 %.
作者
黄翔
潘天文
魏朋博
孙益洲
虞致国
顾晓峰
HUANG Xiang;PAN Tian-wen;WEI Peng-bo;SUN Yi-zhou;YU Zhi-guo;GU Xiao-feng(Engineering Research Center of IoT Technology Applications , Ministry of Education, Department of Electronic Engineering, Jiangnan University, Wuxi 214122, China)
出处
《传感器与微系统》
CSCD
2018年第6期79-82,共4页
Transducer and Microsystem Technologies
基金
江苏省六大人才高峰资助项目(2013-DZXX-027)
中央高校基本科研业务费专项资金资助项目(JUSRP51510
JUSRP51323B)
江苏省研究生科研与实践创新计划项目(SJLX16_0500
KYLX16_0776
SJCX17-0510)
关键词
压缩感知
无线体域网
心电信号
低功耗
贝叶斯学习算法
compressed sensing（CS）
wireless body area networks
electrocardiogram（ECG） signal
low powerconsumption
Bayesian learning algorithm
