研究了TEXAS公司的TMS320F2812的ADC(Analog-to Digital Converter)的工作原理;根据电力系统微机继电保护中对多路电压及电流信号的采样精度和速度的高要求,结合TEXAS公司关于TMS320F2812的校正方案,介绍了利用软件程序对芯片TMS320F281...研究了TEXAS公司的TMS320F2812的ADC(Analog-to Digital Converter)的工作原理;根据电力系统微机继电保护中对多路电压及电流信号的采样精度和速度的高要求,结合TEXAS公司关于TMS320F2812的校正方案,介绍了利用软件程序对芯片TMS320F2812中内置ADC采样校正的软件实现方案,通过试验进一步验证了该方案的有效性。所提出的方案对电力系统自动控制装置的软、硬件设计具有一定的参考价值。展开更多
Slow trends in the RR interval(RRI) series should be removed in the preprocessing step to get a reliable result of heart rate variability(HRV) analysis. Re-sampling is required to convert the unevenly sampled RRI seri...Slow trends in the RR interval(RRI) series should be removed in the preprocessing step to get a reliable result of heart rate variability(HRV) analysis. Re-sampling is required to convert the unevenly sampled RRI series into evenly sampled time series when using the widely accepted smoothness priors approach(SPA). Noise is introduced in this process and the information quality is thus compromised. Empirical mode decomposition(EMD) and its variants, were introduced to directly process the unevenly sampled RRI series. Besides, a RR interval model was proposed to fascinate the introduction of standard metrics for the evaluation of the detrending performance. Based on standard metrics including signal-to-noise-ratio in d B(ISNR), mean square error(EMS), and percent root square difference(DPRS), the effectiveness of detrending methods in RR interval analysis were determined. Results demonstrate that complementary ensemble EMD(CEEMD, a variant of EMD) based method has a higher ISNR, a lower EMS and a lower DPRS as well as a better RRI series detrending performance compared with the SPA method, which would in turn lead to a more accurate HRV analysis.展开更多
文摘研究了TEXAS公司的TMS320F2812的ADC(Analog-to Digital Converter)的工作原理;根据电力系统微机继电保护中对多路电压及电流信号的采样精度和速度的高要求,结合TEXAS公司关于TMS320F2812的校正方案,介绍了利用软件程序对芯片TMS320F2812中内置ADC采样校正的软件实现方案,通过试验进一步验证了该方案的有效性。所提出的方案对电力系统自动控制装置的软、硬件设计具有一定的参考价值。
基金Project(41227803)supported by the National Natural Science Foundation of ChinaProject(KF11011)supported by the State Key Laboratory of Automotive Safety and Energy(Tsinghua University),ChinaProject(DTNH22-08-C-00082)supported by the National Highway Traffic Safety Administration,USA
文摘Slow trends in the RR interval(RRI) series should be removed in the preprocessing step to get a reliable result of heart rate variability(HRV) analysis. Re-sampling is required to convert the unevenly sampled RRI series into evenly sampled time series when using the widely accepted smoothness priors approach(SPA). Noise is introduced in this process and the information quality is thus compromised. Empirical mode decomposition(EMD) and its variants, were introduced to directly process the unevenly sampled RRI series. Besides, a RR interval model was proposed to fascinate the introduction of standard metrics for the evaluation of the detrending performance. Based on standard metrics including signal-to-noise-ratio in d B(ISNR), mean square error(EMS), and percent root square difference(DPRS), the effectiveness of detrending methods in RR interval analysis were determined. Results demonstrate that complementary ensemble EMD(CEEMD, a variant of EMD) based method has a higher ISNR, a lower EMS and a lower DPRS as well as a better RRI series detrending performance compared with the SPA method, which would in turn lead to a more accurate HRV analysis.
