Blind source extraction （BSE） is particularly at- tractive to solve blind signal mixture problems where only a few source signals are desired. Many existing BSE methods do not take into account the existence of nois...Blind source extraction （BSE） is particularly at- tractive to solve blind signal mixture problems where only a few source signals are desired. Many existing BSE methods do not take into account the existence of noise and can only work well in noise-free environments. In practice, the desired signal is often contaminated by additional noise. Therefore, we try to tackle the problem of noisy component extraction. The reference signal carries enough prior information to dis- tinguish the desired signal from signal mixtures. According to the useful properties of Gaussian moments, we incorporate the reference signal into a negentropy objective function so as to guide the extraction process and develop an improved BSE method. Extensive computer simulations demonstrate its validity in the process of revealing the underlying desired signal.展开更多
Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive B...Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.展开更多
传统的Gm-C滤波器OTA输入晶体管大多工作在饱和区,存在输入动态范围较小和跨导值较大等不足,难以满足生物医学电信号处理滤波器所要求的超低截止频率、低功耗与大输入动态范围等要求,采用将输入晶体管钳位到线性工作区的方法,设计了跨...传统的Gm-C滤波器OTA输入晶体管大多工作在饱和区,存在输入动态范围较小和跨导值较大等不足,难以满足生物医学电信号处理滤波器所要求的超低截止频率、低功耗与大输入动态范围等要求,采用将输入晶体管钳位到线性工作区的方法,设计了跨导线性可调的OTA以提高滤波器能够处理的信号幅度。并应用该OTA综合了一种五阶Gm-C超低频低通滤波器。仿真结果表明,该滤波器在1.8 V电源,800 m Vpp输入条件下实现了283 Hz的超低低通角频率,-6.4 d B的带内增益,51 d B的三次谐波失真,功耗仅为22μW,适用于可穿戴式生物医学电信号读取电路。展开更多
文摘Blind source extraction （BSE） is particularly at- tractive to solve blind signal mixture problems where only a few source signals are desired. Many existing BSE methods do not take into account the existence of noise and can only work well in noise-free environments. In practice, the desired signal is often contaminated by additional noise. Therefore, we try to tackle the problem of noisy component extraction. The reference signal carries enough prior information to dis- tinguish the desired signal from signal mixtures. According to the useful properties of Gaussian moments, we incorporate the reference signal into a negentropy objective function so as to guide the extraction process and develop an improved BSE method. Extensive computer simulations demonstrate its validity in the process of revealing the underlying desired signal.
文摘Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.
文摘传统的Gm-C滤波器OTA输入晶体管大多工作在饱和区,存在输入动态范围较小和跨导值较大等不足,难以满足生物医学电信号处理滤波器所要求的超低截止频率、低功耗与大输入动态范围等要求,采用将输入晶体管钳位到线性工作区的方法,设计了跨导线性可调的OTA以提高滤波器能够处理的信号幅度。并应用该OTA综合了一种五阶Gm-C超低频低通滤波器。仿真结果表明,该滤波器在1.8 V电源,800 m Vpp输入条件下实现了283 Hz的超低低通角频率,-6.4 d B的带内增益,51 d B的三次谐波失真,功耗仅为22μW,适用于可穿戴式生物医学电信号读取电路。
