Noise Reduction Technique of Switched-Capacitor Low-Pass Filter Using Adaptive Configuration

Author(s):  
Retdian NICODIMUS ◽  
Takeshi SHIMA
2013 ◽  
Vol 562-565 ◽  
pp. 1132-1136
Author(s):  
Xiao Wei Liu ◽  
Jian Yang ◽  
Song Chen ◽  
Liang Liu ◽  
Rui Zhang ◽  
...  

In this paper, we design a high-order switched capacitor filter for rapid change parameter converter. This design uses a structure which consists of three biquads filter sub-units. The design is a 6th-order SC elliptic low-pass filter, and the sample frequency is 250 kHz. By the MATLAB Simulink simulation, the system can meet the design requirements in the time domain. In this paper, the 6th-order switched capacitor elliptic low-pass filter was implemented under 0.5 um CMOS process and simulated in Cadence. The final simulation results show that the pass-band cutoff frequency is 10 kHz, and the maximum pass-band ripple is about 0.106 dB. The stop-band cutoff frequency is 20 kHz, and the minimum stop-band attenuation is 74.78 dB.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Soojun Kim ◽  
Huiseong Noh ◽  
Narae Kang ◽  
Keonhaeng Lee ◽  
Yonsoo Kim ◽  
...  

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.


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