Narrow Results

To solve the problems of unit noise and vibration causing inconvenience to residents, this paper proposes a unit noise and vibration device and designs a unit noise and vibration control system. Adaptive active noise and vibration control is adopted, and DSP is used as the core of the entire system, which includes the acoustic sensor part, the peripheral signal conditioning part and the signal processing control part, so that it can achieve real-time control of unit noise and vibration. In addition, the improved independent component analysis method is used to achieve the noise reduction effect of unit noise and vibration. Finally, a unit noise and vibration detection system are designed, MEMS microphone detection is applied to achieve real-time detection of unit noise and vibration. Experiments show that the noise reduction effect of the system studied in this paper is relatively good. The vibration effect is better when the distance from the unit is 1m, and the noise value is reduced from 67dB to 48dB. Meanwhile, when the vibration distance from the unit is 22m, the noise drops to 37dB, and the noise value detected by the system in this paper is not much different from the actual noise value.

Removing the respiratory signal is a crucial topic to the high-quality ECG. But, not all models are available in the project. The use of digital filtering, signal averaging, adaptive processing and wavelet transform to remove the respiratory interference have some problems. The ICA algorithm for cancellation of respiratory interference is proposed. It is found that this method is more available to reconstruct high-quality ECG and de-noising artifacts comparing with the wavelet transform. Three steps are performed in the paper. From the simulation aspect, and from the evolution for the ability of de-noising the respiratory signal, as well as from reconstructing ECG, the comparisons between the results using ICA and that using wavelet transform are fulfilled. It is shown that the ICA algorithm is more powerful and more effective to de-noising the respiratory signal from ECG, almost not destroying the original ECG.