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Discrete Wavelet Transform (DWT) is an efficient tool for signal and image processing applications which has been utilized for perfect signal reconstruction. In this paper, twenty seven optimum combinations of three different wavelet filter types, three different filter reconstruction levels and three different kinds of signal for multi-level perfe...
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Citations
... Therefore, these delays must be taken into account for perfect reconstruction of the required reference signal. By equalizing these delays over all filter paths, perfect reconstruction can be achieved [12], [21]. Consider a 3-level filter bank structure Fig. 4, in which three filter pairs are used in each decomposition and reconstruction block. ...
... DWT hardware block for reference signal generationFig. 3. Hardware co-simulation results of MIT-BIH record 100 for (A) MA (B) BW and (C) EMG artifacts suppression Fig. 4. Level-3 perfect reconstruction filter bank[21] ...
... The MF application is used widely in the image and signal processing application (Jiang, 1998;Shafiq and Ejaz, 2010). In dynamic structural performance analysis, the used of multi filter is still limited. ...
This study presents Mansoura-Steel Railway Bridge, Egypt, movement analysis in response to passing trains. This bridge was used for two types of traffics which are trains on the middle and one vehicle lane on each side of the bridge. A Monitoring system is designed based on the real time kinematic Global Positioning System (RTK-GPS) to monitor and assess the bridge behaviour and movements under the effect of trains’ loads. Two methods are used to de-noise the GPS data, which are moving average filter (FM) and wavelet transform (WT). In addition; the wavelet decomposition is used to classify the noise effects. The time and frequency domain GPS observation analysis results are discussed and the results showed that the bridge is safe under affected train loads in time and frequency domain. Also, it can be concluded that the WT can be used to filter the GPS monitoring data but can’t be used to extract the dynamic frequencies. Otherwise, the multiple filters are more suitable method to extract dynamic frequencies for the low sampling GPS frequency instruments.
In this paper, a healthcare platform is designed to establish a mobile telecare environment with the digital signal processing (DSP) ability. Although mobile health (mHealth) is not an innovative research topic, the improvements of wearable sensing technologies and today's mobile devices can make such systems more consummate and powerful than before. In the proposed platform, an electrocardiography (ECG)/respiration (RESP) prototype is developed to record single-lead ECG signal and thorax impedance variation caused by respiration of the users. The prototype weights about 8 g, and the size of prototype, which is 25×35×6 mm3, is smaller than electrode. With easy-to-use user interface (UI), it could be applied in many kinds of platform such as mobile phone and tablet as a monitoring device. The recorded data can be uploaded to Dropbox automatically. The other thing that's worth mentioning is the ability to process digital data. With discrete wavelet transform (DWT) we can not only preprocess the biomedical signals for noise reduction but also detect P wave, QRS complex and T wave in real-time. By verifying with MIT-BIH QT database, the results show that the sensitivity of R peak achieves to 99.5%, P wave 97.87% and T wave 92.91%. Ignoring the data recorded from sudden death, the sensitivity of T wave can be up to 98.38%. Equipped with the developed DSP algorithms, the system can provide noise cancellation and motion artifact removal when moving , stretching and sleeping in user's daily life. According to the experiment results, the sensitivity of R wave still performs well even running on treadmill at speed of 15km/h. Therefore, our design can be applied in various fields such as art, sport and medical care.
The high frequency switching noises due to space vector pulse width modulated (SVPWM) inverter driving squirrel cage induction motor (SCIM) usually contaminate voltage/current signals required for estimation of its various parameters. Various mathematical operations are required to be performed in estimating those parameters. The estimation of derivatives of these signals is difficult especially in practice due to the presence of unwanted noise and non-stationary nature of the signal of interest. Hence, wavelet transform based filtering (WTF) technique is proposed in this work to extract dominant slip frequency components from the voltage/current signals. The suggested filtering technique is effective over the useful range of the slip frequency and no tuning is necessary with change in slip. The experimental results while implementing WTF in dSPACE, confirm the effectiveness of WTF in denoising low frequency current signals contaminated with high frequency noise.
