Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.
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http://dx.doi.org/10.3390/s18072086 | DOI Listing |
Environ Monit Assess
December 2024
School of Big Data and Statistics, Anhui University, Hefei, 230601, Anhui, China.
The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.
Waste sorting is a key part of sustainable development. To maximize the recovery of resources and reduce labor costs, a waste management and classification system is established. In the system, we use Internet of Things (IoT) and edge computing to implement waste sorting and the systematic long-distance information transmission and monitoring.
View Article and Find Full Text PDFRev Sci Instrum
December 2024
School of Mechatronic Engineering, Southwest Petroleum University, Chendu 610500, China.
The early fault characteristics of rolling bearings are weak, especially in a strong noise environment, which are more difficult to extract; therefore, a method based on wavelet packet decomposition, multi-verse optimizer, and maximum correlated kurtosis deconvolution for weak fault feature extraction of rolling bearings is proposed. First, the original vibration signal is decomposed using wavelet packet decomposition, followed by proposing a signal reconstruction method combining the Pearson correlation coefficient and energy ratio to effectively remove noise from the original signal. Second, the parameters L and M of Maximum Correlated Kurtosis Deconvolution (MCKD) are optimized using the multi-verse optimizer algorithm to obtain optimal filter settings.
View Article and Find Full Text PDFRev Sci Instrum
December 2024
College of Intelligent Manufacturing, Long Dong University, Qingyang, Gansu 745000, China.
The deflector jet pressure servo valve (DJPSV), a critical component of the aircraft brake servo system, requires a precise foundational model for performance analysis, optimization, and enhancement. However, the complexity of the jet process within the V-groove of the deflector plate presents challenges for accurate mathematical modeling. To address this issue, the paper takes the DJPSV as the research object, carries out detailed mathematical modeling of its components, analyzes the influencing factors of the performance of the key component-the front stage-and optimizes the design of the key factors.
View Article and Find Full Text PDFJ Comput Aided Mol Des
December 2024
Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China.
Conotoxins, being small disulfide-rich and bioactive peptides, manifest notable pharmacological potential and find extensive applications. However, the exploration of conotoxins' vast molecular space using traditional methods is severely limited, necessitating the urgent need of developing novel approaches. Recently, deep learning (DL)-based methods have advanced to the molecular generation of proteins and peptides.
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