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Temperature is an essential quality index in storage. Prediction of temperature can help the grain storage industry to apply the appropriate operations such as ventilation or drying to improve the quality of grain and extend the suitable storage time. Traditional machine learning methods usually cannot accurately predict the temperature data of the grain considering the complexity of environmental factors and grain warehouse conditions. To make better use of the temporal data such as temperature/humidity information of grain itself and its environment, this paper proposes a gated recurrent unit (GRU)-based algorithm to predict the change of the data. The grain warehouse environmental data are collected by multi-functional sensors inside a grain depot, including temperature, humidity, wind speed, air pressure, etc. Some of these data features such as rain or snow days are sparse data features. Excessive sparse features can affect the training accuracy of the model. At the same time, due to sensor aging or extreme weather conditions, the data collected may not be accurate, and the data contain noise, which also has a significant impact on the training of the model. To improve the performance of the proposed GRU framework, multivariate linear regression is used for feature generation to optimize the volatility of weather data, strengthen and construct the characteristics of datasets, and wavelet filtering is used to denoise the corresponding features. This paper focuses on the data sparse and noise problem and applies the MLR and wavelet filtering to improve the GRU prediction framework for grain warehouse temporal data. According to our experiment, the temperature prediction results based on the GRU deep fusion model have better improvement in prediction accuracy and time than the existing neural network algorithms such as long–short-term memory (LSTM), GRU, and transformer.

Thanks to its capability of providing seamless massive access and extended coverage, satellite communication has been envisioned as a promising complementary part of the future 6G network. Due to the large satellite-to-ground propagation loss, noise mitigation is one of the most important considerations for implementing key interface technologies on boarding a satellite, e.g., random access (RA). This paper aims at developing an effective preamble detection method with noise suppression for 5G-ntegrated satellite RA systems. Specifically, according to the satellite ephemeris and user equipment location, we first perform the pre-compensation of timing and frequency offset before preamble transmission to determine all the possible correlation peak positions in advance. By leveraging the advantage of the wavelet transform in signal-to-noise separation, we further design a novel detection framework based on wavelet denoising, which can efficiently reconstruct preamble signature from the noisy power delay profile. Simulation results validate the feasibility of the proposed method, and show that our method can achieve a notably improved detection performance under extremely low signal-to-noise ratio conditions, in comparison with the conventional one.

The current standard technique for blood pressure determination is by using cuff/stethoscope, which is not suited for infants or children. Even for adults such an approach yields 60% accuracy with respect to intra-arterial blood pressure measurements. Moreover, it does not allow for continuous monitoring of blood pressure over 24 h and days. In this paper, a new methodology is developed that enables one to calculate the systolic and diastolic blood pressures continuously in a non-invasive manner based on the heart beats measured from the chest of a human being. To this end, we must separate the first and second heart sounds, known as S₁ and S₂, from the directly measured heart sound signals. Next, the individual characteristics of S₁ and S₂ must be identified and correlated to the systolic and diastolic blood pressures. It is emphasized that the material properties of a human being are highly inhomogeneous, changing from one organ to another, and the speed at which the heart sound signals propagate inside a human body cannot be determined precisely. Moreover, the exact locations from which the heart sounds are originated are unknown a priori, and must be estimated. As such, the computer model developed here is semi-empirical. Yet, validation results have demonstrated that this semi-empirical computer model can produce relatively robust and accurate calculations of the systolic and diastolic blood pressures with high statistical merits.

This paper presents a new method for fault diagnosis in ball-bearings using a combination of the Independent Component Analysis (ICA) and the Wavelet Transform. In the ICA, the number of sensors should be equal to the number of independent sources. We introduce a new method to replace the second vibration signal required by the ICA by a virtual one in order to increase the accuracy of the diagnosing system and also to simplify the system hardware. Using real and simulated signals, it is shown that the proposed algorithm outperforms the HFD algorithm.

A successful pre-processing stage based on wavelet denoising algorithm for electromyography (EMG) signal recognition is proposed. From the limitation of traditional universal wavelet denoising, the optimal weighted parameter is assigned for universal thresholding method. The optimal weight for increasing EMG recognition accuracy is 50–60% of traditional universal threshold with hard transformation. Experimental results show that it improved approximately from 2 to 50% of recognition accuracy for EMG with signal-to-noise ratio (SNR) in the range of 20 to 0 dB compared to a baseline system (without pre-processing stage) and traditional universal wavelet denoising. The results are evaluated through a large EMG dataset with seven kinds of hand movements and eight types of muscle positions.

A new method for detecting weak linear frequency modulated (LFM) pulse signals buried in additive white Gaussian noise (AWGN) is presented in this paper. The method is based on the features of wavelet transform modulus maxima (WTMM) denoising and auto-correlation filtering theory. Firstly, the frequency-domain information is extracted by auto-correlation matched filtering, and is used to deduce the optimal wavelet decomposition scales. Secondly, let the signal modulus dominate on the biggest scale after the optimal scales decomposition, then keeping the signal modulus and removing the noise modulus at each scale are performed by utilizing the different propagation properties of signal and noise wavelet modulus maxima across the scales. Finally, a reconstructed signal is obtained from the reserved signal modulus with an improved signal-to-noise ratio (SNR), and is used for time-domain information extraction. At the same time, wavelet denoising depends on selecting an optimum wavelet that matches well the shape of the signal. The cross correlation coefficients between signal and db wavelets are calculated and the optimal wavelet to analysis the LFM signal is selected. Simulations show that the method can extract time-frequency information of LFM signal when SNR ≤ -6 dB.

For the non-stationary signal denoising, an effective method for dropping ambient noise is based on discrete wavelet transform. Also, in order to minimize the loss of useful signal and get high SNR in the wavelet denoising, it is very important that the thresholding is suitable for the characteristics of signal. In this paper, we propose new thresholding method to reduce an ambient noise and to detect effectively the useful signal. First, we analyze four kinds of previous wavelet threshold functions (Hard, Soft, Garrote and Hyperbola) and propose new wavelet threshold function compromised between Garrote and Hyperbola threshold functions. Next, a threshold value is selected by value to reduce exponentially according to the wavelet decomposition level. We also analyze a continuity and monotonicity, and prove the logic of new threshold function. The results of theoretical analysis show that new threshold function solves the problems of constant error and discontinuity of previous threshold functions, and minimizes the information loss of useful signal. The results of experiment show that SNR of new thresholding method is highest and RMSE and Entropy are smallest. The results of theoretical analysis and experiment show that new thresholding method is more appropriate to wavelet denoising for dropping ambient noise than previous methods.

Removing the ambient noise and increasing the signal-to-noise ratio are very important for detecting defects and corrosions of conductive material by using the electromagnetic acoustic transducer. It is still an issue to remove the ambient noise without losing the original signal information. The aim of this paper is to solve the issue by using a new closed-form shrinkage function based on Gauss–Laplace mixture distribution in wavelet domain. First, we prove that Gauss–Laplace mixture distribution is well fitted to the statistical model for wavelet coefficients of noise-free signal of electromagnetic acoustic transducer. As well, we use Gauss–Laplace mixture distribution and Gauss distribution for statistical modeling on the wavelet coefficients of noise-free signal and ambient noise, respectively. Using these distributions, we derive a new closed-form shrinkage function that is an analytical solution of a Bayesian maximum a posteriori estimator. Next, we evaluate the denoising performance of new shrinkage function compared with various shrinkage functions in terms of the improved signal-to-noise ratio, root mean squared error and entropy. The experiment results show that the wavelet denoising method using the proposed shrinkage function effectively removes the ambient noise than the other existing denoising methods for noisy signal of electromagnetic acoustic transducer.

Dropping the ambient noise from microseismic signals is very important for disaster monitoring such as a rockburst and early warning system using microseismic monitoring techniques in the mine and coal mines. Currently, it is still a challenge to remove high and low-frequency noise simultaneously without losing the useful information of microseismic signal. The aim of this paper is to remove the low-frequency noise contained in microseismic signal effectively, while preserving the useful signal information by using a stochastic approach. We first statistically model the wavelet coefficients in the approximation subband of noisy microseismic signal. In addition, we evaluate qualitatively and quantitatively the fitness of Gauss–Laplace mixture distribution and the statistical modeling of data. Then, we propose a novel denoising algorithm to remove the ambient noise effectively from the noisy microseismic signals in wavelet domain. This algorithm removes the low-frequency noise by using a stochastic approach and the high-frequency noise by using a traditional wavelet thresholding method. The low-frequency noise is removed by using a closed-form shrinkage function based on Gauss–Laplace mixture distribution, while the high-frequency noise is removed by using a threshold function combined with Garrote and hyperbolic threshold functions. Next, we evaluated the ambient denoising performance of our novel denoising algorithm by comparing it with various denoising methods with different test signals. Experimental results show that the ambient denoising performance of the proposed method is better than the other seven existing methods.

A steel pipeline of about 60 m long containing several pipes and structural singularities (bends, welds, clamps, etc.) is inspected in this work using a guided-waves technique. The inspection system is a pair of transducer-rings operating with the torsional mode T(0,1) and allows the long-range fast screening of the structure from defined measurement points. Recorded signals have submitted some numerical treatments in order to make them interpretable. The wavelet analysis is one of them and serves for denoising the raw signals. Besides, the Hilbert transform (HT) is applied in order to obtain the wave signals' envelopes leading to simplified curves easy to interpret. The processed signals are analyzed to identify defects' reflections from structural-singularities' echoes in the pipeline. The inspection system prove its efficiency for a global screening of such a long-range pipeline by detecting and localizing the defects.

As the Laser Doppler Velocimeter (LDV) has been applied to flow velocity measurement for many years, it is still difficult to process the Doppler signal in solid velocity measurement, especially with high acceleration. In order to address this issue, an Instantaneous Frequency (IF) extraction method for Doppler signal is demonstrated in this paper. First, the Doppler signal is denoised by wavelet to make the signal smoother. Then, the denoised signal is normalized by empirical Amplitude and Frequency Modulation (AM-FM) decomposition. At last, the improved Direct Quadrature (DQ) method is adopted to extract the IF from the normalized signal. A simulated Doppler signal whose IF is changed from about 25 KHz to 80 KHz in 50 milliseconds is used to validate the effectiveness of this method. The result indicates that this method is able to extract the IF with high accuracy and computation speed.

This paper studies a fiber optic perimeter security system based on dual Mach Zehnder interferometer. The structure and principles of the system are analyzed. With the capture card, photoelectric detectors and other hardware circuit designed, this paper points out the key factors that cause the error of the system, and puts forward some measures to improve it. The experimental results show that the wavelet denoising and improved cross-correlation algorithm have significant effect to improve the positioning accuracy.