Wavelet Denoising Research Articles

Multi-Event Location Denoising Scheme for φ-OTDR Based on FFDNet Network

In order to improve the signal-to-noise ratio (SNR) of vibration sensing in the phase-sensitive optical time-domain reflectometer (φ-OTDR) system, a fiber sensing signal processing method based on the FFDNet convolutional neural network is proposed in this paper. In the network, the concept of residual learning is introduced, which involves constructing a residual mapping and utilizing multi-layer convolutional neural networks to learn the noise distribution present in the original image. The denoised result can be obtained by subtracting the learned noise from the original image. We have built a φ-OTDR system based on coherent detection, using three PZTs as simulated vibration sources and a series of experiments at 200 Hz, with each experiment simulating a single vibration event or multiple vibration events by setting different intensities. The experimental results demonstrate that the FFDNet based fiber optic sensing signal processing method enhances the SNR to 37.84 dB, 37.11 dB, and 37.31 dB, respectively, while preserving vibration signal details more effectively than wavelet denoising and Gaussian filtering techniques. The trained FFDNet model has great potential for improving the performance of the φ-OTDR system and has some practical application value.

The Improved Wavelet Denoising Scheme Based on Robust Principal Component Analysis for Distributed Fiber Acoustic Sensor

The Improved Wavelet Denoising Scheme Based on Robust Principal Component Analysis for Distributed Fiber Acoustic Sensor

Fast Feature Extraction Method for Brillouin Scattering Spectrum of OPGW Optical Cable Based on BOTDR.

Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.

A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time-frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

Welding Penetration Monitoring for Ship Robotic GMAW Using Arc Sound Sensing Based on Improved Wavelet Denoising

The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an improved wavelet threshold denoising method is proposed to eliminate interference and purify the arc sound signal. The non-stationary random distribution characteristics of GMAW noise interference are also estimated by using the high-frequency detail coefficients in different domains after wavelet transformation, and a mode of measuring scale that is logarithmically negatively correlated with the wavelet decomposition scale is created to update the threshold. The gradient convergent threshold function is established using the natural logarithmic function structure and concave–convex gradient to enable the nonlinear adjustment of the asymptotic rate. Further, some property theorems related to the optimized threshold function are proposed and theoretically proven, and the effectiveness and adaptability of the improved method are verified via the denoising simulation of speech synthesis signals. The four traditional denoising methods and our improved version are applied in the pretreatment of the GMAW arc sound signal, respectively. Statistical analysis and short-time Fourier transform are used to extract eight-dimensional time and frequency domain feature parameters from the denoised signals with randomly time-varying characteristics, and the extracted joint feature parameters are used to establish a nonlinear mapping model of penetration state identification for ship robotic GMAW using the pattern classifiers of RBFNN, PNN and PSO-SVM. The simulation results yielded by visual penetration classification and the multi-dimensional evaluation index of the confusion matrix indicate that the improved denoising method proposed in this paper achieves a higher accuracy in the extraction of penetration state features and greater precision in the identification of pattern classification.

Detection and evolution of disaster sources in high slopes of open-pit mines based on time-lapse high-density electricity method

High and steep slopes in open pit mines used for ore production require careful monitoring to ensure stability and safeguard lives, property, and social welfare. Understanding the evolutionary characteristics of hazard sources within these slopes is crucial for effective slope management. This article presents a novel approach to identify the evolution of hazard sources within slopes by utilizing the derivative of normalized inversion data versus time as a characteristic feature. To analyze the evolution of hazard sources, raw apparent resistivity data collected at different times are processed using smoothing and wavelet denoising algorithms to reduce noise. Geological constraints are then applied to the collected data through the parametric region method. The least squares method is employed for independent inversion, and the inversion results are standardized. Subsequently, the standardized inversion results are used to derive the derivative of resistivity versus time equation, which reveals the evolution of hazard sources within the slope. A case study is conducted on a large open pit rocky slope to analyze the effectiveness of the proposed approach. The results demonstrate the successful reduction of noise through the smoothing and wavelet threshold denoising algorithms, effectively suppressing interference caused by acquisition equipment and environmental electromagnetic noise. The application of the parametric region method with geological constraints highlights the spatial and temporal evolution characteristics of hazard sources within the slope. The findings indicate varying degrees of evolution of hazard sources within the slope, with significant changes observed mainly in the shallow part influenced by water erosion and blasting vibration. Areas with lithological stability show minimal changes, while original hazard source areas have evolved to different extents, distinguishable based on the size and sparsity of contour values. By combining inversion data analysis, noise reduction techniques, and geological constraints, the proposed approach provides valuable insights into the evolution of hazard sources within slopes. This information can aid in the development of effective slope management strategies in open pit mines, contributing to enhanced safety and protection of valuable assets.

Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding.

Novel noise reduction and QRS detection algorithms in Electrocardiogram (ECG) signal based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and the Modified Sigmoid Thresholding Function (MSTF) are proposed in this paper. EMD and EEMD algorithms are used to decompose the noisy ECG signal into series of Intrinsic Mode Functions (IMFs). Then, these IMFs are thresholded by the MSTF for reduction of noises and preservation of QRS complexes. After that, the thresholded IMFs are used to obtain the clean ECG signal. The characteristic points P, Q, R, S and T peaks are detected using peak detection algorithm. The proposed methods are validated through experiments on the MIT-BIH arrhythmia database and Additive White Gaussian Noise (AWGN) is added to the clean ECG signal at different input SNR (SNR in). Standard performance parameters output SNR (SNR out), mean square error (MSE), root mean square error (RMSE), SNR improvement (SNR imp) and percentage root mean square difference (PRD) are employed for evaluation of the efficacy of the proposed methods. The results showed that the proposed methods provide significant quantitative and qualitative improvements in denoising performance, compared with existing state-of-the-art methods such as wavelet denoising, conventional EMD (EMD-Conv), conventional EEMD (EEMD-Conv, Stockwell Transform (ST) and Complete EEMD with Adaptative Noise with hybrid interval thresholding and higher order statistic to select relevant modes (CEEMDAN-HIT). A detail quantitative analysis demonstrate that for abnormal ECG records 207 m and 214 m at input SNR of-2 dB the SNR imp value is 12.22 and 11.58 dB respectively, which indicates that the proposed algorithm can be used as an effective tool for denoising of ECG signals.

Effectiveness of Wavelet Denoising on Secondary Ion Mass Spectrometry Signals

Wavelet theory has already achieved huge success. For Secondary Ions Mass Spectrometry (SIMS) signals, denoising the secondary signal, which is altered by the measurement, is considered that an essential step prior to applying such a signal processing technique that aims enhance the SIMS signals.The most efficient and widely used wavelet denoising method is based on wavelet coefficient thresholding. This process involves three important steps; wavelet decomposition: the input signals are decomposed into wavelet coefficients, thresholding: the wavelet coefficients are modified according to a threshold, and reconstruction: the modified coefficients are used in an inverse transform to obtain the noise-free-signal. Several researchers have used thresholding wavelet denoising techniques.
 The choice of wavelet type and the level of resolution can have a significant influence; it is important to note that the choice of resolution level depends on the type of signal we are dealing with, the nature of the present noise, and our specific goals for the denoised signal. It is generally recommended to test different resolution levels and evaluate their impact on the quality of the denoised signal before making a final decision. Moreover, the results obtained in wavelet denoising can be significantly influenced by the selection of wavelet types. The chosen wavelet type plays a crucial role in the extraction of signal details. Indeed, the effectiveness of denoising the MD6 sample has been demonstrated by the results obtained with sym4, db8, Haar and coif5 wavelets? These wavelets have effectively reduced noise while preserving crucial signal information, leading to an enhancement in the quality of the denoised signal.

Acoustic Signal based Non-Contact Ball Bearing Fault Diagnosis Using Adaptive Wavelet De-Noising

This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural networks (CNN). The health conditions of the ball bearing are monitored by microphone under noisy condition. To eliminate noise, adaptive wavelet denoising method based on kurtosis-entropy (KE) index is proposed. Multiple acoustic features are extracted base on expert knowledge. The 1D ResNet is used to classify the health conditions of the bearings. Case study is presented to examine the proposed method’s capability to monitor the condition of ball bearings. The fault diagnosis results were compared with and without the adaptive wavelet denoising. The results show its effectiveness of the proposed fault diagnostic method using acoustic signals.

Efficient Nanosystem for Nanomedicine Applications Based on Molecular Communications

The authors propose an efficient nanosystem based on molecular communication technology. Molecular communication via diffusion (MCvD) is a promising trend for exchanging biochemical signals between a nanotransmitter (NT) and a nanoreceiver (NR) in aqueous media over short distances. Nanosystem-based MCvD has recently received a lot of attention in advanced targeted nanomedicine applications such as targeted drug delivery and healthcare monitoring (disease/diagnosis/analysis). However, the random nature of molecular diffusion causes counting noise, which significantly degrades the performance of the nanosystem-based molecular communication. In this paper, a reliable and simple denoising technique, namely Savitzky–Golay (SG) filter, is developed in the nanosystem-based MCvD to provide high accuracy of molecular information reception. The performance of the proposed nanosystem is evaluated in terms of bit error rate (BER) and correlation efficiency. The results reveal that the nanosystem-based MCvD using the proposed SG filter outperforms the MCvD using current denoising techniques such as moving average filter, wavelet denoising and I-filter. Actually, it was found that the SG filter increases the gain efficiency in terms of the correlation coefficient by more than 60% in comparison to the I-filter at low and high signal-to-noise ratios (SNRs), whereas in comparison to wavelet denoising, the SG filter achieves more than 10% enhancement in gain efficiency at low SNRs.

A Partial Discharge Acoustic Detection Method of the Switchgear Based on Wavelet Denoising

To ensure the safe operation of the power grid and accurately identify the acoustic signal of the partial discharge of the switchgear, a partial discharge acoustic detection method for switchgear based on wavelet denoising is proposed in this paper. First, the Gaussian mixture algorithm model is used to denoise the discharge acoustic signal; then, the feature extraction and training of the acoustic signal samples are carried out; finally, a classifier is used to complete the discharge state recognition. The experimental results show that the detection accuracy of the proposed detection algorithm can reach more than 90%, and the robustness is good in different noise environments.

Parameter Optimisation of Wavelet Denoising for Pulsed Eddy Current Signals Based on Particle Swarm Optimisation Algorithm

ABSTRACT To ensure the reliable operation of high voltage cables, it is crucial to routinely inspect the lead sealing using pulsed eddy current. But nevertheless, the signals can be impacted by noise in actual engineering. While retaining the useful high-frequency information in the original signal, wavelet denoising can filter the clutter efficiently. However, previous research on wavelet denoising has only used it as a straightforward filtering tool, ignoring the impact of changing the parameters on the actual denoising effect. In this study, a more accurate evaluation index called P NCC is constructed for pulsed eddy current testing signals with a focus on maintaining the peak information. This index is then adopted as a fitness function for the particle swarm optimisation algorithm to determine the befitting wavelet denoising parameters and denoised signals. The results show that the signal-to-noise ratio of the signals after denoising is approximately 25 dB, and the distortion at the peak position is stable at about 1%. It demonstrates that the comprehensive index P NCC is a reliable metric for assessing the quality of pulsed eddy current signals. The optimal wavelet denoising parameters for pulsed eddy current signals are found to be the Sym 4 wavelet, 10-layer decomposition and Median threshold function.

Distributed High-Temperature Sensing Based on Non-Local Haar Transform in OFDR

We demonstrate a short-time long distance distributed high-temperature sensing by non-local Haar transform (NLH) in optical frequency domain reflectometry (OFDR). By searching similar pixels across a non-local region, NLH makes good use of incomplete space similarity of information contained in 3D cross-correlation distribution of local Rayleigh scattering spectra measured by OFDR, which can be used to enhance image denoising performance and retain rare details of original spectra data. With the proposed method, we have achieved a short-time distributed high-temperature sensing ranging from 950°C to 1050°C over 102 m by reduced-cladding single mode fiber (RC-SMF) with a sensing spatial resolution of 2 cm. Compared with traditional image denoising methods including Gaussian filter (GF), block-matching three dimensional filter (BM3D) and wavelet denoising (WD), the proposed NLH method has a best performance to restore the consistency of spectral shift distribution caused by the same temperature change without deterioration of sensing spatial resolution.

A novel physiological signal denoising method coupled with multispectral adaptive wavelet denoising(MAWD) and unsupervised source counting algorithm(USCA)

In order to improve the quality of physiological signals, a combined study of blind source separation and wavelet thresholding methods was conducted, resulting in the proposal of a multispectral adaptive wavelet denoising (MAWD) method. This method was employed in conjunction with an improved unsupervised source counting algorithm (USCA). To evaluate the effectiveness of the proposed approach, three methods were used to calculate signal-to-noise ratio (SNR) and root mean square error (RMSE): soft thresholding, hard thresholding, and adaptive thresholding. The results demonstrated that the proposed method exhibited strong applicability under soft thresholding. Specifically, compared to hard thresholding, the enhanced signal using soft thresholding showed an approximately 44.2% increase in SNR and a 28.8% decrease in RMSE, along with a 1.4% reduction in processing time. Moreover, when compared to adaptive thresholding, soft thresholding exhibited approximately 706% improvement in SNR, a 16.7% decrease in RMSE, and a 3.0% reduction in processing time. Multiple experiments were conducted to determine the optimal peak detection threshold range for USCA, which was found to be within the interval [0.001, 0.0001]. This range facilitated the separation of more sources, thereby enhancing the separation effectiveness and accuracy. To substantiate the effectiveness of the USCA method, tests were conducted on publicly available datasets of EMG, ECG, and EEG signals, all of which consistently demonstrated the advantages of this approach. Data AvailabilityThe authors do not have permission to share data.

Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model

Accurate short-term forecasting of building cooling load (CLF) in an integrated energy system (IES) is essential for effective building energy management. However, the existing CLF models for IES often treat each building as an independent entity and neglect the spatiotemporal correlation among buildings. To address this research gap and achieve accurate CLF, this paper proposes a new hybrid deep learning model that considers spatiotemporal coupling. First, the coupled spatial–temporal features among different buildings were analyzed, and the meteorological factors were screened based on the Spearman's rank order correlation coefficient (SROCC). Second, synchrosqueezing wavelet denoising (SWT) was adopted to denoise the historical cooling load (CL) data, remove high-frequency noise, and improve data quality. Third, the TTGAT-GTC model was constructed for the CLF of an IES. A temporal trend-aware graph attention network (TTGAT) captured the spatial correlation of CL between buildings. A gated temporal convolution layer (GTC) was constructed to extract the trend in the dynamic temporal variation in historical load. Residual and skip connections were applied to avoid gradient disappearance and increase the computational efficiency of the model. To validate the effectiveness of the proposed SWT-TTGAT-GTC model, this paper compared the proposed model with four benchmark models using MAPE, RMSE, MAE, and R2. The experimental results showed that the performance of the proposed CL forecasting model is superior and that the proposed model appropriately introduces the spatio-temporal coupling information between buildings.

Identification of heavy metal pollutants in wheat by THz spectroscopy and deep support vector machine

This paper proposes to detect heavy metal pollutants in wheat using terahertz spectroscopy and deep support vector machine (DSVM). Five heavy metal pollutants, arsenic, lead, mercury, chromium, and cadmium, were considered for detection in wheat samples. THz spectral data were pre-processed by wavelet denoising. DSVM was introduced to further enhance the accuracy of the SVM classification model. According to the relationship between the accuracy and the training time with the number of hidden layers ranging from 1 to 4, the model performs the best when the hidden layer network has three layers. Besides, using the back-propagation algorithm to optimize the entire DSVM network. Compared with Deep neural network (DNN) and SVM models, the comprehensive evaluation index of the proposed model optimized by DSVM has the highest accuracy of 91.3 %. It realized the exploration enhanced the classification accuracy of the heavy metal pollutants in wheat.

Multiwavelet based-approach to detect shared congestion in computer networks

Internet paths sharing the same congested link can be identified using several shared congestion detection techniques. The new detection technique which is proposed in this paper depends on the previous novel technique (delay correlation with wavelet denoising (DCW) with new denoising method called Discrete Multiwavelet Transform (DMWT) as signal denoising to separate between queuing delay caused by network congestion and delay caused by various other delay variations. The new detection technique provides faster convergence (3 to 5 seconds less than previous novel technique) while using fewer probe packets approximately half numbers than the previous novel technique, so it will reduce the overload on the network caused by probe packets. Thus, new detection technique will improve the overall performance of computer network.

Wavelet Denoising Image Processing Based on MATLAB

This article uses MATLAB to simulate the wavelet denoising algorithm. The threshold denoising was simulated using the function method, triple standard deviation method, fixed threshold algorithm, Haar, sym5, Coif3, and db3 wavelet bases. The simulation results show that using the fixed threshold method, the Haar wavelet has the best denoising effect.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands.

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.

Speech Enhancement Based on Array-Processing-Assisted Distributed Fiber Acoustic Sensing

Distributed Fiber acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted lots of attention due to unique advantages. In security monitoring, speech recognition and other relevant fields, DAS’s ability to acquire speech signals is a key indicator for the practical application. However, the speech signal has broadband and non-stationary characteristics, so the conventional filtering is not sufficient, and the speech enhancement algorithm based on the characteristics of the speech signal is needed; on the other hand, the acoustic sensitivity of conventional fiber layout is not high enough, resulting in poor signal quality. In this paper, speech enhancement based on array-processing-assisted DAS system is proposed to recover speech signal with high-fidelity. Pulse compression Φ-OTDR, combined with multichannel speech enhancement algorithm based on optimally modified log-spectral amplitude (OM-LSA) is used. As far as we know, this is the first time that the multichannel OM-LSA speech enhancement is proposed for DAS. Compared with the signal before processing, the speech signal processed by this method has a great improvement in the signal distortion, background noise intrusiveness and signal overall quality. In addition, other signal enhancement methods, such as wavelet denoising and beamforming are also analyzed and compared. Our scheme outperforms in above three aspects. This scheme does not need to obtain a large number of data sets in advance, and can be transplanted to other DAS systems, which has great significance for realizing high performance speech acquisition.