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Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary In wireless communication, impulsive noise often degrades channel quality, which poses challenges for equalizers. Although robust equalization methods offer some effectiveness, the occurrence of impulsive noise after training significantly impacts the symbol error rate (SER). To mitigate this issue, we propose a method that involves denoising the received signal using robust wavelet decomposition before equalization. This approach combines the discrete wavelet transform with median and morphological filters to reduce impulsive noise. A pre‐impulsive noise detection mechanism triggers denoising only when impulsive noise is detected. We evaluate the SER performance of the proposed technique using simulations of a 16‐QAM orthogonal frequency division multiplexing (OFDM) system with kernel interpolation‐based frequency domain equalization (FDE) in a Rayleigh fading channel with impulsive noise. Results show that our approach achieves SER levels comparable to conventional methods in Gaussian noise scenarios, demonstrating its effectiveness in challenging wireless communication environments. The simulation results demonstrate that the proposed technique in non‐Gaussian noise gives the SER on par with the FDE in a Gaussian noise environment. [ABSTRACT FROM AUTHOR]

Interrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti‐jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex‐valued convolutional auto‐encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex‐valued convolutional shrinkage network (CVCSNet) and a complex‐valued UNet (CVUNet) are developed. Based on the time‐domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming‐free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de‐chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal‐to‐noise ratios, jamming‐to‐signal ratios and jamming parameters. [ABSTRACT FROM AUTHOR]

Radar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness. [ABSTRACT FROM AUTHOR]

The threshing cylinder will wear and deform during the threshing process, causing dynamic balance problems. The combine harvester has multiple vibration excitation sources and a complex vibration environment, making it challenging to extract weak unbalanced signals from strong background noise. A novel three-step filtering framework is proposed in this paper. A zero phase filter was used as the pre-processing layer to filter out the high frequency components in the original signal and reduce the number of parameter-adaptive variational mode decompositions (PAVMD) needed. The PAVMD was used to decompose the non-stationary vibration signal before Adaptive Neuron Linear (Adaline) function was used to fit sinusoidal signal parameters. A measurement index, termed the correlation amplitude (CA) index, is constructed. The parameterisation of PAVMD was guided by the CA index, and the modal component of the unbalanced fault features were located. The simulation and real cylinder signals proved that the proposed method could effectively extract unbalanced signals under noise interference, and the unbalance was identified accurately by the influence coefficient method. Experiments on a threshing cylinder showed that the amplitude identification error was <24 g in single-sided unbalance identification, and the amplitude identification error was <27 g in double-sided unbalance identification. The proposed method had high robustness and small identification error, especially under short-time working conditions, compared with other similar approaches. • Unbalance signal extraction method based on parameter-adaptive VMD was proposed. • It uses a novel three-step filtering framework for signal extraction. • Experiments on threshing cylinder demonstrated the effectiveness and advantages. [ABSTRACT FROM AUTHOR]

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 PNCC 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 PNCC 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. [ABSTRACT FROM AUTHOR]

In acoustic signal–based natural gas pipeline leak detection, it is a technical difficulty to effectively remove the strong background noise hidden in the acoustic signal. Unfortunately, existing techniques usually study denoising in isolation from the model, and do not consider the synergistic effect of integrating denoising with the model to update filtering parameters adaptively, which limits the performance development of the diagnostic model. To solve this problem, this paper proposes a fault diagnosis method that incorporates acoustic signal hybrid filtering and a one-dimensional convolutional neural network (1D-CNN) to jointly update the denoising parameters. Firstly, the acoustic signal is sampled at a fixed frequency by the sensor unit. Secondly, a hybrid filtering method with low-frequency characteristics is designed, which integrates low-pass filtering and median filtering to eliminate background noise. Finally, an adaptive fault diagnosis network that fuses acoustic signal denoising with 1D-CNN feature extraction is developed. The network updates the filtering parameters during training based on the model recognition accuracy, so that the denoising parameters and model weights change dynamically. The experimental results show that the method is capable of performing adaptive parameter search with 97.07% identification accuracy of fault type. [ABSTRACT FROM AUTHOR]

Esophageal speech (ES) is a pathological voice that is often difficult to understand. Moreover, acquiring recordings of a patient's voice before a laryngectomy proves challenging, thereby complicating enhancing this kind of voice. That is why most supervised methods used to enhance ES are based on voice conversion, which uses healthy speaker targets, things that may not preserve the speaker's identity. Otherwise, unsupervised methods for ES are mostly based on traditional filters, which cannot alone beat this kind of noise, making the denoising process difficult. Also, these methods are known for producing musical artifacts. To address these issues, a self-supervised method based on the Only-Noisy-Training (ONT) model was applied, consisting of denoising a signal without needing a clean target. Four experiments were conducted using Deep Complex UNET (DCUNET) and Deep Complex UNET with Complex Two-Stage Transformer Module (DCUNET-cTSTM) for assessment. Both of these models are based on the ONT approach. Also, for comparison purposes and to calculate the evaluation metrics, the pre-trained VoiceFixer model was used to restore the clean wave files of esophageal speech. Even with the fact that ONT-based methods work better with noisy wave files, the results have proven that ES can be denoised without the need for clean targets, and hence, the speaker's identity is retained. [ABSTRACT FROM AUTHOR]

Electrocardiogram is the most essential diagnostic test for heart disease detection in this era, where it has low frequency and small amplitude, making it vulnerable to a variety of stimuli, including high/low-frequency noises resulting in diagnostic quality suffers. Implementation of adaptive filter removes noise from the signal in a better way. Adaptive filters in signal processing plays a major role in biomedical applications for denoising different types of noises such as Power Line Interference noise which is generated by the power line electromagnetic field and it exhibits its peak from 50 to 60 Hz, Baseline Wander noise which occurs due to the variation of electrode skin impedance, Motion Artifacts which is generated by the electrode motions away from the contact zone on the skins and Muscle Noise, this is due to the Electromyographic signals, originating from the skeletal muscle contractions. In order to eliminate the above-mentioned noises, the most common adaptive filters such as Least Mean Square (LMS) and Recursive Least Squares (RLS) are used. In existing method, they used Cascaded Least Mean Square adaptive filters to reduce noises at a much higher rate than the Least Mean Square Adaptive filter and to achieve the higher Signal to Noise Ratio (SNR). The Cascaded RLS filter can be used to obtain faster range of convergence than the Cascaded LMS. The filter is designed with adaptive variable step size which calculates the step size according to the input signal of the filter, this is used to increase the stability of the filter. The systolic architecture and associativity of the VLLMS filter is implemented to reduce the number of adders and multipliers along the critical path of the filter. This work comprises of designing the 4 tap, 8 tap and 16 tap cascaded systolic associative variable leaky least mean square adaptive filters using Simulink and the SNR comparisons have been made between normal LMS cascaded filter and cascaded VLLMS filter. The area and delay between normal filter and systolic associative filter are compared by converting the Simulink design into Verilog and by implementing in Xilinx. [ABSTRACT FROM AUTHOR]

Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method. [ABSTRACT FROM AUTHOR]

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water–slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase. [ABSTRACT FROM AUTHOR]

Random noise attenuation is significant in seismic data processing. Supervised deep learning-based denoising methods have been widely developed and applied in recent years. In practice, it is often time-consuming and laborious to obtain noise-free data for supervised learning. Therefore, we propose a novel deep learning framework to denoise prestack seismic data without clean labels, which trains a high-resolution residual neural network (SRResnet) with noisy data for input and the same valid data with different noise for output. Since valid signals in noisy sample pairs are spatially correlated and random noise is spatially independent and unpredictable, the model can learn the features of valid data while suppressing random noise. Noisy data targets are generated by a simple conventional method without fine-tuning parameters. The initial estimates allow signal or noise leakage as the network does not require clean labels. The Monte Carlo strategy is applied to select training patches for increasing valid patches and expanding training datasets. Transfer learning is used to improve the generalization of real data processing. The synthetic and real data tests perform better than the commonly used state-of-theart denoising methods. [ABSTRACT FROM AUTHOR]

In speech coding, denoising of the speech signal is essential as well as crucial. The filters for minimizing errors through denoising employ the autoregressive moving average (ARMA) approach, introducing higher computational complexity in speech coder design. This research work presents the design and implementation of an effective perceptual weighting filter (PWF) for speech coding. The high-level synthesis of the fixed-point PWF filter is optimized by multiple optimization techniques along with detailed design space exploration using the weighted sum (WS) method. To enhance the performance, an FPGA-based hardware accelerator is proposed using hardware/software (HW/SW) co-design in an embedded environment. Simulative analysis in Vivado HLS and final accelerator design in the Vitis IDE tool validate the proposed architecture by using real-time speech samples, demonstrating a 50% reduction in area and a 99% execution improvement. This makes it well-suited for use in modern speech codecs, enhancing the efficiency. [ABSTRACT FROM AUTHOR]

Lidar (Light Detection and Ranging), utilizes laser based remote sensing data to measure distances and properties of objects by analysing reflected light, serving diverse applications. Lidar signals in the middle atmosphere face challenges like noise and atmospheric uncertainties. Lidar signal denoising and temperature retrievals are crucial for accurate measurements, improving data reliability in atmospheric research. Lidar signal denoising is the process of reducing unwanted noise or interference from Lidar data, enhancing accuracy and reliability. To address the issues Shuffle Attention with Encoder and Decoder based Deep Convolutional Neural Network (SAED-DCNN) is proposed. The Lidar signal undergoes preprocessing, including normalization, followed by convolution and pooling encoding in the autoencoding layer of SAED-DCNN. Leveraging Deep Convolutional Neural Network (DCNN) and encoder-decoder Framework, with shuffle attention, enhances spatial interactions for reducing Lidar signal noise. the introduction of enhanced spider wasp optimization addresses computational complexity, optimizing SAED-DCNN parameters, presenting an innovative Lidar signal enhancement approach. The suggested framework exhibits outstanding performance, achieving signal-to-noise ratio and root mean squared error of 28.135 dB and 68.113, respectively. These metrics underscores the mode's efficacy in overcoming denoising challenges, establishing it as a robust denoising solution. [ABSTRACT FROM AUTHOR]

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance. • A sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed to achieve fading-free sensing. [ABSTRACT FROM AUTHOR]

The alarming mortality rates associated with cardiac abnormalities emphasize the critical need for early and accurate detection of heart disorders to mitigate severe health consequences for patients. Electrocardiograms (ECG) are commonly employed instruments for the examination of cardiac disorders, with a preference for noise-free ECG signals to ensure precise interpretation. However, ECG signal recordings are susceptible to environmental interferences, including patient movement and electrode positioning. This paper introduces a hardware implementation for denoising ECG signals, leveraging a novel method by integrating high-order Synchrosqueezing Transform, Detrended Fluctuation Analysis, and Non-Local-Mean filter optimized by Particle Swarm Optimization (HSST-DFA-PSO-NLM) techniques on Field-Programmable Gate Array (FPGA) platforms. FPGA-based processing units are chosen for their outstanding performance attributes, including high re-programmability, speed, architectural flexibility, and low power consumption, resulting in efficient signal processing. The effectiveness of the designed filtering algorithm is evaluated using key criteria, including Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE) for performance assessment. Additionally, resource utilization metrics such as Look-Up Tables (LUTs), Flip Flops, and DSP Blocks, as well as power consumption measures including dynamic power and static or leakage power, are analysed across various FPGA boards (Virtex and Zedboards) utilizing the VIVADO environment. Comparative analyses are conducted to identify the most suitable FPGA board for implementation, highlighting the superior performance of the proposed design. Remarkably, the proposed denoising solution gives excellent SNR of 29.56, 29.68, and 28.86 by denoising various ECG noises. The RMSE attained by the model is also less than 0.05. This research advances the field of cardiac disorder detection by providing a reliable and efficient FPGA-based solution for ECG signal denoising, thereby enhancing the accuracy of early diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Abstract Interrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti‐jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex‐valued convolutional auto‐encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex‐valued convolutional shrinkage network (CVCSNet) and a complex‐valued UNet (CVUNet) are developed. Based on the time‐domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming‐free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de‐chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal‐to‐noise ratios, jamming‐to‐signal ratios and jamming parameters.

Abstract Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.

Advances in AI techniques have fueled research on using EEG data for psychiatric disorder diagnosis. Despite EEG's cost-effectiveness and high temporal resolution, low Signal-to-Noise Ratio (SNR) hampers critical marker extraction and model improvement, while denoising techniques will lead to a loss of effective information in EEG. The aim of this study is to employ AI methods for the processing of raw EEG data. The primary objectives of the processing are twofold: first, to acquire more reliable markers for schizophrenia, and second, to construct a superior automatic classification for schizophrenia. To remove the noises and retain task-related (classification tasks) effective information mostly, we introduce an Effective Information Estimation Framework (EIEF) based on three key principles: the task-centered approach, leveraging 1D-CNNs' test metrics to gauge effective information proportion, and feedback. We address a theoretical foundation by integrating these principles into mathematical derivations to propose the mathematical model of EIEF. In experiments, we established a paradigm pool of 66 denoising paradigms, with EIEF successfully identifying the optimal paradigms (on two datasets) for restoring effective information. Utilizing the processed dataset, we trained a 3D-CNN for automatic schizophrenia diagnosis, achieving outstanding test accuracies of 99.94 % on dataset 1 and 98.02 % on dataset 2 in subject-dependent evaluations, and accuracies of 89.85 % on dataset 1 and 98.02 % on dataset 2 in subject-independent evaluations. Additionally, we extracted 38 features from each channel of both processed and raw datasets, revealing that 20.86 % (dataset 1) of feature distribution differences between the patients and the healthy exhibited significant changes after implementing the optimal paradigm. We enhance model performance and extract more reliable electrobiological markers. These findings have promising implications for advancing the field of the clinical diagnosis and pathological analysis of Schizophrenia. [ABSTRACT FROM AUTHOR]

Seismic data are typical nonlinear and nonstationary data. In the acquisition and processing of seismic data, many factors interfere with it. Seismic data contain both effective waves and random noises, seriously affecting the quality of seismic data and not conducive to the goal of fine interpretation of subsequent seismic data. Therefore, studying new seismic data denoising methods is beneficial for improving the quality of seismic data and plays a very important role in subsequent seismic data interpretation. In this paper, the principle of variational mode decomposition (VMD) and 2D-VMD is introduced in detail, and the seismic profile with a simple signal and fault model is denoised. Compared to traditional empirical mode decomposition (EMD), the 2D-VMD method has the best seismic data denoising effect. The test results of the synthesised signal show that the 2D-VMD method has a signal-to-noise ratio of 47.14 dB after denoising, which is higher than the signal-to-noise ratio after EMD and VMD denoising, indicating that it has a better denoising effect. The VMD and 2D-VMD methods are applied to the denoising of actual seismic data. The application results show that the 2D-VMD method can effectively improve the quality of the seismic data, enhance the continuity and reliability of the seismic data, and is conducive to the fine interpretation of subsequent seismic data.

In this paper, we propose a Transformer-based encoder architecture integrated with an unsupervised denoising method to learn meaningful and sparse representations of vibration signals without the need for data transformation or pre-trained data. Existing Transformer models often require transformed data or extensive computational resources, limiting their practical adoption. We propose a simple yet competitive modification of the Transformer model, integrating a trainable noise reduction method specifically tailored for failure mode classification using vibration data directly in the time domain without converting them into other domains or images. Furthermore, we present the key architectural components and algorithms underlying our model, emphasizing interpretability and trustworthiness. Our model is trained and validated using two benchmark datasets: the IMS dataset (four failure modes) and the CWRU dataset (four and ten failure modes). Notably, our model performs competitively, especially when using an unbalanced test set and a lightweight architecture. [ABSTRACT FROM AUTHOR]

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization–variational mode decomposition (PSO–VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO–VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising. [ABSTRACT FROM AUTHOR]

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings. [ABSTRACT FROM AUTHOR]

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dryelectrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG's QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10-6 and 1.65 × 10-7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG's QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals. [ABSTRACT FROM AUTHOR]

The ability to predict and preempt insulator failures holds the potential to enhance the reliability of electrical power grids. The increase in insulator leakage current is an indication that failures may occur. By harnessing historical data and employing time series forecasting models, it is possible to identify potential faults before they escalate into disruptive failures. In this paper, a hybrid model for time series prediction is proposed by combining the Christiano–Fitzgerald random walk filter for signal denoising with an ensemble bootstrap aggregation model for leakage current forecasting. A comparison between bootstrap aggregation, boosting, random subspace, and stacked generalization ensemble learning models is presented. With a root mean square error of 7.62 × 10 - 4 (in a statistical evaluation), the ensemble bootstrap aggregation model with Christiano–Fitzgerald random walk filter proved to be a promising approach to be applied for time series fault forecasting. The proposed method was shown to be more promising than the original ensemble bootstrap aggregation model and the long short-term memory. [ABSTRACT FROM AUTHOR]

Effective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of resources. Survival models can be used to predict survival probabilities in such trials. However, survival data from clinical trials are sparse, and DeepSurv cannot accurately capture their effective features, making the models weak in generalization and decreasing their prediction accuracy. In this paper, we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder (DAE) and DeepSurv models. The DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training, and then the robust features are provided to DeepSurv as input for training. The clinical trial dataset for training the model was obtained from the ClinicalTrials.gov dataset. A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant women. The experimental results showed that the denoising autoencoder and deep survival regression (DAE‐DSR) model was able to extract meaningful and robust features for survival analysis; the C‐index of the training and test datasets were 0.74 and 0.75 respectively. Compared with the Cox proportional hazards model and DeepSurv model, the survival analysis curves obtained by using DAE‐DSR model had more prominent features, and the model was more robust and performed better in actual prediction. [ABSTRACT FROM AUTHOR]

In response to the challenges of weak fault characteristics and complex and variable working conditions of roll-ing bearings in strong noise environments, a diagnostic methodology is proposed. This method integrates a multi-scale residual neural network (MResNet) and a long short-term memory (LSTM) neural network to achieve fault diagnosis of bearings under both constant and variable rotational speed working conditions. First, complete an ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that is applied for vibration signal denoising. Secondly, a dropout layer is introduced between multi-scale residual blocks to prevent net-work overfitting, and the powerful time series information capture ability of LSTM is combined to improve di-agnostic accuracy. Finally, experimental verification is conducted using constant and variable speed bearing data sets. The results show that the proposed approach maintains strong diagnostic capabilities when the rotat-ing speed conditions change, and the signal is heavily polluted by noise. [ABSTRACT FROM AUTHOR]

As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration. [ABSTRACT FROM AUTHOR]

This study developed a noise-reduction method for acoustic logging array signals using a deep neural network algorithm in the time-frequency domain. Initially, we derived analytical solutions for the received waveforms when the acoustic logging tool was positioned either at the centre or eccentrically within the borehole. To simulate the received waveforms across various formations, we developed a real-axis integration algorithm. Subsequently, we devised a noise-reduction algorithm workflow based on a convolutional neural network and configured the structure and parameters of the U-net using TensorFlow. To address the scarcity of open datasets, we established both signal and noise datasets. The signal dataset was generated using theoretical simulation encompassing various model parameters, while the noise dataset was collected during tool testing and downhole operations. The trained model demonstrated substantial noise-reduction capabilities during validation. To validate the effectiveness of the algorithm, we applied noise reduction to actual data collected during downhole operations in a TangGu oil field, yielding impressive results across different types of noisy data. Therefore, the U-net-based time-domain noise-reduction algorithm proposed in this paper holds the potential to significantly improve the quality of acoustic logging array signals. [ABSTRACT FROM AUTHOR]

The emerging distributed fiber-optic acoustic sensing (DAS) technology has broad prospects for application in vertical seismic profiles (VSP). However, the acquired DAS-VSP data often suffers from coupled noise that seriously affects data quality. Traditional methods for suppressing coupled noise are usually time-consuming and not suitable for the large-scale denoising of DAS-VSP data. To address this, a coupled noise suppression method based on the U-Net network is proposed, and a self-attention (SA) block is introduced to enhance the denoising ability of the network. Transfer learning is employed to achieve coupled noise suppression from synthetic data to field data. Denoising results demonstrate that the network can effectively suppress coupled noise in DAS-VSP data while preserving signal energy to a certain extent, exhibiting strong generalization capability. Upon completion of network training, denoising results can be obtained within seconds, making it more convenient and efficient compared to traditional methods. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input signals into compact feature representations and a decoder that reconstructs the cleaned signals. The proposed architecture was tested using the MIT-BIH Noise Stress Test Database, which includes ECG recordings with simulated noise conditions. Performance evaluation metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Mean Absolute Deviation (MAD) were used to assess the model's effectiveness. Results showed a low MSE of 0.034, a high SNR of 15.8 dB, and a MAD of 0.754, indicating significant noise reduction and high-quality signal reconstruction. These findings demonstrate that the 1D CNN architecture effectively reduces various types of noise in ECG signals, thereby enhancing signal quality and facilitating more accurate analysis and diagnosis. The model's ability to maintain the integrity of crucial ECG features while removing noise suggests its potential utility in clinical applications for improving cardiovascular disease diagnosis. [ABSTRACT FROM AUTHOR]

Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target's high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition monitoring and feature extraction techniques. Firstly, the integration of CEEMDAN addresses modal aliasing and intermittent signal challenges, while the proposed low-pass filtering method autonomously selects valuable signal components. Additionally, the application of the WT in the unselected components enhances the extraction of useful information, presenting a unique and advanced approach to electrostatic signal denoising. Moreover, the proposed method is applied to simulated signals with different input signal-to-noise ratios and experimental fault electrostatic signals of a micro-turbojet engine. The comparison with several traditional approaches in a denoising test for the simulated signals and experimental signals reveals that the proposed method performs better in extracting the effective components of the signal and eliminating noise. [ABSTRACT FROM AUTHOR]

For guaranteeing the safe and effective functioning of aircraft, image processing techniques can be a valuable tool to detect and evaluate aircraft panel values. In the pursuit of this objective, a dataset covering multiple aircraft models, various sessions, and different lighting conditions was compiled. Four tasks were examined through comparative analysis: object detection, display classification, needle masking, and needle angle detection. YOLOv8 demonstrated high performance in object detection and classification. In the classification task, the adaptability of needle-type device reading was examined by using the well-established models VGG16, Mobilenet V2, and Xception. Denoising autoencoder, U-net, and GrabCut methods were examined for needle masking, and the least squares method was applied to detect needle angle. As we move from the proof-of-concept phase to envisioning the development of an end-to-end system, this work provides significant analysis of image processing methodologies for reading aircraft dashboards. [ABSTRACT FROM AUTHOR]

Distributed optical fiber acoustic sensing (DAS) technique has been applied in pipeline health monitoring, and the commonly used sensor is phase-sensitive optical time domain reflectometry. Most DAS monitoring systems can localize leakages of a pipeline but fail to identify potential non-destructive damages like bolt looseness on joints before the leakage occurs. An early damage identification is indispensable to averting severe leakages and secondary disasters. In this study, an optical phase mode analysis method is proposed for identifying pipeline bolt looseness. This method combines structure mode analysis and distributed optical phase demodulation to extract damage-related phase mode parameters. Two algorithms are specially designed for denoising and selecting signals essential for mode analysis. Phase time histories are retrieved from the original optical phase, which are decomposed to acquire phase mode shapes that can localize bolt looseness through Hilbert-Huang transform enhanced with bandwidth restricted empirical mode decomposition. Phase damping ratio is proposed to further quantify the looseness degree. Polarization diversity technique is employed to avoid polarization fading. An experiment was conducted upon a 3.2 m steel pipeline with flange joints. Bolt looseness on three joints are respectively localized even if only one bolt is loosened, obtaining a localization error of 0.07 m and 85.7% recognition ratio. The phase damping ratio shows apparent positive correlation with the number of loose bolts. The error of quantified loose bolt number is 0.79. The present study demonstrates how to localize and quantify pipeline bolt looseness through dynamical mode analysis for distributed optical phase. The developed method can identify potential damages that change the mechanical properties of a pipeline before they get severe, and holds promise in the long-distance health monitoring of other structures. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method's denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals. [ABSTRACT FROM AUTHOR]

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals. Second, based on the peak value properties, a novel health index (HI) is designed to analyze the health conditions for the denoising signal, showing a high sensitivity for the degradation of bearings. Finally, for better prognostics and health management of the rotating machinery, based on convolutional layers and LSTM, an autoencoder can achieve a transform convolutional LSTM network to develop a convolutional LSTM autoencoder (ALSTM) model that can be applied to forecast the health trend for rotating machinery. Compared with the SVM, CNN, LSTM, GRU, and DTGRU methods, our experiments demonstrate that the proposed approach has the greatest performance for the prediction of the remaining useful life of rotating machinery. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) pacemakers are sophisticated medical devices that deliver electrical signals to targeted areas of the brain via implanted electrodes, effectively regulating abnormal brain activity and relieving symptoms of treatment-resistant neurological disorders. However, proximity to other electromagnetic equipment may introduce additional noise, which can be disruptive to individuals. To mitigate this issue, we propose a novel polymer-based nanocomposite for pacemakers for signal denoising. This research focused on the development and analysis of nanocomposites comprising polypropylene (PP) combined with montmorillonite nanoclay and graphene nanosheets (GNs). The nanocomposites were created by blending them through melting, using varying ratios of clay to GNs, with a total loading of 4 wt.%. This study focused on enhancing the signal-to-noise ratio for brain pacemakers by using nanocomposites. It investigated the noise reduction properties of PP nanocomposites, specifically in the outlet gate of the pacemaker. This research aimed to find the ideal ratio of clay to GNs in the PP matrix. X-ray diffraction (XRD) and differential scanning calorimetry (DSC) were conducted to analyze the crystalline structure and filler dispersion, as well as thermal behavior and filler–matrix interactions in the material. Scanning electron microscopy was employed to observe the dispersion of the nanofillers in the PP, and sound tube testing was conducted to evaluate the noise levels of the composites. The findings indicated that a porous structure of the nanocomposite with dispersed microspheres within the PP matrix and a long pathway facilitated increased dissipation of acoustic waves, making it suitable for denoising in brain pacemakers. Furthermore, the nanocomposite containing 2.75 wt.% of nanoclay and 1.25 wt.% of graphene components within the polypropylene matrix demonstrated a favorable signal-to-noise ratio compared to other evaluated nanocomposites. [ABSTRACT FROM AUTHOR]

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop. [ABSTRACT FROM AUTHOR]

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis. Thus, denoising needs to be utilized as an essential step of vibration signal processing. Traditional denoising methods need expert knowledge to select hyperparameters. And data-driven methods based on deep learning lack interpretability and a clear justification for the design of architecture in a "black-box" deep neural network. An approach to systematically design neural networks is by unrolling algorithms, such as learned iterative soft-thresholding (LISTA). In this paper, the multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived by embedding a designed multi-layer sparse coder to the convolutional extension of LISTA. Then the multi-layer convolutional dictionary learning (ML-CDL) network for mechanical vibration signal denoising is proposed by unrolling ML-CLISTA. By combining ML-CDL network with a classifier, the proposed denoising method is applied to the explainable rolling bearing fault diagnosis. The experiments on two bearing datasets show the superiority of the ML-CDL network over other typical denoising methods. • The multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived. • The multi-layer convolutional dictionary learning network is proposed. • The proposed method is applied to the explainable rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

The primary reason most people invest in stocks is the potential return compared to alternatives such as bank certificates of deposit, gold, and Treasury bonds. This requires accurate information about the stock market, price changes and predicting future trends. The main purpose of this study is to present a method based on wavelet denoising and dynamic time warping to identify the stock price pattern in the Tehran Stock Exchange. Instead of focusing and summarizing different and numerous methods to predict stock prices, this research concentrates on neural networks and wavelet denoising, and dynamic time warping to identify the stock price patterns. This methodology has been approved by researchers as a new effective technique. In this regard, first, using the wavelet denoising preprocessing step, noise is removed from the stock price time series, and then the extracted data was used as input to the dynamic time warping prediction model. MATLAB software version 9.11 was used to analyze the research data. The statistical population of the present study includes 3 shares among the shares of steel industry companies of Tehran Stock Exchange. The research was conducted in the period 2016 to 2020. The results show that the predictions obtained from the dynamic time warping method equipped with the wavelet denoising preprocessing step in comparison with the predictions obtainedfrom the dynamic time warping method without the wavelet denoising preprocessing step in the sample, have been associated with much less accuracy and error. [ABSTRACT FROM AUTHOR]

Controlled-source audio frequency magnetotellurics (CSAMT) is an artificial-source electromagnetic technique that partially mitigates the limitations of weak natural-field signals. However, practical field surveys inevitably encounter strong interference, severely affecting signal quality. Traditional methods such as Fourier transformation, which directly computes apparent resistivity from frequency-domain information, are inadequate in this context, so we need alternative denoising approaches. However, research on CSAMT denoising is currently limited. Given the excellent performance of long short-term memory (LSTM) neural networks in the processing of magnetotelluric (MT) data, as demonstrated by previous studies, this paper proposes the use of LSTM to denoise CSAMT signals in the time domain. Unlike traditional denoising methods, we aim to directly extract the target frequency signal from the time-series data for denoising. For MT data, target frequency signals and noise are all mixed together, so noise suppression can only be achieved by identifying noise characteristics in the time series. However, unlike MT data, CSAMT data has an artificial transmitting source, and the frequency of the valid signal is fixed within a time interval. This allows for the direct extraction of target frequency signals without considering the complex characteristics of noise. In this study, we developed a neural network based on bidirectional LSTM to accomplish the task of noise suppression. After conducting both simulated and measured data tests, this method was able to, on average, improve the signal-to-noise ratio (SNR) of CSAMT data by approximately 20 dB and partially address the challenge of denoising when the data's SNR falls below 0 dB. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance. [ABSTRACT FROM AUTHOR]

Purpose: The cutting tool wear is one of the major physical phenomena to be studied in order to optimize the production and to guarantee the quality of manufactured products. Indeed, the wear affects the quality of the machined surfaces, the durability of the cutting tool and the imposed geometric tolerances. Since uncontrolled wear can lead to premature tool breaking and therefore a drop in productivity, monitoring the machining process is a necessary important task. Methods: In this study, aims to combine experimental results from vibration signature and cutting forces with experimental and numerical methodology allowing the prediction of optimal lifetime of the tool's wear, and to detect the brutal damage of this last, based especially an optimized wavelet multi-resolution analysis (OWMRA), allowed the denoising of the measured signals. Results: The OWMRA revealed two peaks, which appear below and above the tool's resonance frequency. The amplitude evolution of these two peaks is directly related to the effect of the tool wear on the natural frequency band. The strategy adopted is based on a slight modification of a new spectral indicator and in particular the modified overall level (OLmod) calculated from the cutting forces and reconstructed signals. Conclusions: The modified overall level (OLmod) has also shown its effectiveness of providing the moment of transition of the wear of its normal phase to accelerated phase around the band of the cutting tool characteristic frequencies, corresponding to catastrophic wear leading to stopping machining at the appropriate time and which can alert the user as soon as the criterion of the transition to catastrophic wear is reached. [ABSTRACT FROM AUTHOR]