Search Results (270)

Traditional ultrasonic bolt stress measurement is hindered by high power consumption. Lowering excitation voltage reduces power but degrades signal-to-noise ratio (SNR), compromising accuracy. This paper proposes a synergistic algorithm combining Empirical Mode Decomposition (EMD) with Adaptive Threshold Wavelet Denoising (ATWD). The method preserves transient features by reconstructing high-frequency components via EMD, then suppresses noise by precisely processing low-frequency components using ATWD. Finally, cross-correlation estimates ultrasonic delay. Evaluated at excitation voltages from 12 V to 0.5 V, the EMD-ATWD method maintains measurement errors below 10% even at 0.5 V, improving accuracy by over 48% compared to conventional Finite Impulse Response (FIR) and Threshold Wavelet Denoising (WTD) methods, while enhancing key echo waveform fidelity by over 35%. This method provides a reliable low-power bolt stress monitoring idea for engineering applications. Full article

Non-stationary, multi-component vibration signals in rotating machinery are easily contaminated by strong background noise, which masks weak fault features and degrades diagnostic reliability. This paper proposes a joint denoising method that combines an improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and an improved wavelet thresholding scheme. ICFO, enhanced by Chebyshev chaotic initialization, a longitudinal–transverse crossover fusion mutation operator, and a thinking innovation strategy, is used to adaptively optimize the SVMD penalty factor and number of modes. The optimized SVMD decomposes the noisy signal into intrinsic mode functions, which are classified into effective and noise-dominated components via the Pearson correlation coefficient. An improved wavelet threshold function, whose threshold is modulated by the sub-band signal-to-noise ratio, is then applied to the effective components, and the denoised signal is reconstructed. Simulation experiments on nonlinear, non-stationary signals with different noise levels (SNR = 1–20 dB) show that the proposed method consistently achieves the highest SNR and lowest RMSE compared to VMD, SVMD, VMD–WTD, CFO–SVMD, and WTD. Tests on CWRU bearing data and gearbox vibration signals with added −2 dB Gaussian white noise further confirm that the method yields the lowest residual variance ratio and highest signal energy ratio while preserving key fault characteristic frequencies. Full article

Early fault vibration signals of rolling bearings are non-stationary and nonlinear, with weak fault signatures easily masked by noise. Traditional denoising methods (e.g., wavelet thresholding, empirical mode decomposition (EMD)) struggle to accurately extract effective features. Although variational mode decomposition (VMD) overcomes mode mixing, its core parameters rely on empirical selection, making it prone to local optima and limiting its denoising performance. To address this critical issue, this study aims to propose a hybrid model with adaptive parameter optimization and efficient denoising capabilities, enhancing the signal-to-noise ratio (SNR) and feature discriminability of early fault signals in rolling bearings. The novelty of this work is reflected in three aspects: (1) An improved edge strategy whale optimization algorithm (IEWOA) is proposed, incorporating six enhancements to balance global exploration and local exploitation. Using the minimum average envelope entropy as the objective function, the IEWOA achieves adaptive global optimization of VMD parameters. (2) A hybrid framework of “IEWOA-VMD + dataset-specific wavelet thresholding for secondary denoising” is constructed. The optimized VMD first decomposes signals to separate noise and effective components, followed by secondary denoising, ensuring both adaptable signal decomposition and precise denoising. (3) Comprehensive validation is conducted across five models using two public datasets (Case Western Reserve University (CWRU) and Paderborn Universität (PU)). Key findings demonstrate that the proposed method achieves a root-mean-square error (RMSE) as low as 0.00013–0.00041 and a Normalized Cross-Correlation (NCC) of 0.9689–0.9798, significantly outperforming EEMD, traditional VMD, and VMD optimized by single algorithms. The model effectively suppresses noise interference, preserves the fundamental and harmonic components of fault features, and exhibits strong robustness under different loads and fault types. This work provides an efficient and reliable signal preprocessing solution for early fault diagnosis of rolling bearings. Full article

To mitigate the pervasive noise interference present in the measured vibration signals of radial steel gates and to address the limitations of conventional wavelet-threshold denoising, this study proposes a coupled “decomposition–denoising” theoretical framework for vibration-signal purification. The key novelty lies in a smooth and tunable thresholding strategy that enables controlled filtering while preserving key structural characteristics within an integrated denoising workflow. In the proposed approach, the measured signal is decomposed into intrinsic mode components using a data-driven decomposition method, noise-dominated components are identified using multiscale permutation entropy, and only these components are selectively denoised before signal reconstruction. Both qualitative and quantitative analyses conducted on synthetic signals demonstrate the effectiveness of the proposed framework and confirm the enhanced smoothness and robustness of the improved thresholding scheme. Performance is evaluated using objective measures such as signal-to-noise ratio and root-mean-square error, together with spectral-consistency checks for field measurements. Furthermore, two field-measured engineering cases involving radial steel gates substantiate the engineering applicability and generalization capability of the proposed method, showing clearer signals and more stable diagnostic-relevant indicators. Finally, the study integrates the decomposition, denoising, and parameter-selection modules into a user-oriented vibration-signal denoising system, establishing an efficient workflow for engineering signal processing and subsequent structural-health monitoring applications. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

In this paper, we present a novel method for background estimation and updating in video sequences, utilizing an innovative approach that combines an intelligent truncated mean, the stationary wavelet transform (SWT), and advanced thresholding techniques. This method aims to significantly enhance the accuracy of moving object detection by mitigating the impact of outliers and adapting background estimation to dynamic scene conditions. The proposed approach begins with a robust initial background estimation, followed by moving object detection through frame subtraction and gamma correction. Segmentation is then performed using SWT, coupled with adaptive thresholding methods, including hard and soft thresholding. These techniques work in tandem to effectively reduce noise while preserving critical details. Finally, the background is selectively updated to integrate new information from static regions while excluding moving objects, ensuring a precise and robust detection system. Experimental evaluation on the CDnet 2014 and SBI 2015 datasets demonstrates that the proposed method improves the F1 score by 12.5 percentage points (from 0.7511 to 0.8765), reduces false positives by up to 65%, and achieves higher PSNR values compared to GMM_Zivk, SuBSENSE, and SC_SOBS. These results confirm the robustness of the hybrid approach based on truncated mean and SWT in dynamic and challenging environments. Full article

In response to the safety threats posed by unauthorized unmanned aerial vehicles (UAVs), the importance of anti-UAV systems is becoming increasingly apparent. In tasks involving UAV detection, small UAVs are particularly difficult to detect due to their low resolution. Therefore, this study proposed YOLO-CoOp, a lightweight multi-module collaborative optimization framework for detecting small UAVs. First, a high-resolution feature pyramid network (HRFPN) was proposed to retain more spatial information of small UAVs. Second, a C3k2-WT module integrated with wavelet transform convolution was proposed to enhance feature extraction capability and expand the model’s receptive field. Then, a spatial-channel synergistic attention (SCSA) mechanism was introduced to integrate spatial and channel information and enhance feature fusion. Finally, the DyATF method replaced the upsampling with Dysample and the confidence loss with adaptive threshold focal loss (ATFL), aiming to restore UAV details and balance positive–negative sample weights. The ablation experiments show that YOLO-CoOp achieves 94.3% precision, 93.1% recall, 96.2% mAP⁵⁰, and 57.6% mAP⁵⁰⁻⁹⁵ on the UAV-SOD dataset, with improvements of 3.6%, 10%, 5.9%, and 5% over the baseline model, respectively. The comparison experiments demonstrate that YOLO-CoOp has fewer parameters while maintaining superior detection performance. Cross-dataset validation experiments also demonstrate that YOLO-CoOp exhibits significant performance improvements in small object detection tasks. Full article

FY-4B/GIIRS (Geostationary Interferometric Infrared Sounder) is a new-generation infrared hyperspectral atmospheric vertical sounder onboard a Chinese geostationary meteorological satellite. Its observations with high spatial and temporal resolution play an important role in high-impact weather forecasts. The GIIRS data assimilation module is developed in the GSI (Gridpoint Statistical Interpolation) assimilation system. Super Typhoon Doksuri in 2023 (No. 5) is taken as an example based on this module in this paper. Firstly, the sensitivity of analysis fields to five data thinning schemes at four daily assimilation times from 22 to 28 July 2023 is analyzed: the wavelet transform modulus maxima (WTMM) scheme, the grid-distance schemes of 30 km, 60 km, and 120 km in the GSI assimilation system, and a center field of view (FOV) scheme. Taking the ERA5 reanalysis fields as true, it is found that the mean error of temperature and humidity analysis for the WTMM scheme is the smallest, followed by the 120 km thinning scheme. Subsequently, a 72 h cycling assimilation and forecast experiments are conducted for the WTMM and 120 km thinning schemes. It is found that the root mean square error (RMSE) profiles of temperature and humidity forecast fields with no thinning scheme are the largest at all pressure levels and forecast times. The temperature forecast error decreases after data thinning at altitudes below 300 hPa. Since the WTMM scheme has assimilated more observations than the 120 km scheme, the accuracy of its temperature and humidity forecast fields gradually increases with the forecast time. In terms of typhoon track and intensity forecast, the typhoon intensities are underestimated before landfall and overestimated after landfall for all thinning schemes. As the forecast time increases, the advantage of the WTMM is increasingly evident, with both the forecast intensity and track being closest to the actual observations. Similarly, the forecasted 24 h accumulated precipitation over land is overestimated after typhoon landfall compared with the IMERG Final precipitation products. The location of precipitation simulated by no thinning scheme is more westward overall. The forecast accuracy of the locations and intensities of severe precipitation cores and the typhoon’s outer spiral rain bands over the South China Sea has been improved after thinning. The Equitable Threat Scores (ETSs) of the WTMM thinning scheme are the highest for most precipitation intensity thresholds. Full article

To enhance the denoising performance of satellite signals at a low signal-to-noise ratio (SNR), we propose an improved wavelet threshold denoising correlation (IWTDC) method that utilizes wavelet decomposition to analyze the received satellite signals. By dynamically selecting the threshold based on the transformation of the wavelet coefficients of each layer, we reconstruct the signal using processed coefficients for effective denoising. To validate the denoising effect of the proposed IWTDC method, we compare it with methods such as traditional cross-correlation, adaptive filtering correlation, and wavelet threshold correlation. Simulation results indicate that compared to other methods, SNR can be improved by up to 5 dB using the IWTDC method, leading to superior signal correlation and enhanced detection performance for the related peaks. Furthermore, experimental data collected during the study were consistent with the simulation results, confirming the performance of the IWTDC method. Thus, our proposed method can overcome the limitations of using traditional threshold functions through adaptive threshold selection, preserve signal details, and significantly improve the denoising performance. Full article

This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods—including SWT, DTCWT, and hybrid approaches—across multiple metrics. It achieves a ΔSNR improvement of 1.68–10.00 dB, ΔSINAD improvement of 1.68–9.98 dB, RMSE reduction of 0.02–0.56, and PRD reduction of 2.88–183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems. Full article

Non-contact heart rate estimation technology based on frequency-modulated continuous wave (FMCW) radar has garnered extensive attention in single-target scenarios, yet it remains underexplored in multi-target environments. Accurate discrimination of multiple targets and precise estimation of their heart rates constitute key challenges in the multi-target domain. To address these issues, we propose a novel scheme for multi-target heart rate estimation. First, a high-precision distance-bin selection (HDBS) method is proposed for target localization in the range domain. Next, multiple-input multiple-output (MIMO) array processing is combined with the Root-multiple signal classification (Root-MUSIC) algorithm for angular domain estimation, enabling accurate discrimination of multiple targets. Subsequently, we propose an efficient method for interference suppression and vital sign extraction that cascades variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) termed as VLW, which enables high-quality heartbeat signal extraction. Finally, to achieve high-precision and super-resolution heart rate estimation with low computational burden, an improved fast iterative interpolated beamforming (FIIB) algorithm is proposed. Specifically, by leveraging the conjugate symmetry of real-valued signals, the improved FIIB algorithm reduces the execution time by approximately 60% compared to the standard version. In addition, the proposed scheme provides sufficient signal-to-noise ratio (SNR) gain through low-complexity accumulation in both distance and angle estimation. Six experimental scenarios are designed, incorporating densely arranged targets and front-back occlusion, and extensive experiments are conducted. Results show this scheme effectively discriminates multiple targets in all tested scenarios with a mean absolute error (MAE) below 2.6 beats per minute (bpm), demonstrating its viability as a robust multi-target heart rate estimation scheme in various engineering fields. Full article

Field verification of contact-type ultrasonic sensors enables rapid evaluation of their sensitivity performance, thereby ensuring the accuracy of partial discharge (PD) ultrasonic monitoring results. However, during the verification process, both the standard sensor and the sensor under testing are inevitably affected by ambient noise when receiving verification signals, which can result in significant errors in the verification outcome. To address this issue, a noise suppression method is proposed in this study, which integrates multivariate variational mode decomposition (MVMD) optimized by an improved particle swarm optimization (IPSO) algorithm with a hyperbolic tangent-modulated exponential decay wavelet thresholding technique. First, the IPSO algorithm is employed to automatically optimize the parameters of MVMD. Then, the dominant components of the verification signal are selected based on the energy entropy of each decomposed mode. Subsequently, a novel wavelet threshold function incorporating hyperbolic tangent modulation and exponential decay is constructed and combined with an improved thresholding strategy to denoise the residual noise in the dominant components. Finally, a verification platform based on a real-type transformer is established. Both simulated and measured signals are denoised and subjected to sensitivity verification using the proposed method. Comparative analysis with noise-affected verification results demonstrates that the proposed method effectively suppresses noise in the verification signals and improves the accuracy of the sensitivity verification. Full article

Impedance flow cytometry (IFC) enables label-free, real-time characterization of cells and particles, but its performance depends critically on accurate event detection and feature extraction under varying noise and acquisition conditions. Conventional pipelines typically rely on multi-stage thresholding, wavelet transforms, template-based correlation methods, or neural-network models. These approaches generally require additional preprocessing steps and involve multiple parameters or hyperparameter tuning. In this work, we present a simple derivative-based signal processing framework that enables baseline-drift suppression, event detection, and feature extraction within a single computational step. The derivative approach improved precision and recall by approximately 20% and reduced the false discovery rate by 15–25% compared with simple thresholding, while requiring only 22–55% of the processing time across all the test conditions. The algorithm operates in linear time with minimal memory overhead and does not rely on template matching or trained parameters, making it well-suited for real-time or embedded, resource-constrained IFC platforms. We further demonstrate that derivative-extracted features enable accurate real-time classification of microparticles, achieving >98% accuracy while maintaining a processing speed that is approximately two orders of magnitude faster than the data-acquisition rate. Full article

To address the issue of weak geoelectric current field signals that are severely affected by noise and cannot be directly used for geological structure analysis in injected geoelectric current field detection technology, this study proposes a complete ensemble empirical mode decomposition with adaptive noise and improved wavelet thresholding collaborative denoising (CEEMDAN-IWT) method to enhance the interpretation accuracy of geoelectric current signals. The method performs signal decomposition through CEEMDAN and selects the effective intrinsic mode function (IMF) components based on the variance contribution criterion for preliminary denoising. It then combines the improved wavelet thresholding function for further fine denoising and reconstruction, obtaining high signal-to-noise ratio (SNR) electrical data. Simulation and real-world data validation show that in a simulation experiment with an initial SNR of −5 dB, the method improves the $SNR$ to 18.65 dB, and the SNR enhancement is superior to traditional methods under various noise intensities. In practical applications, the normalized cross-correlation (NCC) between the denoised signal and the original injected signal reaches as high as 0.9254, significantly outperforming traditional methods. At the same time, it balances the preservation of signal features with noise suppression, offering significant application value for improving the reliability of injected geoelectric current field detection data. Full article

Infrasound leakage signals, with low propagation energy loss, are ideal for long-distance and small leakage detection but suffer severe background noise interference. Existing wavelet denoising methods using traditional soft/hard threshold functions face critical limitations: soft thresholds introduce constant deviation, while hard thresholds cause discontinuities, both leading to suboptimal noise reduction for infrasound signals—this gap hinders accurate leakage detection. To address this, we propose a wavelet denoising method with an improved threshold function, analyze its process via the Mallat algorithm, and prove its continuity and convergence. Comparative experiments on infrasound leakage data show that, at the optimal decomposition level, our method reduces RMSE by 41.19% and increases SNR by 5.1326 dB compared to the soft threshold method; versus the hard threshold method, RMSE decreases by 34.65% and SNR increases by 4.2148 dB. It also separates background noise more thoroughly in time–frequency domains, demonstrating strong feasibility for pipeline infrasound leakage detection. Full article