Noise Suppression Research Articles

Bearing fault diagnosis by sparse frequency spiral spectrum driven NAF-LDM under strong noise and small samples

Abstract The complexity of background noise and the scarcity of real fault samples seriously affect the diagnostic accuracy of the model. To address this, a noise-robust two-dimensional feature map, the sparse frequency spiral spectrum (SFSM), based on sparse representation theory, is proposed. A bridge penalty coefficient is applied to the sparse representation model to accurately select impact components, and the fast iterative shrinkage threshold algorithm is used to solve for sparse representation coefficients. Sparse reconstructed signals are obtained by convolving the impact patterns with these coefficients, leading to a sparse reconstruction algorithm with reduced computational complexity. Furthermore, the novel non-linear activation-free blocks (NAF Blocks) are embedded into the latent diffusion model to augment small samples, significantly improving image generation speed and quality. The integration of the Swin transformer for feature extraction and classification further enhances diagnostic performance. The superiority of this method is validated on the XJTU-SY dataset, a bearing experimental platform dataset, and enterprise engineering dataset. Experimental results demonstrate that the structural and generalization advantages of NAF Blocks are crucial for improving image quality and inference speed. The noise suppression capability of the proposed method, facilitated by the SFSM feature processing technique, is confirmed through ablation and noise robustness tests. Finally, the Swin transformer’s excellent feature extraction and classification capabilities for SFSM are verified. The proposed method achieves diagnostic accuracies of 99.10% and 98.7% on the XJTU-SY and experimental platform datasets, respectively.

Detection of optic disc in human retinal images utilizing the Bitterling Fish Optimization (BFO) algorithm

Early detection and correct identification of the optic disc (OD) on scanned retinal images are significant for diagnosing and treating several ophthalmic conditions, including glaucoma and diabetic retinopathy. Conventional methods for detecting the OD often struggle with processing retinal images due to noise, changes in illumination, and complex overlapping images. This study presents the development of effective and accurate fixation of the optic disc using the Bitterling Fish Optimization (BFO) algorithm, which enhances the processes of OD imaging in speed and precision. The proposed method begins with image enhancement and noise suppression for preprocessing, followed by applying the BFO algorithm to locate and delineate the OD region. The performance evaluation of the algorithm was conducted within several public domain retinal images, including DRIVE, STARE, ORIGA, DRISHTI-GS, DiaRetDB0, and DiaRetDB1 datasets about some internal metrics: sensitivity (SE), specificity (SP), accuracy (ACC), DICE overlap coefficient, overlap and time of processing respectively. The technique based on BFO provided better results, with 99.33%, 99.94%, and 98.22% accuracy achieved for OD in DRIVE, DRISHTI-GS, and DiaRetDB 1, respectively. The approach also demonstrated high overlaps and good DICE results, with a DICE coefficient of 0.9501 for the DRISHTI-GS database. On average, the processing time per image was less than 2.5 s, proving the approach’s efficiency in computations. The BFO approach has demonstrated its effectiveness and scalability in detecting optic discs in retinal images in an automated manner. It showed impressive performance levels in terms of computation time and accuracy and was variation resistant irrespective of the quality of the image and the pathology present on it. This method holds significant potential for clinical use, providing a meaningful way of diagnosing and managing ocular disease at an early stage.

Multifunctional GAN-based optimization for X-ray tomography under different conditions

Based on the generative adversarial network (GAN), we present a multifunctional X-ray tomographic protocol for artifact correction, noise suppression, and super-resolution of reconstruction. The protocol mainly consists of a data preprocessing module and multifunctional GAN-based loss function simultaneously dealing with ring artifacts and super-resolution. The experimental protocol removes ring artifacts and improves the contrast-to-noise ratio (CNR) and spatial resolution (SR) of reconstructed images successfully, which shows the capability to adaptively rectify ring artifacts with varying intensities and types while achieving super-resolution. Compared with the main existing deep learning models or conventional tomographic correction methods, it also enables higher processing speed and minimal information loss, especially for images of smaller dimensions. This study provides a robust optimization tool for the equivalent realization of large fields of view and high-resolution X-ray tomography. The experimental datasets were collected from a series of X-ray cone-beam computed tomography scans of biological samples.

Radon-domain acoustic and elastodynamic interferometric redatuming of VSP data

Summary The virtual source method (VSM) is a useful tool for imaging and monitoring below complex and time-varying overburden. When it is applied to VSP geometries, the redatumed virtual source data often suffer from artifacts and aliasing due to the violation of theoretical acquisition conditions and partial focusing of virtual multiples, which further degrade seismic imaging quality. While the conventional Radon-domain VSM (R-VSM) mitigates these issues to some extent, it is still possible to improve upon the imaging. This study develops a simple and effective high-resolution Radon-domain VSM (HR-VSM) to effectively address these issues, offering superior noise and artifact suppression compared to traditional VSM and R-VSM. HR-VSM shows a strong performance across a wide range of VSP configurations and data types, especially in applications to multi-component and passive seismic data, demonstrating its versatility and effectiveness in seismic exploration and showing potential for use in earthquake seismology. To extend subsurface illumination, we combined correlation-type and convolution-type HR-VSM for automatically redatuming virtual surface seismic profile (SSP) shot gathers with a complete receiver array covering all surface source locations, without the additional pre-processing steps typically required by VSM. It was also tested on reverse VSP (RVSP) synthetic active data and passive seismic data, proving effective in constructing virtual shot gathers and showing potential for extending its use to earthquake data and ambient-noise seismology. Moreover, HR-VSM can perform elastodynamic interferometric redatuming, enabling the redatuming of pure P-P and pure P-SV waves from multi-component VSP data. We applied HR-VSM to redatum virtual single well profile (SWP) data containing fault-related P-P and P-SV reflections from VSP data, which were further used for fault imaging. Finally, HR-VSM was used to generate virtual crosswell data with receivers in both boreholes, eliminating the need for downhole sources. The obtained virtual crosswell containing direct waves, as well as P-P and P-SV reflections, enable constructing a velocity model and imaging the structure between wells.

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

A novel wafer defocus measurement method for spot-scanning imaging system using laser triangulation

Abstract For spot scanning dark-field scattering technology, the defocus caused by the change in wafer surface height decreases the defect detection rate and size measurement repeatability. The demand for accurately and rapidly measuring the wafer surface height online is becoming increasingly urgent. The defect detection system integrates scattering, reflection, film thickness, and topography defect detection channels. The integration of laser-triangulation-based defocus measurement into the bright-field optical path is proposed. The laser beam is directed onto the surface of the wafer, and the reflected light is transmitted through a lens onto the photosensitive surface of a four-quadrant detector. This detector captures both the strength and position of the reflected signal simultaneously. In this paper, a triangulation-modified model is established to obtain the height of each inspection spot on the wafer surface through the position signal of the reflected image. When the signal-to-noise ratio of the simulated reflection image position is 20 dB, the signal-to-noise ratio of the height measurement resolved by the triangulation-modified model is 41 dB, and the measurement error is less than 0.2 μm, indicating that the model has a noise suppression effect. The spot-scanning imaging system is established, and the height measurement results of the triangulation measurement model are verified using a laser interferometer. Within the travel range of 69 μm, the system has a measurement accuracy better than ±1 μm and a measurement repeatability better than ±0.2 μm, which meet the measurement requirements of the spot-scanning imaging system. The system is utilized for defect detection and defocus measurement of the wafer surface, along with the motion mechanism, to accurately and rapidly obtain the height distribution of the wafer surface.

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution.

In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. These data are not only contaminated by various types of noise but also exhibit fault features that vary across different scales. Consequently, the effective suppression of extraneous noise and accurate extraction of multi-scale fault features are crucial issues. To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. The MFRANet employs a multi-scale deep separable convolution module to thoroughly investigate the fundamental characteristics of the original vibration signals in both the time and time-frequency domains. To enhance the detailed analysis of diagnostic data and mitigate the risks of overfitting and noise interference, an efficient residual channel attention module is incorporated to weight and denoise the feature maps. Additionally, an external attention module is introduced to create implicit connections between the denoised multi-scale feature maps and to highlight potential correlations within the sample data, thereby improving the accuracy of fault diagnosis. Experimental evaluations on a gearbox fault dataset demonstrate that the proposed method surpasses several benchmark and state-of-the-art techniques in terms of diagnostic performance, exhibiting robust noise resilience across various noise levels. This indicates enhanced reliability and accuracy in gearbox fault diagnosis, providing an innovative and efficient solution for fault diagnosis in rotating machinery. The study underscores the contributions of artificial intelligence through the innovative structure of the method and the integration of advanced deep learning modules, while its engineering application is evidenced by addressing practical challenges in rotating machinery fault diagnosis. This work meets the urgent need for reliable diagnostic methods in industrial environments.

Improvement of Criminisi’s Stripe Noise Suppression Method for Side-Scan Sonar Images

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results.

Active disturbance rejection control for systems with uncertainties and noise using super twisting differentiator-based event-triggered ESO

This paper focuses on the noise suppression issue of high-gain extended state observer (ESO) for systems with uncertainty and unknown measurement noise in the active disturbance rejection control (ADRC) paradigm. A special combination of super twisting differentiator (STD) and event-triggered ESO is devised with a designed event-triggered mechanism. Specially, the proposed STD-ETESO incorporates a finite-time switching strategy, which improves the poor transient performance of the STD acting as a pre-filter to prepare relatively smooth signals for the ESO. The locally asymptotic boundedness of the estimation error can be guaranteed by the Input-state-stability (ISS) and existing research for the STD. The effectiveness of the proposed method is validated by a general example and a practical motion control system. Different integral criteria is introduced to evaluate the overall tracking accuracy and energy usage for comparison with existing different ESOs, which reveals the superiority of the proposed STD-ETESO in noise attenuation and disturbance rejection.

Modal analysis and structural noise control of vehicle body frame

The structural noise inside the vehicle cabin is mainly low-frequency vibration, which is closely related to the modal characteristics of the vehicle frame. The finite element method was used to simulate and calculate the body frame under free modal conditions, and the first four effective modal shapes were obtained. The calculation error of the natural frequencies was verified through modal experiments. Taking structural stiffness into account, a sound-structure coupled model was established. The suspension connection point was selected as the excitation point, and a one-way dynamic load was applied to obtain the noise and vibration responses of the front and rear rows. Based on the modal analysis results, the top-roof reinforcement scheme was adopted to verify the noise suppression effect of the structure. The results show that the optimized structure can effectively suppress structural noise, which plays an important role in improving the NVH characteristics.

An optimization of the Fourier self-calibration algorithm for improved noise suppression capability

Abstract Self-calibration emerges as a promising approach in ultra-precision field, overcoming the limitations imposed by instrument capabilities in conventional calibration. In the self-calibration research, the algorithm based on the Fourier transform firstly established a rigorous theoretical framework. However, despite advancements in alternative self-calibration methods that have improved noise suppression, the traditional Fourier algorithm still faces challenges, particularly in mitigating random measurement noise in the separation results. In this study, we introduce methods on enhancing the noise suppression capabilities of the Fourier self-calibration algorithm. We investigate the hypothesis in the traditional algorithm and find that the computation of weighted averages had minimal effects. Then, we present our method by augmenting measurement positions. Considering both the increase in the number of measurement positions and the enhancement of noise suppression capabilities, augmenting translation positions in the opposite direction is proved to be the most effective strategy. Our algorithm can effectively suppress measurement noise when one-sided point number of the artifact is below 21. While the additional data processing slightly increases runtime, it does not alter the algorithm’s computational complexity, yet it significantly improves noise suppression. The effectiveness of our method is validated through a Monte Carlo comparison of uncertainties with the original algorithm. Experiments on Fizeau interferometer further prove robustness and practical feasibility of our proposed algorithm, with the measurement repeatability being reduced by 26.34% and 25.15% in the errors separated from the stage and the artifact.

Generation of squeezed vacuum state in the millihertz frequency band

The detection of gravitational waves has ushered in a new era of observing the universe. Quantum resource advantages offer significant enhancements to the sensitivity of gravitational wave observatories. While squeezed states for ground-based gravitational wave detection have received marked attention, the generation of squeezed states suitable for mid-to-low-frequency detection has remained unexplored. To address the gap in squeezed state optical fields at ultra-low frequencies, we report on the first direct observation of a squeezed vacuum field until Fourier frequency of 4 millihertz with the quantum noise reduction of up to 8.0 dB, by the employment of a multiple noise suppression scheme. Our work provides quantum resources for future gravitational wave observatories, facilitating the development of quantum precision measurement.

Design and Analysis of Reliable and High Data Rate Multicarrier DCSK Transceiver With Index Modulation and Noise Suppression

Design and Analysis of Reliable and High Data Rate Multicarrier DCSK Transceiver With Index Modulation and Noise Suppression

Low Complexity Digital Resolution Enhancer for Clipping and Quantization Noise Suppression With Low-Resolution DAC

Low Complexity Digital Resolution Enhancer for Clipping and Quantization Noise Suppression With Low-Resolution DAC

High dynamic range land wavefield reconstruction from randomized acquisition

Compressive sensing (CS) is an alternative to regular Shannon sampling that captures similar information from reduced measurements. It relies on randomized sampling patterns and a sparse data representation to reconstruct the regularly sampled object. CS is an important ingredient in affordable seismic acquisition, which can lead to improvements in the near-surface mapping and noise suppression for land data. However, the near surface traps most of the source-generated energy, resulting in data that are rich in high-wavenumber content and have amplitudes spanning several orders of magnitude. When dealing with such high dynamic range nonstationary data, the Fourier domain is not optimal for providing a sparse representation — a necessary condition for successful application of CS. In contrast, a discrete complex wavelet transform can localize high-energy features, has good directional selectivity, and is near-shift invariant. Combined, these properties allow complex wavelets to represent detail-rich wavefields in a compact form. To leverage these features and achieve good CS reconstructions, we develop a scale- and orientation-dependent iterative soft thresholding (IST) scheme for reconstructing high dynamic range wavefields. Our approach requires little parameterization, is easy to implement, and is robust to reconstructed wavefield sampling grid and dynamic range. We test IST on different wavefields with randomly missing traces and compare the data reconstructions with the spectral projected gradient solver and projection onto convex sets. We quantify the reconstructions by a direct comparison of Fourier coefficients between fully sampled and reconstructed wavefields. Taking log10 of Fourier coefficients prior to computing the quality metric deemphasizes the importance of magnitude match while highlighting Fourier coefficient support accuracy, which usually translates into good structural fidelity of reconstructed data. We find that IST performs consistently among all examples, yielding good phase match while performing gentle denoising.

Frequency noise suppression of the high-power pumping laser in the SERF atomic magnetometer

Abstract A high-sensitivity SERF atomic magnetometer based on a noise-suppressed master oscillator power amplifier (MOPA) system is demonstrated. The spectrum and frequency noise of the laser are measured and it is proved that the frequency noise in the MOPA system is affected by the amplified spontaneous emission (ASE) stimulated from the power amplifier. The frequency noise of the MOPA system is decreased by filtering out the ASE background in the spectrum through a bandpass filter. The sensitivity of the SERF atomic magnetometer is improved by using the frequency noise-suppressed MOPA system as a pumping laser. This method can also be applied to other precision measurement fields based on the MOPA systems that have high requirement for frequency noise of laser.

Staged suppression of main-lobe compound jamming using airborne distributed arrays

For the compound jamming scenario where noise suppression jamming and interrupted sampling repeater jamming simultaneously exist, we propose a method of staged suppression of them using distributed arrays. The basic principle is using the generalized inner product for the selection of non-uniform samples. By using the generalized inner product quadratically for the staged selection of the samples, we can obtain the statistical feature estimates of the noise suppression jamming and the interrupted sampling repeater jamming, respectively, and then complete the robust suppression of the compounded jamming using the distributed array spatial domain adaptive processing. With typical configuration of parameters, we validate and analyze the effectiveness and performance of the proposed method through numerical simulation experiments.

Removing random noise and improving the resolution of seismic data using deep‐learning transformers

AbstractPost‐stack data are susceptible to noise interference and have low resolution, which impacts the accuracy and efficiency of subsequent seismic data interpretation. To address this issue, we propose a deep learning approach called Seis‐SUnet, which achieves simultaneous random noise suppression and super‐resolution reconstruction of seismic data. First, the Conv‐Swin‐Block is designed to utilize ordinary convolution and Swin transformer to capture the long‐distance dependencies in the spatial location of seismic data, enabling the network to comprehensively comprehend the overall structure of seismic data. Second, to address the problem of weakening the effective signal during network mapping, we use a hybrid training strategy of L1 loss, edge loss and multi‐scale structural similarity loss. The edge loss function directs the network training to focus more on the high‐frequency information at the edges of seismic data by amplifying the weight. Additionally, the verification of synthetic and field seismic datasets confirms that Seis‐SUnet can effectively improve the signal‐to‐noise ratio and resolution of seismic data. By comparing it with traditional methods and two deep learning reconstruction methods, experimental results demonstrate that Seis‐SUnet excels in removing random noise, preserving the continuity of rock layers and maintaining faults as well as being strong robustness.

Rapid Aircraft Wake Vortex Identification Model Based on Optimized Image Object Recognition Networks

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights.