Noise Suppression Method Research Articles

Noise suppression method for OPGW transmission line galloping monitoring by using adjacent FBG sensors

To solve the problem of transmission line galloping monitoring for optical power grounded waveguides (OPGWs) in external field environments, we propose a low-noise monitoring array based on adjacent sensors with low reflectivity fiber Bragg grating (FBG). We analyze the interference signal models for adjacent FBG sensors, and based on them, a noise suppression method by using adaptive filter input is constructed. Then we simulate the noise suppression effect of the proposed algorithm under different noise conditions. Finally, we deploy the low-noise OPGW transmission line galloping monitoring system based on low reflectivity FBGs in China’s western autonomous prefecture with a 220 kV transmission line. The experimental results show that, after adaptive filtering using adjacent sensors, the average noise power spectral density is reduced by 6.5 dB, and the algorithm optimizes the monitoring intensity spectrum. Oscillation events of about 100 m can be clearly observed within the monitoring interval. It creates certain conditions for further improving the typical event classification data processing and pattern recognition database for OPGWs and demonstrates promising prospects for engineering applications.

A baseline correction and noise suppression method based on fitting neural network for CH4/C2H6 dual gas sensing system

We designed a methane/ethane (CH4/C2H6) dual-gas sensor on the basis of direct absorption spectroscopy (DAS) technology and explored the potential for low power consumption and miniaturization of the detection system using the effective optical range is only 25 cm for absorption cell and an uncooled photodetector. The problem of baseline drift introduced by uncooled photodetectors has been improved by using of a fitting neural network approach, and absorbance spectrum denoising has been realized. We established the mathematical relationship between absorbance and concentration for each gas of CH4/C2H6 through experimental detection, which shows strong linearity. And the correlation coefficients (R2) reached 0.99995 and 0.99998, demonstrating the robustness of the sensor. Through 3 h of measured and evaluated system, we obtained the minimum detectable column densities of 0.588 ppm⋅m and 0.128 ppm⋅m for CH4 and C2H6, respectively, which proved that the sensor has high sensitivity, strong stability, and has broad application prospects.

A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel) Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression. It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem. However, in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers. The presence of this kind of noise, called erratic noise, makes singular spectrum analysis (SSA) reconstruction unstable and has undesirable effects on the final results. We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA (RD-SSA) method. It incorporates the damped SSA, an improved version of SSA, into a reweighted framework. The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise. The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations. The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance. The feasibility of RD-SSA is validated via both synthetic and field data examples.

Speckle noise suppression method in a holographic display based on pixel processing.

In this paper, we propose a method to suppress the speckle noise in a holographic display based on pixel processing. Through the separation of object pixels in space, the recorded object is divided into multiple object point groups. The complex amplitude of the light field for each object point group is recorded as a sub-computer-generated hologram (sub-CGH). The phase of each pixel on a sub-CGH is optimized to generate the final sub-CGH. Therefore, the pixels of the recorded object and sub-CGH are processed. In the reconstruction process, the final sub-CGHs are loaded on the spatial light modulator sequentially. The speckle noise of the reconstructed image is suppressed by reducing the algorithm error and the overlapping area of adjacent image points. The experimental results prove the feasibility of the proposed method.

Gaussian wavelet basis expansion based phase noise suppression method for coherent optical OFDM systems

In this paper, we developed a phase noise suppression method based on Gaussian wavelet basis expansion (GWBE) for coherent optical orthogonal frequency-division-multiplexing (CO-OFDM) systems. Compared with traditional phase noise suppression methods, the GWBE method significantly enhances the system’s robustness to laser phase noise, providing improved phase noise suppression, especially in scenarios with high linewidth. Additionally, the method demonstrates excellent performance in 32-QAM and 64-QAM. By employing the proposed GWBE method, the impact of laser phase noise on CO-OFDM can be effectively mitigated.

Explainable artificial intelligence‐driven mask design for self‐supervised seismic denoising

AbstractThe presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self‐supervised denoising circumvents the common requirement of deep learning procedures of having noisy‐clean training pairs. However, self‐supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to ‘see inside the black box’ that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias‐free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time‐correlated noise to complex pseudo‐rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre‐processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace‐wise noise in common shot gathers from the Volve marine data set and coloured noise in post‐stack seismic images from a land seismic survey.

Quantification and suppression of optical non-orthogonality and light intensity noise in all-optical hot atomic sensing systems

All-optical hot atomic sensing systems such as optical pumping magnetometers (OPM) and comagnetometers, nuclear magnetic resonance (NMR) magnetometers and gyroscopes, have the ability to sense magnetic field, anomalous field and inertial rotation, thus having a wide potential for applications in precision measurement, fundamental physics research and inertial navigation. System noise limits the minimum value of the signal that can be measured and there is coupling effect between the noises. Therefore, the quantification and suppression of various noise are challenging and should be investigated. As one of the major noises, the light intensity fluctuation of the probe light and the pump light affects the sensitivity of the all-optical hot atomic sensing systems. However, there has been no in-depth research on the quantification of the light intensity noise. In this paper, a method of measuring the light intensity noise is proposed, which can be applied to kinds of all-optical hot atomic sensing systems. For verification, the effects of probe and pump light intensity fluctuations on system noise are simulated and experimentally verified in a spin exchange relaxation free (SERF) comagnetometer. Besides, it is found that the optical non-orthogonality amplifies the probe light intensity noise by more than 3.4 times and a quantification method of optical non-orthogonality is proposed. Finally, a suppression method of the light intensity noise is presented by suppressing the optical non-orthogonality and the noise is suppressed by 59%. A universal light intensity noise quantification method is proposed for all-optical hot atomic sensing systems, and a light intensity noise suppression method is proposed for SERF comagnetometers, which can be applied to the suppression of noises coupled with atomic polarization.

Real-Time Noise Suppression Method Using Deep Learning Algorithm

In this study, we present an innovative approach to real-time facial feature detection utilizing the MTCNN (MultiTask Cascaded Neural Network) deep learning architecture. Unlike traditional methods, our novel framework combines advanced techniques to achieve unparalleled precision and efficiency in facial feature localization. Through a meticulous exploration of MTCNN's capabilities, we unveil a transformative methodology that significantly enhances the speed and accuracy of real-time facial detection. Our research focuses on pushing the boundaries of existing facial recognition technologies, introducing a fresh perspective on the application of MTCNN. By leveraging its unique architecture, we not only address the challenges associated with real-time detection but also enhance the overall robustness of the system. The proposed approach showcases the untapped potential of MTCNN, establishing it as a key player in the realm of facial feature detection. Through rigorous experimentation and evaluation, we demonstrate the superiority of our approach over conventional methods, highlighting its effectiveness in diverse scenarios. This work contributes to the ongoing evolution of deep learning applications in computer vision, with implications for security, surveillance, and various human-computer interaction domains. Our findings open new avenues for researchers and practitioners seeking cutting-edge solutions in the dynamic field of real-time facial feature detection.

Laser phase noise suppression method for a CO-OFDM-OQAM communication system with real-imaginary-alternate pilots

Laser phase noise suppression method for a CO-OFDM-OQAM communication system with real-imaginary-alternate pilots

Study on the Effect of Micro-Force Perturbations and Temperature Fluctuation on Interferometer for the Taiji Program.

To increase the interferometric measurement resolution in the Taiji program, we present a noise suppression method in this paper. Taking the specific micro-force perturbation and temperature fluctuation in the Taiji-1 interferometer as an example, we set up and experimentally verified the corresponding transfer function to quantify the effect of both noise sources on the interferometric results. Consistent results were obtained between the numerical and experimental results for the transfer function. It is instructive to eliminate the micro-force perturbations and temperature fluctuations during on-orbit interferometric measurement for as long as the acquisition of the force or temperature distribution of related surfaces and the corresponding transfer functions. This indicates that the method can be used for noise sensing and more in the field of noise elimination and measurement resolution improvement for future Taiji program interferometers.

QANS: Toward Quantized Neural Network Adversarial Noise Suppression

Neural network quantization techniques play an important role in efficiently deploying deep learning models on the hardware with limited computing and storage resources. Numerous applications of this technology, such as autopilot, necessitate not just efficiency, but also robustness. Research on the robustness of quantized networks against adversarial attacks is becoming one of the major points of interest. In this work, we rethink the impact of quantization on adversarial attacks and explore the boundary of the robustness of quantized neural networks. This study reveals that activation quantization can be used as a defense to weaken adversarial noise, but the robustness of quantized models is still limited by the amplification effect of network errors, including quantization errors and adversarial noise. To address this problem, we propose the Quantization Adversarial Noise Suppression (QANS) method that employs a Gaussian kernel regularization constraint to stabilize the model by restricting the perturbation error within two levels of tolerance. Extensive experiments are conducted with Wide ResNet and VGG-16 models on CIFAR-10 and SVHN datasets under different attack methods, including several white-box and black-box attacks. Experimental results show the proposed method achieves superior robustness to prior works.

Research of coherent noise suppression method based on rotational scatterer in dual-wavelength holographic microscopy

Laser light source is widely used in digital holography as a strongly coherent light source, its strong correlation will also introduce some unavoidable coherent noise to the system and affect the imaging quality. This paper proposes a method to suppress system noise by adding a rotational scatterer to the reflective dual-wavelength digital holographic microscope. In this paper, the magnification of the system is calibrated with the 1951 USAF resolution target. A one-dimensional micro- and nano-grid is used to determine the system resolution. By comparing the hologram reproduction results under the same experimental conditions both with and without a rotational scatterer, it is demonstrated that the addition of a rotational scatterer can effectively suppress the coherent noise of this system. The system is also verified to have certain stability through the experiments of repeatability measurement and multi-position measurement on the chip. Furthermore, this research offers a fresh measuring approach for microchannel detection in opaque chips.

Improved Initial Orbit Determination Based on the Gooding Method of Low Earth Orbit Space Debris Using Space-Based Observations

Initial orbit determination (IOD), as a basis for initial orbit association and accurate orbit determination (OD), has a crucial role in the process of obtaining space debris orbit information. Among the traditional methods, the Gooding method has better convergence and stability. In this study, the Gooding method is enhanced to solve the issues discovered. A novel initial orbit determination (IOD) method is developed using the proposed improvement measures of the single-parameter initial value determination (SIVD) method, the fitted-curve noise suppression method, the restricted corrective value solution method, the removal of trivial solutions, etc. The experimental results verify the effectiveness of the improved method. The success rate of the initial orbit determination reached 99%, and the accuracy of the solved orbit parameters was significantly improved, especially the semi-major axis (SMA) error of less than 50 km, accounting for 88% of the total. It can be seen that the method meets the demand of space-based space debris cataloging for initial orbit association and can serve in the field of space situational awareness, which has important practical significance and application potential.

Fading noise suppression method of Ф-OTDR system based on non-local means filtering

Distributed optical fiber acoustic sensing (DAS) is a kind of optical fiber sensing system based on phase sensitive optical time domain reflection technology (Ф-OTDR). It has the characteristics of high sensitivity, long distance and real-time detection, and has been widely used in the positioning and monitoring of various engineering events. However, the high coherent detection light source used in the Ф-OTDR technology will produce coherent fading phenomenon in the fiber, which causes fading noise. This will seriously affecting the monitoring effect of the DAS system. Therefore, in order to address the fading noise problem in DAS system, a system fading noise suppression method based on non-local means filtering is proposed, which can effectively suppress the system fading noise in the signals and improve signal-to-noise ratio while preserving the characteristics of the target event signals. The experimental results show that the proposed method has better DAS system noise suppression effect in the face of different intensities and types of noise interference. Further, the proposed method is compared with EEMD, wavelet and outlier noise suppression methods. The results show that the noise suppression effect is significantly better than other methods, and the signal-to-noise ratio is improved by more than 30%, which verifies the effectiveness and superiority of the proposed method.

Improvement and application of electromagnetic vibration and noise suppression method for electric vehicle motor

Motor induced Electromagnetic Vibration and Noise (EVN) are mainly affected by Radial Electromagnetic Force Wave (REFW) and related harmonic amplitude. Therefore, in the past discussion on the suppression of motor induced EVN, it usually starts from this aspect. For this reason, this study proposes a method to suppress EVN. Firstly, the influence of the quantity of rotor slots on the function of Induction Motor (IM) is analyzed; Then, the optimization strategy of rotor slot fit is proposed according to the results; Finally, an optimization strategy for the skewness of IM is proposed. Based on the above contents, the experiment realized the suppression of EVN induced by electric vehicle motor. The experimental results show that the parameters of the motor are optimal when the quantity of rotor slots is 53. It is better to weaken the harmonic amplitude related to the low-order REFW. The peak values of the motor using the research strategy at the second and third vibration modes are - 35 db Hz and - 38 db Hz respectively, which are lower than those of the traditional motor. To sum up, the strategies proposed in the study can remarkably suppress the EVN of IM and improve the function of electric vehicles, which will promote the electric vehicle industry ‘s development.

Seismic random noise attenuation using DnCNN with stratigraphic dip constraint

Abstract In this paper, a method for seismic random noise detection and suppression using a denoising convolutional neural network (DnCNN) is presented. Thanks to the residual learning and batch normalization, deep learning networks can converge faster, the gradient descent and disappearance due to the increase of network layers are solved, and the residual results can be predicted more accurately. For seismic data, the variance estimation method is useful for obtaining an accurate noise distribution model and statistical parameters that provide a useful assessment of the noise level. With the variance estimation method based on weak texture blocks, a noise distribution model and statistical parameters can be derived with high accuracy, and this method effectively estimates seismic noise levels. The DnCNN networks are trained, and non-Gaussian noise reduction technology is used to achieve blind noise reduction at unknown levels, improving the noise reduction of seismic data. In addition, stratigraphic dip characteristics related to layer structure are used as DnCNN training network constraints to prevent effective signals in seismic data from being corrupted by conventional DnCNN noise reduction methods. Geological features such as faults and fracture-cavities can be effectively protected. Carbonate faults in the Tarim Basin in China are affected by the desert surface and the depth at which reservoirs are buried. The seismic data has a low signal-to-noise ratio, and the effective signals of the reservoir are low resolution. The seismic data can be effectively enhanced with this method for noise reduction in this area, the fracture-cavity is effectively displayed, and the fault features are also highlighted.

Using auditory texture statistics for domain-neutral removal of background sounds

IntroductionHuman communication often occurs under adverse acoustical conditions, where speech signals mix with interfering background noise. A substantial fraction of interfering noise can be characterized by a limited set of statistics and has been referred to as auditory textures. Recent research in neuroscience has demonstrated that humans and animals utilize these statistics for recognizing, classifying, and suppressing textural sounds.MethodsHere, we propose a fast, domain-free noise suppression method exploiting the stationarity and spectral similarity of sound sources that make up sound textures, termed Statistical Sound Filtering (SSF). SSF represents a library of spectrotemporal features of the background noise and then compares this against instants in speech-noise-mixtures to subtract contributions that are statistically consistent with the interfering noise.ResultsWe evaluated the performance of SSF using multiple quality measures and human listeners on the standard TIMIT corpus of speech utterances. SSF improved the sound quality across all performance metrics, capturing different aspects of the sound. Additionally, human participants reported reduced background noise levels as a result of filtering, without any significant damage to speech quality. SSF executes rapidly (~100× real-time) and can be retrained rapidly and continuously in changing acoustic contexts.DiscussionSSF is able to exploit unique aspects of textural noise and therefore, can be integrated into hearing aids where power-efficient, fast, and adaptive training and execution are critical.

Phase drift and noise suppression method based on SEE-SGMD-PCC in a distributed acoustic sensor

The problems of laser source frequency drift (LSFD) and phase noise in distributed acoustic sensor (DAS) make it difficult to recover the true vibration phase curve, which limits its application scope in the field of seismic exploration. In order to suppress the influence of phase noise and phase drift on the phase signal and improve the signal-to-noise ratio (SNR) of the phase curve, this paper proposes a method based on symmetric extreme value expansion, symplectic geometry mode decomposition and Pearson correlation coefficient (SEE-SGMD-PCC). Firstly, the mathematical principles and processing flow of the SEE-SGMD-PCC algorithm were introduced, and the effectiveness of this method was verified through multiple sets of simulation experiments. Secondly, The DAS system implemented using digital heterodyne coherent detection technology was used as the experimental platform, and the phase drift and phase noise sources of the phase signal were analyzed in detail. Next, in single frequency and multi frequency vibration signal experiments, compared with other methods, the SNR of the phase signal is significantly improved, and the phase information is effectively restored. Finally, the feasibility of the proposed method was demonstrated through two on-site experiments. The proposal of this method further promotes the application process of DAS in complex seismic exploration environments.

A novel seismic random noise suppression method based on wavelet threshold and Lipschitz

The seismic random noise suppression is very important in the processing and interpretation of seismic data. Wavelet transform is an effective tool to remove random noise. Aiming at the problems of signal propagation point omission and misselection, insignificant denoising effect and poor stability of the traditional wavelet maximum mode (WMM) method, this paper proposes an improved wavelet mode maximum value (IMWMM) denoising method. The Lipschitz of wavelet transform with signal and noise has different features used to distinguish signal and noise. Comparing the propagation points of adjacent scales and removing the points with reduced mode values achieve the separation of seismic signal and noise. The synthetic data, Stanford Earthquake Dataset (STEAD) data and real-field seismic signal experimental results prove the validity of the IMWMM method for both suppressing random noise and preserving seismic signal. The real-field seismic data came from Wenchuan experimental results present that the proposed IMWMM method has effectiveness of random noise suppression with SNR above 28.4527 dB, that has improvement for quality of seismic data.

A vital signsignal noise suppression method for wearable piezoelectric devices.

This paper tackles the problem of noise suppression during vital signsignal monitoring. Physiological signal monitoring is a significant and promising medical monitoring method, and wearable medical monitoring devices based on piezoelectric polymer sensors are a trending way for their advantages of being flexible in the shape, portable to use, and comfortable to wear. However, this raises the question that the measured signal contains much more noise components. To avoid the following shortcoming of low signal to noise ratio (SNR), a noise suppression method based on improved wavelet threshold and empirical mode decomposition combined with singular value decomposition (SVD) screening the intrinsic mode function (IMF) components is proposed. A wavelet transform is first used under the combination of hard and soft thresholds to focus the target range in the low-frequency region where the energy of the physiological signal is concentrated. Then, a complete ensemble empirical mode decomposition is used to decompose the signal effectively, which can resist the influence of random noises. Meanwhile, a SVD decomposition procedure was used to filter out the lower correlated IMF components to retain the validity of the original signal. We verified the effectiveness of the proposed method through simulated and measured experiments as well as the advantages and disadvantages of the algorithm compared with other physiological signal denoising algorithms through SNR filtering results, power spectrum distribution, and other perspectives. The results proved that the proposed method could effectively remove more detailed noise and improve the SNR of the signal efficiently, which is more conducive to the demand for auxiliary medical diagnosis in the future.