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Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial–temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area. [ABSTRACT FROM AUTHOR]

Range-resolved CH4 concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH4 concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH4 concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH4 profile by 85.54%. The standard deviation of CH4 concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH4 concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH4 concentration at observation sites. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated. [ABSTRACT FROM AUTHOR]

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefiting from a low background noise level. Enhancing strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio, is both valuable and feasible. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at the surface, to achieve noise suppression through signal reconstructions. Both the theoretical analysis and the practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data. [ABSTRACT FROM AUTHOR]

Proton magnetic resonance spectroscopy (1H‐MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H‐MRS. Eighty‐three/forty‐two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short‐echo‐time point‐resolved spectroscopy. The acquired raw 1H‐MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post‐noise‐suppression 1H‐MRS showed significantly elevated signal‐to‐noise ratios (P <.05, Wilcoxon signed‐rank test), stable full width at half‐maximum (P >.05, Wilcoxon signed‐rank test), and significantly higher classification accuracy (P <.05, Wilcoxon signed‐rank test). Specifically, the cross‐validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H‐MRS. The study shows that fitting‐based signal‐to‐noise ratios of clinical 1H‐MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post‐noise‐suppression 1H‐MRS may have better diagnostic performance for paediatric brain tumours. [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]

Voice activity detection (VAD) plays a crucial role in speech processing, serving as a fundamental component for various applications such as speech recognition and communication systems. Numerous approaches have been explored to address the VAD issue, but their effectiveness diminishes significantly in the presence of noise. In response to this challenge, we present a novel technique designed to enhance the robustness of VAD under noisy conditions. Our proposed system incorporates a background noise suppression module based on the minimum mean square error spectrum power estimator using zero crossing (MMSE-SPZC). This module is integrated before the semi-supervised Gaussian mixture model-based VAD (SSGMM-VAD). The effectiveness of the proposed VAD system is evaluated across four distinct types of noise and low signal-to-noise ratio (SNR) levels. Our experimental results reveal significant improvements in VAD accuracy, demonstrating the system's ability to maintain robust performance even in the presence of challenging background noise conditions. [ABSTRACT FROM AUTHOR]

Accurately extracting weak signals is extremely important for overall performance and application in optoelectronic imaging and optical communication systems. While weak signals are susceptible to noise, adaptive filtering is a commonly used noise removal method. Still, its convergence speed is slow, the steady-state error is large, and the anti-interference ability is weak. To solve the above problems, this paper proposes a new type of variable-step-length adaptive filtering algorithm (DSLMS) based on the minutiae function, which effectively reduces the noise component in error through its combination with the pair cancelation system, utilizing the low correlation property of the noise signal, to improve the anti-noise interference ability of the adaptive filter. Using FPGA and Matlab (2018b) for experimental verification, the results show that this algorithm shows significant advantages in noise suppression, accelerated algorithm convergence, and low steady-state error, and it has effectiveness and application potential for the optoelectronic detection of weak signal processing. [ABSTRACT FROM AUTHOR]

The seismic signals collected underground in coal mines often exhibit complex waveforms accompanied by significant noise interference, leading to a reduction in the accuracy of first arrival time picking of seismic signals and thereby impacting the inversion and interpretation of seismic data. In response to the low signal-to-noise ratio seismic signals collected in complex interference environments, a method for seismic noise suppression and first arrival extraction based on the Variational Mode Decomposition (VMD) and the Genetic Algorithm-optimized Support Vector Machine (GA-SVM) is proposed to enhance the quality of seismic signals under complex noise conditions in coal mines. The approach employs the Variational Mode Decomposition for adaptive decomposition of the noisy seismic signals, yielding several Variational Mode Components (IMF). The feature extraction is applied to the IMFs obtained from VMD decomposition, utilizing the extracted signal features as the basis for discerning signal validity. Genetic Algorithm is utilized to optimize the Support Vector Machine model, obtaining the optimal penalty factor (c) and kernel function parameter (g). The optimized SVM model is then employed for the validity discrimination of the IMF components, reconstructing them into high signal-to-noise ratio signals. By applying the noise suppression algorithm to artificially noised seismic signals, the common types of noise encountered in coal mines are effectively suppressed, validating the feasibility of the algorithm. Noise suppression processing is applied to the seismic records obtained from mine roadways, successfully mitigating interference noise in the data and significantly improving the signal-to-noise ratio of the seismic records, thereby enhancing the accuracy of first arrival time picking. The results indicate that the seismic noise suppression and first arrival extraction method based on the VMD and GA-SVM can effectively separate and extract valid signals from noisy seismic records, improving first arrival time picking accuracy. This approach demonstrates a significant potential for its application in complex interference conditions in mines, which is of significance for the seismic exploration in the mining environments with complex interference conditions.

Here, the single-target parameterization of alternatives to leading-edge noise is carried out using analytical models based on the Wiener–Hopf technique. Four leading-edge serration profiles with different frequencies, amplitudes, and phases are implemented to aid the understanding of sound suppression mechanisms. The effects of the serrated shape factor, wavelength, and amplitude are analyzed at tip-to-root ratios of 0.5, 1, and 2, respectively. An effective double-wavelength sinusoidal serration design can substantially reduce the noise emissions of 5.2 dB at h ¯ = 2. Additionally, compared to single-wavelength serrations, an additional 1.47 dB noise reduction effect can be obtained by double-wavelength serrations under the appropriate design parameters. The surface pressure and phase distribution of different spanwise-varying leading edges indicate that the phase interference effect affected by source-radiated noise reduction is enhanced by this serration at the hills for serrations with a small curvature, and noise emission in the low-frequency band is more effectively suppressed. The sharper the serration is, the more conducive it is to a reduction in high-frequency noise. Nevertheless, the effectiveness of serrations is usually partially limited by the non-negligible trailing-edge self-noise. The sound source intensity of the root is decreased by the ogee-shaped serrations with a large curvature transition. A secondary noise reduction mechanism with a local source cut-off effect caused by nonlinearity is demonstrated. [ABSTRACT FROM AUTHOR]

The main advantage of full-waveform inversion (FWI) is the ability to obtain useful subsurface structure information, such as velocity and density, from complex seismic data. We have developed a novel inversion algorithm to improve the capability of FWI to achieve high-resolution imaging, even under complex conditions caused by random noise contamination, initial model dependence, or the selection of parameters to be estimated. Our algorithm considers an effective image processing and dimension reduction tool, randomized singular value decomposition-weighted truncated nuclear norm regularization, for embedding FWI to achieve high-resolution imaging results. This algorithm obtains a truncated matrix approximating the original matrix by reducing the rank of the velocity increment matrix, thus achieving the truncation of noisy data, with the truncation range controlled by weighted truncated nuclear norm regularization. Subsequently, we employ an inexact augmented Lagrangian method algorithm in the optimization to compress the solution space range, thus relaxing the dependence of FWI and randomized singular value decomposition-weighted truncated nuclear norm regularization on the initial model and accelerating the convergence rate of the objective function. We tested on one set of synthetic data, and the results show that, compared with traditional FWI, our method can more effectively suppress the impact of random noise, thus obtaining higher resolution and more accurate subsurface model information. This work indicates that the combination of randomized singular value decomposition-weighted truncated nuclear norm regularization and FWI is an effective imaging strategy that can help to solve the challenges faced by traditional FWI. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China 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.)

Spectral transform, known as a curvelet, enables sparse representations of complex data. Denoising wave propagation in disordered media and pattern recognition are just a few of the numerous domains in which denoising has a potential application. Based on directional basis functions, this spectral method represents objects with discontinuities along a smooth curve. In this study, we used this technique to eliminate ground roll, an unwanted feature signal that can be seen in seismic data obtained by sonating the earth's geological formations. Additionally, we improved the curvelet transform threshold denoising method combined with a fast non-local mean to remove seismic random noise. First, cyclic translation and block complex domain threshold methods were introduced into the curvelet transform threshold denoising, and the traditional curvelet threshold denoising method was improved to obtain the best denoising results; Subsequently, the removed noise was filtered using a fast non-local mean method to obtain a valid signal. Finally, the data obtained in the above two steps were added to obtain the final denoising result. The results of the model tests and actual seismic data denoising showed that the denoising results obtained using this method had a higher signal-to-noise ratio and fidelity than that using the other methods. [ABSTRACT FROM AUTHOR]

Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures. [ABSTRACT FROM AUTHOR]

A tone mapping operator (TMO) is a module in the image signal processing pipeline that is used to convert high dynamic range images to low dynamic range images for display. Currently, state-of-the-art TMOs typically take complex algorithms and are implemented on graphics processing units, making it difficult to run with low latency on edge devices, and TMOs implemented in hardware circuits often lack additional noise suppression because of latency and hardware resource constraints. To address these issues, we proposed a low-latency noise-aware TMO for hardware implementation. Firstly, a locally weighted guided filter is proposed to decompose the luminance image into a base layer and a detail layer, with the weight function symmetric concerning the central pixel value of a window. Secondly, the mean and standard deviation of the basic layer and the detail layer are used to estimate the noise visibility according to the human visual characteristics. Finally, the gain for the detail layer is calculated to achieve adaptive noise suppression. In this process, luminance is first processed by the log2 function before being filtered and then symmetrically converted back to the linear domain by the exp2 function after compression. Meanwhile, the algorithms within the proposed TMO were optimized for hardware implementation to minimize latency and cache, achieving a low latency of 60.32 μ s under video specification of 1080 P at 60 frames per second and objective metric smoothness in dark flat regions could be improved by more than 10% compared to similar methods. [ABSTRACT FROM AUTHOR]

To mitigate the impact of low-frequency noise from the tunnel magnetoresistance (TMR) current sensor and ambient stray magnetic fields on weak current detection accuracy, we propose a high-resolution modulation-demodulation test method. This method modulates and demodulates the measurement signal, shifting low-frequency noise to the high-frequency band for effective filtering, thereby isolating the target signal from the noise. In this study, we developed a Simulink model for the TMR current sensor modulation-demodulation test method. Practical time-domain and frequency-domain tests of the developed high-resolution modulation-demodulation method revealed that the TMR current sensor exhibits a nonlinearity as low as 0.045%, an enhanced signal-to-noise ratio (SNR) of 77 dB, and a heightened resolution of 100 nA. The findings indicate that this modulation-demodulation test method effectively reduces the impact of low-frequency noise on TMR current sensors and can be extended to other types of resistive devices. [ABSTRACT FROM AUTHOR]

Automotive fans, small wind turbines, and manned and unmanned aerial vehicles (MAVs/UAVs) are just a few examples in which noise generated by the flow's interaction with aerodynamic surfaces is a major concern. The current work shows the potential of a new airfoil shape to minimize noise generation, maintaining a high lift-to-drag ratio in a prescribed Reynolds regime. This investigation uses a multifidelity approach: a low-fidelity semiempirical model is exploited to evaluate the sound pressure level (SPL). Fast evaluation of a low-cost function enables the computation of a large range of possible profiles, and accuracy is added to the low-fidelity response surface with high-fidelity CFD data. The constraint of maintaining a predefined range of the lift coefficient and lift-to-drag ratio ensures the possibility of using this profile in usual design procedures. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

Wearable devices have been widely used for the home monitoring of physical activities and healthcare conditions, among which ambulatory electrocardiogram (ECG) stands out for the diagnostic cardiovascular information it contains. Continuous and unobtrusive sensing often requires the integration of wearable sensors to existing devices such as watches, armband, headphones, etc.; nonetheless, it is difficult to detect high-quality ECG due to the nature of low signal amplitude at these areas. In this paper, a high-performance system with multi-channel signal superposition for weak ECG real-time detection is proposed. Firstly, theoretical analysis and simulation is performed to demonstrate the effectiveness of this system design. The detection system, including electrode array, acquisition board, and the application (APP), is then developed and the electrical characteristics are measured. A common mode rejection ratio (CMRR) of up to 100 dB and input inferred voltage noise below 1 μV are realized. Finally, the technique is implemented in form of ear-worn and armband devices, achieving an SNR over 20 dB. Results are also compared with the simultaneous recording of standard lead I ECG. The correlation between the heart rates derived from experimental and standard signals is higher than 0.99, showing the feasibility of the proposed technique. [ABSTRACT FROM AUTHOR]

Frequency‐domain electromagnetic (FDEM) instruments are effective tools for detecting underground electrical abnormal targets. However, due to the influence of various noises in the detection process, would lead to l poor signal‐to‐noise ratio of the system and would ultimately affect the detection accuracy. And FDEM detection systems have a defined measuring range. Excessive noise will lead to saturation of the system acquisition and distortion of the measurement. Because of the above problems, an acquisition technique for the FDEM receiving system is proposed to achieve noise suppression in the detection process. The scheme proposes a multi‐frequency LC filtering circuit to optimize the analog signal acquisition module of the receiver system. The circuit converts the measured voltage signal into a current signal, improving the system's immunity to interference. At the same time, the noise of non‐target frequencies in the measured signal is suppressed, avoiding saturation of the receiving system acquisition, and realizing high signal‐to‐noise acquisition of multiple target frequencies. First, the principle of circuit design is analyzed, and the design flow of circuit parameters is proposed. Further, the validity of the design is verified via simulation results. Finally, the optimization scheme is validated with an actual circuit. The results show that for the measurement environment set in this paper, this optimization scheme can suppress the noise of non‐target frequency in the measured signal, so that the noise amplitude can be reduced by more than 90%, and the signal‐to‐noise ratio of the filtered signal can be improved by 23.35 dB. This study helps to facilitate FDEM instruments to achieve high signal‐to‐noise ratio detection. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper examines the potential of utilizing a Faraday anomalous dispersion optical filter (FADOF) to enhance noise reduction in a FMCW LiDAR (Frequency-Modulated Continuous-Wave Laser Detection And Ranging) system. While the FMCW LiDAR itself demonstrates a certain level of robustness, it may fail to operate effectively in the presence of significant background noise interference. To address this challenge, we have proposed a validation experiment utilizing FADOF technology. Specifically, we have introduced a FADOF component at the optical receiving end of the FMCW LiDAR, and we have implemented a dynamic frequency stabilization method that combines frequency stability with frequency modulation for the laser source in the transmitter. By integrating FADOF into the FMCW LiDAR system to mitigate external noise, we conduct indoor and outdoor validation experiments. In the outdoor experiment, we introduce real sunlight background noise into the system, allowing it to directly illuminate the receiver. We then filter the light using FADOF and a 10 nm filter, separately. The experimental results confirm that the FMCW LiDAR system, when equipped with FADOF, can operate normally in intense background light noise, with a noise resistance capability at least twice as effective as the 10 nm filter. Additionally, the system achieves a detection range of at least 17 meters under direct sunlight conditions. This study has the potential to extend the application of LiDAR systems to more extreme environments with strong background noise, enabling their functionality in a broader range of scenarios.

Range-resolved CH4 concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH4 concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH4 concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH4 profile by 85.54%. The standard deviation of CH4 concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH4 concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH4 concentration at observation sites.

Featured Application: A deep learning application to improve speech clarity in digital audio affected by environmental noises, showing potential for enhancing real-time streaming communication in noisy settings. This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality. [ABSTRACT FROM AUTHOR]

Low-light image enhancement is an important task in computer vision. Deep learning-based low-light image enhancement has made significant progress. But the current methods also face the challenge of relying on a wide variety of low-light/normal-light paired images and amplifying noise while enhancing brightness. Based on existing experimental observation that most luminance information concentrates on amplitudes while noise is closely related to phases, an unsupervised low-light image enhancement method in the Fourier transform domain is proposed. In our method, the low-light image is firstly transformed into the amplitude component and phase component via Fourier transform. The luminance of low-light image is enhanced by CycleGAN in the amplitude domain, and the phase component is denoising. The cycle consistency losses both in the Fourier transform domain and spatial domain are used in training. The proposed method has been validated on publicly available test sets and shows that our method achieves superior results than other approaches in low-light image enhancement and noise suppression. [ABSTRACT FROM AUTHOR]

One of the significant challenges in processing and analyzing seismic data is the contamination of the seismic signal with noise from various sources. Coherent or incoherent noise, multiples, and other types of noise can all be eliminated using a number of techniques, but it is still complicated to attenuate random noise to the desired level. The non-homogeneous curvelet transform is first applied in this study, and the regularized calculation is then carried out using the inverted operator. Following the application of the linear calculation method, the threshold value is modified to remove the coefficient noise during the iteration process, allowing the homogeneous coefficients and noise-free seismic data to be obtained. We choose some real data as an example and apply the suggested curvelet theory to obtain homogeneous data in order to verify the denoising effect. Tests on real data show that, when the suggested approach interpolates sampled data to be homogeneous, random noise can be effectively reduced. The important benefit of this approach is that features like multi-resolution, and locality introduce minimal overlapping between coefficients representing signal and noise in curvelet domain. [ABSTRACT FROM AUTHOR]

Computer vision algorithms are widely used in solving a number of applied problems. The correct operation of such algorithms depends on the photo and video data that they receive at the input, which are subject to the effect of noise; hence, noise suppression is an important stage in low-level digital image processing. In this work, the Wiener filtering of normal white noise with using neural networks in the domain of the discrete wavelet transform is studied. The architecture of the networks and the algorithm developed for their application for filtering in the domain of a discrete wavelet transform are described. The proposed algorithm is tested on the BSDS500 dataset at various noise levels. The filtering quality is evaluated by the calculated signal-to-noise ratio (SNR) and structural similarity index (SSIM) values. The results of processing test images indicate that the developed algorithm is superior in noise reduction quality to most of the other considered filters, including Wiener filtering without the use of neural networks in the domain of the discrete wavelet transform. [ABSTRACT FROM AUTHOR]

In communication systems, when the loudspeaker and the microphone are coupled together, it creates acoustic echoes. With the increasing demand for mobile communication and online conference, it is urgent to solve the problem of acoustic echo cancellation (AEC) in communication systems. Due to the existence of nonlinear distortion, background noise and other reasons, traditional AEC methods can no longer solve the problem of echo cancellation well. Although some traditional methods consider the problem of nonlinear distortion, the effect of echo suppression is still not ideal. In this paper, we propose an echo cancellation method based on frequency domain mask, which is defined as a supervised speech separation problem. The use of the temporal convolutional network and optimal ratio mask to obtain the predicted mask, as well as the use of SISNR as the loss function, have been shown to effectively reduce echo in double-talk, nonlinear distortion, and background noise. This method is a significant advancement in the field of AEC and can be used in for mobile communication and online conference. [ABSTRACT FROM AUTHOR]

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch–McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation. [ABSTRACT FROM AUTHOR]

Traditional magnetotelluric signal processing usually uses time–frequency transformation method. Wavelet is also a time–frequency transformation method that used to suppress the magnetotelluric noise. However, the selection of the threshold is very significant, and the unsuitable threshold will lead to excessive distortion of the reconstructed signal. Thus, we propose a method for magnetotelluric noise suppression using grey wolf optimized wavelet threshold. First, the magnetotelluric signal is decomposed by wavelet with appropriate wavelet basis and decomposition layers. The generalized cross‐validation criterion is used as the fitness function of grey wolf optimizer algorithm, which optimizes the threshold of each decomposition layer. Then, the detail coefficients of each layer and the maximum layer of the approximation coefficients are used in the optimized threshold. Next, the inverse wavelet transform is performed. Finally, the noise contour is obtained through iteratively searching for the optimal threshold, and the useful magnetotelluric signal is reconstructed. Simulation experiments and measured magnetotelluric data processing show that the large‐scale interference can effectively be suppressed, and the reconstructed magnetotelluric signal retains the more abundant low‐frequency of useful information. Compared with the remote reference method, fixed threshold method and Birge–Massart layered threshold method, the proposed method realizes the wavelet denoising with adaptive threshold selection in the magnetotelluric noise suppression. The results obtained show smoother and more continuous apparent resistivity–phase curves, which verifies the effectiveness of the optimization. [ABSTRACT FROM AUTHOR]

In diversified optical applications, such as optical imaging and optical metrology, it is evidently significant to pursue low quantum fluctuations of light to promise the enhancement of measurement precision. Recently, it has been demonstrated that the technique of wavefront shaping provides an effective avenue to suppress the excess quantum noise of quadrature-squeezed light through a disordered medium in the presence of one-sided illumination. Nevertheless, the minimum suppressed noise is still above the initial squeezed noise level. Inspired by two-sided modulation enabling novel optical phenomena, we propose an alternative noise-reduction scheme combining two-sided illumination with wavefront shaping. It is clarified that our approach is able to further reduce the excess quantum fluctuation. The exact condition for the reduced noise to reach below the standard quantum limit is given. Intriguingly, the excess noise can be fully erased in certain case. Therefore, our scheme paves another way for elimination of the excess noise induced by multiple scattering in disordered media.

Abstract In this paper, we propose a technique for removing a specific type of interference from a monaural recording. Nonstationary interferences are generally challenging to eliminate from such recordings. However, if the interference is a known sound like a cell phone ringtone, music from a CD or streaming service, or a radio or TV broadcast, its source signal can be easily obtained. In our method, we define such interference as an acoustic object. Even if the sampling frequencies of the recording and the acoustic object do not match, we compensate for the mismatch and use the maximum likelihood estimation technique with the auxiliary function to remove the interference from the recording. We compare several probabilistic models for representing the object-canceled signal. Experimental evaluations confirm the effectiveness of our proposed method.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial–temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefiting from a low background noise level. Enhancing strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio, is both valuable and feasible. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at the surface, to achieve noise suppression through signal reconstructions. Both the theoretical analysis and the practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Accurately extracting weak signals is extremely important for overall performance and application in optoelectronic imaging and optical communication systems. While weak signals are susceptible to noise, adaptive filtering is a commonly used noise removal method. Still, its convergence speed is slow, the steady-state error is large, and the anti-interference ability is weak. To solve the above problems, this paper proposes a new type of variable-step-length adaptive filtering algorithm (DSLMS) based on the minutiae function, which effectively reduces the noise component in error through its combination with the pair cancelation system, utilizing the low correlation property of the noise signal, to improve the anti-noise interference ability of the adaptive filter. Using FPGA and Matlab (2018b) for experimental verification, the results show that this algorithm shows significant advantages in noise suppression, accelerated algorithm convergence, and low steady-state error, and it has effectiveness and application potential for the optoelectronic detection of weak signal processing.