Search

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]

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]

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]

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]

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]

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]

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]

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.

When a multi-degree-of-freedom manipulator operates in underground coal mines, its communication signal is easily affected by external factors such as pulses and electromagnetic interference. It reduces the reliability of signal transmission and affects the flexibility and control precision of the multi-degree-of-freedom manipulator. To improve the communication signal quality of underground multi-degree-of-freedom manipulator, a filtering method for underground manipulator communication signal based on improved time-frequency peak filtering is proposed. The method extracts carrier features based on the carrier nodes of the manipulator communication signal, and extracts transmission period features based on Hilbert transform theory. The method uses wavelet decomposition to solve the wavelet transform coefficient of each level, obtains the disturbance frequency of communication signals, and completes the classification of communication signal features. The method introduces phase compensation factors to improve time-frequency peak filtering. The reduction threshold is calculated according to the scaling factor and reduction factor, so as so adjust the fixed window length. The improved time-frequency peak filtering is used to filter the noise in communication signals. Thus the noise suppression of the multi-degree-of-freedom manipulator communication signal is achieved. A 3-degree-of-freedom manipulator is taken as the experimental object. The pulse width is set to be 8 μs. The Baud rate is 120 bit/s, the communication signal transmission interval is 2 ms, and the signal amplitude is 1. 30 dB Gaussian white noise is added to the communication signal. The experimental results show that the output signal-to-noise ratio of the communication signal processed by the filtering method for underground manipulator communication signal based on improved time-frequency peak filtering is 94 dB. The bit error rate is basically stable at 0.6%, and the average frame loss rate is reduced from 2.9% before filtering to 0.8%. The method effectively improves the communication signal quality and transmission reliability of the multi-degree-of-freedom manipulator.

The performance of free-space optical (FSO) communications that using an optical amplifier (OA) in the scheme of an amplify-received (AR)-relaying has a major drawback in the detection of input signal quality under the effects of turbulence. As an OA is based on a fiber-detection (FD) method to receive and delivers a signal at the amplification process stage, there is an opportunity to implement an optical spatial filter (OSF) to improve the quality of an input signal. In this paper, as the continuation of previous work on the direct-detection, the OSF is applied on the AR-relaying. The novelty proposed in this work is the improvement of FD method where the OSF is designed as the integration of cone reflector, pinhole and multi-mode fiber with an OA. The OSF produces an optical signal, the input of the OA, which minimizes the effects of turbulence, background noise and signal fluctuation. Thus, OA in AR-relaying produces signal output with high power and rise up below threshold level. Additionally, an OSF with a lower pinhole diameter produces the best quality of the signal spectral to be delivered into an EDFA. Through this implementation, the performance of optical relaying on FSO can be significantly improved.

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. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is an efficient and multi-functional control scheme for synchronous permanent magnet generators (PMSGs). However, the effective management of traditional MPC depends on precise system models. Multiple uncertainties of permanent magnet flux, motor inductance, filter inductance and parameter measurement noise will limit MPC's performance. The conventional linear extended state observer (ESO) can perform robust predictive control of the ultralocal model of the PMSG system to cope with parameter mismatches. However, the ESO is limited in balancing disturbance rejection with measurement noise attenuation. Since the amplification of high-frequency noise pollution can lead to both poor control performance and system instability, this challenge is of significant importance. To solve the problem, a new hybrid parallel cascaded ESO (PCESO) model-free predictive control framework is proposed using the three-level neutral-point-clamped (NPC) power electronic converter, on both the machine side and grid side. Analytical discussions of the time and frequency domain characteristics of the PCESO demonstrate its superior characteristics over the ESO. The proposed method can effectively balance parameter mismatch, disturbance rejection and high-frequency noise suppression. Finally, the effectiveness of the proposed method, under uncertainties of parameter mismatches, measurement noise and permanent magnet flux, is verified through real-time hardware-in-the-loop tests on a back-to-back grid-tied PMSG interfaced with an NPC power converter. [ABSTRACT FROM AUTHOR]

Continuous-variable quantum key distribution (CV-QKD) is a promising protocol that can be easily integrated with classical optical communication systems. However, in the case of quantum-classical co-transmissions, such as dense wavelength division multiplexing with classical channels and time division multiplexing with large-power classical signal, a quantum signal is more susceptible to crosstalk caused by a classical signal, leading to signal distortion and key distribution performance reduction. To address this issue, we propose a noise-suppression scheme based on carrier frequency switching (CFS) that can effectively mitigate the influence of large-power random noise on the weak coherent state. In this noise-suppression scheme, a minimum-value window of the channel's noise power spectrum is searched for and the transmission signal frequency spectrum shifts to the corresponding frequency to avoid large-power channel noise. A digital filter is also utilized to filter out most of the channel noise. Simulation results show that compared to the traditional fixed carrier frequency scheme, the proposed noise-suppression scheme can reduce the excess noise to 1.8 % , and the secret key rate can be increased by 1.43 to 2.86 times at different distances. This noise-suppression scheme is expected to be applied in scenarios like quantum–classical co-transmission and multi-QKD co-transmission to provide noise-suppression solutions. [ABSTRACT FROM AUTHOR]

These years, disease-causing and disabling diseases have caused great concern. Neurological musculoskeletal disorders are diverse and affect people of a wide range of ages. And the lack of comprehensive diagnostic methods places a huge burden on healthcare systems and social economies. In this paper, the current status of clinical research on neuromuscular diseases is introduced, and the advantages of magnetic field measurement compared with clinical diagnostic methods are illustrated. A comprehensive description of the related technology of superconducting quantum interference devices (SQUIDs), magnetic field detection noise suppression scheme, the development trend of the sensor detection system, and the application and model establishment of the neuromuscular magnetic field is also given in this paper. The current research and development trends worldwide are compared simultaneously, and finally the conclusions and outlook are put forward. Based on the description of the existing literature and the ideas of other researchers, the next development trends and my own research ideas are presented in this paper, that is, starting from the establishment of a neuromuscular model, combining medical and industrial work, designing a sensor system that meets clinical needs, and laying the foundation for the clinical application of a bio-magnetic system. This review promotes a combination between medicine and industry, and guides researchers on considering the challenges of sensor development in terms of clinical needs. In addition, in this paper, the development trends are described, including the establishment of the model, the clinical demand for sensors, and the challenges of system development so as to give certain guidance to researchers. [ABSTRACT FROM AUTHOR]

In order to generate high-quality single-photon emission computed tomography (SPECT) images under low-dose acquisition mode, a sinogram denoising method was studied for suppressing random oscillation and enhancing contrast in the projection domain. A conditional generative adversarial network with cross-domain regularization (CGAN-CDR) is proposed for low-dose SPECT sinogram restoration. The generator stepwise extracts multiscale sinusoidal features from a low-dose sinogram, which are then rebuilt into a restored sinogram. Long skip connections are introduced into the generator, so that the low-level features can be better shared and reused, and the spatial and angular sinogram information can be better recovered. A patch discriminator is employed to capture detailed sinusoidal features within sinogram patches; thereby, detailed features in local receptive fields can be effectively characterized. Meanwhile, a cross-domain regularization is developed in both the projection and image domains. Projection-domain regularization directly constrains the generator via penalizing the difference between generated and label sinograms. Image-domain regularization imposes a similarity constraint on the reconstructed images, which can ameliorate the issue of ill-posedness and serves as an indirect constraint on the generator. By adversarial learning, the CGAN-CDR model can achieve high-quality sinogram restoration. Finally, the preconditioned alternating projection algorithm with total variation regularization is adopted for image reconstruction. Extensive numerical experiments show that the proposed model exhibits good performance in low-dose sinogram restoration. From visual analysis, CGAN-CDR performs well in terms of noise and artifact suppression, contrast enhancement and structure preservation, particularly in low-contrast regions. From quantitative analysis, CGAN-CDR has obtained superior results in both global and local image quality metrics. From robustness analysis, CGAN-CDR can better recover the detailed bone structure of the reconstructed image for a higher-noise sinogram. This work demonstrates the feasibility and effectiveness of CGAN-CDR in low-dose SPECT sinogram restoration. CGAN-CDR can yield significant quality improvement in both projection and image domains, which enables potential applications of the proposed method in real low-dose study.

The complex working environment of mine leads to the degradation of the infrared image. The existing infrared image enhancement algorithm is easy to lose the scene details or causes the target edge blur while improving the signal-to-noise ratio and contrast. In order to solve the above problems, a mine infrared image enhancement algorithm based on dual domain decomposition coupling improved Gaussian Laplacian (ILoG) factor and contrast limited adaptive histogram equalization (CLAHE) (ILoG-CLAHE) is proposed. Firstly, the dual domain decomposition model is used to decompose the mine infrared image into a detailed sub-images containing high-frequency information and a basic sub-images containing low-frequency information. Secondly, the CLAHE algorithm is used to improve the brightness, contrast and definition of the basic sub-images to highlight the general features of the monitoring scene. The constructed ILoG operator is used to suppress noise and sharpen edges of detail sub-images and eliminate gradient inversion. Thirdly, the reconstructed image with improved image quality is obtained through the basic sub-image and detail sub-image after reconstruction processing. Finally, a Gamma correction function of gray level redistribution is designed to adjust the brightness of the reconstructed image. The mine infrared-enhanced image is obtained. The performance of the algorithm is analyzed by subjective vision and objective indicators. The results show that the overall visual effect and objective index of the mine infrared image enhanced by the mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE have been greatly improved. The comprehensive enhancement performance and robustness are better. Compared with the original mine infrared image and the six comparison algorithms, the comprehensive evaluation index values of this algorithm are increased by 0.28, 0.11, 0.23, 0.38, 0.57, 0.04, and 0.10 respectively. The six algorithms include CLAHE algorithm, bilateral filtering(BF) decomposition and CLAHE enhancement of basic sub-images (BF-CLAHE) algorithm, BF decomposition and Gamma transform (BF-Gamma) algorithm, guided filtering and Gamma transform (GF-Gamma) algorithm, adaptive histogram equalization(AHE) coupled Laplacian transform (AHE-LP) algorithm, and un-sharp mask(UM) based layer fusion (LF-UM) algorithm. The brightness, clarity and contrast of images are greatly improved, and noise suppression and edge sharpening are realized. It shows that the algorithm is suitable for the enhancement of infrared images in the complex working environment of mine.

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.

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.

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.

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 μs 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.

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.

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.

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.

Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods.

Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation 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.)

The transient electromagnetic method (TEM) is a geophysical exploration method that can efficiently acquire subsurface electrical parameters. For airborne, towed and other mobile platforms, TEM systems, large data volumes and the traditional one-dimensional denoising method with low efficiency and low signal-to-noise ratio (SNR) of late-time are the main bottlenecks limiting its reliable application. To address this problem, this paper proposes a neural network structure suitable for two-dimensional (2D) TEM data processing. The proposed structure combines a classical convolutional neural network denoising autoencoder with a gated recurrent neural network autoencoder, called the CNN-GRU dual autoencoder (CG-DAE). This method can directly input 2D TEM response data as images into the network for processing, which greatly improves data processing efficiency compared to single-time-channel processing. The simulation experiments verified the effectiveness of CG-DAE. After using CG-DAE denoising, the SNR of late-time (0.2–1 ms) signals is improved to nearly 29 dB, the 2D anomaly layer position is clear and the relative error (RE) between the denoised data and the corresponding clean data is <1.41%, while the RE of the late-time signals can be reduced to 3.68%. The proposed method can lay the foundation for fast processing of TEM data based on mobile platforms such as those that are airborne and/or towed. [ABSTRACT FROM AUTHOR]

We present a low-intensity noise single-frequency Yb-doped all-fiber amplifier oriented to space-borne gravitational wave detection. Relative intensity noise (RIN) below −70 dBc/Hz @ 1 mHz~1 Hz was achieved by virtue of feedback-loop-based intensity noise suppression. Based on systematic noise analysis and experimental investigation, we found that the pump noise and temperature-dependent noise of the fiber splitter and the photodetector contributed mainly to the RIN of the fiber amplifier. Therefore, we carefully designed a feedback-loop-based Yb-doped all-fiber amplifier, and finely stabilized the temperature of the pump diode, fiber splitters, and photodetectors. Consequently, the RIN can be suppressed down to −72.5 dBc/Hz around 1 mHz. This low-intensity all-fiber Yb-doped amplifier can be used for space-borne gravitational-wave detection. [ABSTRACT FROM AUTHOR]

The incorporation of effective denoising techniques is a crucial requirement for seismic data processing during the acquisition phase due to the inherent susceptibility of the seismic data acquisition process to various forms of interference, such as random and coherent noise. For random noise, the Residual Neural Network (Resnet), with its notable ability to effectively suppress noise in seismic data, has garnered widespread utilization in removing unwanted disturbances or interference due to its elegant simplicity and outstanding performance. Despite the considerable advancements achieved by conventional Resnet in the field of suppressing noise, it is irrefutable that there is still room for amelioration in their ability to filter out unwanted disturbances. As a result, this paper puts forth a novel attention-based methodology for Resnet, intended to overcome the present constraints and attain an optimal seismic signal enhancement. Specifically, we add the convolutional block attention module (CBAM) after the convolutional layer of the residual module and add channel attention on the shortcut connections to filter out the disturbance. We replace the commonly used ReLU activation function in the network with ELU, which is better suited for suppressing seismic noise. Empirical assessments conducted on both synthetic and authentic datasets have demonstrated the efficacy of the proposed methodology in amplifying the denoising prowess of Resnet. Our proposed method remains stable even when dealing with seismic data that has complex waveforms. The findings of this investigation evince that the recommended approach furnishes a substantial augmentation in the signal-to-noise ratio (SNR), thereby facilitating the efficient and robust extraction of the underlying signal from the noisy observations. [ABSTRACT FROM AUTHOR]

Geiger-mode avalanche photodiode (Gm-APD) array LiDAR has become the current research focus due to its sensitive response, high precision, and easy integration. However, due to the limitations of the fabrication process and manufacturing cost, the images collected by Gm-APD array LiDAR have very serious image low-resolution problems. Since the intensity map and depth map of the target can be obtained simultaneously by relying on single-source data from Gm-APD array LiDAR, we propose a Gm-APD LiDAR single-source data self-guided method. We propose to first perform Gm-APD LiDAR intensity map superresolution, and then use the processed intensity map with the corresponding depth map for guided superresolution. The advantage of this process is that instead of using high-resolution (HR) imaging devices from different domains, it relies only on single-source data from Gm-APD LiDAR to obtain HR depth map, thus eliminating the need for additional image registration work and providing wider applicability. We investigate the feasibility of the proposed single-source data processing method and evaluate our method on the real Gm-APD LiDAR single-source data with an average peak signal-to-noise ratio of 42.21, which better preserves the original distance information of the targets while providing visually sharper outputs.

The performance of free-space optical (FSO) communications that using an optical amplifier (OA) in the scheme of an amplify-received (AR)-relaying has a major drawback in the detection of input signal quality under the effects of turbulence. As an OA is based on a fiber-detection (FD) method to receive and delivers a signal at the amplification process stage, there is an opportunity to implement an optical spatial filter (OSF) to improve the quality of an input signal. In this paper, as the continuation of previous work on the direct-detection, the OSF is applied on the AR-relaying. The novelty proposed in this work is the improvement of FD method where the OSF is designed as the integration of cone reflector, pinhole and multi-mode fiber with an OA. The OSF produces an optical signal, the input of the OA, which minimizes the effects of turbulence, background noise and signal fluctuation. Thus, OA in AR-relaying produces signal output with high power and rise up below threshold level. Additionally, an OSF with a lower pinhole diameter produces the best quality of the signal spectral to be delivered into an EDFA. Through this implementation, the performance of optical relaying on FSO can be significantly improved. [ABSTRACT FROM AUTHOR]

The Xe isotope comagnetometer in the nuclear magnetic resonance regime can be used as a promising high-precision inertial measurement unit because of the absolute frequency measurement and high bandwidth. The fluctuation of the electron spin polarization leads to equivalent magnetic noise in the Xe isotope comagnetometer, which is one of the main factors limiting the stability of the comagnetometer. Here, we demonstrate systematic research of equivalent magnetic noise suppression and analyze the influence of the electron spin polarization on the Xe isotope comagnetometer. Based on the spin–exchange method between Xe isotopes and alkali metal atoms through the Fermi contact hyperfine interaction, the error equation of the Xe Larmor frequency is established. The equivalent magnetic noise can be suppressed by controlling the static magnetic field. This suppression method for Xe isotope comagnetometers improved the stability while maintaining high bandwidth. The experimental results show that this method can reduce the fluctuations of the 129 Xe and 131 Xe frequencies by 75% and 68.6%, respectively. [ABSTRACT FROM AUTHOR]

In image enhancement, maintaining the texture and attenuating noise are worth discussing. To address these problems, we propose a low-light image enhancement method with contrast increase and illumination smooth. First, we calculate the maximum map and the minimum map of RGB channels, and then we set maximum map as the initial value for illumination and introduce minimum map to smooth illumination. Second, we use the histogram-equalized version of the input image to construct the weight for the illumination map. Third, we propose an optimization problem to obtain the smooth illumination and refined reflectance. Experimental results show that our method can achieve better performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]