Search

The picking efficiency of seismic first breaks (FBs) has been greatly accelerated by deep learning (DL) technology. However, the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio (SNR) situations. To address this issue, we propose a regression approach to pick FBs based on bidirectional long short-term memory (BiLSTM) neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region. We employ a regressive model that represents the relationships among the elevation of shots, offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs, from common-shot gathers with sparsely distributed traces. Different from image segmentation methods which automatically extract image features and classify FBs from seismic data, the proposed method can learn the inner relationship between field geometry and FBs. In addition, the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution. The picking results of synthetic data shows that the proposed method has low dependence on label data, and can obtain reliable and similar predicted results using two types of label data with large differences. The picking results of 9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data. The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method. The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application. [ABSTRACT FROM AUTHOR]

Magnetic resonance sounding is a geophysical method for quantitatively determining the state for groundwater storage that has gained international attention in recent years. However, the practical acquisition of magnetic resonance sounding signals, which are on the nanovolt scale, is susceptible to various types of interference, such as power-line harmonics, random noise, and spike noise. Such interference can degrade the quality of magnetic resonance sounding signals and, in severe cases, be completely drowned out by noise. This paper introduces an adaptive Gaussian filtering algorithm that is well-suited for handling intricate noise signals due to its adaptive solving characteristics and iterative sifting approach. Notably, the algorithm can process signals without relying on prior knowledge. The adaptive Gaussian filtering algorithm is applied for the envelope extraction of noisy magnetic resonance sounding signals, and the reliability and effectiveness of the method are rigorously validated. The simulation results reveal that, even under strong noise interference (with original signal-to-noise ratios ranging from −7 dB to −25 dB), the magnetic resonance sounding signal obtained after algorithmic processing is compared to the ideal signal, with 16 sets of data statistics, and the algorithm ensures an initial amplitude uncertainty within 4nV and restricts the uncertainty of the relaxation time within a 6 ms range. The signal-to-noise ratio can be boosted by up to 53 dB. The comparative assessments with classical algorithms such as empirical mode decomposition and the harmonic modeling method confirm the superior performance of the adaptive Gaussian filtering algorithm. The processing of the field data also fully proved the practical application effects of the algorithm. [ABSTRACT FROM AUTHOR]

Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts. Often, regression models are not auto-calibrated. We propose to apply isotonic recalibration to a given regression model to restore auto-calibration. Our main result proves that under a low signal-to-noise ratio, this isotonic recalibration step leads to an explainable pricing system because the resulting isotonically recalibrated regression function has a low complexity. [ABSTRACT FROM AUTHOR]

In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance. [ABSTRACT FROM AUTHOR]

Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data. [ABSTRACT FROM AUTHOR]

In low signal-to-noise ratio (SNR) conditions, to address the problem of poor speech enhancement effect of traditional neural networks, this paper combines Convolution Recurrent Neural Network (CRN) with Gated Linear Units (GLU) to extract speech characteristics. Meanwhile, considering the advantages of the Empirical Mode Decomposition (EMD) algorithm, this paper put forward a model for speech enhancement that is based on Adaptive Mean Median Empirical Modal Decomposition and Multilayer Gated Convolutional Recurrent Neural Networks (ME-MGCRN). The model first uses the improved EMD algorithm to split the original noisy speech into low-frequency and high-frequency components. After that, the noisy speech is denoised using noise correlation and completes the feature extraction process. Finally, the extracted feature information is fed into the MGCRN network to realize speech enhancement further. This paper analyzes the performance of the Adaptive Mean Median Empirical Modal Decomposition algorithm using the LibriSpeech ASR dataset. This paper compares and investigates the enhanced performance of the ME-MGCRN model with the baseline model and the traditional model in terms of evaluation metrics such as the perceptual evaluation of speech quality (PESQ) and short-time objective intelligence (STOI). The research indicates that the ME-MGCRN model proposed in this paper has improved in the evaluation indexes, such as PESQ and STOI, respectively, by 0.22% and 5.6% than the baseline model and the traditional model, and the speech enhancement effect is better when Huber is selected as the loss function.

In the window function spectrum of the low signal-to-noise ratio photon Doppler signal after the short-time Fourier transform, the weak beat frequency cannot be obtained by extracting the maximum amplitude spectrum frequency of each window because the amplitude of the noise spectrum exceeds the weak beat frequency. In this article, the value of the beat frequency is first estimated by the Kalman filter. Then, a multiple analytical bandpass filter is constructed to refine the spectrum of the high noise signal with the value of the beat frequency estimate as the spectrum band center. This technique extracts only the beat frequencies from the narrowband refinement spectrum by removing the interference spectrum band range. We use this technique to process the photon Doppler signals from the tiny high-speed flying fragments explosion experiment. After data processing, the beat frequency value heavily affected by high-frequency noise can reduce the error by up to 64.9%. The beat frequency value of the low noise signal can be accurate to 106 Hz, equivalent to 0.775 m/s after velocity demodulation. This method fully considers the positioning and protection of the beat frequency characteristics. It makes weak beat frequencies more obvious in the refined narrowband spectrum without changing the signal amplitude. This article describes a method for extracting the weak beat frequency of the photon Doppler signal or a signal optimization algorithm for needing high-precision beat frequencies in a test environment.

Seismic data collected from desert areas contain a large amount of low-frequency random noise with similar waveforms to the effective signals. The complex noise characteristics make it difficult to effectively identify and recover seismic signals, which will adversely affect subsequent seismic data processing and imaging. In order to recover the complex seismic events from low-frequency random noise, we propose an attention mechanism guided deep convolutional autoencoder network (ADCAE) to assign different importance to different features at different spatial position. In ADCAE, an attention module (AM) is connected to the deep convolutional autoencoder network (DCAE) with soft-thresholded symmetric skip connection that helps to enhance the ability of feature extraction. By combining the global features of the input data and the output local features of DCAE, AM generates an attention weight matrix, which assigns different weights to the features associated with the seismic events and random noise during the training process. In this way, AM can guide the update of the target gradient, thus retains the complex structure of the seismic events in the denoised results and improves the training efficiency of the model. The ADCAE is applied to the synthetic data and field seismic data, and denoised results show that ADCAE has achieved satisfactory denoising performance in signals recovery and low-frequency random noise suppression at the low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods.

Magnetic resonance sounding is a geophysical method for quantitatively determining the state for groundwater storage that has gained international attention in recent years. However, the practical acquisition of magnetic resonance sounding signals, which are on the nanovolt scale, is susceptible to various types of interference, such as power-line harmonics, random noise, and spike noise. Such interference can degrade the quality of magnetic resonance sounding signals and, in severe cases, be completely drowned out by noise. This paper introduces an adaptive Gaussian filtering algorithm that is well-suited for handling intricate noise signals due to its adaptive solving characteristics and iterative sifting approach. Notably, the algorithm can process signals without relying on prior knowledge. The adaptive Gaussian filtering algorithm is applied for the envelope extraction of noisy magnetic resonance sounding signals, and the reliability and effectiveness of the method are rigorously validated. The simulation results reveal that, even under strong noise interference (with original signal-to-noise ratios ranging from −7 dB to −25 dB), the magnetic resonance sounding signal obtained after algorithmic processing is compared to the ideal signal, with 16 sets of data statistics, and the algorithm ensures an initial amplitude uncertainty within 4nV and restricts the uncertainty of the relaxation time within a 6 ms range. The signal-to-noise ratio can be boosted by up to 53 dB. The comparative assessments with classical algorithms such as empirical mode decomposition and the harmonic modeling method confirm the superior performance of the adaptive Gaussian filtering algorithm. The processing of the field data also fully proved the practical application effects of the algorithm.

The traditional BP-PID control algorithm uses the gradient descent method to solve, which has problems such as slow convergence speed, easy trapping in local extremum, and performance degradation under low signal-to-noise ratio (LSNR) conditions. In order to solve the above problems, a BP PID belt conveyor speed control method (ImGSAA-BP-PID) based on improved genetic simulated annealing algorithm (ImGSAA) optimization is proposed. Firstly, the values of crossover and mutation probabilities are correlated with the iteration time. The inverse cosine function is introduced to enhance the dynamic adjustment and nonlinear change adaptability of GSAA. Secondly, by weighting the traditional Metropolis criterion, a weighted Metropolis criterion is proposed to modify the new population individuals and improve the noise robustness of genetic simulated annealing algorithm (GSAA). Finally, ImGSAA is used to optimize the initial parameters of BP-PID, automatically determining the optimal parameter combination for BP-PID. It improves its real-time parameter tuning, control precision, and adaptability to the LSNR environment. The experimental results show the following points. ① ImGSAA only needs 11 iterations to converge, indicating that optimizing the GSAA using the proposed improved crossover and mutation strategies and weighted Metropolis criteria can effectively improve the convergence speed and real-time performance of the algorithm. ② The control error of ImGSAA-BP-PID is −0.468 5-0.572 3 m/s, which is 224.88%, 104.07%, and 38.33% higher than the control methods based on genetic algorithm (GA)-BP PID, particle swarm optimization (PSO)-BP PID, and GSAA-BP-PID, respectively. ③ The performance of ImGSAA is least affected by LSNR. It converges to the global optimal solution after 15 iterations, which has strong noise robustness. ④ Under LSNR conditions, the average control error of ImGSAA-BP-PID decreases by 3.54%. The control performance is significantly better than GA-BP-PID, PSO-BP-PID, and GSAA-BP-PID, which better meets the practical engineering application requirements.

Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods. [ABSTRACT FROM AUTHOR]

Aiming at the nonlinear and low signal-to-noise ratio characteristics of mine hoist vibration signal in complex environments, a mine hoist vibration signal fusion denoising algorithm based on complete EEMD with adaptive noise (CEEMDAN) and adaptive wavelet threshold is proposed. Firstly, the CEEMDAN algorithm is used to decompose the noisy mine hoist vibration signal to obtain the intrinsic mode component (IMF) and the residual. The IMF component is judged for high and low frequency. The t-test method is used to test whether the mean value is significantly different from 0. The IMF component which tends to 0 is the high-frequency component, and the IMF component which is significantly different from 0 is the low-frequency component. Secondly, the appropriate wavelet basis function and decomposition level are selected. The high-frequency IMF component is denoised by using the adaptive wavelet threshold method. Finally, the processed high-frequency IMF components and the unprocessed low-frequency IMF components are reconstructed with the residuals to obtain the de-noised vibration signal from the fusion algorithm. The CEEMDAN denoising method, CEEMD-wavelet threshold combined denoising method, CEEMDAN-wavelet threshold combined denoising method and CEEMDAN-adaptive wavelet threshold fusion denoising method are used to denoise the simulated signal respectively. The results show the following points. ① The signal denoised by the CEEMDAN-adaptive wavelet threshold fusion denoising method is similar to the original signal in local waveform features and signal peak values. Some features of the signal waveform have been restored well. The feature information of the original signal has been well preserved in the process of denoising. ② The composite evaluation index H is used as the objective evaluation standard. The H value of the CEEMDAN-adaptive wavelet threshold fusion denoising method is the smallest. This shows that the denoising effect of the fusion denoising algorithm for the simulation signal is better than that of other denoising methods. The experiment is carried out on the running mine hoist in a mine in Heilongjiang Province. The results show the following points. ① The db4 wavelet basis function is used to decompose the noisy IMF component in four layers. The signal de-noised by CEEMDAN-adaptive wavelet threshold fusion de-noising method is smooth. Some waveform features of the signal have also been restored well. While removing the noise, the feature information of the original signal has been retained to the greatest extent. ② In the actual mine hoist vibration signal denoising process, the CEEMDAN-adaptive wavelet threshold fusion denoising method has the smallest H value and the best denoising effect.

Most of the existing acoustic imaging studies in reverberant field ignore the influence of signal-to-noise ratio. As a result, commonly used beamforming algorithms in reverberant backgrounds have poor imaging accuracy for low signal-to-noise ratio sound sources. In response to that problem, an improved adaptive beamforming algorithm called SC-DAMAS is put forward in this paper. The algorithm replaces the free-field Green's function with the impulse response function, making the algorithm more suitable for acoustic imaging of low signal-to-noise ratio in a reverberant environment. Besides, the comparative simulation results with the conventional beamforming method and orthogonal matching pursuit algorithm-based DAMAS, as well as sound source acoustic imaging experiments are carried out to analyze its effectiveness. It is indicated that, in the reverberation field, the SC-DAMAS has no obvious sidelobes and achieves higher positioning accuracy for acoustic imaging of low signal-to-noise ratio sound source than the abovementioned counterparts, and its imaging test result is consistent with the actual situation, which verifies the effectiveness of the algorithm. [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.)

Because the traditional pulse laser high-precision ranging method has the problems of low ranging accuracy and long ranging time, a pulse laser high-precision ranging algorithm under low signal-to-noise ratio is proposed. Based on the analysis of the basic principle of pulse laser ranging, the pulse laser signal under low signal-to-noise ratio is obtained by modeling the pulse laser echo analog signal waveform. The wavelet denoising method of improved threshold function is used to denoise the pulse laser signal under low signal-to-noise ratio. According to the processing results, the pulse laser high-precision ranging is carried out through sinusoidal amplitude time conversion. The simulation results show that the proposed algorithm has high precision and short ranging time.

With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm's robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods. [ABSTRACT FROM AUTHOR]

The traditional speech enhancement algorithm based on generative adversarial networks (SEGAN) enhances speech in the time domain,and completely ignores the distribution of speech samples in frequency domain.Under the condition of low signal-to-noise ratio,the speech signal will be submerged in noise,and the time-domain distribution information of noisy speech is difficult to capture.Therefore,the enhancement performance of SEGAN will drop sharply,and the speech quality and speech intelligibility of its enhanced speech are very low.To solve this problem,this paper proposes a speech enhancement algorithm (time-frequency domain SEGAN,TFSEGAN) based on time-frequency domain generation confrontation network.TFSEGAN adopts the model structure of the time-frequency domain dual discriminator,and a time-frequency L1 loss function.The input of time domain discriminator is time domain feature of the speech sample,and the input of frequency domain discriminator is frequency domain feature of the speech sample.In the training process,time-domain discriminator uses the time-domain distribution information of speech sample as the criterion,and frequency-domain discriminator uses the frequency-domain distribution information of the speech sample as the criterion.Under the action of two discriminators,the generator of TFSEGAN could simulta-neously learn the distribution rules and information of speech samples in time domain and frequency domain.Experiments prove that,compared with SEGAN,the speech quality and intelligibility of TFSEGAN improve by about 17.45% and 11.75% respectively at low signal-to-noise ratio.

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 Shack-Hartmann wavefront sensor produces incorrect wavefront measurements when some sub-spots are weak and missing. In this paper, a method is proposed to predict the centroids of these sub-spots for the Shack-Hartmann wavefront sensor based on the deep neural network. Using the centroid information of present sub-spots, the method is able to predict the absent sub-spots’ positions. The feasibility and effectiveness of this method are verified by a large number of numerical simulations. The method is applied to wavefront measurement of light with non-uniform near-field intensity. The simulation results show that the proposed method is of great help to improve the measurement accuracy and the Strehl ratio of the focal spot. For wavefronts outside of the training sample, the proposed method shows good generalization and adaptability. In addition, the experiment results demonstrate that the proposed method can predict the missing sub-spots’ centroid displacements accurately even though a large proportion of sub-spot is lost randomly.

Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically expressed range time series signals. First, the two-dimensional echo signal was processed graphically. Second, the graphical echo signal was detected by the improved convolutional neural network. The simulation results under the condition of low signal-to-noise ratio show that, compared with the multi-pulse accumulation detection method, the detection method based on convolutional neural network proposed in this paper has a higher target detection probability, which reflects the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional deep learning algorithms have unsatisfactory speech enhancement effect under low Signal-to-Noise Ratio (SNR) conditions, this paper improves Deep Neural Network (DNN) and Multiresolution Residual U Network (MultiResU_Net) to discuss the speech enhancement effect. In this paper, the Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator (MMSE-STSA) is used to reduce the noise of the Multi-Resolution Cochleagram (MRCG) features that including the local and global characteristics of speech and using the improved features Improved Multi-Resolution Cochleagram (I-MRCG) as the input of Skip-DNN network to build Skip-DNN speech enhancement model. The short-time Fourier transformed features of the speech signal at low SNR are used as the input of MultiResU_Net network and the output features of residual path and upsampling are rearranged by the hybrid channel to build an improved MultiResU_Net speech enhancement model. Using LibriSpeech ASR corpus, under different SNR conditions, analyze the speech enhancement effects of different network models and different features as the input of the Skip-DNN model. The experimental results show that when the SNR is -5dB, the perceptual evaluation effect of the average speech quality is better that I-MRCG is used as the feature input of the Skip-DNN model. The average short-time objective intelligibility (STOI), the average perceptual evaluation of speech quality (PESQ) and Source-to-Distortion Ratio (SDR) of the improved MultiResU_Net speech enhancement model are higher than other models. It can be seen that both the improved Skip-DNN and MultiResU_Net proposed in this paper have better speech enhancement effect, especially the improved MultiResU_Net network is more suitable for speech enhancement at low SNR. [ABSTRACT FROM AUTHOR]

With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm’s robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods.

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.)

Speech enhancement in the frequency-domain can achieve a good noise reduction effect and improve speech communication quality under the condition of a high signal-to-noise ratio. However, speech enhancement performance in frequency-domain decreases significantly under low SNR conditions. To address this problem, this paper applied a mask based on speaker embedding to a frequency-domain speech enhancement network, and used the a priori information of speaker embedding to improve the network's ability to distinguish between speakers and noise. To further improve the performance of speech enhancement under low SNR conditions, this paper proposes a speech enhancement network with the introduction of mapping-based speaker embedding to avoid the problem of speech distortion, which may be caused by using the mask scheme. The experimental results show that the speech enhancement network with the introduction of mapping-based speaker embedding performs better in the low signal-to-noise ratio condition when the same speaker information is applied, especially in improving the speech distortion. Compared with the mask- based network, the mapping-based network achieves a relative improvement of 6.40%, 1.46% and 24.84% in the three metrics of PESQ, STOI and SSNR, respectively. respectively. respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press 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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically expressed range time series signals. First, the two-dimensional echo signal was processed graphically. Second, the graphical echo signal was detected by the improved convolutional neural network. The simulation results under the condition of low signal-to-noise ratio show that, compared with the multi-pulse accumulation detection method, the detection method based on convolutional neural network proposed in this paper has a higher target detection probability, which reflects the effectiveness of the method proposed in this paper.

In order to solve the problem of low coherent signal DOA estimation accuracy and low resolution in low signal to noise ratio, this paper proposes a DOA estimation algorithm for coherent signal under low SNR. This method mainly uses the advantages of spatial smoothing algorithm and ES-MUSIC algorithm. Coherent signals are decohered through spatial smoothing, and then ES-MUSIC algorithm is used to estimate DOA. In this paper, the DOA estimation success probability, DOA estimation mean square error and DOA estimation successful resolution are compared. The test results show that compared with the traditional method, the proposed method has higher DOA estimation accuracy and resolution at low signal to noise ratio.

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology 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.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

Summary: According to the characteristics of the chaotic oscillator in detecting weak signals, an algorithm based on Duffing chaotic oscillator array was proposed for acquisition of weak GLONASS signals. By means of GLONASS intermediate frequency and Duffing chaotic oscillator models, Lyapunov exponent is adopted to judge the critical condition of phase track and decide whether GLONASS signals exist or not by the change of the critical condition, and numeric iteration procedure is used to calculate the exponent. Finally, the performance of algorithm is simulated using GLONASS intermediate frequency data. The results indicate that acquisition algorithm can satisfy the need of object positioning in low signal‐to‐noise ratio environment. [ABSTRACT FROM AUTHOR]

For the challenges of filter bank multi-carrier (FBMC) test applications in the fifth-generation mobile communication network (5G) system, presented is a time domain synchronisation estimation algorithm based on power synchronisation in frequency domain. Through MATLAB simulation, the proposed algorithm is verified to achieve good synchronisation at low signal-to-noise ratio (SNR) and large frequency offset without signal quality changes. As a result, it can be applied in FBMC signal vector analysers, promoting FBMC signal vector analysis functions used in 5G test applications.

A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10−3 can be obtained at a signal-to-noise ratio of −8 dB.

Conventional ultrasonic imaging systems suffer from poor resolution imposed by the diffraction limit. The authors have recently demonstrated the use of holey-structured metamaterials (HSMs) to enable resolution beyond the diffraction limit in the ultrasonic regime. However, imaging with HSM requires acquisition of data at fine spatial intervals. Although the use of laser Doppler vibrometers (LDV) as a receiver can be a solution for this as reported in earlier experimental studies, they are highly sensitive to ambient disturbances, suffer from low signal-to-noise ratio and are also expensive, hampering a wider practical implementation. This Letter presents experimental results using hollow cone attachments coupled with conventional ultrasonic transducers for achieving subwavelength resolution imaging at a relatively higher speed and minimal cost. The proposed methodology can be implemented easily for practical inspections and has great potential for commercialisation. [ABSTRACT FROM AUTHOR]

The authors analyse the effect of correlation between the forward and backward links on the capacity of backscatter communication systems. With that aim, we obtain an analytical expression for the average capacity under a correlated Rayleigh product fading channel, as well as closed-form asymptotic expressions for the high and low signal-to-noise ratio (SNR) regimes. The proposed works' results show that correlation is indeed detrimental for a fixed target SNR; contrarily to the common belief, the authors also see that correlation can be actually beneficial in some instances when a fixed power budget is considered. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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.)

Passive bistatic radar detects targets by exploiting available local broadcasters and communication transmissions as illuminators, which are not designed for radar. The signal usually contains a time-varying structure, which may result in high-level range ambiguity sidelobes. Because the mismatched filter is effective in suppressing sidelobes, it can be used in a passive bistatic radar. However, due to the low signal-to-noise ratio in the reference signal, the sidelobe suppression performance seriously degrades in a passive bistatic radar system. To solve this problem, a novel mismatched filtering algorithm is developed using worst-case performance optimization. In this algorithm, the influence of the low energy level in the reference signal is taken into consideration, and a new cost function is built based on worst-case performance optimization. With this optimization, the mismatched filter weights can be obtained by minimizing the total energy of the ambiguity range sidelobes. Quantitative evaluations and simulation results demonstrate that the proposed algorithm can realize sidelobe suppression when there is a low-energy reference signal. Its effectiveness is proved using real data. [ABSTRACT FROM AUTHOR]

Aiming at the problem that single-network hopping signals have not fully utilised its frequency domain sparse characteristic in the parameter estimation, this study proposes a parameter estimation of frequency hopping (FH) signal based on multi-measurement vector sparse Bayesian learning (MSBL) in modulation wideband converter (MWC). Since the FH signal is sparse in the frequency domain, the authors apply the MSBL method to estimate its parameters. After the signal is sampled by the MWC, the MSBL algorithm is used to reconstruct its support set. Then the time–frequency ridge method is used to estimate the signal's hop duration, time-hopping, and carrier frequency based on the time–frequency map. Simulation experiments show that under the condition of low signal-to-noise ratio, the parameter estimation performance in the case can be improved by up to 65% and anti-noise performance can be improved up to 6 db compared with the existing method. The result is very close to the Nyquist full sampling and can greatly improve the accuracy of the FH signal parameter estimation in the MWC system and relieve the pressure of the hardware. [ABSTRACT FROM AUTHOR]

Image denoising under low signal‐to‐noise ratio (SNR) and non‐Gaussian noise is still a challenging problem in image processing. In this study, the authors prose a kind of improved convolution neural network auto‐encoders for image denoising. Different from other priors based methods, the denoising auto‐encoders (DAEs) can learn end‐to‐end mappings from noisy images to the target ones. This study research statistical features of image residual between the restored images and target images. According to the maximum entropy principle, the training loss function of the ordinary DAEs was modified with residual statistics as the constraint condition, and an improved training algorithm was proposed based on augmented Lagrange function method. Thus, the quality of restored image can be improved through removing image information from residual more efficiently. Experiments show not only the denoising effects of improved DAEs is superior to the original mean‐square‐error loss function DAEs in both peak SNR and Riesz feature similarity metric indexes, but also has the ability to suppress the different types of noises with different levels through a single model. [ABSTRACT FROM AUTHOR]

A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. In this paper we offer a formalism to study this question and subsequently introduce computationally efficient multiscale edge detection algorithms designed to detect faint edges in noisy images. In our formalism we view edge detection as a search in a discrete, though potentially large, set of feasible curves. First, we derive approximate expressions for the detection threshold as a function of curve length and the complexity of the search space. We then present two edge detection algorithms, one for straight edges, and the second for curved ones. Both algorithms efficiently search for edges in a large set of candidates by hierarchically constructing difference filters that match the curves traced by the sought edges. We demonstrate the utility of our algorithms in both simulations and applications involving challenging real images. Finally, based on these principles, we develop an algorithm for fiber detection and enhancement. We exemplify its utility to reveal and enhance nerve axons in light microscopy images. [ABSTRACT FROM AUTHOR]

For the challenges of filter bank multi-carrier (FBMC) test applications in the fifth-generation mobile communication network (5G) system, presented is a time domain synchronisation estimation algorithm based on power synchronisation in frequency domain. Through MATLAB simulation, the proposed algorithm is verified to achieve good synchronisation at low signal-to-noise ratio (SNR) and large frequency offset without signal quality changes. As a result, it can be applied in FBMC signal vector analysers, promoting FBMC signal vector analysis functions used in 5G test applications. [ABSTRACT FROM AUTHOR]

In order to improve the frequency measurement accuracy of low signal‐to‐noise ratio (SNR) sinusoidal signal, a frequency measurement approach based on a linear model is proposed. Firstly, the precision of frequency measurement affected by random noise while transforming the wave is analysed. Then, a linear model of frequency measurement is established according to the characteristics of frequency measurement, and the model is fitted by least‐square regression analysis with the measured data of several periods. Finally, the formula for frequency calculation is obtained from the regression results, and the relative error of frequency measurement is deduced and compared with other method. Simulation results and frequency measurement experimental results show that, the proposed method can effectively suppress noise and improve the frequency measurement accuracy of low SNR sinusoidal signal. [ABSTRACT FROM AUTHOR]

The mechanical noise in the cabin of the ship is so large that the leakage of high-pressure fluid is not easily noticed. In view of this situation, a near-field acoustic holography for high-frequency weak sound source under low signal-to-noise ratio is proposed. The method uses the empirical mode decomposition method to add weights to the time-domain sampling signals of each array element, and then uses the plane equivalent source near-field acoustic holography combined with compressive sensing to find the holographic surface acoustic pressure distribution. The simulation and experiment show that this method has certain feasibility under low signal-to-noise ratio, and the results are better than the method based on Fourier transform and the traditional boundary element method. It is of positive significance to apply it to engineering practice. [ABSTRACT FROM AUTHOR]

In random frequency-hopping pulse–Doppler radar, the transmitted frequencies are randomly distributed in the given frequency bandwidth. As a result, this kind of radar has the advantage of anti-jamming. However, because of the sidelobe level fluctuating randomly, the sidelobe of a large scatterer can mask the presence of nearby smaller scatterers. The adaptive pulse compression (APC) algorithm has been shown capable of effectively suppressing the range sidelobes, but the performance gain is diminished whenever the return signals have a low signal-to-noise ratio (SNR). Furthermore, moving targets with high speed bring about the range migration problem. To solve these problems, the coherent integration APC (CIAPC) algorithm is proposed. The simulation results indicate that in a low SNR scenario, the CIAPC algorithm can suppress the range sidelobes of large scatterers and can improve detection performance. [ABSTRACT FROM AUTHOR]

Here, a wideband signal direction-of-arrival (DOA) estimation method was proposed. By applying the keystone transform, the phase difference caused by different frequency is eliminated, and then the narrowband DOA estimation method can be used directly. Compared with the traditional method, the proposed approach can achieve better estimation accuracy than incoherent subspace method and coherent subspace method at low signal-to-noise ratio. The theoretical derivation of the method is given. Monte–Carlo simulations are provided to demonstrate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

For a surface ship carrying a towed array, its in-band radiated self-noise is one of the near-field strong interference, which will seriously limit the performance of the underwater acoustic (UWA) signal detection system. Typically, the conventional ship noise cancellation methods requires time-synchronized hydrophone arrays, such as the towed linear array (TLA), to suppress its self-noise by using spatial filters. However, the spatial filter based methods fail when the direction of arrival of the desired signal is in the ship noise masking area. In cooperative scenarios, the prior knowledge of the template signal provides additional temporal information, which can be utilized to design a time-domain representations based detection system. In this paper, a multiscale time self-attention network (MTSA-Net) is proposed to mitigate the ship radiated self-noise and enhance the desired signal to improve the performance of signal detection system. Experimental results based on sea trial data indicate the effectiveness of our proposed method. • This study reformulates the issue of reducing ship radiated self-noise as a signal separation task involving two types of signals: the desired signal from a cooperative source and the self-noise from the towed ship. • This paper introduces a novel approach, the multiscale time self-attention network (MTSA-Net), which is designed based on the cooperative source structure. • The proposed model MTSA-Net can obtain a high coefficient gain when maintaining sufficient discrimination. [ABSTRACT FROM AUTHOR]

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

High-intensity focused ultrasound (HIFU) therapy represents an image-guided and non-invasive surgical approach to treat uterine fibroid. During the HIFU operation, it is challenging to obtain the real-time and accurate lesion contour automatically in ultrasound (US) video. The current intraoperative image processing is completed manually or semi-automatic. In this Letter, the authors propose a morphological active contour without an edge-based model to obtain accurate real-time and non-rigid US lesion contour. Firstly, a targeted image pre-processing procedure is applied to reduce the influence of inadequate image quality. Then, an improved morphological contour detection method with a customised morphological kernel is harnessed to solve the low signal-to-noise ratio of HIFU US images and obtain an accurate non-rigid lesion contour. A more reasonable lesion tracking procedure is proposed to improve tracking accuracy especially in the case of large displacement and incomplete lesion area. The entire framework is accelerated by the GPU to achieve a high frame rate. Finally, a non-rigid, real-time and accurate lesion contouring for intraoperative US video is provided to the doctor. The proposed procedure could reach a speed of more than 30 frames per second in general computer and a Dice similarity coefficient of 90.67% and Intersection over Union of 90.14%.