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Ultrasonic nondestructive testing (NDT) provides a valuable insight into the integrity of stainless steel structures, but the noise caused by the scattering of stainless steel microstructure often limits the effectiveness of inspection. This work presents a novel adaptive filtering approach to enhance the signal-to-noise ratio (SNR) of a measured ultrasonic signal from the inspection of a stainless steel component, enabling the detection of hidden flaws under strong noise. After the spectral modeling of the noisy ultrasonic NDT signal, the difference between the spectral characteristics of a flaw echo and that of grain noise is highlighted, and a reference spectrum model to estimate the frequency spectrum of the echo reflected by any possible flaw is developed. Then, the signal is segmented and the similarity between the spectra of data segments and the reference spectra is evaluated quantitatively by the spectral similarity index (SSI). Based on this index, an adaptive time-frequency filtering scheme is proposed. Each data segment is processed by the filtering to suppress the energy of noise. The processed data segments are recombined to generate the de-noised signal after multiplying weighting coefficients, which again is determined by the SSI. The performance of the proposed method for SNR enhancement is evaluated by both the simulated and experimental signal and the effectiveness has been successfully demonstrated. [ABSTRACT FROM AUTHOR]

The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective. [ABSTRACT FROM AUTHOR]

Noise pollution remains a challenging problem requiring the development of novel systems for noise insulation. Extensive work in the field of acoustic metamaterials has led to various ventilated structures which, however, are usually demonstrated for rather narrow regions of the audible spectrum. In this work, the idea of metamaterial‐based systems is further extended, developing the concept of a metahouse chamber representing a ventilated structure for multiple band noise insulation. Broad stop‐bands originate from strong coupling between pairs of Helmholtz resonators constituting the structure. The averaged transmission −18.6 dB are demonstrated numerically and experimentally within the spectral range from 1500 to 16 500 Hz. The sparseness of the structure together with the possibility to use optically transparent materials suggest that the chamber may be also characterized by partial optical transparency depending on the mutual position of structural elements. The obtained results are promising for development of novel noise‐insulating structures advancing urban science. [ABSTRACT FROM AUTHOR]

This article presents a new method that employs the active disturbance rejection control (ADRC) paradigm to handle the control problem for a class of nonlinear systems with unknown disturbances and measurement noise. An improved extended state observer (ESO) is devised to separate disturbance estimation from state reconstruction and a new parameter is used to adjust disturbance-rejection performance independently. This increases the flexibility of system design and makes the gain of the improved ESO (IESO) small compared to that of a conventional ESO in the ADRC. A low-pass filter is added to the IESO. This changes the slope of the magnitude curve of the Bode diagram from $-20$ dB/dec to $-40$ dB/dec at high frequencies for measurement noise, which improves the noise-suppression performance. Comparison with a conventional ESO reveals the superiority of the IESO in disturbance rejection and noise suppression. The presented method is validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to noise suppression using multiple distributed recording devices with stereo microphones. In the proposed method, noise suppression based on phase information is applied to the synchronous stereo signals captured by each recording device and then output signals are utilized for transfer-function-gain nonnegative matrix factorization (NMF) as extra input signals. We intended to estimate the target signal more accurately by transfer-function-gain NMF. Experiments using impulse responses measured in a meeting room have shown that the proposed method outperformed conventional methods using transfer-function-gain NMF in terms of the signal-to-distortion ratio (SDR) and signal-to-interference ratio (SIR). [ABSTRACT FROM AUTHOR]

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Lead (Pb) halide perovskites have attracted significant attention in the field of optoelectronics. However, due to the disadvantages associated with the presence of Pb in perovskite, its use should be minimized, if not completely eliminated. Herein, an efficient Pb‐free Sn‐based halide perovskite photodetector (PD) that is sensitive to visible and near‐infrared light is developed. The Sn perovskite is characterized with low toxicity and a narrow bandgap, and exhibits lower shunt leakage and a higher built‐in potential compared with a Pb‐based perovskite. Also, the relatively stronger bond strength between Sn and iodine ions in comparison with that observed in the Pb‐based perovskite effectively prevents charge injections that are vulnerable to dark current suppression. The Sn‐based perovskite PD demonstrates a high signal‐to‐noise ratio (SNR) of 51.12 dB, as well as an excellent cut‐off frequency of 763 kHz. To the best knowledge, this device is one of the fastest and most efficient photodiode‐type Sn‐based perovskite PDs developed to date. Unlike the Pb‐based perovskite device, the Sn‐based perovskite PD retains its high SNR over time. This effect may be attributed to the low movement of ion vacancies and degree of vertical shunt leakage in the perovskite material. [ABSTRACT FROM AUTHOR]

In this letter, a quasi-resonant extended state observer-based predictive current control (QRESO-based PCC) strategy is proposed for three-phase pulse width modulation (PWM) rectifier. Specifically, the QRESO is utilized to estimate the predictive value of system total disturbance and grid current at time instant k+1 in the stationary αβ frame, and the required control input at time instant k+1 is then calculated based on the principle of deadbeat predictive control. The proposed control scheme can achieve robust control against to the electrical parameter variation, accommodate the measurement noise, and also assure the satisfactory steady-state and dynamic performance for the PWM rectifier. Besides, the stability analysis of QRESO and the whole current close-loop control system with the consideration of QRESO in the discrete-time domain are presented. Finally, several hardware-in-the-loop test results of the proposed control strategy are provided and compared with the model free predictive current control based on linear extended state observer and the generalized integrator-extended state observer-based PCC strategy, which validate the effectiveness and superiority of proposed QRESO-based PCC strategy. [ABSTRACT FROM AUTHOR]

Spin systems in solid state materials are promising qubit candidates for quantum information in particular as quantum memories or for quantum sensing. A major prerequisite here is the coherence of spin phase oscillations. In this work, we show a control sequence which, by applying RF pulses of variable detuning, allows to increase the visibility of spin phase oscillations. We experimentally demonstrate the scheme on single NV centers in diamond and analytically describe how the NV electron spin phase oscillations behave in the presence of classical noise models. We hereby introduce detuning as the enabling factor that modulates the filter function of the sequence, in order to achieve a visibility of the Ramsey fringes comparable to or longer than the Hahn-echo T2 time and an improved sensitivity to DC magnetic fields in various experimental settings. [ABSTRACT FROM AUTHOR]

High-gain nature of extended state observer (ESO), which forms an integral part of active disturbance rejection control (ADRC) technique, results in the following limitations: 1) Sensitivity to high-frequency measurement noise which limits closed-loop performance in practical applications. 2) Escalation of observer gains up to a power $\boldsymbol{n+1}$ of observer bandwidth, which complicates numerical implementation when system order $(\boldsymbol{n})$ or observer bandwidth is large. To overcome these limitations, a low-power higher order ESO is proposed in the present work for practical application of ADRC scheme in noisy environment. Moreover, a recently proposed cascade ESO (CESO), designed for noise suppression, is analyzed in the frequency-domain to reveal an underlying similarity with higher-order ESO, which is not reported in literature. Presented analysis justifies the performance improvement obtained over conventional ESO and provides a guideline for selecting the number of cascade levels based on the expected nature of disturbance. Case study performed on a dc–dc boost converter illustrates the practical advantages of proposed scheme over CESO in terms of improved immunity to high-frequency measurement noise, precise regulation in presence of time-varying disturbance, low observer gains that facilitate numerical implementation, and ease of tuning due to a single observer parameter. [ABSTRACT FROM AUTHOR]

A hybrid method incorporating the simulations of noise sources with delayed detached eddy simulation (DDES) and calculations of far-field noise with the Ffowcs Williams–Hawkings (FW-H) equation is used to study the suppression technique for the aerodynamic noise of a Faiveley CX-PG pantograph. Considering that China's Fuxing bullet trains operate at 350 km/h, the inflow velocity of 350 km/h is applied in this paper. The noise radiated from the panhead area, middle area, and bottom area at an inflow velocity of 350 km/h is distinguished. The noise intensities at the standard observer show that the noise radiated from the panhead area is the strongest, and the sound pressure level spectrum value is larger than the other two in the range above 500 Hz. The influence of applying the wavy rods and modifying the contact strip shape on the aerodynamic noise is discussed in detail. By comparing the acoustic source distribution and the far-field noise intensity, it is found that applying the wavy rods can effectively reduce the panhead noise, especially around the peak frequency. Modifying the shape of the contact strip to a hexagon can suppress the vortex shedding, leading to a lower surface pressure level. Combining the strip modification and wavy rods, the total noise intensity can be diminished by about 3.0 dB. [ABSTRACT FROM AUTHOR]

Helicopter has achieved rapid development and wide application in various fields, due to its unique flight mode. However, the noise is also a particular feature of the helicopter, which people cannot bear without protection. With the requirements of helicopter comfort and low noise pollution, its noise has become an urgent problem that must be solved. According to the main sources and propagation ways of external noise and internal noise of the helicopter, the current situation of material and structure-based helicopter noise control in domestic and international scope was reviewed. The noise control characteristics and effects of conventional sound absorbing materials, intelligent piezoelectric control materials, acoustic super material/structure and damping material were introduced in this paper. Traditional materials are no longer suitable for the current lightweight requirements of the helicopter. Intelligent composite materials, new sound-absorbing structures, and acoustic super material/structure have become potential selections of noise suppression. Finally, combined with the current development status of helicopter vibration and noise reduction materials, the development trend of helicopter noise reduction material/structure in the future such as active noise reduction technology, resonance sound absorption, metamaterial acoustic band gap, damping material noise reduction and so on, is proposed, and feasible research ideas for the development direction of helicopter noise suppression materials/structures are put forward.

Dual energy computed tomography (DECT) plays an important role in the application of advanced imaging due to its material decomposition capability. Image domain decomposition can directly invert CT images through by linear matrix, but the decomposed material images will be seriously affected by noise and artifacts. Although various regularization methods have been proposed to solve this problem, they still face two challenges: tedious parameter adjustment and the loss of image details resulted from over-smoothing. Therefore, in this paper we proposes a dual energy CT image material decomposition algorithm based on iterative residual network. Direct inversion is used as the initial base image, and a stacking two-channel convolutional neural network is used to replace the regularization items in the iterative decomposition model to form a deep iterative decomposition network. This method can realize material decomposition and noise suppression simultaneously. Experimental results show that the iterative residual network proposed in this paper is superior to other comparison methods and can effectively suppress noise and artifacts while maintaining the edge details of the base image.

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD's sensitivity to threshold selection, along with the RPCA's limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging. • LP-fRPCA is introduced for clutter filtering in ultrasound microvascular imaging. • Low-rank prior and accelerated algorithm is used for rapid convergence. • Superior microvascular visualization is demonstrated with enhanced CR and CNR. • Robust performance in both phantom and in vivo rabbit kidney experiments is shown. • Processing time is significantly reduced, improving clinical applicability. [ABSTRACT FROM AUTHOR]

Nowadays, redundant manipulators have received a lot of attention from industry and academia because of their ability to adapt to complex tasks. Specifically, the motion control is one of the key problems, especially in trajectory tracking. Nevertheless, it is very challenging to achieve precise motion control without relying on their model. In this paper, for the precise and robust motion control of redundant manipulators under different noises, an integral-type design formula combined with zeroing neurodynamics is developed to establish a model-free noise-immune control scheme. Then, an inverse-free version of the control scheme is further proposed to eliminate the matrix inversion operation. At the same time, a filtering method is applied to compensate for the demands for velocity and acceleration information. The robustness and stability of this scheme have been rigorously analyzed, confirming its reliability and effectiveness for high-precision tasks in noisy environments. Simulations and practical experiments on the basis of different manipulators are performed under different noise conditions, and the comparison of different methods shows the superiorities of the proposed scheme with respect to accuracy and robustness in the motion control task. [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.

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.

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.

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.

Hyperspectral anomaly detection is a widely used technique for exploring target of interest in hyperspectral images (HSIs). In recent years, the low-rank and sparse-decomposition-based anomaly detection model has attracted extensive attention. However, these models suffer from two main problems. First, it is difficult for them to completely separate the low-rank background and the sparse anomaly. Moreover, the extracted sparse component is inevitably contaminated by noise. Second, the incorporation of various constraints increases the cost of selecting the optimal parameters. To solve the two key problems, we propose a self-adaptive low-rank and sparse decomposition (SLaSD) method for hyperspectral anomaly detection in this article. The proposed method decomposes the sparse (anomaly) part of the HSI through a novel self-adaptive alternating direction method (S-ADM). The noise of the sparse part is suppressed through a dual strategy of integrating guided filter and the difference between the S-ADM-derived sparse features of pixels. The performance of the proposed method is evaluated by comparing with ten state-of-the-art methods using six real HSIs. It is shown that the proposed SLaSD method can produce more accurate detection results than the ten benchmark methods.

Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-noise ratio. In this study, we propose an improved cycle-consistent generative adversarial network (CycleGAN) seismic random noise suppression method. First, the generator replaces the original cycle-consistent generative adversarial network generator network structure with the Unet structure combined with the Resnet structure in order to increase the diversity of seismic data feature extraction and decrease the loss of seismic data details. Second, in order to improve the network’s stability, the feature extraction effect, the event texture preservation effect, and the signal-to-noise ratio, the Least Square GAN (LSGAN) square difference loss is used in place of the conventional generative adversarial network cross-entropy loss. The feasibility of the proposed method was confirmed using model and real seismic data, both of which demonstrated that the improved cycle-consistent generative adversarial network method effectively suppressed random noise in seismic data. In addition, the denoising effect was superior to both the widely used FX deconvolution denoising method and original cycle-consistent generative adversarial network denoising method.

Due to high spatial resolution, low cost, and wide bandwidth, distributed optical fiber acoustic sensing (DAS) is regarded as a potential tool for data acquisition in vertical seismic profile (VSP) surveys. However, in real DAS-VSP records, desired signals are often seriously plagued by various noise, which does not appear in the conventional seismic data received by electronic geophones. Exploring a high-performing attenuation method for the background noise can significantly improve the quality of DAS-VSP records and has essential impacts on the following imaging and interpretation. Deep-learning-based methods, especially convolutional neural network (CNN), have shown remarkable performance in seismic data denoising. However, the conventional CNN-based methods may degrade when dealing with DAS-VSP records in low signal-to-noise ratio. In this study, we propose a novel multi-scale dense-connection denoising network (MDD-Net) to achieve high-accuracy processing of the complex DAS background noise. Unlike conventional multi-scale networks, MDD-Net utilizes widen convolution block to capture the multi-scale features of the analyzed data. On this basis, dense connection operations are employed to fuse the features and improve the network efficiency. Meanwhile, an enhanced spatial attention (ESA) block is designed to reinforce the features, which are helpful for noise suppression and weak signal recovery. Both synthetic and field DAS-VSP records are processed to verify the effectiveness of MDD-Net. Meanwhile, we also compare the denoising results with other competing methods. The experimental results demonstrate that MDD-Net can significantly attenuate the complex DAS background noise and restore the desired signals, even for the weak upgoing signals.

In recent years, distributed optical fiber acoustic sensing (DAS) technology has been increasingly used for vertical seismic profile (VSP) exploration. Even though this technology has the advantages of high spatial resolution, strong resistance to high temperature and pressure variations, long sensing distance, DAS seismic noise has expanded from random noise to optical abnormal noise, fading noise and horizontal noise, etc. This seriously affects the quality of the seismic data and brings huge challenges to subsequent imaging, inversion and interpretation. Moreover, the noise is more complex and more difficult to simultaneously suppress using traditional methods. Therefore, for the purpose of effectively improving the signal-to-noise ratio (SNR) of DAS seismic data, we introduce a denoising network named attention-guided denoising convolutional neural network (ADNet). The network is composed of four blocks, including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB). The network uses different kinds of convolutions alternately to enlarge the receptive field size and extract global feature of the input. Meanwhile, the attention mechanism is introduced to extract the hidden noise information in the complex background. The network predicts the noise, and denoised data are obtained by subtracting the predicted results from the noisy inputs. In addition, we uniquely construct a large number of complex forward models for pure seismic data training set to enhance the network suitability. The combination design improves the denoising performance and reduces computational cost and memory consumption. The results obtained from both synthetic- and field data illustrate that the network has the ability to denoise the seismic images and retrieve weak effective signals better than conventional methods and common networks.

Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and strong resistance to the harsh environment. Noise attenuation is an essential step in seismic data processing. However, there are two main difficulties faced by the denoising task of DAS seismic data. On the one hand, some background noise in DAS seismic data, such as optical low-frequency noise, horizontal noise, and fading noise, is unique and not presented in the conventional seismic data; on the other hand, the signal-to-noise ratio (SNR) of DAS seismic data is relatively low. Recently, a convolutional neural network (CNN) has shown superior denoising performance compared to the traditional method. To follow this promising trend, we propose a multi-scale interactive convolutional neural network (MSI-Net) and apply it to denoise the challenging DAS seismic data. Different from most of the existing CNN architecture used in seismic data denoising, the MSI-Net considers both coarse-scale and fine-scale features by improving the inherent serial convolution to multi-scale parallel convolution, which is beneficial to recover detailed information. Moreover, we utilize some connections to achieve the information interaction between different scales, which promotes the flow of information and enables the network to extract more informative multi-scale features from the DAS seismic data. Moreover, both synthetic and real examples demonstrate that the proposed MSI-Net can effectively attenuate a variety of unique DAS background noise and also completely recover the weak signals. Compared with conventional CNN architecture, MSI-Net exhibits better performance in global SNR and local details.

We present a method for speech enhancement of data collected in extremely noisy environments, such as those obtained during magnetic resonance imaging (MRI) scans. We propose an algorithm based on dictionary learning to perform this enhancement. We use complex nonnegative matrix factorization with intra-source additivity (CMF-WISA) to learn dictionaries of the noise and speech+noise portions of the data and use these to factor the noisy spectrum into estimated speech and noise components. We augment the CMF-WISA cost function with spectral and temporal regularization terms to improve the noise modeling. Based on both objective and subjective assessments, we find that our algorithm significantly outperforms traditional techniques such as Least Mean Squares (LMS) filtering, while not requiring prior knowledge or specific assumptions such as periodicity of the noise waveforms that current state-of-the-art algorithms require.

Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks have been successfully used to denoise seismic data in a supervised fashion. However, supervised learning always comes with the often unachievable requirement of having noisy-clean data pairs for training. Using blind-spot networks, we redefine the denoising task as a self-supervised procedure where the network uses the surrounding noisy samples to estimate the noise-free value of a central sample. Based on the assumption that noise is statistically independent between samples, the network struggles to predict the noise component of the sample due to its randomicity, whilst the signal component is accurately predicted due to its spatio-temporal coherency. Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as post-stack inversion. To conclude our study, the suggested approach is applied to field data and the results are compared with two commonly used random denoising techniques: FX-deconvolution and sparsity-promoting inversion by Curvelet transform. By demonstrating that blind-spot networks are an efficient suppressor of random noise, we believe this is just the beginning of utilising self-supervised learning in seismic applications.

Noise is ever present in seismic data and arises from numerous sources and is continually evolving, both spatially and temporally. The use of supervised deep learning procedures for denoising of seismic datasets often results in poor performance: this is due to the lack of noise-free field data to act as training targets and the large difference in characteristics between synthetic and field datasets. Self-supervised, blind-spot networks typically overcome these limitation by training directly on the raw, noisy data. However, such networks often rely on a random noise assumption, and their denoising capabilities quickly decrease in the presence of even minimally-correlated noise. Extending from blind-spots to blind-masks has been shown to efficiently suppress coherent noise along a specific direction, but it cannot adapt to the ever-changing properties of noise. To preempt the network’s ability to predict the signal and reduce its opportunity to learn the noise properties, we propose an initial, supervised training of the network on a frugally-generated synthetic dataset prior to fine-tuning in a self-supervised manner on the field dataset of interest. Considering the change in peak signal-to-noise ratio, as well as the volume of noise reduced and signal leakage observed, using a semi-synthetic example we illustrate the clear benefit in initialising the self-supervised network with the weights from a supervised base-training. This is further supported by a test on a field dataset where the fine-tuned network strikes the best balance between signal preservation and noise reduction. Finally, the use of the unrealistic, frugally-generated synthetic dataset for the supervised base-training includes a number of benefits: minimal prior geological knowledge is required, substantially reduced computational cost for the dataset generation, and a reduced requirement of re-training the network should recording conditions change, to name a few. Such benefits result in a robust denoising procedure suited for long term, passive seismic monitoring.

It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cyclic statistics CWD time-frequency images and the YOLOv5 deep network model, which can quickly dissociate, label, and sort the multi-emitter signal features in the time-frequency domain under a low SNR environment. First, the denoised signal is extracted based on the low-order cyclic statistics of the typical modulation types of radiation source signals. Second, the time-frequency graph of multisource signals was obtained through CWD time-frequency analysis. The cyclic frequency was controlled to balance the noise suppression effect and operation time to achieve noise suppression of multisource signals at a low SNR. Finally, the YOLOv5s deep network model is used as a classifier to sort and identify the received signals from multiple radiation sources. The method proposed in this paper has high real-time performance. It can identify the radiation source signals of different modulation types with high accuracy under the condition of a low SNR. [ABSTRACT FROM AUTHOR]

With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed. [ABSTRACT FROM AUTHOR]

Up to date, the existing methods for nonlinear optimization with time-dependent parameters can be classified into two types: 1) static methods are capable of handling inequality constraints but may generate large lagging errors in the solution of the intrinsically time-dependent constrained nonlinear optimization (TDCNO) problem due to the hypothesis of short-time invariance and 2) time-variant methods, e.g., zeroing neural networks, are able to remedy the lagging error but fail to solve the TDCNO problem under inequality constraints. To resolve this contradiction, a noise-suppressing neural dynamics (NSND) model is proposed to solve the TDCNO problem subject to both equality and inequality constraints via the nonlinear complementary problem (NCP) function. The proposed method allows inequality constraints for unknown variables, removes the short-time invariance hypothesis, and further eliminates lagging errors during the solving process in the presence of noises. Besides, the rapid convergence, global stability, and noise processing of the NSND model are verified by the theoretical analyses. Simulation results of illustrative examples, including dimensionality reduction on principal component analyses (PCA) and a robot motion control, show that the NSND model outperforms the existing models for the TDCNO problem. [ABSTRACT FROM AUTHOR]

The fiber optic gyro (FOG), which is currently one of the most widely used fiber optic sensors, has firmly occupied a considerable market share of inertial sensors and offers the benefits of high stability and easy integration. The resonant FOG, also known as the RFOG, is a sensor that identifies a rotation rate by employing the use of a fiber ring resonator. It possesses a higher application potential in terms of optical stability and system miniaturization because it is an FOG of the second generation. This review begins with an exploration of the RFOG principle and then moves on to concentrate on the two aspects of RFOG optical noises suppression and signal processing in order to examine the progression of RFOG in the most recent years. Several distinct types of RFOG schemes that have a high application potential are dissected individually; these are the types of schemes that are anticipated to become mainstream application schemes in the not-too-distant future. In the end, the prospect and research challenge of RFOG are presented to help researchers clarify research directions. This article presents an in-depth analysis of the current state of research regarding RFOG-related technologies and offers a forecast regarding the path that the inertial sensor will take in the foreseeable future. [ABSTRACT FROM AUTHOR]

High-resolution gradient power amplifiers are required to generate high-fidelity gradient fields in magnetic resonance imaging (MRI) systems. Various aspects of gradient power amplifiers have been the subject of research in the past years, however, systematic analysis and design methods for its resolution improvement have not been sufficiently addressed. This article addresses this research gap with comprehensive analysis and design methods for resolution improvement. First, a method for systematic resolution characterization of the MRI gradient power amplifier with gradient coil is proposed. Second, noise modeling with respect to the critical aspects in the system resolution degradation is developed and discussed. Third, the resolution improvement methods of bandwidth optimization, pseudorandom code modulation, and utilizing silicon carbide (SiC) switching power devices are proposed and analyzed, achieving a resolution at the one part in ${10^6}$ level. Finally, the resolution improvement methods are experimentally validated in a 1000 V/400 A SiC gradient power amplifier. The experimental results show a 3.6 times reduction in the peak spurious signals and resolution improvement at all current levels and pulse lengths, compared to a conventional insulated gate bipolar transistor GPA. These results prove the validity of the proposed methods for resolution improvement in a high-power MRI gradient power amplifier, enabling future high-power and high-resolution amplifier designs. [ABSTRACT FROM AUTHOR]

In the acoustics-based power transformer fault diagnosis, a transformer acoustic signal collected by an acoustic sensor is generally mixed with a large number of interference signals. In order to separate transformer acoustic signals from mixed acoustic signals obtained by a small number of sensors, a blind source separation (BSS) method of transformer acoustic signal based on sparse component analysis (SCA) is proposed in this paper. Firstly, the mixed acoustic signals are transformed from time domain to time–frequency (TF) domain, and single source points (SSPs) in the TF plane are extracted by identifying the phase angle differences of the TF points. Then, the mixing matrix is estimated by clustering SSPs with a density clustering algorithm. Finally, the transformer acoustic signal is separated from the mixed acoustic signals based on the compressed sensing theory. The results of the simulation and experiment show that the proposed method can separate the transformer acoustic signal from the mixed acoustic signals in the case of underdetermination. Compared with the existing denoising methods of the transformer acoustic signal, the denoising results of the proposed method have less error and distortion. It will provide important data support for the acoustics-based power transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

Noise suppression sheets (NSS) composed of magnetic materials are widely used to mitigate the electromagnetic (EM) emission of radiation from electronic equipment as well as EM interference between devices within it. Numerical full-wave EM simulators, such as a finite-difference time-domain method, have been essential to design NSS. Although numerical EM simulations are rigorous, they require large memory and CPU time. This article validates an analytical transmission-line method for calculating the inter-decoupling ratio of NSS measured with a pair of loop antennas. Furthermore, this article devises a simple method on the basis of a variational method and EM duality concept for calculating the transmission attenuation power ratio of NSS placed on a microstrip line. The devised method is shown to be able to reproduce the measured transmission attenuation power ratios of commercially available NSS within 10% accuracy and with computational time much shorter than numerical full-wave EM simulators. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) technology is a novel technology applied in vertical seismic profile (VSP) exploration, which has many advantages, such as low cost, high precision, strong tolerance to harsh acquisition environment. However, the field DAS-VSP data are often disturbed by complex background noise and coupling noise with strong energy, affecting the quality of seismic data seriously. Therefore, we develop a time–frequency analysis method based on low-rank and sparse matrix decomposition (LSMD) and data position points distribution maps (DPM) to separate signals from noise. We adopt Multisynchrosqueezing Transform to construct the approximate ideal time–frequency representation of DAS data, which reduces the difficulty of signal to noise separation and avoids the loss of some effective information to a certain extent. The LSMD is performed to separate the signal component and noise component preliminarily. In addition, combined with the separated low-rank matrix and sparse matrix, we propose the DPM to improve the accuracy of signal component extraction and the recovery ability of the method for weak signals through the joint analysis of the maps in time domain and frequency domain. Both synthetic and field experiments show that the proposed method can suppress coupling noise and background noise and recover weak energy signals in DAS VSP data effectively. [ABSTRACT FROM AUTHOR]

In elastic-wave reverse-time migration (ERTM), the reverse-time reconstruction of source wavefield takes advantage of the computing power of GPU, avoids its disadvantages in disk-access efficiency and reading and writing of temporary files, and realizes the synchronous extrapolation of source and receiver wavefields. Among the existing source wavefield reverse-time reconstruction algorithms, the random boundary algorithm has been widely used in three-dimensional (3D) ERTM because it requires the least storage of temporary files and low-frequency disk access during reverse-time migration. However, the existing random boundary algorithm cannot completely destroy the coherence of the artificial boundary reflected wavefield. This random boundary reflected wavefield with a strong coherence would be enhanced in the cross-correlation image processing of reverse-time migration, resulting in noise and fictitious image in the migration results, which will reduce the signal-to-noise ratio and resolution of the migration section near the boundary. To overcome the above issues, we present an ERTM random boundary-noise suppression method based on generative adversarial networks. First, we use the Resnet network to construct the generator of CycleGAN, and the discriminator is constructed by using the PatchGAN network. Then, we use the gradient descent methods to train the network. We fix some parameters, update the other parameters, and iterate, alternate, and continuously optimize the generator and discriminator to achieve the Nash equilibrium state and obtain the best network structure. Finally, we apply this network to the process of reverse-time migration. The snapshot of noisy wavefield is regarded as a 2D matrix data picture, which is used for training, testing, noise suppression, and imaging. This method can identify the reflected signal in the wavefield, suppress the noise generated by the random boundary, and achieve denoising. Numerical examples show that the proposed method can significantly improve the imaging quality of ERTM. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) denoising is a challenging task in medical imaging that has garnered considerable attention. Supervised networks require a lot of noisy-clean image pairs, which are always unavailable in clinical settings. Existing self-supervised algorithms for suppressing noise with paired noisy images have limitations, such as ignoring the residual between similar image pairs during training and insufficiently learning the spectrum information of images. In this study, we propose a Residual Image Prior Network (RIP-Net) to sufficiently model the residual between the paired similar noisy images. Our approach offers new insights into the field by addressing the limitations of existing methods. We first establish a mathematical theorem clarifying the non-equivalence between similar-image-based self-supervised learning and supervised learning. It helps us better understand the strengths and limitations of self-supervised learning. Secondly, we introduce a novel regularization term to model a low-frequency residual image prior. This can improve the accuracy and robustness of our model. Finally, we design a well-structured denoising network capable of exploring spectrum information while simultaneously sensing context messages. The network has dual paths for modeling high and low-frequency compositions in the raw noisy image. Additionally, context perception modules capture local and global interactions to produce high-quality images. The comprehensive experiments on preclinical photon-counting CT, clinical brain CT, and low-dose CT datasets, demonstrate that our RIP-Net is superior to other unsupervised denoising methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The article presents a method for estimating the level of discrete white noise in an image, based on the use of linear difference operators with a vector mask. Two variants of a new method for estimating the noise level are proposed, which differ in the accuracy of the obtained estimates and computational complexity. The first version of the method can be attributed to the class of block methods, whereas the second one is intended for the rapid image analysis and is based on processing a small number of rows or columns of an image.

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.

In this paper, we propose a coupled theoretical model used for noise processing at nodes of hybrid core fiber with single core fiber (SMF)-multicore fiber (MCF), and intra-core noises and inter-core noises are considered in the model. Then, we propose an on-demand quantum secured future fronthaul architecture and a channel allocation algorithm over hybrid core fibers, and the proposed coupled theoretical model is used to calculate the noises in architecture. For fronthaul optical signals, wavelength modes and the number of transceiver modules in the distributed unit (DU) can be dynamically adjusted according to requirements, and the function of load aggregation is adopted. For quantum key distribution (QKD), quantum signals are transmitted in a dedicated core, and wavelength-time multiplexing is adopted to increase the number of quantum secured base station (BSs) while improving wavelength utilization. Finally, simulation results show that proposed channel allocation algorithm can suppress noise about 60% compared to First-Fit. The architecture can save up to 83.3% modules in 7-core feeder fiber, and it is feasible to support 128 BSs. The experimental and simulation results show that the proposed coupled theoretical model matches well with the experimental results, and the quantum bit error rates in experiment are both lower than 2.5%. The results show that the architecture has the advantages of allocating time-space-frequency resources on-demand, saving power consumption, and expanding the number of users. Therefore, it provides a feasible solution for future optical fronthaul networks secured by QKD. [ABSTRACT FROM AUTHOR]

A noise-suppressing low-light image enhancement approach is proposed in this paper based on the extent of exposedness at each image pixel. To this end, a progressive, structure-aware exposedness estimation procedure is presented that quantifies local and global exposedness. These exposedness values are leveraged to produce a locally smooth pixel-level map that signifies the required degrees of enhancement at image pixels. This map is subsequently used in an enhancement function, which satisfies a few important properties, to generate the enhanced image. Before the enhancement, inherent noise in the low-light image is diminished employing a detail-preserving, low gradient magnitude suppression method. Subjective and quantitative analysis of results on a wide variety of natural and synthetically generated low-light images from standard databases using PSNR, iRSE, SSIM, and measures of perceptual quality, natural image statistics and brightness preservation suggests that our approach in general outperforms the state-of-the-art. Ablation studies and further experiments show the importance of a few components of our approach, and that our approach is computationally fast. [ABSTRACT FROM AUTHOR]

As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation. Micro-seismic data are generated in complex underground environments which are significantly affected by random noise. These data greatly influence subsequent micro-seismic source location, energy estimation, and disaster monitoring. In this paper, a new denoising method based on Time-Frequency Peak Filtering (TFPF) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed for micro-seismic data. Micro-seismic data are decomposed into several Intrinsic Mode Functions (IMFs) by CEEMDAN. Then, discriminant factors are used to determine which IMF needs to be denoised by TFPF. Finally, the denoised result is reconstructed by inverse CEEMDAN. By comparing and analyzing different entropies, Fuzzy Entropy (FE) is selected as the best discriminant factor. The CEEMDAN-FE-TFPF denoising method can effectively avoid the influence of fixed window length of the conventional TFPF method. The effectiveness and superiority of this method are verified by experiments of synthetic and actual data. [ABSTRACT FROM AUTHOR]

Terahertz lensless phase retrieval imaging is a promising technique for non-destructive inspection applications. In the conventional multiple-plane phase retrieval method, the convergence speed due to wave propagations and measures with equal interval distance is slow and leads to stagnation. To address this drawback, we propose a nonlinear unequal spaced measurement scheme in which the interval space between adjacent measurement planes is gradually increasing, it can significantly increase the diversity of the intensity with a smaller number of required images. Both the simulation and experimental results demonstrate that our method enables quantitative phase and amplitude imaging with a faster speed and better image quality, while also being computationally efficient and robust to noise. [ABSTRACT FROM AUTHOR]

The shielding of electromagnetic noise is critical in obtaining magnetic resonance imaging measurements in the ultra-low magnetic field regime where the intrinsic signal-to-noise ratio is very small. The traditional approach of using an enclosure for electromagnetic shielding is expensive and hinders system portability. We describe here the use of a CNN-based software gradiometer to suppress the effect of electromagnetic ambient background noise sources that inductively couple into the signal detection coils. The system involves three ambient noise monitoring coils placed at a distance from the magnetic resonance signal detector. The three coils were used to synthesize the ambient noise captured by the signal detector; a convolutional neural network approach was used. Mathematical foundations are provided to justify the noise suppression framework. The results show that as much as 20-fold noise suppression can be achieved using an optimized convolutional neural network and simultaneous ambient noise measurements. The proposed approach has the potential to replace the requirement for magnetically shielded enclosures and make ultra-low field magnetic resonance imaging truly portable. [ABSTRACT FROM AUTHOR]