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Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). Materials and methods We retrospectively collected 100 patients (median age, 61 years [IQR, 53–70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). Results The median effective dose was 0.16 (IQR, 0.14–0.18) mSv for QLD CT and 0.65 (IQR, 0.57–0.71) mSv for LDCT. The radiologists’ evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P

[Introduction] Due to the dense load, complex grid structure, and close electrical connections in the Guangdong power grid area, there are three major problems for a long time: the risk of large-scale power outages, the interaction between AC and DC, and the excessive short-circuit current, making it difficult to meet the needs of future power load development and flexible system regulation.[Method] To address the above issues, a high-performance VSC-HVDC (Voltage Source Converter-based High Voltage Direct Current) back-to-back project was constructed in the core area of Guangdong Power Grid, and a new generation of high-performance VSC-HVDC back-to-back technology was studied and adopted. The VSC-HVDC interconnection control of complex power grids, the development of highly reliable and high-performance converter valves, and the design of green and efficient converter stations were deeply studied. The upgrade of VSC-HVDC back-to-back technology was achieved. [Result] The project successfully achieved automatic control of asynchronous power flow, significantly reducing the link delay of the control and protection system, developed high-performance and highly reliable VSC-HVDC converter valves, and optimized the design in terms of noise reduction, loss reduction, water conservation, and other aspects of the converter station. A comprehensive perception system for the converter station was built, achieving intelligent operation and maintenance. At present, the project is running well. [Conclusion] The VSC-HVDC back-to-back project of Guangdong Power Grid has solved the three major stability problems faced by the multi DC feeder receiving end grid for a long time and enhanced the long-term safe and reliable power supply capacity of the Guangdong-Hong Kong-Macao Greater Bay Area.

Wavelet theory has already achieved huge success. For Secondary Ions Mass Spectrometry (SIMS) signals, denoising the secondary signal, which is altered by the measurement, is considered that an essential step prior to applying such a signal processing technique that aims enhance the SIMS signals.The most efficient and widely used wavelet denoising method is based on wavelet coefficient thresholding. This process involves three important steps; wavelet decomposition: the input signals are decomposed into wavelet coefficients, thresholding: the wavelet coefficients are modified according to a threshold, and reconstruction: the modified coefficients are used in an inverse transform to obtain the noise-free-signal. Several researchers have used thresholding wavelet denoising techniques. The choice of wavelet type and the level of resolution can have a significant influence; it is important to note that the choice of resolution level depends on the type of signal we are dealing with, the nature of the present noise, and our specific goals for the denoised signal. It is generally recommended to test different resolution levels and evaluate their impact on the quality of the denoised signal before making a final decision. Moreover, the results obtained in wavelet denoising can be significantly influenced by the selection of wavelet types. The chosen wavelet type plays a crucial role in the extraction of signal details. Indeed, the effectiveness of denoising the MD6 sample has been demonstrated by the results obtained with sym4, db8, Haar and coif5 wavelets? These wavelets have effectively reduced noise while preserving crucial signal information, leading to an enhancement in the quality of the denoised signal.

Passive control techniques of impinging jets are of high interest for many industrial applications and particularly for noise generation issues encountered in such configurations. Thus, an experimental study was carried out to simultaneously show the effect of a mechanism of control on the acoustic and the dynamic fields involved in a rectangular jet of air impinging on a slotted plate. A Reynolds number of Re = 5900 presenting an intense acoustic level was considered. The mechanism of control consists on a thin rod which was introduced in different positions of the flow. A total number of 1085 spatial positions of the rod were tested in order to identify the optimal position for noise reduction. Combined Stereoscopic Particle Image Velocimetry measurements were performed to obtain the kinematic field in the whole area of interest from the both sides of the introduced rod. A new representation of the acoustic levels (cartography of acoustic level as function of the location of the rod) is provided to identify the optimal positions of control. It was found that when the self-sustaining tone loop disappears, the sound pressure levels can drop by almost 23% depending on the location of the rod. A Dynamic Mode Decomposition (DMD) was established and cross-correlations were calculated between temporal modes and acoustic signals for both controlled and not controlled cases. The cross-correlations between the acoustic signal and the temporal modes were found to be insignificant in case of controlled flow. Moreover, in case of controlled flow, spatial modes were found to be significant far from the slot which plays a principal role in the self-sustaining tones by interacting with the passage of vortices through it. These results are of interest since the visualization of the flow dynamics and the corresponding vortex activity explains the disappearance of the self-sustaining loop and the sound pressure level changes. Such results are of high interest for developing new strategies of noise control.

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability.

With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of −15 is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusions: AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

In permanent magnet synchronous motor (PMSM) sensorless drive systems, the motor inductance is a crucial parameter for rotor position estimation. Variations in the motor current induce changes in the inductance, leading to core magnetic saturation and degradation in the accuracy of rotor position estimation. In systems with constant load torque, the saturated inductance remains constant. This inductance error causes a consistent error in rotor position estimation and some performance degradation, but it does not result in speed estimation errors. However, in systems with periodic load torque, the error in the saturated inductance varies, consequently causing fluctuations in both the estimated position and speed errors. Periodic speed errors complicate speed control and degrade the torque compensation performance. In this paper, we propose a wavelet denoising-group method of data handling (GMDH) based method for accurate inductance estimation in PMSM sensorless control systems with periodic load torque compensation. We present a method to analyze and filter the collected three-phase current signals of the PMSM using wavelet transformation and utilize the filtered results as inputs to GMDH for training. Additionally, a method for magnetic saturation compensation using the inductance parameter estimator is proposed to minimize periodic speed fluctuations and improve control accuracy. To replicate the load conditions and parameter variations equivalent to the actual system, experiments were conducted to measure the speed ripples, inductance variations, and torque component of the current. Finally, software simulation was performed to confirm the inductance estimation results and verify the proposed method by simulating load conditions equivalent to the experimental results.

Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target’s high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.

In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°.

The multi-frequency noisy vibration of an autonomous underwater vehicle (AUV) is a significant factor affecting the performance of shear probes mounted on the head of AUVs. Many efforts have been made to suppress mechanical radiation noise; however, conventional noise reduction methods have their limitations, such as mode mixing. In order to extract thorough information from the aliasing modes and achieve multi-frequency mode targeted correction, a multi-frequency noise reduction method is proposed, based on secondary decomposition and the multi-mode coherence correction algorithm. Weak impulses in aliasing shear mode are enhanced, and mixing frequencies are isolated for thorough decomposition. Noisy mechanical vibrations in the shear modes are eliminated with the use of the acceleration modes along the identical central frequency series. The denoised modes are used to reconstruct the cleaned shear signal, and the updated spectra are aligned with the standard Nasmyth spectrum. Compared with the raw profiles, the variation in the dissipation rate estimated from the corrected shear is reduced by more than an order of magnitude.

Reducing the tonal noise from airfoil instabilities has attracted significant interest from the aeronautical community in the past few years. The aim of this paper is to investigate the effect of structured porous trailing edges on the tonal noise reduction performance of a symmetrical NACA 0012 airfoil. Detailed parametric testing was performed in an open-jet wind tunnel between the baseline solid trailing edge and seventeen structured porous trailing edges with different sub-millimeter-scale pores. The experimental results demonstrate that structured porous trailing edges can reduce the noticeable tonal noise of the symmetrical NACA 0012 airfoil. Moreover, the design parameters for the structured porous edges have slightly different impacts on the tonal noise reduction performance between a zero angle of attack (α = 0°) and a non-zero angle of attack (α = 10°): better airfoil tonal noise reduction is due to the porous parameters of small pore coverage, small-to-moderate chordwise spacing, and moderate spanwise spacing at α = 0°. On the other hand, the optimal combination of the structured porous edge at α = 10° is the configuration with larger pore coverage, smaller chordwise spacing, and spanwise spacing.

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

Effective noise reduction is essential because it can influence the health and well-being of living organisms significantly, both physically and psychologically. This necessitates the use of appropriate absorbing materials to minimize the negative impact of noise. In recent years, there has been growing interest in using jute, coir and natural fibre-based acoustic materials in architectural and interior applications due to their exceptional acoustic properties, eco-friendliness, cost-effectiveness and widespread availability. The study focuses on analysing the effects of some scrutinised testing parameters of jute, coir and jute-coir blended non-woven fabric samples on noise reduction coefficient through observing sound absorption coefficient. The sound absorption coefficient was measured using the impedance tube method as per the ASTM E-1050 standard. The mean value of the sound absorption coefficient observed at frequencies 250 Hz, 500 Hz, 1000 Hz, and 2500 Hz was used to calculate the noise reduction coefficient. A three-factor & three-level Box–Behnken experimental design was used for designing research and non-woven jute, coir and jute coir blended fabric sample planning. The experimental work showed that with an increase in non-woven jute fabric sample thickness from 15 mm to 45 mm, blending jute 50 % with coir 50% and non-woven fabric samples tested by creating air gaps (15 mm to 30 mm) resulted in higher sound absorption coefficient and improved noise reduction coefficient. The experimental findings and statistical analysis revealed that blending jute with coir and increasing the thickness of non-woven fabric led to a significant improvement in the acoustic performance of the tested samples. However, increasing the air gap resulted in only a marginal improvement in acoustic performance.

Objective(s): This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques.Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients’ data. The inputs to the network were LC images and its outputs were denoised PET (LC + DL) images, which should resemble FC images. To evaluate the LC + DL images, Gaussian and non-local mean (NLM) filters were applied to the LC images (LC + Gaussian and LC + NLM, respectively). To create reference images, a Gaussian filter was applied to the FC images (FC + Gaussian). The usefulness of our denoising model was objectively and visually evaluated using test data set of thirteen patients. The coefficient of variation (CV) of background fibroglandular tissue or fat tissue were measured to evaluate the performance of the noise reduction. The SUVmax and SUVpeak of lesions were also measured. The agreement of the SUV measurements was evaluated by Bland–Altman plots.Results: The CV of background fibroglandular tissue in the LC + DL images was significantly lower (9.10 2.76) than the CVs in the LC (13.60 3.66) and LC + Gaussian images (11.51 3.56). No significant difference was observed in both SUVmax and SUVpeak of lesions between LC + DL and reference images. For the visual assessment, the smoothness rating for the LC + DL images was significantly better than that for the other images except for the reference images.Conclusion: Our model reduced the noise in dbPET images acquired in about half the emission time while preserving quantitative values of lesions. This study demonstrates that machine learning is feasible and potentially performs better than conventional post-image filtering in dbPET denoising.

PURPOSEThis study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in terms of image quality.METHODSA total of 35 patients who underwent MRE for Crohn’s disease between August 2021 and February 2022 were included in this retrospective study. The enteric phase CE-T1W MRE images of each patient were reconstructed with conventional reconstruction and no image filter (original), with conventional reconstruction and image filter (filtered), and with a prototype version of AIRTM Recon DL 3D (DLR), which were then reformatted into the axial plane to generate six image sets per patient. Two radiologists independently assessed the images for overall image quality, contrast, sharpness, presence of motion artifacts, blurring, and synthetic appearance for qualitative analysis, and the signal-to-noise ratio (SNR) was measured for quantitative analysis.RESULTSThe mean scores of the DLR image set with respect to overall image quality, contrast, sharpness, motion artifacts, and blurring in the coronal and axial images were significantly superior to those of both the filtered and original images (P < 0.001). However, the DLR images showed a significantly more synthetic appearance than the other two images (P < 0.05). There was no statistically significant difference in all scores between the original and filtered images (P > 0.05). In the quantitative analysis, the SNR was significantly increased in the order of original, filtered, and DLR images (P < 0.001).CONCLUSIONUsing DLR for near-isotropic CE-T1W MRE improved the image quality and increased the SNR.

A novel type of integral damping gear （IDG） is developed and designed to study the influence of different damping liquid viscosity of IDG on the vibration and noise reduction characteristics of the gear transmission system. The IDG damping mechanical model is established, and the effectiveness of IDG control of gear system vibration is analyzed. An experimental platform for the first-order spur gear transmission system is built to compare the vibration at the support of the ordinary spur gear system and the IDG system with different damping liquid viscosity. The results show that IDG can effectively control the vibration of the gear transmission shaft system. In the range of damping fluid viscosity used in this experiment, with the increase of the damping fluid viscosity used in IDG, the vibration of the gear shaft system decreases gradually, and the average vibration reduction amplitude of IDG under the maximum viscosity is more than 50%. At the same time, IDG can effectively reduce the noise of the gear system, and the maximum viscosity can reduce 7.6 dB（A） noise. It is further proved that IDG has excellent vibration and noise reduction characteristics, which ensures the smooth operation of the gear transmission system and provides a new idea for vibration and noise reduction of the gear system.

In view of the noise generated during the operation of the RV reducers, the tooth surface of the involute gear and the cycloid gear are modified, the modification amount is obtained based on the empirical modification formula, and the orthogonal test table is established. With the RV110E type reducer as the research object, the noise value of the reducer under different modifications is obtained based on the RecurDyn software. The results show that the noise reduction effect of the RV reducer is significant for the involute gear modification, and the maximum noise value is reduced by 7.78 dB(A). The virtual prototype of the RV110E type reducer is built with Romax software, and the parameters of involute gear modification are imported. Comparing the simulation results before and after the modification, it can be seen that the transmission error of the combined modified involute gear is reduced, the peak load per unit length of the tooth surface is reduced, the problem of tooth surface off-load and noise are improved, and the transmission stability of the reducer is effectively improved. The research results have certain reference significance for gear modification of different types of RV reducers.

It is essential to maintain the aerodynamic performance of the air-conditioning system meanwhile reducing the noises (including aerodynamic, broadband, and discrete noises), determining the consumer's comfort level. In this work, depending on the coupling of the wavy leading-edge and the seagull airfoil, the aeroacoustics noise and aerodynamic performance of the impellers with the coupling bionic blade were investigated in detail. The results indicate the aerodynamic performance was improved by the coupling bionic optimization. Moreover, the total pressure efficiency (η) of the coupling bionic blade increases by 2.28% in comparison to the original blade. Furthermore, A smaller static differential pressure is observed between the suction and pressure sides, and vortices and backflows from the pressure side to the suction side are hampered, causing a reduction in turbulence noise. Additionally, the broadband noise of the coupling bionic blade decreases by 3.59 dB. Besides, the coupling bionic blade improves the directivity of the sound pressure level, especially in the middle-frequency and low-frequency region, resulting in a decrease of 7.9 dB for the aeroacoustics noise of the coupling bionic blade. What's more, the modal analysis demonstrates the security of the designed coupling bionic blade. In generally, this work provides some inspiration to design axial flow fans with excellent aerodynamic performance and low-noise characteristics.

Training a neural network to reconstruct images from time-series waveforms obtained from fiber optic probes not only yields high-quality, content-aware images but can also acquire different types of images from lower quality training images. Image reconstruction, as an inverse problem, involves using collected signals and system models to retrieve desired images, encountering mathematical challenges like distortion and degradation. In this paper, we introduce REcaNet, a multi-mode fiber image restoration model based on an enhanced residual convolutional neural network (CNN). The network employs a symmetrical architecture that downscales the image before upscaling it for restoration, and it reconstructs the high-level semantic feature map generated by the encoder to the original image resolution. Additionally, we incorporate weight initialization, attention mechanisms, and residual connections to enhance the final restored feature map with more low-dimensional features and promote fusion of features from distinct layers. The algorithm performs well on three datasets collected by multi-mode fibers, namely Minist, Clothes, and Omiglot. Among them, various indicators such as SSIM have been significantly improved.

This research addresses the need for quieter and safer road environments by evaluating low-noise-drainage pavements with varying coarse aggregate sizes. Thorough physical property tests were conducted on pavements with aggregate sizes of 8 mm, 10 mm, 13 mm, and 19 mm. Field tests assessed pavement characteristics over service years, including permeability, roughness, friction resistance, and road traffic noise. Notably, the 19 mm pavement exhibited optimal permeability, highlighting efficient water drainage. The 13 mm variant demonstrated superior roughness, indicating enhanced durability. Surprisingly, the 10 mm pavement's roughness improved after seven years. Slip resistance met standards in all but the 19 mm type, with the 10 mm and 13 mm pavements excelling in promoting road safety. Noise assessment showed an 8.7 dB(A) reduction in the low-noise-drainage pavement post-construction, but age-related effects were observed. Recognizing the potential for performance decline, this research emphasizes dedicated maintenance practices. This study offers insights for various pavement types, shaping designs, strategies, and noise mitigation measures for safer roads.

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications. The absorption ([Formula: see text]) and scattering ([Formula: see text]) coefficients of biological tissues are inversely analyzed from their diffuse reflectance (R) and total transmittance (T), which are measured using a double integrating spheres (DIS) system. The inversion algorithms, for example, inverse adding doubling method and inverse Monte Carlo method, are sensitive to noise signals during the DIS measurements, resulting in reduced accuracy during determination. In this study, we propose an artificial neural network (ANN) to estimate [Formula: see text] and [Formula: see text] at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties. Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprising [Formula: see text], [Formula: see text], R and T. Measurement noise signals were added to R and T, and the ANN model was then trained using the noise-added datasets. Numerical results showed that the trained ANN model reduced the effects of noise in [Formula: see text] and [Formula: see text] estimation. Experimental verification indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average, resulting in measurement time reduction. The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times, thus reducing changes in the optical properties due to desiccation of the samples.

Transmission line fault detection using drones provides real-time assessment of the operational status of transmission equipment, and therefore it has immense importance in ensuring stable functioning of the transmission lines. Currently, identification of transmission line equipment relies predominantly on manual inspections that are susceptible to the influence of natural surroundings, resulting in sluggishness and a high rate of false detections. In view of this, in this study, we propose an insulator defect recognition algorithm based on a YOLOv5 model with a new lightweight network as the backbone network, combining noise reduction and target detection. First, we propose a new noise reduction algorithm, i.e., the adaptive neighborhood-weighted median filtering (NW-AMF) algorithm. This algorithm employs a weighted summation technique to determine the median value of the pixel point's neighborhood, effectively filtering out noise from the captured aerial images. Consequently, this approach significantly mitigates the adverse effects of varying noise levels on target detection. Subsequently, the RepVGG lightweight network structure is improved to the newly proposed lightweight structure called RcpVGG-YOLOv5. This structure facilitates single-branch inference, multi-branch training, and branch normalization, thereby improving the quantization performance while simultaneously striking a balance between target detection accuracy and speed. Furthermore, we propose a new loss function, i.e., Focal EIOU, to replace the original CIOU loss function. This optimization incorporates a penalty on the edge length of the target frame, which improves the contribution of the high-quality target gradient. This modification effectively addresses the issue of imbalanced positive and negative samples for small targets, suppresses background positive samples, and ultimately enhances the accuracy of detection. Finally, to align more closely with real-world engineering applications, the dataset utilized in this study consists of machine patrol images captured by the Unmanned Aerial Systems (UAS) of the Yunnan Power Supply Bureau Company. The experimental findings demonstrate that the proposed algorithm yields notable improvements in accuracy and inference speed compared to YOLOv5s, YOLOv7, and YOLOv8. Specifically, the improved algorithm achieves a 3.7% increase in accuracy and a 48.2% enhancement in inference speed compared to those of YOLOv5s. Similarly, it achieves a 2.7% accuracy improvement and a 33.5% increase in inference speed compared to those of YOLOv7, as well as a 1.5% accuracy enhancement and a 13.1% improvement in inference speed compared to those of YOLOv8. These results validate the effectiveness of the proposed algorithm through ablation experiments. Consequently, the method presented in this paper exhibits practical applicability in the detection of aerial images of transmission lines within complex environments. In future research endeavors, it is recommended to continue collecting aerial images for continuous iterative training, to optimize the model further, and to conduct in-depth investigations into the challenges associated with detecting small targets. Such endeavors hold significant importance for the advancement of transmission line detection. [ABSTRACT FROM AUTHOR]

Passive control techniques of impinging jets are of high interest for many industrial applications and particularly for noise generation issues encountered in such configurations. Thus, an experimental study was carried out to simultaneously show the effect of a mechanism of control on the acoustic and the dynamic fields involved in a rectangular jet of air impinging on a slotted plate. A Reynolds number of Re = 5900 presenting an intense acoustic level was considered. The mechanism of control consists on a thin rod which was introduced in different positions of the flow. A total number of 1085 spatial positions of the rod were tested in order to identify the optimal position for noise reduction. Combined Stereoscopic Particle Image Velocimetry measurements were performed to obtain the kinematic field in the whole area of interest from the both sides of the introduced rod. A new representation of the acoustic levels (cartography of acoustic level as function of the location of the rod) is provided to identify the optimal positions of control. It was found that when the self-sustaining tone loop disappears, the sound pressure levels can drop by almost 23% depending on the location of the rod. A Dynamic Mode Decomposition (DMD) was established and cross-correlations were calculated between temporal modes and acoustic signals for both controlled and not controlled cases. The cross-correlations between the acoustic signal and the temporal modes were found to be insignificant in case of controlled flow. Moreover, in case of controlled flow, spatial modes were found to be significant far from the slot which plays a principal role in the self-sustaining tones by interacting with the passage of vortices through it. These results are of interest since the visualization of the flow dynamics and the corresponding vortex activity explains the disappearance of the self-sustaining loop and the sound pressure level changes. Such results are of high interest for developing new strategies of noise control. [ABSTRACT FROM AUTHOR]

Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by generating the minority class samples is one of the most well-known methods to improving the quality of datasets; however, it often introduces overfitting noise to datasets. To better improve the quality of these datasets, this paper proposes a method called US-PONR, which uses undersampling to remove duplicate samples from version iterations and then uses oversampling through propensity score matching to reduce class imbalance and noise samples in datasets. The effectiveness of this method was validated in a software prediction experiment that involved 24 versions of software data in 11 projects from PROMISE in noisy environments that varied from 0% to 30% noise level. The experiments showed a significant improvement in the quality of datasets pre-processed by US-PONR in noisy imbalanced datasets, especially the noisiest ones, compared with 12 other advanced dataset processing methods. The experiments also demonstrated that the US-PONR method can effectively identify the label noise samples and remove them. [ABSTRACT FROM AUTHOR]

Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery. Nevertheless, previous research has primarily focused on the internal noise of the system. Neglecting to include external factors that impact the WSN system in the study might lead to findings that are not true to reality. Hence, this research takes into account both internal and external noise factors, such as rain, fog, or snow conditions. Moreover, in order to maintain the temporal characteristics and intersensor relationships, the data from multiple sensor nodes are consolidated into a two-dimensional matrix format. The stacked convolutional autoencoder (SCAE) model is proposed, which has the capability to extract data features. The stack design of the SCAE enables it to effectively mitigate the issue of vanishing gradients. Moreover, the weight sharing approach used between the two subnetworks also enhances the efficiency of the weight initialization procedure. Thorough experiments are conducted using both simulated WSN systems and real-world sensing data. Experimental results demonstrate that the SCAE outperforms existing methods for reconstructing noisy data. [ABSTRACT FROM AUTHOR]

Objective: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). Materials and methods: We retrospectively collected 100 patients (median age, 61 years [IQR, 53–70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). Results: The median effective dose was 0.16 (IQR, 0.14–0.18) mSv for QLD CT and 0.65 (IQR, 0.57–0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P =.11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P =.12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P =.15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P <.001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P =.35) or the AUFROCs (0.77 vs. 0.78; P =.68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). Conclusion: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images. [ABSTRACT FROM AUTHOR]

Speech enhancement methods differ depending on the degree of degradation and noise in the speech signal, so research in the field is still difficult, especially when dealing with residual and background noise, which is highly transient. Numerous deep learning networks have been developed that provide promising results for improving the perceptual quality and intelligibility of noisy speech. Innovation and research in speech enhancement have been opened up by the power of deep learning techniques with implications across a wide range of real time applications. By reviewing the important datasets, feature extraction methods, deep learning models, training algorithms and evaluation metrics for speech enhancement, this paper provides a comprehensive overview. We begin by tracing the evolution of speech enhancement research, from early approaches to recent advances in deep learning architectures. By analyzing and comparing the approaches to solving speech enhancement challenges, we categorize them according to their strengths and weaknesses. Moreover, we discuss the challenges and future directions of deep learning in speech enhancement, including the demand for parameter-efficient models for speech enhancement. The purpose of this paper is to examine the development of the field, compare and contrast different approaches, and highlight future directions as well as challenges for further research. [ABSTRACT FROM AUTHOR]

Atmospheric lidar is susceptible to light attenuation, sky background light and detector dark current during detection, which results in a lot of noise in the lidar return signal. In order to improve the SNR and extract useful signals, this paper proposes a new joint denoising method EEMD-GWO-SVD, which includes empirical mode decomposition (EEMD), grey wolf optimization (GWO) and singular value decomposition (SVD). Firstly, the grey wolf optimization algorithm was used to optimize two parameters of EEMD algorithm according to moderate values: the standard deviation Nstd of adding Gaussian white noise to the signal and the number NE of adding Gaussian white noise. Secondly, the mode components obtained by EEMD-GWO decomposition are screened and reconstructed according to the correlation coefficient method. Finally, the SVD algorithm with strong noise reduction ability was used to further remove the noise in the reconstructed signal, and the lidar return signal with high SNR was obtained. In order to verify the effectiveness of the proposed method, the proposed method was compared with empirical mode decomposition (EMD), complete ensemble empirical modal decomposition (CEEMDAN), wavelet packet decomposition and EEMD-SVD-lifting wavelet transform (EEMD-SVD-LWT). The results show that the noise reduction effect of the proposed method was better than that of the other four methods. This method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. In fact, the denoised signal is not distorted, the waveform is smooth, the far-field noise interference can be suppressed and the denoised signal is closer to the real signal with higher accuracy, which indicates the feasibility and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Gestures have been used for nonverbal communication for a long time, but human–computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noise is removed, and intensity is adjusted for feature extraction. The hand gesture is first detected through the images, and the skeleton is computed through mathematical computations. From the skeleton, the features are extracted; these features include joint color cloud, neural gas, and directional active model. After that, the features are optimized, and a selective feature set is passed through the classifier recurrent neural network (RNN) to obtain the classification results with higher accuracy. The proposed model is experimentally assessed and trained over three datasets: HaGRI, Egogesture, and Jester. The experimental results for the three datasets provided improved results based on classification, and the proposed system achieved an accuracy of 92.57% over HaGRI, 91.86% over Egogesture, and 91.57% over the Jester dataset, respectively. Also, to check the model liability, the proposed method was tested on the WLASL dataset, attaining 90.43% accuracy. This paper also includes a comparison with other-state-of-the art methods to compare our model with the standard methods of recognition. Our model presented a higher accuracy rate with a markerless approach to save money and time for classifying the gestures for better interaction. [ABSTRACT FROM AUTHOR]

Human communication is mostly used in a variety of ways, including human-machine communication, technical equipment, and even virtual support or search engines. This kind of communication is often carried out using a device that is sensitive to background noise. It has a detrimental impact on the message’s or content’s comprehension, and it also lowers communication quality. The quality of the voice signal could be highly reduced as it travels from the transmission to reception. Improved nonlinear filter analysis has been attempted to solve step size difficulties as well. However, each approach has its drawbacks. As a result, an effective method is required to overcome all current disadvantages. The objective of the proposed work is to detect speech degradations in both sustained vowels and speech. This research recommends utilizing a Variable Step Size Normalized Differential Least Mean Square (VSSNDLMS) algorithm to detect speech degradations in both sustained vowels and speech. The value of the alpha parameter is changed to reduce background noise in speech communications. The suggested system’s performance is evaluated and contrasted with that of the currently used methods. Using the suggested technology, noise has been reduced in the voice signals. [ABSTRACT FROM AUTHOR]

Noise interference seriously affects the data processing and imaging interpretation of aviation transient electromagnetic, and how to effectively denoise has become an important content of aviation transient electromagnetic research. Based on the characteristics of aviation transient electromagnetic noise and the multi-scale characteristics of curvelet transform, the second generation fast discrete curvelet transform(FDCT)was used to reconstruct and denoise actual aviation transient electromagnetic data. Firstly, a 3D theoretical model with anomalous bodies was established, and a direct time domain transient electromagnetic 3D simulation program was used for forward modeling. Then, three different types of noise, including random noise, square wave noise and industrial noise, were added to the theoretical electromagnetic response data, and images of different scales were generated through curvelet transform and reconstruction. Low scales reflect large background fields, while high scales reflect local details or high-frequency noise. Thirdly, the denoising effect of the reconstructed data was evaluated through parameters such as signal-to-noise ratio and relative error. Finally, the method was applied to the measured data of aviation transient electromagnetic and compared with several traditional denoising methods, including median filtering, singular value decomposition and wavelet transform. The results show that for the original data with random noise and industrial noise, the relative error after noise reduction is below 2.6%; for the original data with square wave noise, the relative error after noise reduction is below 6.5%, and the signal-to-noise ratio is also higher than traditional denoising methods, proving that the second generation curvelet transform can be applied to noise reduction of airborne transient electromagnetic data. [ABSTRACT FROM AUTHOR]

This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms' combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS. [ABSTRACT FROM AUTHOR]

Objective To investigate the effects of the Silent Laboratory Optimization System (SLOS), a technical-noise reduction and communication-management system, on noise load and stress among medical-laboratory workers. Methods We conducted a quasiexperimental field study (20 days with SLOS as the experimental condition, and 20 days without SLOS as the control condition) in a within-subjects design. Survey data from 13 workers were collected before and after the shift. Also, a survey was conducted after the control and experimental conditions, respectively. Noise was measured in dBA and as a subjective assessment. Stress was operationalized via a stress composite score (STAI and Perkhofer Stress Scale), the Perceived Stress Scale (PSS), an exhaustion score (Leipziger StimmungsBogen in German [LSB]), and salivary cortisol values in µg/L. Results SLOS users perceived significantly less noise (V  = 76.5; P =.003). Multilevel models revealed a stress reduction with the SLOS on the composite score, compared with a stress increase in the control condition (F [1, 506.99] = 6.00; P  = .01). A lower PSS score (F [1,13] = 4.67; P  = .05) and a lower exhaustion level (F [1, 508.72] = 9.057; P  = .003) in the experimental condition were found, whereas no differences in cortisol (F [1,812.58.6] = 0.093; P  = .76) were revealed. Conclusion The workers showed reduced noise perception and stress across all criteria except cortisol when using SLOS. [ABSTRACT FROM AUTHOR]