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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. [ABSTRACT FROM AUTHOR]

Control effects of leading-edge grooves on deep cavity noise are investigated numerically. The length-to-depth ratio of the grooves are 0.5, 1, and 4, respectively. The freestream Mach number is equal to 0.16, corresponding to the Re based on the cavity length of 7.7 × 105. The Detached Eddy Simulations (DES) combined with Ffowcs Williams–Hawkings (FW-H) acoustic analogy are adopted to simulate the characteristic information of the flow field and the acoustic field. The analysis results show that all grooves investigated in this paper have a certain noise control effect, and the groove with the most obvious noise reduction effect is the groove with a length-to-depth ratio of 0.5, namely, the deep groove. Narrowband noise generated by the flow-acoustic feedback (100–700 Hz) and the acoustic resonance mechanism (above 300 Hz) and broadband noise caused by the turbulent disturbance in the shear layer of the cavity mouth significantly reduce when the deep groove is used for the noise control. The reason for the noise reduction is that the leading-edge grooves can effectively change the flow characteristics near the mouth of the downstream cavity. When the flow passes the grooves, the groove flow reduces the energy in the fluid, resulting in the significant decrease of flow velocity of the boundary layer of the incoming flow of the cavity. In addition, the use of the deep groove also promotes the vortex concentration position to move towards the bottom of the cavity, pushes the energy in the shear layer of the downstream cavity towards the front-edge as well as the bottom of the cavity, and stabilizes the development of the shear layer near the cavity mouth. [ABSTRACT FROM AUTHOR]

MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. Sixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t -test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test. The acquisition time of DL-reconstructed images was reduced to about 21–58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p -value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences. DL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time. • Deep learning network can reconstruct undersampled MR data with reduced scan time. • Deep learning can produce images of equivalent quality compared to conventional MRI. • Conventional and DL-reconstructed spine MRI are clinically interchangeable. [ABSTRACT FROM AUTHOR]

Tire acoustic cavity resonance (TACR) noise is one kind of low-frequency and narrowband noise that particularly annoys passengers, especially becomes more prominent in electric vehicles. This paper demonstrates a numerical investigation on the TACR noise via the acoustic-structural coupling finite element model. The accuracy of this coupling finite element model is validated both by the analytic method based on the superposition theory of traveling waves and experimental modal tests of tire cavities. According to the simulated models, the influences of external factors including the inflation pressure and road load on the TACR noise are studied. Meanwhile, as the main constituent component in the tire model, the effect of belt cord is also discussed. To design the low-TACR noise tire, various design parameters of the inner contour in tire cavity are analyzed. By the orthogonal test and range analysis, it is found that the contour parameters can slightly affect the modal frequency and sound pressure of TACR noise. Among these, the curvature radius of the tire shoulder ρ 4 , tire sidewall ρ 5 , and half-section height H have a more obvious influence on the TACR noise than the curvature radius of the tire crown region ρ 2 and transition region ρ 3 . Meanwhile, it also illustrates that increasing the curvature radius at the tire shoulder ρ 4 and reducing the half-section height H has a reduction effect on the TACR noise. This study can contribute to the design of low road noise tires. [ABSTRACT FROM AUTHOR]

Most of the existing deep learning-based speech denoising methods rely heavily on clean speech data. According to the traditional view, a large number of noisy and clean speech samples are required for good speech denoising performance. However, the data collection is a technical barrier to this criteria, particularly in economically challenged areas and for languages with limited resources. Training deep denoising networks with only noisy speech samples is a viable option to avoid dependence on sample data size. In this study, the target and input of a DCU-Net were trained using only noisy speech samples. Experimental results demonstrate that, when compared to traditional speech denoising techniques, the proposed approach avoids not only the high dependence on clean targets but also the high dependence on large data sizes. [ABSTRACT FROM AUTHOR]

In the context of the Internet of Things, there is a growing demand for real-time monitoring of water distribution systems (WDS). Among the various leak detection methods, acoustic leak detection is considered to be a suitable method. However, existing methods are not very effective in environments with high daytime ambient noise. To address this issue, this paper conducted on-site data collection experiments and designed a monitoring system that combines traditional nighttime monitoring with daytime monitoring, combining water company pipeline inspections and repair work. A large number of daytime audio samples were collected. In this paper, the logarithmic spectrogram (log spectrogram) was used to represent the features of the leak signal. By comparing the features of the signal during day and night, noisy and quiet environments, and leak and normal signals, we identified the interfering frames that required noise reduction, and applied frame-level noise reduction processing to the signal. Based on this, a log PS-ResNet18 model was developed to identify leaks, and its performance was compared with other classification models [including traditional nighttime detection methods, random forests, XGBoost, and convolutional neural network (CNN)]. The results showed that the log PS-ResNet18 model had the best performance, with an all-day accuracy rate of 99.4% and a daytime accuracy rate of 99.3%. In addition, by conducting ablation experiments to explore the role and contribution of the log PS-ResNet18 and noise reduction methods in the model, the results showed that the log spectrogram and noise reduction methods increased the all-day accuracy rate by 18.8% and 23.2%, respectively, and by 24.7% when used together. In another practical application, the log PS-ResNet18 model achieved an all-day detection accuracy rate of 99.6%. This study demonstrated the applicability of the log spectrogram and CNN combination in daytime leak detection, overcoming research limitations in the field. This research presents the log PS-ResNet18 framework, which combines deep learning models and denoised logarithmic spectrograms to improve leak detection in water supply pipelines under daytime environmental noise. The research focuses on field data collection and analysis of cast iron pipes with different diameters in Hangzhou (HZ). The model was tested on cast iron pipes in Lishui (LS) and proved to be effective. The proposed method is highly versatile and can be applied to different regions and pipe materials after sufficient sample collection and model training validation. The research recommends a comprehensive leak monitoring solution that involves initial intelligent detection using front-end noise meters and secondary identification of suspicious audio signals using the log PS-ResNet18 model in the cloud. This enables water utility operators to respond quickly to pipeline leaks, leading to more efficient water resource conservation and improved water supply service quality. [ABSTRACT FROM AUTHOR]

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°. [ABSTRACT FROM AUTHOR]

This study addresses the noise challenges associated with roots blowers, pivotal devices in water treatment technology, by developing an innovative duct silencer. To construct an efficient internal structure for noise reduction, a novel rectangular design altering sound propagation direction was implemented. Additionally, an array of Helmholtz resonators giving substantial transmission loss within specific frequency ranges, was used. Experimental validation and acoustic analysis were employed to evaluate the silencer's performance, ultimately confirming its noise reduction capabilities. The optimized silencer through a parametric study of the Helmholtz resonator structure exhibited an impressive transmission loss of 14.75 dB, signifying a remarkable 74 % enhancement over the existing circular silencer design. This remarkable performance substantiated its effectiveness in mitigating noise. To validate the optimized design, a physical prototype was manufactured and subjected to noise measurement experiments, revealing a significant noise reduction of 17.3 dB. [ABSTRACT FROM AUTHOR]

Accurate and intelligent leakage localization of the urban directly buried heating pipe network is the key aspect for the urban heating infrastructure safety, energy-saving and carbon-reducing and intelligence operation. In order to improve the localization accuracy of the direct buried hot water heating pipe network leakage by the acoustic wave method, a DN300 diameter large scaled heating experiment system was established. The effects of acoustic wave propagation distances, temperature, pressure and flow rates on the acoustic wave signal characteristics with and without leakage were studied. The acoustic wave time-frequency characteristics from pipeline pump vibration were analyzed, and then the wavelet threshold method was used for signal noise reduction. It obtained the leakage characteristic frequency band range. The results show that temperature has significant effect on the acoustic frequency band of leakage, and higher temperature contributes to larger acoustic wave frequency band. When the temperature rises from 31 ℃ to 86 ℃, the characteristic frequency band range expand from 200 ~ 800 Hz to 50 ~ 1 500 Hz. The propagation distance, pressure and flow rate mainly affect the amplitude of leakage signal, but they have little effect on the band range. The acoustic energy of the pump is mainly within 3 000 ~ 4 000 Hz, and it is beyond the range of the leakage characteristic frequency band range. The wavelet threshold method for the signal noise reduction. It could improve the localization accuracy up to 0. 11%, along with a localization distance deviation within ± 1 m. [ABSTRACT FROM AUTHOR]

Objective To demonstrate that a T2 periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique using deep learning reconstruction (DLR) will provide better image quality and decrease image noise. Methods From December 2020 to March 2021, 35 patients examined cervical spine MRI were included in this study. Four sets of images including fast spin echo (FSE), original PROPELLER, PROPELLER DLR50%, and DLR75% were quantitatively and qualitatively reviewed. We calculated the signal-to-noise ratio (SNR) of the spinal cord and sternocleidomastoid (SCM) muscle and the contrast-to-noise ratio (CNR) of the spinal cord by applying region-of-interest at the spinal cord, SCM muscle, and background air. We evaluated image noise with regard to the spinal cord, SCM, and back muscles at each level from C2-3 to C6-7 in the 4 sets. Results At all disc levels, the mean SNR values for the spinal cord and SCM muscles were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE and original PROPELLER images (P  < .0083). The mean CNR values of the spinal cord were significantly higher in PROPELLER DLR50% and DLR75% compared to FSE at the C3-4 and 4-5 levels and PROPELLER DLR75% compared to FSE at the C6-7 level (P  < .0083). Qualitative analysis of image noise on the spinal cord, SCM, and back muscles showed that PROPELLER DLR50% and PROPELLER DLR75% images showed a significant denoising effect compared to the FSE and original PROPELLER images. Conclusion The combination of PROPELLER and DLR improved image quality with a high SNR and CNR and reduced noise. Advances in knowledge Motion-insensitive imaging technique (PROPELLER) increased the image quality compared to conventional FSE images. PROPELLER technique with a DLR reduced image noise and improved image quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Today, rapid urbanization and accompanying population growth cause an increase in noise levels in residential areas. Noise source-based evaluations have revealed that the main environmental noise source originating from transportation in urban life is road traffic noise. The most efficient and economical way to reduce the adverse effects of road traffic noise is to control the noise at its source. If this implementation is insufficient, acoustic barriers are recommended to be applied between the source and the receiver. Noise barrier systems with different design components are used worldwide for road traffic noise reduction. Appropriate sections (lengths) should be determined before developing the barrier systems to ensure noise control on roads. The areas where acceptable noise levels are exceeded, and the road sections in these areas can be determined by strategic noise mapping. However, a range of criteria should be evaluated before applying barriers in noise-exposure areas. Within the scope of this study, potential road sections where noise barrier systems can be applied were determined for 20 provinces in Turkey for which strategic noise maps were prepared. While determining these sections, exposure exceedance assessments and noise modeling input data were used in the road impact areas determined within the scope of strategic noise mapping studies. It has been determined that noise barriers can be applied to approximately 3.9% of the modeled 620 km major roads. [ABSTRACT FROM AUTHOR]

In order to increase the flexibility of the algorithm as well as improve the performance of the filter, a set of optimal non-causal filters and filtering matrices are developed based on the existing single-channel noise reduction algorithms in Short-Time Fourier Transform (STFT) domain which takes the interframe correlation into account. Specifically, this paper designs a set of single-channel non-causal filters by introducing the non-causal concept to achieve the purpose of noise reduction by exploiting both the past and future information. Based on that, several optimal single-channel filtering matrices are also derived to make the computational burden of the algorithms lower. Combining the aforementioned ideas, a set of single-channel non-causal filtering matrices are deduced. Furthermore, the proposed algorithms are generalized to the multi-channel case to achieve a better performance. Finally, the algorithms developed previously are investigated through simulations. The results show that the proposed algorithms can effectively improve the signal-to-noise ratio of the noisy signal (input signal) and enhance the speech quality compared with the traditional algorithms. [ABSTRACT FROM AUTHOR]

The evaluation of blasting vibrations primarily hinges on two physical quantities: velocity and acceleration. A significant challenge arises when attempting to reference the two types of vibration data in relation to one another, such as different types of seismometers, noise, etc., necessitating a method for their equivalent transformation. To address this, a transformation method is discussed in detail with a case study, and equations for the ratio (Ra) of the particle peak velocity (PPV) to the particle peak acceleration (PPA) are proposed. The findings are twofold: (1) The conventional data conversion processes often suffer from low accuracy due to the presence of trend terms and noise in the signal. To mitigate this, the built-in MATLAB function is used for trend term elimination, complemented by a combined approach that integrates CEEMDAN with WD/WDP for noise reduction. These significantly enhance the accuracy of the transformation. (2) This analysis reveals a positive power function correlation between Ra and the propagation distance of the blast vibrations, contrasted by a negative correlation with the maximum charge per delay. Intriguingly, the Ra values observed in pre-splitting blasting operations are consistently lower than those in bench blasting. The established Ra equations offer a rapid, direct method for assessing the transformation between the PPV and PPA, providing valuable insights for the optimization of blasting design. [ABSTRACT FROM AUTHOR]

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the publication of many research articles regarding the contactless measurement and monitoring of heart rate signals deduced from facial video recordings. The techniques presented in these articles, such as examining the changes in the heart rate of an infant, provide a noninvasive assessment in many cases where the direct placement of any hardware equipment is undesirable. However, performing accurate measurements in cases that include noise motion artifacts still presents an obstacle to overcome. In this research article, a two-stage method for noise reduction in facial video recording is proposed. The first stage of the system consists of dividing each (30) seconds of the acquired signal into (60) partitions and then shifting each partition to the mean level before recombining them to form the estimated heart rate signal. The second stage utilizes the wavelet transform for denoising the signal obtained from the first stage. The denoised signal is compared to a reference signal acquired from a pulse oximeter, resulting in the mean bias error (0.13), root mean square error (3.41) and correlation coefficient (0.97). The proposed algorithm is applied to (33) individuals being subjected to a normal webcam for acquiring their video recording, which can easily be performed at homes, hospitals, or any other environment. Finally, it is worth noting that this noninvasive remote technique is useful for acquiring the heart signal while preserving social distancing, which is a desirable feature in the current period of COVID-19. [ABSTRACT FROM AUTHOR]

The unfiltered derivative fast Fourier transform (dFFT) of degrees higher than two fails flagrantly for encoded time signals. These data are always dominated by noise at larger times of encodings. Such a breakdown is due to processing the unweighted product of the time signal and the time power function. The latter is generated by the frequency derivative operator applied to the fast Fourier transform (FFT). As a result, the unfiltered dFFT cannot separate the overlapped resonances and it dramatically decreases signal-to-noise ratio (SNR) relative to the FFT. This problem is solved by a derivative-adapted optimization with the properly attenuated filters. The ensuing optimized dFFT achieves the long sought simultaneous enhancement of both resolution and SNR. It uncovers the genuine resonances hidden within overlapping peaks to enable quantitative interpretations. It does not impose any model on the input time signals nor on the output lineshape in the spectra. It is computationally expedient as it uses the Cooley-Tukey fast algorithm. The present applications deal with time signals encoded by in vitro NMR spectroscopy from human malignant ovarian cyst fluid. A remarkably successful performance of the optimized dFFT is demonstrated for reconstructed spectra of potentially added value in clinical decision-making. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the shell of twin screw air compressor produces large noise during operation, the noise spectrum of the shell of the twin screw air compressor was studied by using finite element analysis technology, and the noise spectrum diagram of the shell of the twin screw air compressor was obtained at 100 ~ 1 500 Hz. Noise reduction was carried out by adding a muffler, and the structure of the muffler was optimized to eliminate the resonance phenomenon. The designed muffler was installed at the suction port of twin-screw air compressor. It is found that the muffler has better noise reduction effect. The research results can play a certain reference role for noise reduction of twin-screw air compressor. [ABSTRACT FROM AUTHOR]

An inertial guidance system based on a fiber optic gyroscope (FOG) is an effective way to guide long-distance curved pipe jacking. However, environmental disturbances such as vibration, electromagnetism, and temperature will cause the FOG signal to generate significant random noise. The random noise will overwhelm the effective signal. Therefore, it is necessary to eliminate the random noise. This study proposes a hybrid de-noising method, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)—lifting wavelet transform (LWT). Firstly, the FOG signal is extracted using a sliding window and decomposed by CEEMDAN to obtain the intrinsic modal function (IMF) with N different scales and one residual. Subsequently, the effective IMF components are selected according to the correlation coefficient between the IMF components and the FOG signal. Due to the low resolution of the CEEMDAN method for high-frequency components, the selected high-frequency IMF components are decomposed with lifting wavelet transform to increase the resolution of the signal. The detailed signals of the LWT decomposition are de-noised using the soft threshold de-noising method, and then the signal is reconstructed. Finally, pipe-jacking dynamic and environmental interference experiments were conducted to verify the effectiveness of the CEEMDAN-LWT de-noising method. The de-noising effect of the proposed method was evaluated by SNR, RMSE, and Deviation and compared with the CEEMDAN and LWT de-noising methods. The results show that the CEEMDAN-LWT de-noising method has the best de-noising effect with good adaptivity and high accuracy. The navigation results of the pipe-jacking attitude before and after de-noising were compared and analyzed in the environmental interference experiment. The results show that the absolute error of the pipe-jacking pitch, roll, and heading angles is reduced by 39.86%, 59.45%, and 14.29% after de-noising. The maximum relative error of the pitch angle is improved from −0.74% to −0.44%, the roll angle is improved from 2.07% to 0.79%, and the heading angle is improved from −0.07% to −0.06%. Therefore, the CEEMDAN-LWT method can effectively suppress the random errors of the FOG signal caused by the environment and improve the measurement accuracy of the pipe-jacking attitude. [ABSTRACT FROM AUTHOR]

The problems of vibration and noise have seriously affected people's production and life. Hydrogel can be used as damping material because of its good viscoelasticity, but the current damping material is difficult to meet the needs of actual production and life because of the lack of wide damping temperature and frequency range and high loss factor. In this work, ammonium persulfate was used as both initiator and oxidant to form a mixed double network hydrogel of polyacrylamide and oxidized sodium alginate. The double network hydrogel prepared by this method has good adhesion and mechanical properties. In addition, due to the adjustable viscoelasticity of the hydrogel, the hydrogel shows excellent damping properties. When the concentration of MBA is 0.02%, it can reach the temperature range of 0-125 ℃, and the damping factor is more than 0.3. The visualization experiment proves the practical application effect of the hydrogel, so it is expected to be used as a damping material in damping, noise reduction and other fields in the future. [ABSTRACT FROM AUTHOR]

Biomimetics, which draws inspiration from nature, has emerged as a key approach in the development of underwater vehicles. The integration of this approach with computational fluid dynamics (CFD) has further propelled research in this field. CFD, as an effective tool for dynamic analysis, contributes significantly to understanding and resolving complex fluid dynamic problems in underwater vehicles. Biomimetics seeks to harness innovative inspiration from the biological world. Through the imitation of the structure, behavior, and functions of organisms, biomimetics enables the creation of efficient and unique designs. These designs are aimed at enhancing the speed, reliability, and maneuverability of underwater vehicles, as well as reducing drag and noise. CFD technology, which is capable of precisely predicting and simulating fluid flow behaviors, plays a crucial role in optimizing the structural design of underwater vehicles, thereby significantly enhancing their hydrodynamic and kinematic performances. Combining biomimetics and CFD technology introduces a novel approach to underwater vehicle design and unveils broad prospects for research in natural science and engineering applications. Consequently, this paper aims to review the application of CFD technology in the biomimicry of underwater vehicles, with a primary focus on biomimetic propulsion, biomimetic drag reduction, and biomimetic noise reduction. Additionally, it explores the challenges faced in this field and anticipates future advancements. [ABSTRACT FROM AUTHOR]

The accurate original signals of fibre fracture and the veritable correlation between the signal parameters and the fibre tensile stress are investigated in this paper based on a suitable noise reduction method. The physical properties of materials can be indirectly characterised through fracture acoustic emission signals, such as fibres. However, it makes the characterisation difficult and inaccurate due to the fracture signals of most fibres are weaker than the background noise. A suitable noise reduction method based on complete ensemble empirical mode decomposition with adaptive noise and wavelet packet transform denoising is proposed to reduce the noise of the fibre fracture signals and obtain the exact raw signals. The fibre fracture signals are decomposed into several intrinsic mode function series and categorised into two groups. The two groups of series are denoised by different thresholds to minimise the level of noise. The two sets of denoised series are reconstructed as the original fibre fracture signals. The results show the proposed combined denoising method can reduce the noise components and well preserve the components of fibre fracture signals. The signal parameters denoised by the combined noised reduction method have a better correlation with the fibre breaking strength and higher prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we complete pioneering research that indicates the very low frequency (VLF) signal amplitude and phase noise reductions, and short-period wave excitations and attenuations as new potential earthquake precursors. We consider changes in the VLF signal broadcast in Italy by the ICV transmitter and recorded in Serbia that start a few tens of minutes before earthquakes. The sampling interval of the analyzed data is 0.1 s. The main objectives of this study are (1) to complete this research in the time and frequency domains during the periods of the four earthquakes analyzed in the previous studies, and (2) to define the parameters of the VLF signal amplitude and phase in both domains that should be further examined in statistical analyses of the aforementioned potential earthquake precursors. In the first part of this study, we analyze the ICV signal amplitude in the frequency domain during the period around three earthquakes that occurred in November 2010 near the considered signal propagation path. Here, we apply the Fourier transform to the relevant recorded data. In the second part, we compare characteristics of the signal amplitude and phase noise reductions in the time domain, and wave excitations and attenuations in the frequency domain. The results of these comparisons indicate the parameters that should be analyzed in subsequent studies to confirm the connection of the considered VLF signal changes with seismic activity before earthquakes, and potentially establish procedures for their detection are: (a) the start and end times of the noise reductions in the time domain and the excited/attenuated waves in the frequency domain, (b) the differences in the corresponding times, and (c) the wave periods of wave excitations of both the signal amplitude and phase. [ABSTRACT FROM AUTHOR]

The almost unlimited possibilities of modern computational tools create the temptation to study phenomena related to the operation of engineering objects exclusively using complex numerical simulations. However, the fascination with multi-parametric complex computational models, whose solutions are obtained using iterative techniques, may result in qualitative discrepancies between reality and virtual simulations. The need to verify on real objects the conclusions obtained from numerical calculations is therefore indisputable. The enormous cost and uniqueness of large-scale test stands significantly limit the possibility of conducting tests under real conditions. The solution may be an experiment focused on testing features relevant to the given task, while minimising the dimensions of the objects under consideration. Such conditions led to the concept of conducting a series of field experiments to verify the effectiveness of prototype track components, which were developed using numerical simulations to reduce the noise caused by passing trains. The main aim of this study is to examine the acoustic efficiency of prototype porous concrete sound absorbing panels, in relation to the ballasted and ballastless track structures. Presented results of the proposed unconventional experiments carried out on an improvised test stand using the recorded acoustic signals confirm the effectiveness of the developed vibroacoustic isolators. [ABSTRACT FROM AUTHOR]

Background: Radiation dose reduction has been the focus of many research activities in x‐ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients. Purpose: Reconstruction algorithm‐based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high‐dose images (SHD) from the data acquired with low‐dose scans. A representative example is the model‐based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high‐quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low‐dose clinical datasets that overcomes both the noise texture issue and the data availability issue. Methods: Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick‐slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD. Results: The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise‐texture. Conclusions: We present a method to generate SHD datasets from regularly acquired low‐dose CT scans. Images produced with the proposed approach exhibit excellent noise‐reduction with the desired noise‐texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI), notwithstanding the vital information they provide, introduces issues in diagnostic work due to inherent noise. The poor signal-to-noise ratio (SNR) in MRI images demonstrates the importance of post-processing procedures. There are numerous strategies for reducing noise, but there is still a need for a solution that has both high accuracy and a fast convergence speed. In this study, we present a total variation (TV) method for noise reduction in MRI images utilizing a modified Barzilai–Borwein method (MBBM) algorithm. The proposed method is tested on three noisy and blurred images (IXI data set section 3.1). The findings reveal that the reconstructed images have less noise and sharper edges. In comparison to similar existing iterative methods, Gradient projection method (GP) and Barzilai–Borwein method (BBM), the proposed method achieves higher accuracy in a substantially smaller number of iterations. The MBBM technique enhanced the quality metrics, Peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) for the first data set (data 2a). For instance, the PSNR and SSIM for noise level 0.06 improved from 2.62 and 0.22 to 33.12 and 0.90, respectively. This improvement in rating criteria was also seen in the next two experiments (data 2b and data 2c). When compared to similar methods, experimental results reveal that the suggested method is faster, more precise, more successful at preserving edges, and less computationally demanding. Consequently, the presented method is effective and reliable both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

To make the sound absorber easy to fabricate and convenient for practical application, a modular composable acoustic metamaterial with multiple nonunique chambers (MCAM–MNCs) was proposed and investigated, which was divided into a front panel with the same perforated apertures and a rear chamber with a nonunique grouped cavity. Through the acoustic finite element simulation, the parametric studies of the diameter of aperture d, depth of chamber T0, and thickness of panel t0 were conducted, which could tune the sound absorption performances of MCAM–MNCs–1 and MCAM–MNCs–2 for the expected noise reduction effect. The effective sound absorption band of MCAM–MNCs–1 was 556 Hz (773–1329 Hz), 456 Hz (646–1102 Hz), and 387 Hz (564–951 Hz) for T = 30 mm, T = 40 mm, and T = 50 mm, respectively, and the corresponding average sound absorption coefficient was 0.8696, 0.8854, and 0.8916, accordingly, which exhibited excellent noise attenuation performance. The sound absorption mechanism of MCAM–MNCs was investigated by the distributions of the total sound energy density (TSED). The components used to assemble the MCAM–MNCs sample were fabricated by additive manufacturing, and its actual sound absorption coefficients were tested according to the transfer matrix method, which demonstrated its feasibility and promoted its actual application. [ABSTRACT FROM AUTHOR]

The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future. [ABSTRACT FROM AUTHOR]

NOMAD is a suite of spectrometers on the board of the ESA-Roscosmos Trace Gas Orbiter (TGO) spacecraft and is capable of investigating the Martian environment at very high spectral resolution in the ultraviolet–visible and infrared spectral ranges by means of three separate channels: UVIS (0.2–0.65 μm), LNO (2.2–3.8 μm), and SO (2.3–4.3 μm). Among all channels, LNO is the only one operating at infrared wavelengths in nadir-viewing geometry, providing information on the whole atmospheric column and on the surface. Unfortunately, the LNO data are characterized by an overall low level of signal-to-noise ratio (SNR), limiting their contribution to the scientific objectives of the TGO mission. In this study, we assess the possibility of enhancing LNO nadir data SNR by applying the Minimum Noise Fraction (MNF), a well-known algorithm based on the Principal Components technique that has the advantage of providing transform eigenvalues ordered with increasing noise. We set up a benchmark process on an ensemble of synthetic spectra in order to optimize the algorithm specifically for LNO datasets. We verify that this optimization is limited by the presence of spectral artifacts introduced by the MNF itself, and the maximum achievable SNR is dependent on the scientific purpose of the analysis. MNF application study cases are provided to LNO data subsets in the ranges 2.627–2.648 μm and 2.335–2.353 μm (spectral orders 168 and 189, respectively) covering absorption features of gaseous H2O and CO and CO2 ice, achieving a substantial enhancement in the quality of the observations, whose SNR increases up to a factor of 10. While such an enhancement is still not enough to enable the investigation of spectral features of faint trace gases (in any case featured in orders whose spectral calibration is not fully reliable, hence preventing the application of the MNF), interesting perspectives for improving retrieval of both atmospheric and surface features from LNO nadir data are implied. [ABSTRACT FROM AUTHOR]

Background: The application of cone‐beam computed tomography (CBCT) in image‐guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality. Purpose: In this study, we aim to develop a deep learning framework to generate high‐quality CBCT images for therapeutic applications. Methods: The synthetic CT (sCT) generation from the CBCT was proposed using a transformer‐based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine‐tune the general model for further improvement. For subsequent treatment, a patient‐specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing. Results: The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer‐based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed‐rank test). Conclusion: We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow. [ABSTRACT FROM AUTHOR]

The blasting vibration signal in the mine environment contains a lot of high frequency noise, which seriously affects the authenticity of the blasting vibration signals In this paper, targeting the phenomenon that there is a lot of high frequency noise in the blast signals, using blast vibration data from an open pit mine in Yunnan as a research object, this paper proposes research on noise reduction of blast vibration signals based on adaptive WOA VMD MPE blast vibration signal denoising method. WOA optimization algorithms are used to process blast vibration signals from three mines, and the optimal parameter combination of the VMD algorithm K、q is found, which is broken down via the VMD algorithm, and the IMF components with MPE above 0. 6 are removed in order to obtain the effect of the noise reduction. The results show that it can be seen that the signal to noise ratio of the vibration waveforms optimized by the WOA VMD algorithm is the highest, which is 15. 23,25. 51, 27. 2 respectively, and the mean squared error is the lowest, which is 8. 51, 8. 73,5. 91, respectively. Compared to the EEMD and CEEMD algorithms, the signal to noise ratio of the WOA VMD algorithm is increased by 56% and 44%, and the mean squared error is decreased by 46 % and 42 %, respectively. It has a better noise reduction effect than the EEMD and CEEMD algorithms. It can better remove high frequency noise and retain the original vibration waveform information from the blast, thus verifying the universality of the algorithm. [ABSTRACT FROM AUTHOR]

Low-noise pavements, particularly polyurethane pavements with sound absorption and vibration reduction characteristics, are an effective way to reduce traffic noise. However, the noise reduction performance of polyurethane porous elastic mixtures (PPEM) gradually deteriorates owing to void clogging and thermal-oxidative aging. This study investigates and characterizes the noise reduction performance of PPEM influenced by these factors. The tire noise reduction performance of a prepared PPEM was measured based on void-clogging, thermal-oxidative aging, and tire noise tests. The results indicate that the pavement performance of the PPEM meets the Chinese specification requirements, and its dynamic stability can be up to 120,000 times/mm. The void ratio was found to be the most important factor affecting the noise reduction performance of the mixture, and, at a constant void ratio, the PPEM outperformed a porous asphalt concrete mixture. With increased thermal-oxidative aging time, the noise reduction performance of the PPEM first increases and then decreases and is likely to gradually decrease owing to the aging of polyurethane and crumb rubber. [ABSTRACT FROM AUTHOR]

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 ( μ a ) and scattering ( μ s ) 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 μ a and μ s 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 μ a , μ s , 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 μ a and μ s 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • A facile strategy was developed to promote heat dissipation and noise reduction. • Heat-conducting elastic fiber sponges with boron nitride were first constructed. • Sponges exhibit good heat dissipation with thermal conductivity of 0.159 W m−1 K−1. • Elastic PU and crosslinking endow sponges with robust elastic and tensile properties. • Sponges can reduce noise by 28.3 dB with a high noise reduction coefficient of 0.64. Industrial and traffic noise has become increasingly serious with the progress of industrialization. Most existing noise-absorbing materials suffer from poor heat dissipation and insufficient low-frequency (<1000 Hz) noise absorption, which not only reduces working efficiency but also leads to safety risks. Herein, heat-conducting elastic ultrafine fiber sponges with boron nitride (BN) networks were prepared by integrating direct electrospinning and impregnation method. The large acoustic contact area of ultrafine fibers and the vibration effect of BN nanosheets in a three-dimensional direction endow fiber sponges with good noise reduction, which can reduce white noise by 28.3 dB with a high noise reduction coefficient of 0.64. Moreover, thanks to good heat-conducting networks composed of BN nanosheets and porous structures, the obtained sponges exhibit superior heat dissipation with thermal conductivity of 0.159 W m−1 K−1. Besides, the introduction of elastic polyurethane and following crosslinking endow the sponges with good mechanical properties, which have almost no plastic deformation after 1000 compressions, and the tensile strength and strain are as high as 0.28 MPa and 75%. The successful synthesis of heat-conducting elastic ultrafine fiber sponges overcomes poor heat dissipation and low-frequency noise reduction of noise absorbers. [ABSTRACT FROM AUTHOR]

This article explores the acoustic characteristics and the relevant flow features of jets impinging on permeable plates. Noise generated due to the interaction of the jet with permeable plates is compared with jets impinging on an impermeable plate and the corresponding free jet. This study systematically measures various parameters, including pore size, porosity, and pressure drop, to precisely quantify the permeability of the plates using the Forchheimer equation. The focus is on investigating the impact of permeability on noise reduction. An acoustic study is performed by carrying out blow-up and blow-down tests to find the effect of permeability at different nozzle pressure ratios and different nozzle-plate spacings. An extensive directivity study is conducted to find the directionality of acoustic emissions and calculate acoustic power. It is found that the overall sound pressure level is lower for the jets impinging on the permeable plates compared to the impermeable plates in subsonic cases. It is also observed that the insertion of the permeable plates in supersonic jets generates less noise compared to the corresponding free jet. It is found that most of the tones are absent in the case of permeable plates for supersonic jets, whereas the tones are present with lesser amplitude compared to jets impinging on impermeable plates for subsonic and transonic jets. Finally, flow measurement and flow visualization studies are carried out to understand the flow physics responsible for the noise variance. This study illuminates that the passage of flow through the porous plate results in the reduction of wall-jet velocities, thereby suppressing the turbulent mixing noise. The absence of shock oscillations in front of the permeable plate is identified as the cause of the mitigation of impinging tones. [ABSTRACT FROM AUTHOR]

This study investigated the use of trailing edge serrations to reduce the noise generated by axial-flow automotive cooling fans. Three different serration profiles (sinusoidal, rectangular, and triangular) were examined, with the profiles being extended radially along the entire blade length and truncated at half the blade length while keeping the dimensionless wave number k ∗ and wave amplitude 2 h ∗ constant. The simulations employed a hybrid URANS-LES solver for the flow field and Ffowcs Williams-Hawkings analogy for the sound field, corresponding to the maximum volumetric flow rate and fan rotational speed. Acoustic pressure measurements were taken at multiple receivers upstream and downstream of the fan, and the overall sound pressure level was computed based on the results. Furthermore, the study also compared the aerodynamic performance of all serration types with the baseline fan, revealing that the baseline fan was relatively more efficient than their serrated counterparts. Despite the reduced efficiency, the trailing edge serrations offered significant noise reduction benefits of up to 10 dB, making them a promising solution for improving acoustic comfort in automotive cooling systems. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of the fuzzy c-means (FCM) algorithm sensitivity to noise when clustering image pixels. We propose in this regard an improved FCM algorithm that incorporates contextual information at the membership degrees updating stage. For that aim, we introduce two novel parameters: the contextual similarity degree and the intrinsic similarity degree which are used to estimate each pixel's nature (normal or noisy), according respectively to its context and to its specific features. Based on this estimation, we propose a modified membership degrees updating strategy that proceeds by adaptively reinforcing the assignment of a pixel to its context's cluster when this pixel is detected as noisy. Experiments performed on synthetic and real-world images proved that our approach achieves competitive performance compared to state-of-the-art FCM-based methods. [ABSTRACT FROM AUTHOR]

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

To reduce the noise generated by large mechanical equipment, a stackable and expandable acoustic metamaterial with multiple tortuous channels (SEAM–MTCs) was developed in this study. The proposed SEAM–MTCs consisted of odd panels, even panels, chambers, and a final closing plate, and these component parts could be fabricated separately and then assembled. The influencing factors, including the number of layers N, the thickness of panel t0, the size of square aperture a, and the depth of chamber T0 were investigated using acoustic finite element simulation. The sound absorption mechanism was exhibited by the distributions of the total acoustic energy density at the resonance frequencies. The number of resonance frequencies increased from 13 to 31 with the number of layers N increasing from 2 to 6, and the average sound absorption coefficients in [200 Hz, 6000 Hz] was improved from 0.5169 to 0.6160. The experimental validation of actual sound absorption coefficients in [200 Hz, 1600 Hz] showed excellent consistency with simulation data, which proved the accuracy of the finite element simulation model and the reliability of the analysis of influencing factors. The proposed SEAM–MTCs has great potential in the field of equipment noise reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Behavioural audiovisual improvements are reflected by enhanced activation in temporal and occipital regions at P2 latency. • Background noise delays responses within the auditory cortex (N1) and reduces superior frontal cortex activity (P2). • Noise reduction via ForwardFocus improves audiovisual speech performance by allowing the reallocation of attentional resources. Hearing with a cochlear implant (CI) is difficult in noisy environments, but the use of noise reduction algorithms, specifically ForwardFocus, can improve speech intelligibility. The current event-related potentials (ERP) study examined the electrophysiological correlates of this perceptual improvement. Ten bimodal CI users performed a syllable-identification task in auditory and audiovisual conditions, with syllables presented from the front and stationary noise presented from the sides. Brainstorm was used for spatio-temporal evaluation of ERPs. CI users revealed an audiovisual benefit as reflected by shorter response times and greater activation in temporal and occipital regions at P2 latency. However, in auditory and audiovisual conditions, background noise hampered speech processing, leading to longer response times and delayed auditory-cortex-activation at N1 latency. Nevertheless, activating ForwardFocus resulted in shorter response times, reduced listening effort and enhanced superior-frontal-cortex-activation at P2 latency, particularly in audiovisual conditions. ForwardFocus enhances speech intelligibility in audiovisual speech conditions by potentially allowing the reallocation of attentional resources to relevant auditory speech cues. This study shows for CI users that background noise and ForwardFocus differentially affect spatio-temporal cortical response patterns, both in auditory and audiovisual speech conditions. [ABSTRACT FROM AUTHOR]

Background: Photon‐counting‐detector CT (PCD‐CT) enables the production of virtual monoenergetic images (VMIs) at a high spatial resolution (HR) via simultaneous acquisition of multi‐energy data. However, noise levels in these HR VMIs are markedly increased. Purpose: To develop a deep learning technique that utilizes a lower noise VMI as prior information to reduce image noise in HR, PCD‐CT coronary CT angiography (CTA). Methods: Coronary CTA exams of 10 patients were acquired using PCD‐CT (NAEOTOM Alpha, Siemens Healthineers). A prior‐information‐enabled neural network (Pie‐Net) was developed, treating one lower‐noise VMI (e.g., 70 keV) as a prior input and one noisy VMI (e.g., 50 keV or 100 keV) as another. For data preprocessing, noisy VMIs were reconstructed by filtered back‐projection (FBP) and iterative reconstruction (IR), which were then subtracted to generate "noise‐only" images. Spatial decoupling was applied to the noise‐only images to mitigate overfitting and improve randomization. Thicker slice averaging was used for the IR and prior images. The final training inputs for the convolutional neural network (CNN) inside the Pie‐Net consisted of thicker‐slice signal images with the reinsertion of spatially decoupled noise‐only images and the thicker‐slice prior images. The CNN training labels consisted of the corresponding thicker‐slice label images without noise insertion. Pie‐Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy, and compared to a U‐net‐based method that did not include prior information. Results: Pie‐Net provided strong noise reduction, by 95 ± 1% relative to FBP and by 60 ± 8% relative to IR. For HR VMIs at different keV (e.g., 50 keV or 100 keV), Pie‐Net maintained spatial and spectral fidelity. The inclusion of prior information from the PCD‐CT data in the spectral domain was able to improve a robust deep learning‐based denoising performance compared to the U‐net‐based method, which caused some loss of spatial detail and introduced some artifacts. Conclusion: The proposed Pie‐Net achieved substantial noise reduction while preserving HR VMI's spatial and spectral properties. [ABSTRACT FROM AUTHOR]

Indoor air conditioning systems play a crucial role in regulating the environmental conditions of enclosed spaces. The noise generated by such systems can significantly impact human comfort. The multiblade centrifugal fan, as a crucial component of indoor air conditioners, has a significant impact on the performance of the air conditioner. The vortex, which is the primary noise source inside the fan, hinders the aerodynamic noise reduction of the indoor unit. During experimental research and numerical simulation of the indoor unit, the researchers identified the presence of a vortex flow at the outlet of the multiblade centrifugal fan. By analyzing the velocity vector of different cross‐sections in the fan, the researchers determined the formation mechanism and influencing factors of the vortex at the fan's outlet. The addition of fairing sheet plates on the volute wall was explored as a vortex suppression method, which proved effective in weakening and suppressing the vortex. The test results showed that under the same air volume, the noise reduction amplitude of the fan equipped with fairing sheet blades was 1.2 dB at an outlet static pressure of 30 Pa and 0.7 dB at an outlet static pressure of 100 Pa. The research findings suggest that the addition of the fairing sheet can effectively reduce the vortex's intensity, improve the performance of the multiblade centrifugal fan, and reduce the noise. [ABSTRACT FROM AUTHOR]

Aim: Noise levels above 45 dB in a neonatal intensive care unit (NICU) and 60 dB during neonatal transport are recognised hazards, but protective equipment is not standard. We measured noise levels in both settings, with and without noise protection. Methods: Peak sound and equivalent continuous sound levels were measured in a NICU and during road transport, at a mannequin's ear and inside and outside the incubator. Recordings were made without protective earwear, with noise protecting earmuffs and with active noise cancelling headphones. Results: In the NICU, the peak levels at the ear, and inside and outside the incubator, were 61, 68 and 76 dB. The equivalent continuous sound levels were 45, 54 and 59 dB. During road transport, the respective levels were 70, 77 and 83 dB and 54, 62 and 68 dB. In the NICU, 80% of environmental peak noise reached the ear and this was reduced to 78% with earmuffs and 75% with active noise cancelling. The respective figures during transport were 87% without protection and 72% with active noise cancelling, with an unexpected increase for ear muffs. Conclusion: Noise levels exceeded safe limits in the NICU and during transport, but active noise cancelling reduced exposure. [ABSTRACT FROM AUTHOR]

The aeroacoustic characteristics of a power module in the electric vehicle charging pile is studied by experimental and numerical methods. In the free field, a single cooling fan mainly emits tonal noise, while the far-field noise of fans in parallel satisfied the superposition of incoherent sound sources. However, when two fans in parallel delivered air into the power module, the noise increased significantly, especially broadband noise. Referring to the power module, a narrow space model with cooling fan supplying air inward is constructed. The computational fluid dynamics results show that the mass flowrate of the fan is reduced due to the obstacles downstream, which weakens the heat dissipation performance. Part of the fluid forms reflux, interacts with the fan and strengthens the corner vortex. By setting guide holes on the casing near the flow blockage to arrange flow, the flow resistance is reduced and the total mass flowrate of the fan is increased by 2.5%. The reflux and corner vortex are dispersed, and the vortices develop downstream, thus reducing the interaction with the fan. The predicted tonal sound pressure level (SPL) decreases by 3.2 dBA based on acoustic analogy method. The broadband noise caused by shedding vortex is also predicted and discussed by a semi-empirical model. In the noise reduction experiment of the real power module, combining inlet bellmouth on the fan and guide holes on the casing, the total SPL was significantly reduced by 2.9 dBA. Especially, based on bellmouth, guide holes brought an additional 1.9 dBA tonal noise reduction and 1.0 dBA total SPL reduction. The study indicates that appropriate flow control is important to improve the heat dissipation and reduce noise of the power module. These results are helpful to develop higher power charging system. [ABSTRACT FROM AUTHOR]

Derivative estimation in magnetic resonance spectroscopy (MRS) possesses several attractive features. It has the ability to enhance the inaccessible spectral details when time signals encoded by MRS are analyzed by nonderivative shape estimators. These unfolded subtle spectral features can be diagnostically relevant in differentiating between healthy and diseased tissues. Within the realm of shape estimators, the prerequisite for the success of MRS in the clinic is reliance upon accurate derivative signal processing. However, derivative processing of encoded time signals can be very challenging. The reason is that such spectra may suffer from severe numerical instabilities since even small perturbations (noise) in the input data could produce large errors in the predicted output data. Nevertheless, it is presently demonstrated that this obstacle can be surmounted by an adaptive optimization. The benefit is simultaneously increased resolution and reduced noise in quantitatively interpretable lineshapes. The illustrative spectra are reconstructed from time signals encoded by proton MRS with and without water suppression. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]