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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]

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]

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]

Impulse noise is a challenging problem that degrades the quality of an image. In last few decades, Median filtering denoising method has been widely used for impulse noise. Several well-known and efficient algorithms and techniques exist to effectively remove either Gaussian noise or Impulse noise, independently However, in order to remove high noise densities, there has been a shift in the process of filtering methods. New methods adopted are usually computationally expensive. In this paper, Nearest Neighbour median Filter method has been proposed for impulse noise reduction. The proposed method exploited correlation between the pixels of an image. The main objective of proposed approach is detection and reduction of impulse noise in corrupted images without any loss of information. The performance of proposed denoising technique is compared with existing methods on the basis of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Virtual Information Fidelity (VIF) and computational complexity. From the experimental analysis, it is evident that the proposed denoising method removes impulse noise very effectively, especially at higher noise density levels (more than 70%). Moreover, computational complexity of proposed approach is lesser as compared to state-of-the art methods. Graphical abstract of Nearest Neighbour median Filter [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]

Underwater acoustic communication is fraught with challenges, including signal distortion, noise, and interferences unique to aquatic environments. This study aimed to advance the field by developing a novel underwater modem system that utilizes machine learning for signal classification, enhancing the reliability and clarity of underwater transmissions. This research introduced a system architecture incorporating a Lattice Semiconductors FPGA for signal modulation and a half-pipe waveguide to emulate the underwater environment. For signal classification, support vector machines (SVMs) were leveraged with the continuous wavelet transform (CWT) employed for feature extraction from acoustic signals. Comparative analysis with traditional signal processing techniques highlighted the efficacy of the CWT in this context. The experiments and tests carried out with the system demonstrated superior performance in classifying modulated signals under simulated underwater conditions, with the SVM providing a robust classification despite the presence of noise. The use of the CWT for feature extraction significantly enhanced the model's accuracy, eliminating the need for further dimensionality reduction. Therefore, the integration of machine learning with advanced signal processing techniques presents a promising research line for overcoming the complexities of underwater acoustic communication. The findings underscore the potential of data mining methodologies to improve signal clarity and transmission reliability in aquatic environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A methodology that could reduce computational cost and time, combining computational fluid dynamics (CFD) simulations, neural networks, and genetic algorithms to determine a diffuser-augmented wind turbine (DAWT) design is proposed. The specific approach used implements a CFD simulation validated with experimental data, and key parameters are analyzed to generate datasets for the relevant mathematical model established with the backpropagation neural network algorithm. Then, the mathematical model is used with the non-dominant sorting genetic algorithm II to optimize the design and improve the DAWT design to overcome negative constraints such as noise and low energy density. The key parameters adopted are the diffuser's flange height/angle, the diffuser's length, and the rotor's axial position. It was found that the impact of the rotor's axial position on the power output of the DAWT is the most significant parameter, and a well-designed diffuser requires accelerating the airflow while maintaining high-pressure recovery. Introducing a diffuser can suppress the wind turbine's noise, but if the induced tip vortex is too strong, it will have the opposite effect on the noise reduction. [ABSTRACT FROM AUTHOR]

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Russian language instruction emerges as a pivotal course in tertiary education, necessitating novel approaches to maintain instructional quality and efficacy. This study introduces a novel approach to Russian language teaching that combines the robustness of Machine Learning with the security framework of Blockchain technology and is tailored to the unique needs of the Internet of Things (IoT) environment. At its core, the study creates an advanced back-propagation deep neural network enriched with a deep noise-reducing auto-encoder and a support vector machine to improve privacy and access control in IoT-based educational platforms. The proposed model employs a polynomial kernel function and a one-error penalty factor in a single hidden layer, resulting in a system that is not only efficient in handling small-scale data samples but also adept at processing larger data volumes, a common scenario in IoT settings. This design effectively overcomes the problems of overfitting and slow convergence that are common in traditional models. Furthermore, the incorporation of blockchain technology ensures a decentralized and secure data handling framework, reinforcing the privacy and access control aspects that are critical in the digital education domain. The combination of these technologies yields a more rational, scientifically based evaluation system, propelling the standardization and enhancement of Russian language instruction forward. This method not only improves language teaching quality, but it also paves the way for more secure, scalable, and efficient IoT applications in educational settings. [ABSTRACT FROM AUTHOR]

SAR is a self-illuminating imaging method that produces high-resolution images in all weather conditions, day and night. Many application scientists have accepted and used SAR pictures. The SAR images, however, are distorted by speckle noise. Random interference of electromagnetic signals scattered by the object surface within one resolution element causes speckle noise. The amount and distribution of noise that corrupts the image are unpredictably large. Traditional noise filters are quantitative in nature and are not well suited to problems involving uncertainty. Uncertainty can be handled through fuzzy logic. A despeckling approach based on non-subsampled contourlet transform is proposed in this study. Non-subsampled filter banks can influence the directionality, anisotropy, and translation invariance of this transform. The two most desirable qualities of noise filters are noise reduction and image detail preservation. PSNR and MSE are used to assess the suggested fuzzy filter's performance. The performance is measured using SAR images with varied degrees of speckle noise. The effective filter has shown to decrease noise while preserving image edges. [ABSTRACT FROM AUTHOR]

In this study, we investigate the effectiveness of noise reduction in electron holography, based on the wavelet hidden Markov model (WHMM), which allows the reasonable separation of weak signals from noise. Electron holography observations from a Nd2Fe14B thin foil showed that the noise reduction method suppressed artificial phase discontinuities generated by phase retrieval. From the peak signal-to-noise ratio, it was seen that the impact of denoising was significant for observations with a narrow spacing of interference fringes, which is a key parameter for the spatial resolution of electron holography. These results provide essential information for improving the precision of electron holography studies. [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]

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 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]

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Streamflow prediction is one of the critical components of hydrological interactions and a vital step for integrated water resources management for different water-related sectors. Accurate streamflow prediction can provide significant information about flood mitigation, irrigation operation, and land use planning. The study aims to improve data quality and prediction accuracy by remarkably reducing improper noise in streamflow data. In the present study, daily streamflow prediction for Haji Arab station, Gazvin (Iran) from 1969-2020 is conducted for different time scales of 1-week, 2-weeks ahead. First, observed data was analyzed and cleaned with preprocessing techniques to model and predict the streamflow. Due to non-linearity, complexity and erroneous noise of streamflow data, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method (CEEMDAN) was applied on input data to extract oscillations and noise resulting the decomposed streamflow stationary components. In the next step, streamflow data were modeled with some tree methods (M5 tree, RF, REP tree), and the methods were hybridized with the CEEMDAN method (CEEMDAN-M5 tree, CEEMDAN-RF, CEEMDAN-REP tree). Different quantitatively and visually based criteria metrics such as mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe coefficient (NSE), Legate-McCabe index (LMI), and Willmott's Index of the agreement (WI) were applied for model validation. Results revealed that, on the weekly scale, the hybrid CEEMDAN-RF model (NSE:0.924, LMI:0.811, and WI:0.905) outperformed all benchmarked standalone and hybrid models. On the fortnight scale, the hybrid CEEMDAN-M5 tree model (NSE:0.725, LMI:0.504, and WI:0.728) demonstrated superior performance compared to the other models. Preprocessing techniques enhanced the modelling prediction power up to 20% accuracy. [ABSTRACT FROM AUTHOR]

The air distributor is the end component of the ventilation system, and its performance directly determines the comfortable and pleasant feeling of the residence. A perforated plate can be added to the air distributor to convert dynamic pressure into static pressure first and then distribute the air flow, which provides better self-adjustment compared with the embedded guide vane structure. However, the perforated plate can lead to a contradiction between head loss, noise and distribution uniformity. To reconcile this problem, a novel V-shape configuration of the perforated plate was put forward in this study. The internal flow and aeroacoustic properties of different types of perforated plates were systematically studied, and a full-scale test platform was built to verify the flow characteristics. The effects of hole size and installation position of the perforated plate on the uniform distribution of air flow were analyzed by the parametric analysis method. Then, the sound pressure level of the optimized perforated plate air distributor was further analyzed. The results show that, with the optimized perforated plate structure, the uniform flow performance was improved by about 30% and the overall sound pressure level was reduced by up to 12 dB. [ABSTRACT FROM AUTHOR]

The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT. [ABSTRACT FROM AUTHOR]

Noise is one of the key indicators to evaluate axial flow fans, and in many cases, it is also the only indicator for determining their suitability for use. In this study, a new method to reduce axial fan’s noise was proposed for changing the section chord length to transform the blades of two axial fans with the same design parameters but distinct chord lengths to wavy blades. The aerodynamic calculations and noise reduction mechanism of the wavy configuration of the two fans were studied by combining CFD of large eddy simulation with the Lighthill acoustic analogy method. The results showed that the main mechanism contributing to noise reduction through wavy configuration was the promotion of transformation of the blade surface's layered vortex structure into an uncorrelated comb vortex structure. For fan blades with smaller chord lengths, the comb structure with low spanwise correlation was still maintained after the trailing edge, while for fan blades with larger chord lengths, the comb structure of the shedding vortex rapidly dissipated downstream of the trailing edge. Under the rated design conditions, the implementation of wavy leading edge blades resulted in noise reductions of 1.9 dB and 1.5 dB for the two fans, respectively, while wavy trailing edge blades yielded reductions of 2.6 dB and 2.1 dB, respectively. Furthermore, the adoption of wavy configuration induced a phenomenon of pressure increase and efficiency decrease in both axial fans at medium and low flow rates, with minimal impact at high flow rates. These outcomes underscored the superior noise reduction efficacy of the wavy trailing edge blades, offering a promising way for the noise reduction design of axial flow fans.

Abstract In this study, we investigate the effectiveness of noise reduction in electron holography, based on the wavelet hidden Markov model (WHMM), which allows the reasonable separation of weak signals from noise. Electron holography observations from a Nd2Fe14B thin foil showed that the noise reduction method suppressed artificial phase discontinuities generated by phase retrieval. From the peak signal-to-noise ratio, it was seen that the impact of denoising was significant for observations with a narrow spacing of interference fringes, which is a key parameter for the spatial resolution of electron holography. These results provide essential information for improving the precision of electron holography studies.

The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT.

Recognizing and quantifying microcracks on surfaces are crucial for early detection of structural damage, as they can lead to more complex issues in engineering structures. In this study, a dataset reflecting varying surface cracks in various engineering materials from the 2018 Ecuador earthquake was constructed. Furthermore, we proposed deep learning-based methods for recognizing and quantifying complex surface cracks. The methods utilized an enhanced U-Net semantic segmentation model, along with noise reduction and topological parameter extraction post-processing algorithms. The intersection-over-union recognition capacity for simple extended, complex crisscrossed, and microcracks was significantly improved, reaching as high as 91.71%, 89.56%, and 100% compared to the original U-Net. The results showed that the enhanced U-Net architecture had a steady training process and high accuracy in segmenting various types of cracks, especially microcracks, which were difficult to detect through manual observation or conventional imaging techniques. Therefore, the proposed model is suitable for pre-alarming monitoring of multiscale damage. A noise reduction post-processing algorithm was introduced to enhance the segmenting capability by extracting detailed characteristics of prediction samples. Statistical analysis of false-positive samples showed that 90% of misrecognitions were eliminated, compared to the original situations. The study also developed an approach for determining the topological parameters of complicated patterns on hierarchical cracks in the refined masks at the pixel level. The average relative errors of area, perimeter, length, compactness, average width, and centroid coordinate for crack samples in ground-truth annotations and refined masks are below 10.75%, 1.89%, 1.51%, 6.20%, 9.75%, and 0.72%, respectively. Finally, a graphical user interface integrating all algorithms was explored and applied for damage assessment of structural components under various environmental disturbances.

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]

Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%. [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]

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To improve image quality for low-count bone scintigraphy using deep learning and evaluate their clinical applicability. Six hundred patients (training, 500; validation, 50; evaluation, 50) were included in this study. Low-count original images (75%, 50%, 25%, 10%, and 5% counts) were generated from reference images (100% counts) using Poisson resampling. Output (DL-filtered) images were obtained after training with U-Net using reference images as teacher data. Gaussian-filtered images were generated for comparison. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to the reference image were calculated to determine image quality. Artificial neural network (ANN) value, bone scan index (BSI), and number of hotspots (Hs) were computed using BONENAVI analysis to assess diagnostic performance. Accuracy of bone metastasis detection and area under the curve (AUC) were calculated. PSNR and SSIM for DL-filtered images were highest in all count percentages. BONENAVI analysis values for DL-filtered images did not differ significantly, regardless of the presence or absence of bone metastases. BONENAVI analysis values for original and Gaussian-filtered images differed significantly at ≦25% counts in patients without bone metastases. In patients with bone metastases, BSI and Hs for original and Gaussian-filtered images differed significantly at ≦10% counts, whereas ANN values did not. The accuracy of bone metastasis detection was highest for DL-filtered images in all count percentages; the AUC did not differ significantly. The deep learning method improved image quality and bone metastasis detection accuracy for low-count bone scintigraphy, suggesting its clinical applicability. [ABSTRACT FROM AUTHOR]

Dedicated breast positron emission tomography (db-PET) is more sensitive than whole-body positron emission tomography and is thus expected to detect early stage breast cancer and determine treatment efficacy. However, it is challenging to decrease the sensitivity of the chest wall side at the edge of the detector, resulting in a relative increase in noise and a decrease in detectability. Longer acquisition times and injection of larger amounts of tracer improve image quality but increase the burden on the patient. Therefore, this study aimed to improve image quality via reconstruction with shorter acquisition time data using deep learning, which has recently been widely used as a noise reduction technique. In our proposed method, a multi-adaptive denoising filter bank structure was introduced by training the training data separately for each detector area because the noise characteristics of db-PET images vary at different locations. Input and ideal images were reconstructed based on 1- and 7-min collection data, respectively, using list mode data. The deep learning model used residual learning with an encoder-decoder structure. The image quality of the proposed method was superior to that of existing noise reduction filters such as Gaussian filters and nonlocal mean filters. Furthermore, there was no significant difference between the maximum standardized uptake values before and after filtering using the proposed method. Taken together, the proposed method is useful as a noise reduction filter for db-PET images, as it can reduce the patient burden, scan time, and radiotracer amount in db-PET examinations. [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]