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Transformer typically enjoys larger model capacity but higher computational loads than convolutional neural network (CNN) in vision tasks. In this letter, the advantages of such two networks are fused for achieving effective and efficient real image denoising. We propose a hybrid denoising model based on Transformer Encoder and Convolutional Decoder Network (TECDNet). The Transformer based on novel radial basis function (RBF) attention is used as encoder to improve the representation capability of overall model. In decoder, the residual CNN instead of Transformer is adopted to greatly reduce computational complexity of the whole denoising network. Extensive experimental results on real images show that TECDNet achieves the state-of-the-art denosing performance with relatively low computational cost. [ABSTRACT FROM AUTHOR]

Seismic signal denoising is the main task of seismic data processing. This study proposes a novel method for the denoising seismic record on the basis of a two-dimensional variational mode decomposition (2D-VMD) algorithm and permutation entropy (PE). 2D-VMD is a recently introduced adaptive signal decomposition method in which |$K$| and |$\alpha $| are important decomposing parameters to determine the number of modes, and have a predictable effect on the nature of detected modes. We present a novel method to address the problems of selecting appropriate |$K$| and |$\alpha $| values and apply these values to the proposed method. First, for a 2D seismic signal, the 2D-VMD method can decompose it into |$K$| modes with specific direction and vibration characteristics. Next, the PE value of each mode is calculated. Random noise components are eliminated according to the PE value. Finally, the signal components are reconstructed to acquire the denoised seismic signal. Experimental and simulation results indicate that the proposed method has remarkable denoising effect on synthetic and real seismic signals. We hope that this new method can inspire and help evaluate new ideas in this field. [ABSTRACT FROM AUTHOR]

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The aim of this study is to investigate the effectiveness of dose reducing software (ClarityIQ) on patient and staff dose during fluoroscopically guided cardiac procedures. Dose measurements were collected in a room without dose reducing software (n = 157) and compared with similar procedures performed in two rooms with the software (n = 1141). Procedures included diagnostic coronary angiography, percutaneous coronary intervention, deployment of cardiac closure devices (for occlusion of atrial septal defect, patent foramen ovale, and atrial appendage) and insertion of permanent pacemakers. The dose reducing software was found to be effective in reducing patient and staff dose by approximately 50%. This study has added to the limited literature reporting on the capability of dose reducing software to decrease radiation exposure during the implantation of cardiac closure devices, as well as demonstrating a reduction in dose to the cardiologist and nursing staff. Administrators should ensure timely upgrades to angiographic equipment to safeguard patients and staff against the potentially adverse effects of radiation exposure. Regardless of the use of dose reducing software, the mean occupational dose during closure devices was in descending order scout > scrub > cardiologist. Scrub nurse dose was found to be higher than the cardiologist during closure devices (0.98/0.26 μSv) and diagnostic coronary angiograms (1.51/0.82 μSv). Nursing staff should be aware that their levels of radiation dose during some cardiac procedures may come close to or even exceed that of the cardiologist. [ABSTRACT FROM AUTHOR]

The envelope (façade) of a building is the part that forms the primary barrier to its environment. Most of the new and modern office buildings have a glazed envelope and are usually built-in city centers. The concept of a double-skin façade was born as a consequence of urban noise. The principal noise source in urban areas is traffic noise. Using a double-skin façade can be a solution that ensures good sound insulation for a glazed office building. This study presents experimental results obtained both from measurement campaigns carried out over longer periods of time, in connection with heat transfer in the case of this system, and punctual measurements, over shorter periods of time, for sound insulation and interior comfort parameters, from the experimental chamber adjacent to the system. The results from this study indicate that box double-skin façades can contribute to noise reduction, improve the interior thermal comfort and increase the energetic performance compared to normal single-glass façades, but within certain limits. [ABSTRACT FROM AUTHOR]

Studies have been carried out of the prospects of using soundproofing screens to reduce the effect of the impact of noise. Adhesive compositions are used as a noise-absorbing material in the capacity of parts of multilayer screens consisting of metal plates. It has been established that the characteristics of the used glue do not considerably affect the sound-absorption level. It was recommended to use adhesive compositions with the highest technological characteristics validated in the process of applying sound-insulating coatings as sound-insulating materials. [ABSTRACT FROM AUTHOR]

Objective: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals—measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow—are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. Methods: In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. Major Results: We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. Conclusion: ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. Significance: We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement. [ABSTRACT FROM AUTHOR]

Deep unfolding network (DUN) has become the mainstream for compressive sensing MRI (CS-MRI) due to its good interpretability and high performance. Different optimization algorithms are usually unfolded into deep networks with different architectures, in which one iteration corresponds to one stage of DUN. However, there are few works discussing the following two questions: Which optimization algorithm is better after being unfolded into a DUN? What are the bottlenecks in existing DUNs? This paper attempts to answer these questions and give a feasible solution. For the first question, our mathematical and empirical analysis verifies the similarity of DUNs unfolded by alternating minimization (AM), alternating iterative shrinkage-thresholding algorithm (ISTA) and alternating direction method of multipliers (ADMM). For the second question, we point out that one major bottleneck of existing DUNs is that the input and output of each stage are just images of one channel, which greatly limits the transmission of network information. To break the information bottleneck, this paper proposes a novel, simple yet powerful high-throughput deep unfolding network (HiTDUN), which is not constrained by any optimization algorithm and can transmit multi-channel information between adjacent network stages. The developed multi-channel fusion strategy can also be easily incorporated into existing DUNs to further boost their performance. Extensive CS-MRI experiments on three benchmark datasets demonstrate that the proposed HiTDUN outperforms existing state-of-the-art DUNs by large margins while maintaining fast computational speed. 1 For reproducible research, the source codes and training models of our HiTDUN. [Online]. Available: https://github.com/jianzhangcs/HiTDUN. [ABSTRACT FROM AUTHOR]

Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers' EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development. [ABSTRACT FROM AUTHOR]

This study presents an experimental study of the effect of sawtooth trailing-edge serrations on airfoil instability noise. The far-field noise measurements are obtained to investigate the noise radiation characteristics of a NACA-0012 airfoil operated at various angles of attack: 0°, 5°, and 10°, and covered Reynolds numbers of 2.87 × 105, 3.71 × 105, and 5 × 105. It is found that as the Reynolds number increases, the instability noise shifts from tonal to broadband, whereas as the angle of attack increases, it shifts from broadband to tonal. Furthermore, sawtooth trailing-edges are used to minimize instability tonal noise, leading to considerable self-noise reduction. Parametric studies of the serration amplitude 2 h and streamwise wavelength λ are performed to understand the effect of sawtooth trailing-edges on noise reduction. It is observed that the sound pressure reduction level is sensitive to both the amplitude and streamwise wavelength. Overall, the sawtooth trailing-edge with larger amplitude and smaller wavelength produce the greatest amount of noise reduction. [ABSTRACT FROM AUTHOR]

Abstract Objectives In clinical practice, characterization of speech comprehension for cochlear implant (CI) patients is typically administered by a set of suprathreshold measurements in quiet and in noise. This study investigates speech comprehension of the three most recent cochlear implant sound processors; CP810, CP910, and CP1000 (Cochlear Limited). To compare sound processor performance across generations and input dynamic range changes, the state‐of‐the art signal processing technologies available in each sound processor were enabled. Outcomes will be assessed across a range of stimulation intensities, and finally analyzed with respect to normal hearing listeners. Methods In a prospective study, 20 experienced postlingually deafened CI patients who received a Nucleus CI in the ENT department of the University Hospital of SH in Kiel were recruited. Speech comprehension was measured in quiet at 40, 50, and 65 dBSPL with monosyllabic words as well as by speech reception threshold for two‐digit numbers. In noise, speech reception thresholds were measured with the adaptive German matrix test with speech and noise in front. Results We found that high levels of open‐set speech comprehension are achieved at suprathreshold presentation levels in quiet. However, results at lower test levels have remained mostly unchanged for tested sound processors with default dynamic range. Expanding the lower limit of the acoustic input dynamic range yields better speech comprehension at lower presentation levels. In noise the application of ForwardFocus improves the speech reception. Overall, a continuous improvement for speech perception across three generations of CI sound processors was found. Conclusions Findings motivate further development of signal pre‐processing, an additional focus of clinical work on lower stimulation levels, and automation of ForwardFocus. Level of evidence 2.

Abstract Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results. In this paper, we propose a novel components loss (CL) for the training of neural networks for mask-based speech enhancement. During the training process, the proposed CL offers separate control over preservation of the speech component quality, suppression of the noise component, and preservation of a naturally sounding residual noise component. We illustrate the potential of the proposed CL by evaluating a standard convolutional neural network (CNN) for mask-based speech enhancement. The new CL is compared to several baseline losses, comprising the conventional mean squared error (MSE) loss w.r.t. speech spectral amplitudes or w.r.t. an ideal-ratio mask, auditory-related loss functions, such as the perceptual evaluation of speech quality (PESQ) loss and the perceptual weighting filter loss, and also the recently proposed SNR loss with two masks. Detailed analysis suggests that the proposed CL obtains a better or at least a more balanced performance across all employed instrumental quality metrics, including SNR improvement, speech component quality, enhanced total speech quality, and particularly also delivers a natural sounding residual noise component. For unseen noise types, we excel even perceptually motivated losses by an about 0.2 points higher PESQ score. The recently proposed so-called SNR loss with two masks not only requires a network with more parameters due to the two decoder heads, but also falls behind on PESQ and POLQA and particularly w.r.t. residual noise quality. Note that the proposed CL shows significantly more 1st ranks among the evaluation metrics than any other baseline. It is easy to implement, and code is provided at https://github.com/ifnspaml/Components-Loss .

It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.

Sonic boom reduction has been an urgent need to develop the future supersonic transport, because of the heavy damages of the noise pollution. This paper provides an active control method for the supersonic aircraft to reduce the sonic boom, wherein a suction slot near the leading edge and an injection slot near the trailing edge on the airfoil suction surface are opened, and the mass flow sucked in near the leading edge is equal to the mass flow injected near the trailing edge. The diamond and 566 airfoils are adopted as the baseline airfoil to verify the capability of the active control method, and the effects of the suction and injection location, the mass flow rate and the attack angle on the ground boom signature, the maximum overpressure, the drag coefficients and the ratio of lift to drag are studied in detail. The results show that the proposed active control method can significantly reduce the sonic boom, and the reduction of the sonic boom intensity is more sensitive to the injection near the trailing edge than the suction near the leading edge. Applying this active control method to the diamond (NACA0008) airfoil, when the mass flow rate is 6.5 kg/s(7.5 kg/s), the value of maximum positive overpressure is decreased by 12.87%(12.85%), the value of maximum negative overpressure is decreased by 33.83%(56.77%) and the drag coefficient is decreased by 9.50%(10.96%). It can be seen that the method proposed in this paper has great benefits in the reduction of sonic boom and provides a useful reference for designing a new generation of lower sonic boom supersonic aircraft.

CNNs have been widely used for image denoising. An attention mechanism network using block match for laboratory X-ray Microscopy denoising is proposed. The similar block is introduced into a CNN model to capture the global information connections of the images, which complements the information loss by weakening long-distance dependencies with increased convolution network depth. A multi-scale convolutional subnetwork is designed to enhance the receptive field as the noise feature extraction module. Then, by reassigning weights to the feature channels, a residual channel attention block is designed to improve the feature channel's availability. Both experiments on bronze coins and fossils show that the proposed method can perform effective image denoising within 0.1s exposure time, and the image quality is comparable with those within 3s exposure time, reducing the existing acquisition time. • Propose a network that utilizes block matching and apply it for the first time to X-ray microscopy denoising. • Utilize the self-similarity of images to capture the global information connections. • Multiscale convolutional subnetwork is designed to enhance the receptive field as the feature extraction module. • Residual channel attention block is designed to improve the feature channel's availability. • This method improves the image quality of 0.1 s exposure time to comparable to 3 s. [ABSTRACT FROM AUTHOR]

The conventional post-filtering methods in the multi-channel noise reduction application only rely on multi-channel input signals while neglecting the noise reduction capability of the microphone array beamformer, which results in overestimation of the noise power spectral density (PSD) and consequently suboptimal filters in the minimum mean-square-error (MMSE) sense. This paper proposes a novel microphone array post-filter based on accurate PSD estimation, the beamformer output in the microphone array is used to estimate the noise PSD, and a two-step noise reduction method is also employed to obtain accurate post-filter gain function. The error analysis is also given to highlight the advantage of the proposed algorithm over the conventional Zelinski and McCowan post-filters. The performance advantages of the proposed post-filter are demonstrated in terms of segmental SNR (SegSNR), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and deep noise suppression mean opinion score (DNSMOS). • A microphone array post-filter based on accurate PSD estimation is proposed. • Mathematical derivations and simulations analyze the error between the proposed and conventional methods. • Simulations and experiments verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals.DesignThe EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure.ResultsThe results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear.ConclusionWe showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions.

Abstract This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, having reviewed the mathematical modeling of the Micro Electro Mechanical Systems (MEMS) Tunable Capacitors (TCs), and also proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers. Referring to importance and impact of this research, the proposed controller takes advantage of fuzzy control properties such as robustness against noise. TCs are responsible for regulating the reference voltage when integrated into Alternating Current (AC) Voltage Reference Sources (VRS). Capacitance regulation for tunable capacitors in VRS is carried out by modulating the distance of a movable plate. A successful modulation depends on maintaining the stability around the pull-in point. This distance regulation can be achieved by the proposed controller which guarantees the tracking performance of the movable plate in moving towards the pull-in point, and remaining in this critical position. The simulation results of the tracking performance and capacitance tuning are very promising, subjected to measurement noise. Article Highlights This article deals with MEMS tunable capacitor dynamics and modeling, considering measurement noise. It designs and applies fuzzy PID control system for regulating MEMS voltage reference output. This paper contributes to robustness increase in pull-in performance of the tunable capacitor.

During research work, three series of studies were performed to support the importance of using rail lubrication at tramway tracks in Budapest. The first task was to determine the noise reduction efficiency of automated rail lubrication: noise measurements were performed in case of lubricated rails with corrugations, non-lubricated rails with corrugations, grinded rails as well as grinded and lubricated rails. Once the conformity was determined, an additional task was to find the right lubricant. After the noise and braking effect tests of various lubricants, it was a legitimate need to determine the optimum lubricant application because the setting of the devices is based only on the experience of the Operator. Finally, the noise mitigation effect and the friction coefficient affecting the wheel/rail contact with different lubricant application settings were investigated simultaneously. It is important that the lubricant be applied in appropriate amount such that even the safe movement of the trams is guaranteed, but at the same time the noise reduction is satisfactory too. Based on the results, the authors make recommendations for the application of the rail lubrication devices.

Abstract Maximum spatial eigenfiltering improves the accuracy of maximum likelihood direction-of-arrival estimators for closely-spaced signal sources but may interchangeably attenuate widely-spaced signal sources, producing a severe performance degradation. Although this behavior has been observed experimentally, it still lacks a mathematical explanation. In our previous work, we overcame these limitations using a differential spectrum-based spatial filter but this still caused a small degradation in the DOA estimate. In this paper, we develop a mathematical analysis of how the signal source separation and the Karhunen-Loève expansion affect the passbands of the maximum spatial eigenfilter. The farther the sources, the less significant is the maximum eigenvalue of the spatial correlation matrix and its corresponding eigenvector. Then, the magnitude response of the maximum spatial eigenfilter no longer approximates the spatial power spectrum and is not guaranteed to place multiple passbands around the signal sources. Consequently, we propose a spatial filter built from the eigenvectors of the entire signal subspace. This filter showed an overall runtime smaller than that of our previous work. It also provides a significant reduction in the threshold signal-to-noise ratio for closely-spaced signal sources and does not hamper the estimation for widely-spaced signal sources.

Higher derivatives are important to interpret the physical process. However, higher derivatives calculated from measured data often deviate from the real ones because of measurement errors. A novel method for data fitting without higher derivatives violating the real physical process is developed in this paper. Firstly, the research on errors’ influence on higher derivatives and the typical functions’ extreme points distribution were conducted, which demonstrates the necessity and feasibility of adopting extreme points distribution features in neural networks. Then, we proposed a new neural network considering the extreme points distribution features, namely, the extreme-points-distribution-based neural network (EDNN), which contains a sample error calculator (SEC) and extreme points distribution error calculator (EDEC). With recursive automatic differentiation, a model calculating the higher derivatives of the EDNN was established. Additionally, a loss function, embedded with the extreme points distribution features, was introduced. Finally, the EDNN was applied to two specific cases to reduce the noise in a second-order damped free oscillation signal and an internal combustion engine cylinder pressure trace signal. It was found that the EDNN could obtain higher derivatives that are more compatible with physical trends without detailed differentiation equations. The standard deviation of derivatives’ error of the EDNN is less than 62.5 percent of that of traditional neural networks. The EDNN provides a novel method for the analysis of physical processes with higher derivatives compatible with real physical trends.

ECG signal processing is an important basis for the prevention and diagnosis of cardiovascular diseases; however, the signal is susceptible to noise interference mixed with equipment, environmental influences, and transmission processes. In this paper, an efficient denoising method based on the variational modal decomposition (VMD) algorithm combined with and optimized by the sparrow search algorithm (SSA) and singular value decomposition (SVD) algorithm, named VMD–SSA–SVD, is proposed for the first time and applied to the noise reduction of ECG signals. SSA is used to find the optimal combination of parameters of VMD [K,α], VMD–SSA decomposes the signal to obtain finite modal components, and the components containing baseline drift are eliminated by the mean value criterion. Then, the effective modalities are obtained in the remaining components using the mutual relation number method, and each effective modal is processed by SVD noise reduction and reconstructed separately to finally obtain a clean ECG signal. In order to verify the effectiveness, the methods proposed are compared and analyzed with wavelet packet decomposition, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm. The results show that the noise reduction effect of the VMD–SSA–SVD algorithm proposed is the most significant, and that it can suppress the noise and remove the baseline drift interference at the same time, and effectively retain the morphological characteristics of the ECG signals.

Current research shows that the wavy shape can play an important role in drag reduction. Meanwhile, it also has the potential of noise reduction. In the present study, a kind of wavy shape of periodic cosine profile with a large spanwise wavelength and large aspect ratio was applied to the circular cylinder model. The experiments on the influence of various aspect ratios (ratio of wave wavelength to amplitude) on the far-field noise of the wavy cylinder were carried out in a 0.55 m × 0.4 m aeroacoustic wind tunnel. It is shown that the maximum decrease of the far-field SPL (Sound Pressure Level) between the wavy cylinder and baseline cylinder exceeded 37 dB within the frequency between 200 Hz and 1000 Hz. The noise reduction effect of the wavy cylinder will become better along with the increasing aspect ratio. However, there exists a critical aspect ratio near λ/a = 30. If the aspect ratio continues increasing, the noise reduction effect of the wavy cylinder will decrease instead of increasing. Finally, the computational fluid dynamics method is applied to reveal the noise reduction mechanism of this kind of wavy cylinder with a large spanwise wavelength and large aspect ratio. It is found that the periodic shedding vortex is disturbed and tends to be banded instead of showing alternate oscillation. The turbulence intensity and velocity fluctuation around the wavy cylinder will be also reduced. According to the vortex and sound theory, these changes are beneficial to the noise reduction. The large spanwise wavelength and large aspect ratio play a significant role in controlling the shedding vortex variation and adjusting the local flow field around the crest and trough of the wavy cylinder, which is the key factor to change the flow field and reduce the flow-noise of the wavy cylinder.

The leading-edge and trailing-edge serrated guide vanes, inspired by the silent flight of owl wings, were designed to reduce the aerodynamic noise produced by elbows in cruise. Using the acoustic finite element approach in conjunction with the large eddy simulation model, the aerodynamic noise produced by the elbow is calculated. Using an air piping test platform, this hybrid simulation technique is validated. Further simulation results showed that these two bionic guide vanes contributed to the decrease in the aerodynamic noise by streamlining the airflow and lowering the formation of laminar flow separation bubbles. In particular, the leading-edge serrated guide vane reduced noise by 4.6 dB, whereas the trailing-edge serrated guide vane reduced noise by 3.4 dB. [ABSTRACT FROM AUTHOR]

Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one.Graphical Abstract: Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies. [Display omitted] • Use blank sample removal method for noise reduction in LIBS spectral preprocessing. • The noise reduction effect is significant. Compared with the original spectra of six target elements, the spectral data processed by this algorithm shows significant noise reduction, achieving a SNR of up to 36 times. • This method effectively extracts small spectral peaks that cannot be detected in the original spectrum. • For multiple elements, the detection limit of all target elements can be increased by at least one order of magnitude. [ABSTRACT FROM AUTHOR]

[Display omitted] • An improved SSA-based wavelet threshold denoising method was proposed by introducing two adjustable factors. • The Hilbert Huang transform was used to decompose the infrasound signal, and the signal was preliminarily filtered based on Pearson correlation coefficient. • Three wavelet threshold parameters were determined in the infrasound simulation. • Experiments on debris flows with different volumes and velocities were conducted in Jiangjia Gully, and the effectiveness of the method was verified. The measurement and analysis of infrasound are widely utilized in debris flow monitoring and early warning. However, the infrasound signals are often contaminated by noise across various frequencies, posing significant challenges for feature extraction. The current methods for infrasound denoising are somewhat rudimentary and face several constraints. This study introduces a novel method for enhancing infrasound signals through denoising. This approach integrates empirical mode decomposition with an enhanced wavelet threshold algorithm, and improves the conventional wavelet threshold function. This study uses the Signal-to-Noise ratio, smoothness and correlation coefficient to fine-tune the wavelet parameters. Experiments in flumes with varying speeds and volumes were conducted to test the effectiveness of the denoising method. The results demonstrate that, compared to traditional methods, the proposed method improves the SNR by averages of 69.56%, 60.91%, and 55.63%. It offers a new alternative for the noise reduction of debris flow infrasound signals. [ABSTRACT FROM AUTHOR]

• A LSTM model with attention was proposed to predict power of hybrid vehicle. • Training data was collected from dyno with perturbation for data diversity. • The predictors were handled using feature extraction and noise reduction. • The effects were compared and analyzed with different contamination degree data. • The robustness of proposed model was assessed on real data from different vehicles. Hybrid vehicles are pivotal in transitioning to sustainable transportation, but effective energy management is challenged by data contamination. This paper proposes a novel approach, utilizing deep learning trained with diverse data from existing energy management systems, to assess its adaptability to varying contamination levels. The training dataset covers emission test cycles, loadings, and driving styles, with dimensionality and noise reduction applied to control parameters. A Long Short-Term Memory (LSTM) prediction model with attention mechanism forecasts power demand, outperforming five other deep learning models. The study demonstrates exceptional performance, achieving an average Root Mean Square Error (RMSE) of 4.9761, surpassing RMSE values from 7.9497 to 9.1933. Real driving conditions yield a Mean Absolute Error (MAE) of 3.965 and RMSE of 5.908, highlighting the model's adaptability and predictive accuracy despite data contamination. Such robustness makes it valuable for hybrid electric vehicle energy management systems research and development. [Display omitted] [ABSTRACT FROM AUTHOR]

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of K i images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower K i in general as well as incorrect K i in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion K i predictions. • Dynamic PET, Noise reduction, Parametric Imaging, Deep Image Prior. [ABSTRACT FROM AUTHOR]

The purpose of low-dose computed tomography (LDCT) is to reduce patient's radiation dose. However, LDCT imaging often suffers from low image quality due to computed tomography (CT) noise patterns. While conventional methods such as iterative reconstruction and image processing have been studied to address this issue, their clinical application remains limited due to the degradation of critical anatomical structures. Recently, deep-learning-based noise reduction approaches have shown promising results in improving the image quality of LDCT. Particularly, generative adversarial network (GAN)-based noise reduction methods have produced outstanding results, approaching the quality of normal-dose CT (NDCT) images. In this study, we propose a hybrid framework based on Wasserstein-GAN with the non-subsampled contourlet transform for enhanced noise reduction in LDCT. The aim is to meticulously address CT noise patterns and generate CT images similar to NDCT. We implemented the proposed algorithm and conducted an experiment using clinical abdominal CT datasets to demonstrate its feasibility. According to our results, the proposed method effectively reduced noise components in LDCT images while preserving the anatomical structures of NDCT. This was confirmed through both qualitative and quantitative evaluations. The implications of our findings for clinical practice are significant, as the proposed method can improve the accuracy of LDCT and reduce radiation dose to patients, ultimately enhancing patient safety. [ABSTRACT FROM AUTHOR]

• The unique characteristics of acoustic metamaterials (AMMs), which are not found in natural materials, have been explored. • The fundamental structures, application areas, opportunities, and challenges of AMMs have been examined in detail. • The importance of simulation in designing AMMs has been emphasized, and 3D printing has been examined for their fabrication. • It has been suggested that a materials-centered approach holds the potential for revolutionary developments in this field. Acoustic metamaterials (AMMs) are a form of man-made material that can be specifically developed to have a sub-wavelength periodic structure with extraordinary characteristics not found in nature. Recently, the ability to manipulate sound waves and elastic waves, thanks to these properties, has attracted great interest from scientists and engineers and has been the subject of intense research. This review presents general conceptual descriptions of these materials. We have summarized current literature and we investigated basic structures such as Helmholtz resonators, membrane-type structures, locally resonant and space-coiled structures, and application fields of acoustic metamaterials such as sound imaging, sound absorption, acoustical cloaking, noise reduction, and underwater acoustics. As simulation methods are essential for design and performance evaluation of acoustic metamaterials, the studies carried out in the literature were described and 3D printing technologies for the fabrication of these designs were investigated. Finally, we discuss the opportunities and challenges of acoustic metamaterials. While acoustic metamaterials have enormous ability for the field of sound control, AMMs also present challenges such as frequency-dependent operation, optimization requirements, and material costs. This review provides a comprehensive resource to guide researchers and engineers in understanding the importance and future potential of acoustic metamaterials and identifying gaps in the literature. [ABSTRACT FROM AUTHOR]

Electrification of ships offers zero-emission travel and is spreading rapidly, but the relation between electric ship propulsion and noise pollution is not generally understood. Here, three different types of diesel-electric hybrid ferries have been measured in two operating modes: running in hybrid mode with the diesel engine powering an electric generator; and running in fully electric battery powered mode. Measurements were performed to simultaneously quantify airborne and underwater radiated noise. Findings are that on-board diesel engines for electricity generation do not contribute a large extent of the underwater radiated noise, whilst a major source of airborne low-frequency radiation. • Diesel-electric vessels radiate less airborne noise in battery-powered propulsion mode. Potentially disturbing low frequency tones are present in both modes. • Small or no changes in underwater noise levels were observed between electric and hybrid propulsion; diesel engines are a minor contributor to underwater noise at cruise speed. • Airborne and underwater ship noise radiation can be simultaneously measured in route, even in busy shipping lanes. This requires close cooperation with ship operators. [ABSTRACT FROM AUTHOR]

In acoustic emission tests, the effective signal generated by crack initiation and propagation inside a sample is easily affected by the testing machine, earth vibration and other external noise, which increases the difficulty of data processing and analysis. To solve this problem, a gypsum sample under uniaxial compression is taken as an example to analyze and process the acoustic emission signal during the experiment. The test results show that these noises generally have the characteristics of acoustic sources far from the probe and signal types different from the effective signal. Therefore, a series of methods concerning the noise reduction of acoustic emission signals, including acoustic emission signal preprocessing, acoustic source distance denoising, and acoustic source type denoising, is proposed. Through three analyses and treatments of acoustic emission parameters, noise reduction was achieved with good results. According to the principles of these methods, some suggestions have been put forward for the position of probes in acoustic emission tests; that is, the connection of two probes should not be perpendicular to a weak plane (bedding, joints, etc.). These results could provide a theoretical reference for the postprocessing of acoustic emission signals in mechanical tests. • A new method for determining noise signals by acoustic source distances is proposed. • A new method for determining noise signals by acoustic type is proposed. • Several suggestions were proposed for the position of the probe in the acoustic emission test. [ABSTRACT FROM AUTHOR]

Moving differential and dynamic window moving averaging are simple and well-known signal processing algorithms. However, the most common methods of obtaining sufficient signal-to-noise ratios in distributed acoustic sensing use expensive and precise equipment such as laser sources, photoreceivers, etc., and neural network postprocessing, which results in an unacceptable price of an acoustic monitoring system for potential customers. This paper presents the distributed fiber-optic acoustic sensors data processing and noise suppression techniques applied both to raw data (spatial and temporal amplitude distributions) and to spectra obtained after the Fourier transform. The performance of algorithms’ individual parts in processing distributed acoustic sensor’s data obtained in laboratory conditions for an optical fiber subjected to various dynamic impact events is studied. A comparative analysis of these parts’ efficiency was carried out, and for each type of impact event, the most beneficial combinations were identified. The feasibility of existing noise reduction techniques performance improvement is proposed and tested. Presented algorithms are undemanding for computation resources and provide the signal-to-noise ratio enhancement of up to 13.1 dB. Thus, they can be useful in areas requiring the distributed acoustic monitoring systems’ cost reduction as maintaining acceptable performance while allowing the use of cheaper hardware.

Among the several noise-generation mechanisms of airfoil self-noise, trailing-edge bluntness noise is an important noise source, which is caused by the vortex shedding at blunt trailing edges. A numerical study on airfoil trailing-edge bluntness noise control using the bio-inspired wavy leading edges is presented in this paper. The high-fidelity, improved, delayed, detached eddy simulation (IDDES) method was used to calculate the flow field, and the acoustic analogy method was used for noise prediction. For both the blunt-trailing-edge airfoils, a baseline airfoil with a straight leading edge and a bio-inspired airfoil with a wavy leading edge were used in this study. The chord-based Reynolds number was 400,000, and there was no angle of attack. The numerical results show that the trailing-edge bluntness noise of the baseline airfoil was significantly reduced by the wavy leading edges. The sound pressure level reduction was about 3.7 dB at the characteristic frequency, and the maximum sound pressure level reduction was as high as 35 dB. The trailing-edge bluntness noise was decreased at all directional angles. The maximum overall sound pressure level reduction was 6.3 dB at 0°. In addition, by analyzing the pressure fluctuations, wake characteristics, turbulent vortex structures and spanwise correlation and coherence of the flow field, the noise-reduction mechanisms of the bio-inspired airfoil are deeply revealed.

The SWOT (Surface Water Ocean Topography) mission will provide high-resolution and two-dimensional measurements of sea surface height (SSH). However, despite its unprecedented precision, SWOT’s Ka-band Radar Interferometer (KaRIn) still exhibits a substantial amount of random noise. In turn, the random noise limits the ability of SWOT to capture the smallest scales of the ocean’s topography and its derivatives. In that context, this paper explores the feasibility, strengths and limits of a noise-reduction algorithm based on a convolutional neural network. The model is based on a U-Net architecture and is trained and tested with simulated data from the North Atlantic. Our results are compared to classical smoothing methods: a median filter, a Lanczos kernel smoother and the SWOT de-noising algorithm developed by Gomez-Navarro et al. Our U-Net model yields better results for all the evaluation metrics: 2 mm root mean square error, sub-millimetric bias, variance reduction by factor of 44 (16 dB) and an accurate power spectral density down to 10–20 km wavelengths. We also tested various scenarios to infer the robustness and the stability of the U-Net. The U-Net always exhibits good performance and can be further improved with retraining if necessary. This robustness in simulation is very encouraging: our findings show that the U-Net architecture is likely one of the best candidates to reduce the noise of flight data from KaRIn.

With increasing urbanization in China, the traffic-induced urban environmental noise pollution problem is becoming more and more serious, and it has become a common urban malady that cannot be ignored. Traffic green belts are an important part of the urban ecosystem and play a role in traffic noise reduction, and simultaneously provide ecosystem services, such as creating a natural landscape and retaining dust. Therefore, they are a category of Nature-based Solutions (NbSs) that have multiple ecosystem service provisions. The relationship between NbSs and urban ecosystem services is one of the current research hot spots. However, regarding the assessment of ecosystem services on the urban scale, the role of vegetation in reducing noise pollution as a service has rarely been studied. Taking Shenzhen City as an example, through monitoring 217 sample plots in the city, this paper analyzes the relationship between vegetation coverage and the ability of green belts to reduce noise by using the IUEMS platform combined with the high-resolution spatial distribution data of green spaces. Then, we evaluated the product amount and the value of the roadside green belts in Shenzhen when acting as a noise reduction service. The work of this study, to a certain extent, improves the problems related to the inadequate consideration of vegetation characteristics in current urban-scale noise assessment models. The results show the following: (i) In the respective analysis buffer zones of the Grade I to Grade IV roads in Shenzhen, on average, for every 1% increase in the vegetation coverage of green belts, noise can be reduced by 0.4 dB, 1.0 dB, 0.2 dB, and 0.6 dB, respectively. (ii) The product value of the noise reduction service provided by roadside green belts is CNY 1.16 billion in Shenzhen. (iii) The road traffic noise greatly exceeds the standard in Shenzhen, but traffic noise can be decreased by increasing the vegetation coverage of green belts. This is not only beneficial to the scientific understanding of the ecological service value of green spaces by evaluating the noise reduction service of traffic green belts, as well as its influencing factors, but is also beneficial to making improvements in construction and management ideas for urban green spaces.

The passive optical network (PON) is widely used in optical fiber communication thanks to its low cost and low resource consumption. However, the passiveness brings about a critical problem that it requires manual work to identify the topology structure, which is costly and prone to bringing noise to the topology logs. In this paper, we provide a base solution firstly introducing neural networks for such problems, and based on that solution we propose a complete methodology (PT-Predictor) for predicting PON topology through representation learning on its optical power data. Specifically, we design useful model ensembles (GCE-Scorer) to extract the features of optical power with noise-tolerant training techniques integrated. We further implement a data-based aggregation algorithm (MaxMeanVoter) and a novel Transformer-based voter (TransVoter) to predict the topology. Compared with previous model-free methods, PT-Predictor is able to improve prediction accuracy by 23.1% in scenarios where data provided by telecom operators is sufficient, and by 14.8% in scenarios where data is temporarily insufficient. Besides, we identify a class of scenarios where PON topology does not follow a strict tree structure, and thus topology prediction cannot be effectively performed by relying on optical power data alone, which will be studied in our future work.

Coronary computed tomography angiography (CCTA) is widely used due to its improvements in computed tomography (CT) diagnostic performance. Unlike other CT examinations, CCTA requires shorter rotation times of the X-ray tube, improving the temporal resolution and facilitating the imaging of the beating heart in a stationary state. However, reconstructed CT images, including those of the coronary arteries, contain insufficient X-ray photons and considerable noise. In this study, we introduce an image-processing technique for noise reduction using singular value decomposition (SVD) for CCTA images. The threshold of SVD was determined on the basis of minimization of Jensen–Shannon (JS) divergence. Experiments were performed with various numerical phantoms and varying levels of noise to reduce noise in clinical CCTA images using the determined threshold value. The numerical phantoms produced 10% higher-quality images than the conventional noise reduction method when compared on a quantitative SSIM basis. The threshold value determined by minimizing the JS–divergence was found to be useful for efficient noise reduction in actual clinical images, depending on the level of noise.

The emphasis on sustainability in materials related to the construction and transportation sectors has renewed interest in the usage of natural fibers. In this manuscript, a different perspective is taken in adopting oil palm fibers (OPF) to develop composite panels and understand their acoustic, mechanical, and water susceptibility (including warm water analysis) properties to provide an insight into the potential of these panels for further exploration. The binder for these composite panels is a water-based acrylic resin, and for reinforcement purposes, fly ash and other metal oxides are used. It is shown that the presence of fibers positively influences the acoustic absorption coefficient in the critical mid-frequency range of 1000–3000 Hz. Even the noise reduction coefficient values highlighting the octave band are higher by more than 50% in the presence of fibers as compared to traditional refractory boards. Quasistatic indentation and drop-weight tests have also highlighted the excellent performance of the composite panels developed in this work. Though the water immersion tests on composite panels and subsequent analysis showed relatively minor changes in their performance, the immersion of the panels in caustic warm water for 56 days has resulted in their severe degradation with a loss of more than 65% in flexural strength. [ABSTRACT FROM AUTHOR]

Rod–airfoil interaction noise is a major concern in several practical industrial and aeronautical applications. In this study, we constructed bio-inspired gradient distributed porous leading edges to reduce rod–airfoil interaction noise. Noise radiations by NACA 0012 airfoils with nonporous aluminum and porous leading edges were experimentally compared in an anechoic wind tunnel by changing the streamwise gap between the upstream rod and the downstream airfoil, as well as the angle of attack of the airfoil. The results of detailed acoustic tests showed that the proposed gradient distributed porous leading edges can significantly reduce noise radiation around and above the peak frequency of the baseline rod–airfoil interaction. Parametric studies on the piecewise porous characteristics showed that rod–airfoil interaction noise reduction is sensitive to the coverage percentage, position, and arrangement order of the porous materials. Porous leading edges with lower pores per inch, larger coverage, and gradually sparse distributed pores better reduced noise. Moreover, the position of the porous material affected the frequency band of noise reduction, and the noise reduction performance was better when it was located in the downstream strips of the porous leading edge. [ABSTRACT FROM AUTHOR]

Distant Supervision is an approach that allows automatic labeling of instances. This approach has been used in Relation Extraction. Still, the main challenge of this task is handling instances with noisy labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). The approaches reported in the literature addressed this problem by employing noise-tolerant classifiers. However, if a noise reduction stage is introduced before the classification step, this increases the macro precision values. This paper proposes an Adversarial Autoencoders-based approach for obtaining a new representation that allows noise reduction in Distant Supervision. The representation obtained using Adversarial Autoencoders minimize the intra-cluster distance concerning pre-trained embeddings and classic Autoencoders. Experiments demonstrated that in the noise-reduced datasets, the macro precision values obtained over the original dataset are similar using fewer instances considering the same classifier. For example, in one of the noise-reduced datasets, the macro precision was improved approximately 2.32% using 77% of the original instances. This suggests the validity of using Adversarial Autoencoders to obtain well-suited representations for noise reduction. Also, the proposed approach maintains the macro precision values concerning the original dataset and reduces the total instances needed for classification. [ABSTRACT FROM AUTHOR]

Wool of mountain sheep, treated nowadays as a waste or troublesome byproduct of sheep husbandry, was used for the production of sound-absorbing materials. Felts of two different thicknesses were produced from loose fibres. Additionally, two types of yarn, ring spun and core rug, were obtained. The yarns were used for the production of tufted fabric with cut and loop piles. During the examinations, basic parameters of the obtained materials were determined. Then, according to standard procedure with the use of impedance tube, the sound absorption coefficient was measured, and the noise reduction coefficient (NRC) was calculated. It was revealed that felt produced from coarse wool exhibits high porosity, and its sound absorbing capacity is strongly related to the felt thickness. For thicker felt the NRC achieved 0.4, which is comparable with the NRC of commercial ceiling tiles. It was shown that the crucial parameter influencing the sound absorption of the tufted fabrics was the pile height. For both types of yarns, when the height of the pile was increased from 12 to 16 mm, the NRC increased from 0.4 to 0.42. The manufactured materials made from local wool possess good absorption capacity, similar to commercial products usually made from more expensive wool types. The materials look nice and can be used for noise reduction as inner acoustic screens, panels, or carpets. [ABSTRACT FROM AUTHOR]

A key aspect of sustainable pavement construction is the use of environmentally-friendly designed pavement materials. These materials are characterized by the fact that they are renewable raw materials, require a low amount of energy during production and in the best case, are made from a high proportion of recyclable materials in order to reduce waste. A number of recent studies have demonstrated the recyclability of waste materials that can be very well utilized in road construction. This study describes the recycling of a new and innovative topcoat system that already contains recycled materials. However, the focus is on guaranteeing the mechanical performance of the innovative absorption layer where different portions of used material are added. Therefore, low-temperature behaviour, durability, fatigue and noise absorption are investigated in detail and it is concluded that their function is preserved. In order to investigate these characteristics, the impedance measuring tube, the uniaxial cyclic compression test (UCCT), the three point bending test (3PB), the uniaxial tension stress test (UTST) and the thermal stress restrained specimen test (TSRST) are used. However, the examined absorption material can be reused to build innovative roads. [ABSTRACT FROM AUTHOR]

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications. [ABSTRACT FROM AUTHOR]