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Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body's movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively. [ABSTRACT FROM AUTHOR]

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information. [ABSTRACT FROM AUTHOR]

Tyre cavity resonance noise is one kind of low-frequency and narrow-band noise that particularly affects the passengers inside the cabin of vehicle, especially when driving at a medium speed. In this paper, a noise reduction structure made of multiple resonators is proposed to reduce this type of noise. Based on the local resonance principle, the dimension of the resonator unit is determined by the tyre cavity resonance frequency. In order to obtain this characteristic frequency and the acoustic feature, the acoustic-structure coupling model of the tyre and cavity is established by the finite element method (FEM), and the modal frequency and shape of the tyre cavity are calculated and validated by the experimental results. Based on these analyses, the geometric and material parameters of the sound reduction structure are calculated to match the resonant frequency of the tyre cavity. A long belt filled with multiple resonators is designed to fit the profile of the tyre cavity, and simulations and experimental tests are conducted to investigate the noise reduction performance. The results show that the multiple resonators can significantly reduce the sound pressure inside the tyre cavity due to the vibroacoustic coupling effect. This paper provides a novel solution for reducing tyre cavity resonance noise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The purpose of this study was to clarify the appropriate use of a combination of pulse sequences and acoustic noise reduction technology in general-purpose brain magnetic resonance imaging. Five pulse sequences commonly used in brain magnetic resonance imaging examinations—turbo spin-echo T2-weighted imaging, T1-weighted fluid-attenuated inversion recovery, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and magnetic resonance angiography—were performed on healthy participants at three vendors where acoustic noise reduction technology was available. The results showed that acoustic noise reduction technology reduced sound pressure levels and altered image quality in all pulse sequences across all vendors' magnetic resonance imaging scanners. Although T2-weighted imaging and T1-weighted fluid-attenuated inversion recovery resulted in little image quality degradation, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and magnetic resonance angiography had significant image degradation. Therefore, acoustic noise reduction technology should be used with caution. [ABSTRACT FROM AUTHOR]

The phase reconstruction process in digital holographic microscopy involves a trade-off between the phase error and the high-spatial-frequency components. In this reconstruction process, if the narrow region of the sideband is windowed in the Fourier domain, the phase error from the DC component will be reduced, but the high-spatial-frequency components will be lost. However, if the wide region is windowed, the 3D profile will include the high-spatial-frequency components, but the phase error will increase. To solve this trade-off, we propose the high-variance pixel averaging method, which uses the variance map of the reconstructed depth profiles of the windowed sidebands of different sizes in the Fourier domain to classify the phase error and the high-spatial-frequency components. Our proposed method calculates the average of the high-variance pixels because they include the noise from the DC component. In addition, for the nonaveraged pixels, the reconstructed phase data created by the spatial frequency components of the widest window are used to include the high-spatialfrequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages.

In speech enhancement, accurate phase reconstruction can significantly improve speech quality. While phase-aware speech enhancement methods using the complex ideal ratio mask (cIRM) have shown promise, the estimation difficulty of the phase is shared with the real and imaginary parts of the cIRM. The pattern lacking in the imaginary part poses particular difficulties. To address this issue, we proposed a phase-aware speech enhancement method that uses a complex Wiener filter, which delegates the estimation of speech and noise amplitude properties and the phase property to different models, mitigating the issues with the cIRM and improving the effectiveness of neural-network training. Our method uses a speech-variance estimation model with a noise-robust vector-quantized variational autoencoder and a phase corrector that maximizes the scale-invariant signal-to-noise ratio in the time domain. To further improve speech-variance estimation, we propose a loss function that uses a categorical distribution of fundamental frequency (F0) for enhancing the spectral fine structure of estimated speech variance. We evaluated our method on the open dataset released by Valentini et al. to directly compare it with other speech-enhancement methods. Our method achieved a perceptual evaluation of speech quality score of 2.86 and short-time objective intelligibility score of 0.94, better than the state-of-the-art method based on cIRM estimation during the 2020 Deep Noise Challenge. Our comprehensive analysis shows that incorporating the proposed loss function for spectral-fine-structure enhancement improves speech quality, especially when the F0 is low.

Creep is a long-term property of concrete that poses a significant challenge to structural engineering. The research content of this paper concerns how to accurately predict the creep behavior of concrete. The overall purpose is to develop a robust predictive model based on the Northwestern University (NU) database that overcomes the challenges encountered when handling tabular creep data, including the lack of spatial correlations between features and the presence of noise. To achieve this goal, a novel deep learning methodology that leverages convolutional neural networks (CNNs) is introduced. First, the data preprocessing phase involves feature selection and K-nearest neighbor-based data filling to enhance the quality of the given data. A soft-sorted 1D CNN is introduced to transform tabular data into multichannel images, thereby taking advantage of spatial locality and feature sorting. Additionally, this model incorporates noise reduction mechanisms through residual network encoders, achieving enhanced data clarity and model accuracy. The mean absolute error (MAE) and root mean square deviation (RMSE) of the model increase from 7.48 and 10.76 to 5.83 and 8.88, respectively. The major findings of this research indicate a significant predictive accuracy improvement over the existing methods. The model effectively captures the complex relationships between concrete creep and a range of influencing factors, enabling precise and quantitative predictions to be obtained. These findings offer deep insights for the field of civil engineering, enabling enhanced structural design, safety, and performance prediction in concrete applications. This paper contributes to the ongoing exploration of advanced deep learning techniques for addressing complex challenges in the domains of concrete mechanics and structural engineering.

Limited cardiology resources increase the urgency for automated heart disease screening for the general public. Heart sound diagnostic models have been recently employed as a cost-effective solution for the initial screening of heart disease. Noise in heart sound recordings, however, can reduce the performance of such data-driven models. Various quality enhancement approaches have been adopted to alleviate the destructive impact of noise on model performance. One approach is universal noise reduction which applies denoising techniques to recordings, irrespective of their noise level. The second approach is targeted noise reduction, which applies denoising solely to recordings deemed to need it, based on an assessment of signal noise level. The third approach is filtering where instead of noise reduction, the quality of recordings is assessed and the signals falling below a minimum threshold of quality are discarded. This study aims to understand which quality enhancement approach leads to a more accurate heart sound classification. We developed multiple data-driven models using different classifiers and feature representations and analyzed the impact of quality enhancement on the accuracy of those models. The results indicate that noise reduction is associated with an overall performance drop in classification models. We observe that both universal and targeted noise reduction have a destructive impact on models’ performance. However, filtering improves the accuracy of the models, in particular, for the clinically important abnormal class. The findings of this study can be leveraged to inform the design decisions for the pre-processing of heart sound recordings and consequently optimize downstream classification performance.

Speech emotion recognition technology includes feature extraction and classifier construction. However, the recognition efficiency is reduced due to noise interference and gender differences. To solve this problem, this paper used two multi-task learning models based on adversarial multi-task learning(ASP-MTL). The first model took emotion recognition as the main task and noise recognition as the auxiliary task, and removed the noise part identified by the auxiliary task. After identifying the non-noise part, the second model was constructed. The second model took emotion recognition as the main task and gender classification as the auxiliary task. These two multi-task learning models can not only can use shared information to learn the relationship between different tasks, but also can identify specific tasks. This paper used Audio/Visual Emotion Challenge (AVEC) database and AFEW6.0 database,which were recorded in the field environment. Considering the problem of data imbalance between datasets, the data balance operation was carried out on the data sets in the process of data preprocessing. The paper shows an increase of around 10% in terms of accuracy and F1 score with the recent works using AVEC database and AFEW6.0 datasets, which proved that this paper has made a great progress in SER.

A cochlear implant (CI) is the most suitable option for individuals with severe profound hearing loss. CI restores the audibility to near perfection and offers good speech understanding in quiet. However, the speech perception in noise with CIs is less optimal as most speech coding strategies of CIs encode only the temporal envelope. Besides the current CI signal coding strategies lacks sophisticated pre-processing. In the current study, we proposed a novel pre-processing method to improve speech Intelligibility in noise and tested using the acoustic simulations of cochlear implants. The proposed noise reduction technique aims to minimize the mean square error (MSE) between the temporal envelopes of the enhanced speech and its clean speech. Therefore, the proposed method will be suitable for CI applications. This paper provides an analysis of the theoretical derivation of the noise suppression function and also the performance evaluation using objective and subjective tests. The effectiveness of the proposed method was objectively evaluated using the SRMR-CI and ESTOI. Additionally, speech recognition through the acoustic simulations of the cochlear implant was done for the subjective evaluation. Performance of the proposed method was compared with the Weiner filter (WF) and sigmoidal functions. The sinewave vocoder was used to simulate the cochlear implant perception. Both objective and subjective scores revealed that the performance of the proposed technique is superior to the WF and sigmoidal function.

Many different filters can be used to reduce multiplicative speckle noise on radar images. Most of these filters have some parameters whose values influence the result of filtering. Finding optimal values of such parameters may be a non-trivial task. In this paper, a formal automated method for finding optimal parameters of speckle noise reduction filters is proposed. Using a specially designed test image, optimal parameters for the most commonly used filters were found using several image quality assessment metrics, including the Structural Similarity Index (SSIM) and Gradient Magnitude Similarity Deviation (GMSD). The use of filters with optimal parameters allows processing (detection, segmentation, etc.) of radar images with minimal influence of speckle noise.

Over the last couple of decades, there is an increasing demand to control interior noise occurring in aircraft, automobiles, railways, and construction works. Interior noise could impair people’s health, affecting passenger comfort and active safety performance. One of the contemporary noise control technologies is the use of sound-absorbing materials. Traditionally, mineral fibers, foams, and composites are used as sound-absorbing materials. However, they are expensive and energy consumptive as they add to the structure’s weight and affect structural integrity. Natural fibers could contribute to the replacement of conventional mineral fibers with decreased environmental impact, carbon neutrality, and improved sound-absorbing qualities. Also, previous research proved that sound-absorbing materials having natural fiber possess better acoustic properties comparable to those of synthetic fibers. However, high water absorption, termite attack, low fire resistance, and low strength are some of the issues restricting natural fibers usage. Here, an attempt is made to present the potential of natural fibers for acoustic absorbers by understanding the benefits of their inherent porous and tortuous structure. This paper involves a review study of experimental methods and parameters used to regulate the acoustic performance of natural fibers. Natural fibers seem a proven alternative as an acoustic absorber, thereby reducing sustainability concerns related to synthetic materials in acoustics applications.

Impulse noise is one of the common noise types that affect images. Median filtering denoising method has been widely used for impulse noise. However, as the noise density increases beyond 50%, the noise reduction quality of median filter reduces sharply. In this paper, a very simple nearest neighbor filtering method11 The MATLAB code for the proposed NNFM algorithm is given at: https://github.com/rafimiet/NNFM.git is proposed for impulse noise reduction. The proposed method exploits the inter-pixel correlation in images. More spatially closer the pixels within an image, more the correlation amongst them. The filter window is partitioned into multiple clusters based on their distance from the pixel that needs to be corrected. The closest non-noisy pixel is searched and is used to replace the corrupted pixel. The proposed method has been simulated for grayscale as well as color images. The simulation results show that the proposed method outperforms the state-of-the-art methods. Also, the computational complexity of the proposed methods is very small as compared to the state-of-the-art denoising methods. The only method that outperforms the proposed method is median filtering method.

The noise optimization using suitable absorbing materials is imperative due to significant impact on living beings by influencing various physiological and psychological factors. The research investigating the acoustic characteristics of woven fabrics compared to nonwoven and other fabrics are very limited. This research investigates the acoustic characteristics of woven jute fabric in relation to fabric structural variables. The research is designed to fulfil the existing gap on acoustic characteristics of woven jute fabrics. The focus was on the effect of woven jute fabric structural parameters such as weft yarn linear density, weft density and number of weft float over warp on sound absorption coefficient and noise reduction coefficient. The sound absorption coefficient was measured using impedance tube method as per ASTM E1050 standard. Noise reduction coefficient was measured from the mean value of sound absorption coefficient observed at 250 Hz, 500 Hz, 1000 Hz and 2500 Hz. A Box–Behnken experimental design was adopted for research design and woven jute sample planning. The experimental work revealed that the plain woven jute samples prepared with 40 tex weft yarn consisting of 8 weft threads/cm showed higher mean values of sound absorption coefficient and noise reduction coefficient compared to twill and satin weave. The experimental results and statistical analysis showed that the effect of number of weft yarn float over warp and weft yarn linear density is significantly influencing sound absorption coefficient and noise reduction coefficient whereas the effect of weft density is found not significant.

In order to analyze the propagation path of vibration noise, and carry out finite element numerical calculation research based on electro-magnetic–solid-acoustic multiphysics coupling, a single-phase two-column dry-type transformer is used as the noise source in this paper. According to the distribution characteristics of the spatial sound field and the vibration noise amplitude–frequency characteristics of the transformer, a topology design method of the space coiled acoustic metamaterial (SCAMM) unit is proposed and the analytical calculation methods of acoustic parameters such as sound pressure reflection/transmission coefficient of various SCAMM units are deduced. Besides, the effects of parameters such as the longitudinal width a, the waveguide width b, the ratio of waveguide width to longitudinal width b/aand transverse width t on the SCAMM unit sound transmission loss (STL) are studied, and the anti-resonance frequencies are adjusted by parameter optimization method to achieve the optimal sound insulation effect. In the end, the sound field control barrier based on the SCAMM for transformer vibration and noise suppression is designed, and relevant experiments are carried out. The experiments show that the sound field control barrier based on the SCAMM can reduce the transformer noise sound pressure level (SPL) of the main noise frequencies by up to 25 dB with a thickness of only 4 cm and ventilation.

Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning. The imbalanced class distribution misleads classifiers from correctly classifying the minor class. This paper introduces Reduced Noise-SMOTE (RN-SMOTE) for pre-processing imbalanced data. RN-SMOTE firstly, oversamples the training data using SMOTE which introduces noisy oversampled synthetic instances in the minority class. Then, applying DBSCAN to detect and remove noise. Next, the clean artificial instances are combined with the original data. Finally, RN-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. RN-SMOTE is evaluated using 9 different classifiers and 9 different imbalanced datasets with different imbalance ratios and five of them are used for outlier detection. The results proved that the performance of the classifiers has been improved with RN-SMOTE and outperformed the performance with original data and SMOTE with percentage based on the classifier, dataset and evaluation metric. Also, performance of RN-SMOTE has been compared to the performance of the current state of art and resulted in an increase up to 37.41%, 23.28%, 13.95% and 9.07% in terms of Recall, F1, Precision and Accuracy for RN-SMOTE.

Magnetoencephalography is a non-invasive technology for brain function imaging, which is of enormous value to brain science research and clinical application due to its ultra-high temporal and spatial trace resolution. In this paper, we introduce a self-built and atomic magnetometer based wearable magnetoencephalography system. By designing bi-planar coils system and combining with reference sensor array, the residual magnetic field in the subject's head movement area is controlled to be within ±1 nT, which ensures the sensors are maintained within their dynamic range during the moving measurement. At the same time, a virtual gradiometer-based noise reduction method is proposed to suppress the common-mode magnetic-field noise. Finally, the alpha rhythm and auditory evoked magnetic field signals with high signal-to-noise ratio are successfully detected under the subject's natural head movement and the effectiveness of the system is confirmed. This study could provide more possibilities for the application and promotion of moving wearable magnetoencephalography.

Smart voice assistants are being used progressively in new fields and environments. This trend has also reached Polish underground mines, where the first attempts to implement solutions of this type are already being executed. Unfortunately, the underground mine environment places great demands on the operation of such assistants. This problem is even greater when we take into account less popular and more difficult languages like Polish. In this article, we approach the problem of noise in the underground mine environment and the correct transmission of speech to text by a smart voice assistant. In particular, we researched the possibility of using various algorithms for speech enhancement. This was achieved by taking a series of recordings in an underground environment, during which a voice assistant was used in a standardized way by different people. This voice material was then denoised using 10 different algorithms, converted to text, and evaluated. Such a comparison showed that only one of the chosen methods influenced the speech-to-text solution in a positive way, showing that current algorithms are not sufficient for harsh industrial environments similar to underground mines. [ABSTRACT FROM AUTHOR]

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. [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. [ABSTRACT FROM AUTHOR]

Background: To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses. Purpose: To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning. Material and Methods: \We retrospectively collected 100 retrospective ECG-gated CCTAs. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently. Results: DLNR CCTAs showed 64.5% lower image noise (P < 0.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison (P > 0.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77–0.99) with original CCTAs. Conclusion: Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate a silent MR active catheter tracking sequence that allows conducting catheter interventions with low acoustic noise levels. Methods: To reduce the acoustic noise associated with MR catheter tracking, we implemented a technique previously used in conventional MRI. The gradient waveforms are modified to reduce the sound pressure level (SPL) and avoid acoustic resonances of the MRI system. The efficacy of the noise reduction was assessed by software‐predicted SPL and verified by measurements. Furthermore, the quality of the catheter tracking signal was assessed in a phantom experiment and during interventional cardiovascular MRI sessions targeted at isthmus‐related flutter ablation. Results: The maximum measured SPL in the scanner room was 104 dB(A) for real‐time imaging, and 88 dB(A) and 69 dB(A) for conventional and silent tracking, respectively. The SPL measured at different positions in the MR suite using silent tracking were 65–69 dB(A), and thus within the range of a normal conversation. Equivalent signal quality and tracking accuracy were obtained using the silent variant of the catheter tracking sequence. Conclusion: Our results indicate that silent MR catheter tracking capabilities are identical to conventional catheter tracking. The achieved acoustic noise reduction comes at no penalty in terms of tracking quality or temporal resolution, improves comfort and safety, and can overcome the need for MR‐compatible communication equipment and background noise suppression during the actual interventional procedure. [ABSTRACT FROM AUTHOR]

INTRODUCTION: During a deployment of a fighter unit, aircrew began to exhibit ear discomfort and episodes of auricular irritation and ulceration. All affected were using the Attenuating Custom Communications Earpiece System (ACCES). Discomfort was previously discussed in the literature, but neither the prevalence of discomfort nor the occurrence of skin ulcerations had been previously described. METHODS: An anonymous paper questionnaire was used with three fighter squadrons while deployed in 2019. A total of 59 aircrew in the F-15C/E and F-16 airframes participated; aircrew not using ACCES were excluded. RESULTS: Response rate was 57.3%, spread evenly among airframes, with 78% being pilots. A majority of respondents (79.7%) stated they had ACCES problems in the deployed setting. Among those noting problems in the deployed setting, 89% reported ear discomfort and a smaller portion reported skin redness, erosion, and bleeding. DISCUSSION: This study was able to provide a small sample estimate of the prevalence of ear problems among fighter aircrew ACCES users while deployed. This sample exhibited an increase in prevalence of ear discomfort during the deployment. It also showed pathological features absent in home-station flying, such as skin redness and erosion. However, the sample size and study design prevented risk factor characterization, confounder control, or causal inference. While ACCES may contribute to these problems, other confounders such as air characteristics, recall bias, aircrew motivation to report problems, and baseline dermatologic pathology could not be excluded. This data should serve as a baseline for larger studies, which are better equipped for confounder control and assessment of other potential risk factors. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar will be disturbed by various noise signals in the actual use process, and to solve this problem, a ground-penetrating radar denoising was proposed based on wavelet adaptive thresholding method. Conventional wavelet thresholding algorithms for ground-penetrating radar B-scan images may cause image distortion due to the difference in wavelet coefficients before and after processing. In order to make the target signal clearer and eliminate interference such as noise, wavelet thresholds were improved by the way of selecting wavelet thresholds and the threshold function, and the thresholds were identified with the length of subbands of different layers of wavelet transform to achieve adaptive and accurate quantization of thresholds. The experimental results show that the method is applicable to ground-penetrating radar signal processing and improves the good continuity and stability of the threshold function compared with the ordinary wavelet thresholding algorithm, further improves the direct wave removal and noise reduction effects, and better preserves the details of the target image. Comparing the results with other methods shows that the algorithm proposed in this paper improves the peak signal-to-noise ratio and reduces the image entropy. The research results show that the method has a certain application value in the practical use case of ground-penetrating radar. [ABSTRACT FROM AUTHOR]

Background: Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality,especially for multinucleon ions that can fragment,such as helium. Purpose: This work proposes a particle imaging filter, referred to as the Prior Filter,based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. Methods: The Prior Filter uses Fermi–Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3𝜎 filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories.Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of -the-art method including an implementation of the 3𝜎 filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. Results: The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5–6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3𝜎 filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of -the-art filtering method. Conclusions: The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction. [ABSTRACT FROM AUTHOR]

• User friendly short inlet duct design for cooling fan noise reduction. • Far-field noise tests show the stable suppression of short duct on forward propagation tonal noise. • Full passage unsteady CFD reveals the flow control effect of the short inlet duct. • Mode test and acoustic analogy are combined to clarify the principle of short duct noise reduction. • The applicability of short duct to cooling fans with different sizes and configuration is expanded. This paper presents experimental and numerical analyses of the noise-reduction method of applying short inlet ducts to axial-flow cooling fans. Far-field noise tests of three different-sized cooling fans with tip Mach numbers of 0.143, 0.156, and 0.174 demonstrated that tonal noise was the primary source of aerodynamic noise. Through the installation of short inlet ducts on these fans, the upstream propagation of tonal noise was reduced. The average total sound pressure levels were decreased by up to 3.5, 4.8, and 7.1 dBA, respectively. At the blade passing frequency, the dominant upstream-propagating azimuthal acoustic modes were suppressed. Full-passage unsteady computational fluid dynamics calculations revealed that the duct transforms multiple suction vortices into a single annular vortex structure and weakens the interaction with the rotor blades. In addition, the acoustic analogy was used to compute the reduction of far-field noise and the variations in the major acoustic modes. This revealed a noise-reduction trend similar to that seen in the experiments. Essentially, the duct restrains the non-uniform inlet flow to influence the various inlet–rotor–stator interaction modes. The application of this approach to various fan configurations confirmed that a duct with a parameter of L / D (duct length/duct inner diameter) equal to 0.08 can be recommended as a compact and efficient way to reduce noise. [ABSTRACT FROM AUTHOR]

An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist" in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use. [ABSTRACT FROM AUTHOR]