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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‐spatial‐frequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages. [ABSTRACT FROM AUTHOR]

Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the current study is to employ a formal optimization approach and to find the optimal sawtooth shape of trailing-edge serrations that maximize the turbulent boundary layer-trailing edge (TBL-TE) interaction noise reduction. Methods: Lyu's semi-empirical model is used for the optimization approach to investigate a semi-infinite flat plate at zero angle of attack (AoA) and low Mach Number. The ability of sawtooth trailing-edge serrations to reduce TBL-TE noise is analyzed through a single-objective numerical optimization study and trailing-edges tested in a wind tunnel. Four different cases of sawtooth serration geometries are tested for NACA-0012 airfoil and flat-plate trailing edges. Results: Results from the optimization study are used to examine and validate the influence of serration design parameters. For all the cases, the optimum size shows that the maximum noise reduction is located in regions of the design space where the amplitude is maximal, and the wavelength is minimal. Moreover, based on the optimization analyses, the Saw-1 serration provides the greatest noise reduction up to 12.89 dB over a wider range of frequencies. Experimental and optimization results are then compared. Conclusion: It is concluded that numerical and experimental results agree that optimized sawtooth trailing-edge configurations can yield less TBL-TE noise compared to the traditional straight-trailing-edge configuration. The optimization studies underestimated the measurements by about 2 to 12 dB, but the tendency between the different serration geometries was predicted correctly. [ABSTRACT FROM AUTHOR]

Due to light absorption and scattering underwater images suffer from low contrast, color distortion, blurred details, and uneven illumination, which affect underwater vision tasks and research. Therefore, underwater image enhancement is of great significance in vision applications. In contrast to existing methods for specific underwater environments or reliance on paired datasets, this study proposes an underwater multiscene generative adversarial network (UMGAN) to enhance underwater images. The network implements unpaired image-to-image translation between the underwater turbid domain and the underwater clear domain. It has a great enhancement impact on several underwater image types. Feedback mechanisms and a noise reduction network are designed to optimize the generator and address the issue of noise and artifacts in GAN-produced images. Furthermore, a global–local discriminator is employed to improve the overall image while adaptively modifying the local region image effect. It resolves the issue of over- and underenhancement in local regions. The reliance on paired training data is eliminated through a cycle consistency network structure. UMGAN performs satisfactorily on various types of data when compared quantitatively and qualitatively to other state-of-the-art algorithms. It has strong robustness and can be applied to various enhancement tasks in different scenes. [ABSTRACT FROM AUTHOR]

Background/Aims Excessive noise is a considerable problem in intensive care units, interfering with patient rest, recovery times and patient satisfaction. This quality improvement project aimed to implement and evaluate noise-reduction strategies in a coronary intensive care unit in Doha, Qatar. Methods Rapid plan-do-study-act cycles were used to implement several noise reduction strategies, including the introduction of two designated quiet time periods per day (15:00--16:00 and 2:00-4:00), staff education about the impact of noise, environmental adjustments and an audio-visual device to alert staff, patients and visitors of excessive noise levels. A sound metre app was used to measure noise levels for 4 months before the intervention and for nearly 3 years (April 2018-December 2020) after implementation. Surveys were used to collect informal feedback from staff and patients on the ward. Results The average pre-intervention noise level on the unit were 64.6 dB, which was reduced to a consistent average of 24 dB during designated quiet times following the interventions. No adverse effects on communication or medical errors were observed. Patient and staff feedback was positive, with most patients (n=50) and all staff (n=86) being satisifed with the noise levels following the intervention. Conclusions Although the nature of activity in intensive care units makes them noisy environments, it is possible to reduce noise levels by targeting modifiable factors and implementing simple measures, such as dedicated quiet times, staff education and audio-visual noise alerts. [ABSTRACT FROM AUTHOR]

Constructing a bibliographic dataset is fundamental for domain analysis in bibliometric research. However, irrelevant documents(so-called "impurities") in the initial domain dataset are inevitable and difficult to identify, requiring considerable human efforts to eliminate. To solve this problem, we propose a weak-supervised noise reduction approach based on the Positive-Unlabeled Learning (PU-Learning) algorithm to clean the initial bibliographic dataset automatically. The basic idea is to use a batch of "absolutely positive sample sets" already available in the dataset to obtain a collection of "reliable negative sample sets," based on which a training set can be constructed for the downstream supervised classification. This paper conducted a comparative experiment using the Artificial Intelligence (AI) domain of the US National Technical Reports Library (NTIS) report as an example. We compared schemes with different variables to explore the influence of various technical aspects on the final noise reduction performance. Our approach achieved significant improvements compared with the similarity-based unsupervised baseline; the recall rose from 0.3742 to 0.8103, and the precision rose from 0.6621 to 0.7383. We found that the impact of document representation algorithms is crucial while classification strategies and s_ratio in PU-Learning are not. Our approach needs no manual annotation data and thus can provide powerful help for bibliometric researchers to construct high-quality bibliographic datasets. [ABSTRACT FROM AUTHOR]

For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the simulation results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments. [ABSTRACT FROM AUTHOR]

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction. [ABSTRACT FROM AUTHOR]

This paper concerns the management of natural waste fibres. The aim of this research was the production of multifunctional acoustic and thermal insulation materials from natural protein and lignocellulosic fibre wastes, according to a circular bioeconomy. For the manufacture of the materials, local mountain sheep wool and a mixture of bast fibre waste generated by string production were used. Insulating materials in the form of mats produced by the needle-punching technique with different fibre contents were obtained. The basic parameters of the mats, i.e., the thickness, surface weight and air permeability were determined. To assess barrier properties, sound absorption and noise reduction coefficients, as well as thermal resistance and thermal conductivity, were measured. It was shown that the mats exhibit barrier properties in terms of thermal and acoustic insulation related to the composition of the mat. It was found that mats with a higher content of the bast fibres possess a greater ability to absorb sounds, while mats with higher wool contents exhibit better thermal insulation properties. The produced mats can serve as a good alternative to commonly used acoustic and thermal insulating materials. The production of the described materials allows for a reduction in the amount of natural fibre waste and achieves the goal of "zero waste" according to the European Green Deal strategy. [ABSTRACT FROM AUTHOR]

Due to the imaging mechanism of time-of-flight (ToF) sensors, the captured depth images usually suffer from severe noise and degradation. Though many RGB-guided methods have been proposed for depth image enhancement in the past few years, yet the enhancement performance on real-world depth images is still largely unsatisfactory. Two main reasons are the complexity of realistic noise and degradation in depth images, and the difficulty in collecting noise-clean pairs for supervised enhancement learning. This work aims to develop a self-supervised learning method for RGB-guided depth image enhancement, which does not require any noisy-clean pairs but can significantly boost the enhancement performance on real-world noisy depth images. To this end, we exploit the dependency between RGB and depth images to self-supervise the learning of the enhancement model. It is achieved by maximizing the cross-modal dependency between RGB and depth to promote the enhanced depth having dependency with the RGB of the same scene as much as possible. Furthermore, we augment the cross-modal dependency maximization formulation based on the optimal transport theory to achieve further performance improvement. Experimental results on both synthetic and real-world data demonstrate that our method can significantly outperform existing state-of-the-art methods on depth denoising, multi-path interference suppression, and hole filling. Particularly, our method shows remarkable superiority over existing ones on real-world data in handling various realistic complex degradation. Code is available at https://github.com/wjcyt/SRDE. [ABSTRACT FROM AUTHOR]

Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral image (HSI) low-level vision applications, but previous studies often failed to comprehensively exploit the low-rank nature of HSI along different modes in low-dimensional subspace, and unsurprisingly handled only one specific task. To address these challenges, in this paper, we figured out that in addition to the spatial correlation, the spectral dependency of HSI also implicitly exists in the coefficient tensor of its subspace, this crucial dependency that was not fully utilized by previous studies yet can be effectively exploited in a cascaded manner. This led us to propose a unified subspace low-rank learning regime with a new tensor cascaded rank minimization, named STCR, to fully couple the low-rankness of HSI in different domains for various low-level vision tasks. Technically, the high-dimensional HSI was first projected into a low-dimensional tensor subspace, then a novel tensor low-cascaded-rank decomposition was designed to collapse the constructed tensor into three core tensors in succession to more thoroughly exploit the correlations in spatial, nonlocal, and spectral modes of the coefficient tensor. Next, difference continuity-regularization was introduced to learn a basis that more closely approximates the HSI’s endmembers. The proposed regime realizes a comprehensive delineation of the self-portrait of HSI tensor. Extensive evaluations conducted with dozens of state-of-the-art (SOTA) baselines on eight datasets verified that the proposed regime is highly effective and robust to typical HSI low-level vision tasks, including denoising, compressive sensing reconstruction, inpainting, and destriping. The source code of our method is released at https://github.com/CX-He/STCR.git. [ABSTRACT FROM AUTHOR]

The classification of targets is one of the most challenging tasks in radar signal processing. Classifying a target can help radar operators figure out the nature of the target, such as its source and activity. However, it is very difficult to find the labeled data necessary to develop radar target classification models. Generating a radar dataset is an expensive and time-consuming process. To address these issues, we propose a noise reduction method that can be applied to micro-Doppler radar datasets. This method is carried out by averaging the spectrogram of each class in the RadEch micro-Doppler radar datasets and subtracting pixel by pixel from each sample. RadEch dataset has also been augmented with traditional and learning-based data augmentation methods. The learning-based data augmentation method was carried out by using Generative Adversarial Networks. Raw spectrograms, augmented spectrograms and noise-reduced spectrograms have been classified using 5-layer Convolutional Neural Network, VGG-16, and VGG-19. Classification results are compared with state-of-the-art studies. Comparison results show that the classification on noise-reduced spectrogram performs better than current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, a neural network (NN) approach is introduced to estimate the nonnoisy speed and torque from noisy measured currents and voltages in induction motors with variable speed drives. The proposed estimation method is comprised of a neural speed–torque estimator and a neural signal denoiser. A new training strategy is introduced that combines large amount of simulated data and a small amount of real-world data. The proposed denoiser does not require nonnoisy ground-truth data for training, and instead uses classification labels that are easily generated from real-world data. This approach improves upon existing noise removal techniques by learning to denoise as well as classify noisy signals into static and dynamic parts. The proposed NN-based denoiser generates clean estimates of currents and voltages that are then used as inputs to the NN estimator of speed and torque. Extensive experiments show that the proposed joint denoising-estimation strategy performs very well on real data benchmarks. The proposed denoising method is shown to outperform several widely used denoising methods and a proper ablation study of the proposed method is conducted. [ABSTRACT FROM AUTHOR]

The work examines noise barriers and evaluates their effectiveness when changing their geometry and shape. Noise barriers are one of the most effective and widely used methods of reducing noise caused by road and railway transport. The effectiveness of the barrier depends on the materials used in the construction, the barrier's height, geometry, shape, and the acoustic properties of the additional elements installed on top of the barriers. The aim of the work is to review and analyze the scientific literature, which would allow to evaluate the dependence of the acoustic characteristics of noise-reducing barriers on the geometry and shape of the barrier, and to provide recommendations for the design and selection of noise barriers. [ABSTRACT FROM AUTHOR]

Contrast improvement and noise reduction are needed to improve aerial images prone to interference during data collection. Contrast limited adaptive histogram equalization (CLAHE) is a histogram equalization (HE) development method that is commonly used for contrast improvement. Median blur (MB) is one of the methods used for noise reduction. Combining these two techniques helps optimize the preprocessing process before semantic segmentation analysis is carried out in image maps. The results of testing experiments with U-Net VGG-16 and VGG-19 on image maps show a detailed representation of the predicted pixel class. Comparing accuracy with state-of-the-art methods shows that contrast enhancement and noise reduction are better than the previous method. The highest average result for combining CLAHE+MB with U-Net VGG-16 was 76.5 and VGG-19 was 73.8, and the highest accuracy for image sample testing was 87.94 with U-net VGG-16. [ABSTRACT FROM AUTHOR]

Mixed noise pollution severely disturbs hyperspectral image (HSI) processing and applications. Plenty of algorithms have been developed to address this issue via two strategies: model-driven or data-driven strategy. However, model-driven methods exist in the highly time-consuming weakness of iterative optimization and unstable sensitivity of setting parameters. Data-driven methods usually perform poor due to the overfitting effects. To solve these issues, we combine both the deep denoising priors with low-rank tensor factorization (DP-LRTF) for HSI restoration. The proposed method uses Tucker tensor factorization to depict the global spectral low-rank constraint. Then the spectral orthogonal basis and spatial reduced factor are optimized by two deep denoising priors, respectively. Through this integrated strategy, we can simultaneously exploit the intrinsic low-rank property of HSI, and utilize the powerful feature extraction ability by deep learning for HSI restoration. Compared with model-driven and data-driven methods, DP-LRTF outperforms on HSI mixed noise removal and execution efficiency for various simulated/real experiments. [ABSTRACT FROM AUTHOR]

Distributed optical fiber acoustic sensing (DAS) is an emerging acquisition technology in seismic exploration. However, DAS records are always affected by the complex background noise, resulting in a low signal-to-noise ratio (SNR). In addition, the DAS background noise has different properties from the noise existing in conventional seismic data. Thus, conventional denoising methods may degrade the record when dealing with complex DAS data. To improve the denoising capability, a novel denoising network, called residual modular cascaded heterogeneous network (RMCHN), is proposed. In general, the network is based on the idea of heterogeneous convolution and modular convolutional neural networks. Specifically, different modules are designed to extract the discriminatory features of the DAS data through effective information integration. On this basis, heterogeneous convolution combined with long and short path feature learning strategy is employed to fuse the captured features, thereby improving the feature expression capability and avoiding the information loss. Both synthetic and field denoising results indicate that RMCHN can suppress the DAS background noise with excellent performance in signal restoration, even for the weak signals form deep strata. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) signal processing has used mode decomposition methods such as empirical mode decomposition (EMD), variational mode decomposition, and their variations, but these methods are typically constrained by manual mode selection for the extraction of real signals and unwanted noises. In this study, we propose an automatic mode selection scheme for GPR signal denoising, which is demonstrated by the combination of the improved complete ensemble EMD (ICEEMD) and the multiscale principal component analysis (MSPCA). For GPR B-scan data, the intrinsic mode functions (IMFs) are automatically screened using our predefined criteria to remove noise after their spectra and the relationship between signals are first analyzed. Next, the nonlinear and nonstationary signals are adaptively decomposed into many time series by the proposed automated ICEEMD. Each A-scan dataset is then screened and subjected to MSPCA before being reassembled into the final denoised result. The synthetic and real GPR data prove our denoising scheme’s superiority over more established denoising techniques like MSPCA and median filtering. [ABSTRACT FROM AUTHOR]

Predicting surface settlement in deep foundation pit engineering plays a central role in the safety of foundation pit construction. Recently, static or dynamic methods are usually applied to predict ground settlement in deep foundation pit projects. In this work, we propose a model combining wavelet noise reduction and radial basis neural network (XW-RBF) to reduce noise interference in monitoring data. The results show that the XW-RBF model predicts an average relative error of 0.77 and a root average square error of 0.13. The prediction performance is better than the original data prediction results with noise structure and has higher prediction accuracy. The noise data caused by the interference of construction and the surrounding environment in the original data can be removed via the wavelet noise reduction method, with the discreteness of the original data reducing by 30%. More importantly, our results show that the XW-RBF model can reflect the law of data change to predict the future data trend with high credibility. The findings of this study indicate that the XW-RBF model could optimize the deep foundation pit settlement prediction model for high accuracy during the prediction, which inspires the potential application in deep foundation pit engineering. [ABSTRACT FROM AUTHOR]

This article investigates the source of booming noise emanating from the tailgate of an electric sport utility vehicle (SUV), along with proposed strategies to mitigate it. This annoying low-frequency booming noise, which significantly impacts interior sound quality, is less perceptible in conventional internal combustion engine vehicles. However, this noise is more readily detected in electric SUVs, highlighting the necessity for focused measures to reduce it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, ODSs were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes, resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite-element analysis software to propose measures for reducing booming noise. Experimental findings validated this model’s accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to reduce the booming noise resulting from tailgate motion. Under random force inputs, the analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness. This study identifies the mechanism generating booming noise, establishes a practical and simple dynamic model, and proposes improvement measures aimed at reducing the booming noise.

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise–power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.

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

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

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

To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. Firstly, a Hankel matrix is constructed based on the collected discrete time signals. After performing SVD on the Hankel matrix, the ASVD algorithm is used to automatically select the effective singular values to filter out most of the background white noise and retain the useful frequency components with similar energy in the signal. Then, ICEEMDAN combined with the Spearman correlation coefficient method is used to further filter out residual white noise and low-frequency water flows. The noise reduction performance of this combined method is verified through simulation experiments. Filtered by the ASVD-ICEEMDAN method, the signal-to-noise ratio of the simulation signal (50% noise level) is increased from 4.417 to 16.237, and the root mean square error is reduced from 2.286 to 0.586. Based on the practically measured vibration signals of a floodgate at a large hydropower station, the result shows that the ASVD-ICEEMDAN method exhibits good noise reduction performance and feature information extraction abilities for floodgate vibration signals, and can provide support for operational mode analysis and damage identification of practical structures under complex interference conditions.

Due to friction vibration dampers’ inability to effectively dampen low loads during high-frequency dewatering, drum washing machines vibrated intensively. In order to address this problem, in this paper, a novel type of low-cost non-Newtonian fluid damper is proposed and investigated based on the non-Newtonian fluid shear thinning properties’ effect on vibration suppression during the high-frequency dewatering process of the washing machine. In contrast to other commonly used dampers, the homemade non-Newtonian fluid damper significantly suppresses the growth trend of the apparent elastic coefficient at high frequencies. A systematic investigation of damper structural parameters reveals that smaller gap height, higher piston head number, and more viscous fluid viscosity are adequate for vibration suppression and noise reduction. These results demonstrate that the non-Newtonian fluid damper can produce an excellent vibration-damping effect for the entire washing process of the washing machine, especially for the high-frequency dewatering process. The acceleration attenuation ratio can reach up to 83.49%, the energy attenuation is up to 98.44%, and the noise reduction is up to 10.38 dB.

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

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

This study aims to find an alternative sound-absorbing material from nature that is cheap and environmentally friendly, namely the water hyacinth plant (Eichhornia crassipes). The experimental soundproofing tool from a glass box with a placemat skat was developed by integrating science, technology, engineering, arts, and mathematics (STEAM) elements as an alternative project-based learning model (PjBL) for students. The research method used is experimental. Water hyacinth was sun-dried, woven in the form of a placemat with two variations of the test samples at several frequencies of 250 Hz, 500 Hz, 750 Hz, 1000 Hz, and 1500 Hz. Six stages of PjBL are used, and elements of STEAM are integrated. The results of the study obtained the value of Noise Reduction (NR) of sound for test samples 1, test sample 2, or a combination of test samples 1 and 2. Each test sample is effective at 1500 Hz high frequency; namely, test NR 1 = 8.7 dB, test NR 2 = 8.3 dB, and NR Gab=9.3 dB. The combined test sample was also effective at frequencies of 500 Hz and 750 Hz, with NR Gab=4.3 dB and NR Gab=4.7 dB. Following the theory that changes in sound between 3 to 12 dB have effects that humans can feel. This study concludes that natural water hyacinth fiber has fairly good quality as an alternative sound-dampening material. This experimental tool can also be an alternative to the STEAM-integrated PjBL learning model.

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In recent years, noise pollution generated by increasing global urbanization has become an important issue. This study focused on determining the sound absorption coefficients (SACs) of untreated tree trunks as natural fiber-based materials and predicting their noise reduction coefficients (NRCs). It also evaluated the effects of noise reduction using numerical data. One of the most effective acoustic characteristics is the normal incidence SAC; this was obtained for trunks belonging to species of black locust, narrow-leafed ash, stone pine, silver lime, sweet chestnut, sessile oak, maritime pine, cedar, and plane in the frequency range of 50–6000 Hz using an impedance tube based on a two-microphone transfer function method. The absorbance of sound was found to correlate with the tree species, trunk diameter, bark shape, and the bark’s fibrous structure. The results confirmed that when compared with deciduous species, the trunks of coniferous species exhibit higher NRC values which were recorded as 0.302 ± 0.005 for stone pine and 0.233 ± 0.004 for cedar. Furthermore, the surface morphology of the outer bark of different tree species obtained via scanning electron microscopy revealed that significant variations exist in the microstructure of the bark’s fiber cells.

Breast cancer is one of the most dreaded diseases that affects women worldwide and has led to many deaths. Early detection of breast masses prolongs life expectancy in women and hence the development of an automated system for breast masses supports radiologists for accurate diagnosis. In fact, providing an optimal approach with the highest speed and more accuracy is an approach provided by computer-aided design techniques to determine the exact area of breast tumors to use a decision support management system as an assistant to physicians. This study proposes an optimal approach to noise reduction in mammographic images and to identify salt and pepper, Gaussian, Poisson and impact noises to determine the exact mass detection operation after these noise reduction. It therefore offers a method for noise reduction operations called Quantum Inverse MFT Filtering and a method for precision mass segmentation called the Optimal Social Spider Algorithm (SSA) in mammographic images. The hybrid approach called QIMFT-SSA is evaluated in terms of criteria compared to previous methods such as peak Signal-to-Noise Ratio (PSNR) and Mean-Squared Error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison to state-of-arts methods. supported the work.

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. [ABSTRACT FROM AUTHOR]

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD setup to handle multiple degradations adaptively and relief data unbalancing problem in different degradation types. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution together with a simple loss reweighting approach to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Moreover, we also provide an estimation network to predict the condition vector, thus the base network could directly output the restored image without modulation from users. Extensive experiments demonstrate that the proposed CResMD achieves excellent performance on both SD and MD modulation tasks. [ABSTRACT FROM AUTHOR]

Acoustic monitoring has recently shown great potential in the diagnosis of infrastructure conditions. However, due to the severe noise interference in acoustic signals, meaningful features tend to be difficult to infer. It creates a considerable obstacle to an extensive application of acoustic monitoring. To tackle this problem, we propose an acceleration-guided acoustic signal denoising framework (AG-ASDF) based on learnable wavelet transform to automatically denoise the acoustic signal and extract the relevant features based on the acceleration signal. This denoising framework requires the acceleration signal only for the training stage. Therefore, only acoustic sensors (nonintrusive) need to be installed during the application phase, which is convenient and crucial for the condition monitoring of safety-critical infrastructure. A comparative study is conducted among the proposed AG-ASDF and other feature learning/extraction methods, by using a multiclass support vector machine (SVM) to evaluate the detection effectiveness of slab track conditions based on acoustic signals. Different healthy and unhealthy states of slab tracks are imitated with three types of slab track supporting conditions in a railway test line. The classification based on the proposed AG-ASDF features outperforms other feature extraction and learning methods with a significant accuracy improvement. [ABSTRACT FROM AUTHOR]

In this paper, a promising acoustic structure for noise reduction was prepared, in which microperforated nanofibrous resonant membrane together with nonwovens were used. The role of microperforated nanofibrous film, the effect of perforation parameters, cavity and the assembly sequence of the composite fibrous structure on sound absorption performance has been studied. This structure effectively combined the porous sound absorbing, micro-perforated absorbing and membrane resonance mechanisms, which can improve the sound absorbing performance without weight and thickness penalty offering a competitive advantage in noise reduction. In addition, the composite materials exhibited favorable performance in a wide-frequency regime under the condition of appropriate assembly sequence and perforation parameters. [ABSTRACT FROM AUTHOR]

Aiming at the influence of gear body and unbalance on noise, a reducer was taken as the research object, the relevant structural analysis and calculation were carried out, and noise test of different scheme were carried out for comparison. Firstly, modal analysis was carried out by finite element method, and two kinds of gear body of first stage driven gear was determined. Then, the noise performance was analyzed by MASTA software, smaller peak to peak of transmission error and smaller dynamic meshing force was obtained, then the optimized gear body was determined. The dynamic model of rotor shaft-gear was established by considering the difference of unbalance of special-shaped gear body. The inertia force and excitation were analyzed, and the response of different unbalance was obtained. Finally, the bench test was carried out for verify design. The results indicate that the noise of the special-shaped gear body is more than 5 dB lower than that of the traditional structure when reducer works above 8 000 r/min. When the unbalance is less than 9.29 g, the statistical result of noise test is that the P value is greater than 0.05, this indicating that the unbalance has no significant influence on the reducer’s noise within this range. [ABSTRACT FROM AUTHOR]

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances. In this paper, we propose three approaches to perform unsupervised domain adaptation of a depth denoising network from synthetic to real data. These approaches are respectively acting at the input, at the feature and at the output level of the network. The first approach uses domain translation networks to transform labeled synthetic ToF data into a representation closer to real data, that is then used to train the denoiser. The second approach tries to align the network internal features related to synthetic and real data. The third approach uses an adversarial loss, implemented with a discriminator trained to recognize the ground truth statistic, to train the denoiser on unlabeled real data. Experimental results show that the considered approaches are able to outperform other state-of-the-art techniques and achieve superior denoising performances. [ABSTRACT FROM AUTHOR]

Various problems in computer vision and medical imaging can be cast as inverse problems. A frequent method for solving inverse problems is the variational approach, which amounts to minimizing an energy composed of a data fidelity term and a regularizer. Classically, handcrafted regularizers are used, which are commonly outperformed by state-of-the-art deep learning approaches. In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer. In its core, a convolutional neural network extracts local features on multiple scales and in successive blocks. This combination allows for a rigorous mathematical analysis including an optimal control formulation of the training problem in a mean-field setting and a stability analysis with respect to the initial values and the parameters of the regularizer. In addition, we experimentally verify the robustness against adversarial attacks and numerically derive upper bounds for the generalization error. Finally, we achieve state-of-the-art results for several imaging tasks. [ABSTRACT FROM AUTHOR]

Fuzzy system is proven to be one of the well-effective approximation tool in soft computing techniques. The image de-noising in modern multimedia system is strongly demanded issue which has been focussed in this work and an optimal solution with the help of fuzzy inference technique has been provided. An improved and approximated anisotropic diffusion scheme has been proposed by using fuzzy based diffusion coefficient functions. The anisotropic diffusion has been redefined by formulating the diffusion coefficients in terms of degrees of noisiness of each pixel which tends to sufficiently smooth the impulse noisy pixels along with preservation of edge pixels. The proposed fuzzy rule based diffusion coefficient is applied in basic Perona–Malik diffusion as well as selective advanced diffusion scheme and tested on various standard images at different noise densities. The proposed diffusion scheme based on fuzzy rule shows the effective results on images having impulsive noise densities upto 50 % . [ABSTRACT FROM AUTHOR]

Research on bearing fault diagnosis generally uses a single signal processing method to extract features to a certain extent and then empirically judges the fault type at this stage. However, this approach has a poor fault feature extraction effect when the noise interference signal is large and subjective human errors. The diagnosis effect is determined by external interference and has no practical value. This paper proposes a rolling bearing fault diagnosis method based on singular spectrum analysis and a wide convolution kernel neural network, which can effectively extract the fault features of the rolling bearing crack fault signal with a strong noise interference and realize the efficient diagnosis of this kind of fault. Gaussian white noise is added to the standard bearing fault signal to construct the vibration signal with noise interference. Then, the noisy one-dimensional time series signal is preprocessed, including the data division into the training and test sets and the different kinds of numbering processing. Singular spectrum analysis is performed on the preprocessed data. Then, the denoised training set is used as input in a deep convolution neural network with a wide convolution kernel for feature extraction and model training. The trained diagnosis model is used for the fault prediction of the next test set, and the relevant diagnosis results are output. The test results show that this method can ensure the overall accuracy of more than 90% under the background of high noise, and the diagnosis rate of the model under various working conditions is stably maintained at more than 93%, without the collapse of diagnosis stability under the sudden change of noise. The advantages of model diagnosis efficiency and structural improvement fit are prominent. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ($\mathbf{N}\ = \ 385\ \text{subjects}$). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ($\mathbf{SN}{\mathbf{R}_{\mathbf{imp}}}$) and lower mean squared error ($\mathbf{MSE}$) when compared with that of the three previous methods (averaged $\boldsymbol{S\!N\!R}_\text{imp} = 35.25\,{\text{dB}}$ , and ${\boldsymbol{M\!S\!E\ }} = 0.028$ on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. Conclusion: The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data. [ABSTRACT FROM AUTHOR]