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In vibration-based fault diagnosis of rotating machinery, strong impulse noise constitutes a widely encountered source of external interference, thereby posing significant challenges to the effective extraction of diagnostic information. In this context, an adaptive reweighted-Kurtogram (ARKurtogram) method is proposed. The bearing transient fault signatures are accurately extracted through two aspects – that is, precise frequency band division and robust band selection indicator. Specifically, we use the dual-tree complex wavelet packet transform (DTCWPT) as the band division tool and propose the sub-bands rearranged and ensemble DTCWPT which addresses the band disorder problem, yields far richer band division patterns and maintains excellent performance of DTCWPT in signal decomposition. Meanwhile, we introduce a robust indicator characterizing periodic fault impulses to accurately select the fault band. In comparison with the classical kurtosis and the advanced reweighted-kurtosis, this indicator is insensitive to strong external impulse noise and its calculation process contains a sliding window averaging approach which makes the calculation entirely automated without human intervention. The validity of the ARKurtogram is investigated with the analysis of vibration signals measured from fault-injection experiments. The comparisons with the classical fast Kurtogram method and the advanced Autogram method highlight the advantages of the method in extracting bearing transient fault signatures from complex signals with strong external impulse noise. It shows high potential in the application of industrial bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

The internal combustion engine plays a very important role in many fields, and when the internal combustion engine fails, if it is not found in time, it may cause continuous damage to the internal combustion engine, and further affect the life of the entire mechanical system. To solve the above problems, this paper proposes fault detection of internal combustion engines based on a random convolutional neural network. The cylinder burst noise signal of the internal combustion engine is decomposed by a stationary wavelet, and the inclusion matrix is composed of partial decomposition coefficients. Using singular value theory, the singular value containing matrix is extracted as the characteristic of cylinder burst noise signal. The singular value is used as the input of a random convolutional neural network to train and identify faults. The AdaBound optimizer was then applied to adapt the learning rate to changes, thereby accelerating the weight update of the model. At the same time, the neurons in the structure are randomly deactivated by dropout technology to prevent complex collaborative responses to the training data, and the diagnosis results of each network model are integrated by Dempster synthesis rules. The experimental results show that the minimum mean square error of 0.00165 is achieved when there are 15 neurons in the hidden layer, and it gradually increases as the number of neurons increases. Therefore, it is determined that there should be 15 neurons in the hidden layer, and the trainLM algorithm is used. The decision factors for training, validation, cross-validation, and overall data are 0.98947, 0.98597, 0.97738, and 0.9801, respectively, all reaching the control accuracy of 0.95, which indicates that the random convolutional neural network proposed has a high accuracy for internal combustion engine fault detection. For the main bearing Y-directional force, as the gap increases, its fluctuation trend strengthens; the main bearing force changes within a small range at first, then increases sharply when the gap reaches 0.2mm, and some main bearings even experience significant impact loads. [ABSTRACT FROM AUTHOR]

The tensor singular value decomposition‐based model has garnered increasing attention in addressing tensor recovery challenges. However, existing tensor recovery methods exhibit certain inherent limitations. Some ignore the simultaneous effects of noise and missing values, while most can't handle higher‐order tensors, which are not reflective of real‐world scenarios. The information redundancy within tensor data often leads to a prevailing low‐rank structure, making low‐rankness a vital prior in the tensor recovery process. To tackle this pressing issue, a robust low‐rank tensor recovery framework is proposed to rehabilitate higher‐order tensors corrupted by sparse noise and missing entries. In the model, the tensor nuclear norm derived for order‐d tensors (d≥$\ge$ 4) are employed as a representation of the low‐rank prior, while utilizing the L1$\mathtt {L}_1$‐norm to model the sparse noise. To solve the proposed model, an efficient Alternating direction method of multipliers algorithm is developed. A series of experiments are performed on synthetic and real‐world datasets. The results show that the superior performance of the method compared with other algorithms dedicated to addressing order‐d tensor recovery challenges. Notably, in scenarios where the data is severely compromised (noise ratio 40%, sample ratio 70%), the algorithm consistently outperforms its competitors, achieving significantly improved results. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for filtering out impulse noise in digital color images. This work is of critical interest in biomedical imaging, visual tracking, etc. The conventional filtering methods operate by applying a noise reduction scheme, generally the vector median filtering approach and its variants, for the center pixel of a suitably chosen window that iteratively slides along the entire image. These methods consider the window in its entirety in the filtering process. This consideration, however, comprehends the noise within the noisy pixels in the filtering process and could prove detrimental to the overall output. The method proposed in this paper operates by clustering the pixels in the chosen window into two groups, one that corresponds to the pixel intensities that lie in the signal space and the other to those that lie in the noise space. The motivating rationale for this clustering scheme is to marginalize those pixels that lie in the noise space that seemingly do not contribute to the information in the image. The median filter is then applied to the pixels that contribute to the signal, in isolation from the color components, to filter out the impulse noise. Simulation results show that the proposed method outperforms conventional filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. The proposed method is applied to analyze CT scans for improved diagnosis of SARS-COV-2 COVID-19 disease. The method is also applied to a visual tracking example as a preprocessor. [ABSTRACT FROM AUTHOR]

Evaluation of clustering validity to set up an optimal cluster-data space (CDS) is a vital task in many fields related to data mining. Almost existing clustering validity indexes (CVIs) lack stability due to being too sensitive to noise, especially impulse noise. Here, we (1) propose a new CVI named DzI (Dzung Index) or fRisk2 using analysis of fuzzy-set-based accumulated risk degree (FARD), and (2) present a new algorithm named fRisk2-bA for determining the optimal number of data clusters. It is a method of evaluation of the centroid-based fuzzy clustering validity. In essence, the fRisk2 still focuses on enhancing the data compression in each cluster and expanding the separation between cluster centroids. However, these features are exploited indirectly through FARD. As a result, the proposed method not only can avoid the difficulties of the traditional ones relying on the compression and separation properties directly but also can distill better local and global attributes in the data distribution to estimate the CDS more fully. Along with the proved theory basis, surveys, including the ones based on noisy datasets from measurements, showed the compared advantages of fRisk2 as follows. (1) The accuracy, stability, and convergence of the fRisk2 are outstanding. (2) Its total calculating cost is lower than the other surveyed CVIs. [ABSTRACT FROM AUTHOR]

This paper proposes a Generalized Multilevel B-spline Approximation (GMBA) method, which addresses scattered data interpolation problems in image processing. Mathematically, the GMBA provides a better solution for the B-spline control lattice by superimposing identical level B-splines compared with traditional Multilevel B-spline Approximation (MBA). Specifically, the GMBA allows the spacing of next control lattice to be subdivided arbitrarily or remained unchanged, which is determined by a predefined spacing set or the current error level. These improvements bring higher approximation accuracy and more flexibility for algorithm design to avoid over-fitting. In this paper, basic GMBA algorithm and its refined algorithm are compiled for image processing. Finally, six relevant cases are involved to test the GMBA, including surface approximation, image enlargement, image completion, and Salt-and-Pepper (SAP) noise removal. The experimental results show that the GMBA has better performance than the MBA in surface approximation and image processing, performs comparatively fast with the best performance on more than half of the standard test images compared with traditional algorithms, and has partially better performance even than deep learning algorithms. The GMBA can effectively recover meaningful details in images contaminated with even extremely high SAP noise level (up to 99%). • We give a generalized multilevel B-spline approximation (GMBA) method. • GMBA allows the control spacing to be subdivided arbitrarily or remained unchanged. • GMBA offers higher approximation accuracy and greater flexibility than traditional MBA. • GMBA provides superior performance and less run time than many of the state-of-the-art methods for SAP noise removal. • GMBA can recovery meaningful detail at noise levels as high as 99% for SAP noise removal. [ABSTRACT FROM AUTHOR]

In modern society, due to the influence of the 4th Industrial Revolution, image processing technology is used in various fields such as automated processes, smart farms, and intelligent CCTV. If noise occurs in the image used in such systems, unexpected problems may occur, so noise removal is used as a preprocessing process. In this paper, we propose a filtering algorithm based on multi-purge weights to restore images damaged by complex noise. The proposed algorithm performs impulse noise judgment on pixel values in a local mask, switches the filtering process, and sets multiple purge weights according to the reference value. The final output image is obtained by applying the fuzzy weights and noise classification mask to the local mask. To evaluate the proposed algorithm, we performed a simulation in comparison with conventional noise removal technologies and analyzed it using enlarged images and PSNR. [ABSTRACT FROM AUTHOR]

Temporal distributions of Antarctic krill (Euphausia superba) density and aggregation types were characterized and compared using Nortek Signature100 and SIMRAD Wideband Autonomous Transceiver (WBAT) upward-looking echosounders. Noise varied between the two echosounders. With the Signature100, it was necessary to correct data for background, transient, and impulse noises, while the WBAT data needed to be corrected for background noise only. For selected regions with no visible backscatter, the signal-to-noise ratio of S v values (i.e. the ratio between the signal and the background noise level) did not vary between the two echosounders. Surface echo backscatter was similar during similar time periods. Descriptive metrics were used to quantify spatial and temporal krill vertical distributions: volume backscatter, mean depth, center of mass, inertia, equivalent area, aggregation index, and proportion occupied. Krill backscatter density differed between the two instruments but was detected at similar mean depths. Krill aggregations were identified at each mooring location and classified in three types based on morphological characteristics. Each type of aggregation shape differed at the two spatially separated moorings, while the acoustic density of each aggregation type was similar. The Signature100 detected a lower number of krill aggregations (n  = 133) compared to the WBAT (n  = 707). Although both instruments can be used for autonomous deployment and sampling of krill over extended periods, there is a strong caveat for the use of the Signature100 due to significant differences in noise characteristics and krill detection. [ABSTRACT FROM AUTHOR]

The conjugate gradient (CG) directions are among the important components of the CG algorithms. These directions have proven their effectiveness in many applications—more specifically, in image processing due to their low memory requirements. In this study, we derived a new conjugate gradient coefficient based on the famous quadratic model. The derived algorithm is distinguished by its global convergence and essential descent properties, ensuring robust performance across diverse scenarios. Extensive numerical testing on image restoration and unconstrained optimization problems have demonstrated that the new formulas significantly outperform existing methods. Specifically, the proposed conjugate gradient scheme has shown superior performance compared to the traditional Fletcher–Reeves (FR) conjugate gradient method. This advancement not only enhances computational efficiency on unconstrained optimization problems, but also improves the accuracy and quality of image restoration, making it a highly valuable tool in the field of computational imaging and optimization. [ABSTRACT FROM AUTHOR]

Communication systems are greatly hampered by many disruptive noises in powerline communication systems (PLC), which come with strong interference, resulting in the malfunction of PLC systems. Hence, there is a need to model noise and its effect on communication systems. This paper presents noise modelling and mitigation techniques for indoor broadband powerline communication systems. To model the PLC noise, frequency domain measurements employing the GSP‐930 spectrum analyser were carried out to determine the noise frequency response in the frequency range of 1–30 MHz. The results obtained were plotted. While the analytical model for the noise model is presented, furthermore, noise mitigation techniques for multiple input multiple output PLC (MIMO‐PLC) systems in the form of spatial modulation PLC systems have been proposed. The SM‐PLC system employs the indices of the individual transmit lines to increase the data rate, as opposed to the traditional MIMO‐PLC systems, where the symbol to be transmitted is transmitted by duplicating the symbol across all lines. The proposed system uses the maximum likelihood (ML) detector at the receiver to obtain estimates of the transmitted symbols. The simulation results of the SM‐PLC system are compared with the already existing MIMO‐PLC system and show a significant improvement of ≈6 dB and 5.2 dB in signal‐to‐noise ratio (SNR) at a bit error rate of 10(−5) for spectral efficiencies of 4 bits per channel use (bpcu) and 6 bpcu, respectively. On comparison of the SM‐PLC system having a combination of additive white Gaussian noise and impulse noise at the receiver, the SM‐PLC system outperformed the traditional MIMO‐PLC by 3.5 and 3.8 dB in SNR for 4 and 6 bpcu, respectively. [ABSTRACT FROM AUTHOR]

To overcome the biases in estimating the L1-norm data fidelity term and staircase artifacts of the total variation regularization term, we propose a nonconvex+nonconvex model with box constraints to recover images degraded by blurring and impulse noise. Owing to the data fidelity term and the regularization term being nonconvex, we apply a proximal linearized minimization algorithm to solve the problem. To deal with a subproblem, we utilize the alternating direction multiplier method. The global convergence of the proposed algorithm is established under the assumption that the objective function satisfies the Kurdyka-Lojasiewicz property. We also present numerical results to demonstrate that the proposed nonconvex+nonconvex model outperforms existing models in terms of both numerical accuracy and visual quality. The proposed model also exhibits much better performance than the other methods, especially for piecewise-constant images. [ABSTRACT FROM AUTHOR]

In view of the nonlinear self interference coupling problem between the transmitting and receiving antennas of radar jammer, a nonlinear self interference cancellation method based on spline interpolation is studied. The method combines spline interpolation and adaptive filtering, and establishes the spline-based Wiener model and the spline-based Wiener model respectively. By introducing the robust arctangent (ARC) function as the cost function, the adaptive learning rules of the spline control points and filter coefficients under the two models are obtained, and the influence of the number of spline control points on the performance of the self-interference cancelation i analyzed. Simulation experiments show that for signals with a bandwidth of 60 MHz, the proposed ARC-based parameter learning method achieves about 4 dB improvement in the interference cancelation ratio compared with the traditional least mean square parameter learning methods, and the convergence speed can be improved by a factor of one. In addition, the method has good tracking performance and low steady-stale error for the scenario of sudden channel change. When non-Gaussian pulse interference exists in the background noise, the proposed method can effectively cope with the interference in the impulse noise environment. [ABSTRACT FROM AUTHOR]

When a rhythm makes an event predictable, that event is perceived faster, and typically more accurately. However, the experiments showing this used simple tasks, and most manipulated temporal expectancy by using periodic or aperiodic precursors unrelated to stimulus and task. Three experiments tested the generality of these observations in a complex task in which rhythm was intrinsic to, rather than a precursor of, the information needed to respond: listeners averaged the laterality of a stream of noise bursts. We varied presentation rate, degree of periodicity, and average lateralisation. Decisions following a probe tone were fastest after periodic stimuli, and slowest after the most aperiodic stimuli. Without a probe tone, listeners responded sooner during periodic sequences, thus hearing less information. Periodicity did not benefit accuracy overall. This gain in speed but not accuracy for less information is not reported for simpler tasks. Neural entrainment supplemented by cognitive factors provide a tentative explanation. When the task is inherently complex and demands high attention over long durations, both expected-periodic and unexpected-aperiodic stimuli can increase response amplitude, enhancing stimulus representation, but periodicity increases confidence to respond early. Drift diffusion modelling supports this proposal: aperiodicity modulated the decision threshold, but not the drift rate or non-decision time. Together, these new data and the literature point towards task-dependent effects of temporal expectation on decision-making, showing interactions between rhythmic variance, task complexity, and sources of expectation about stimuli. We suggest the implications are worth exploring to extend understanding of rhythmicity on decision-making to everyday situations. [ABSTRACT FROM AUTHOR]

Inexpensive cameras, different acquisition devices and mobile phones have resulted in the production of digital images in all spheres of life. There is common of these images for extracting useful information in many fields to perform image analysis and interpretation. One of the problems being faced in the use of this information is the presence of random valued impulse noise. Impulse noise removal is a crucial step in computer vision. Many articles have been published to remove or suppress impulse noise from grayscale digital images using different approaches. Edges and details are, however, often lost during the restoration of digital images. In this research, a two-stage approach for edge detection, preservation, and impulse noise removal is proposed. An effective statistics rank ordered absolute difference (ROAD) is used in the first stage to detect noisy pixels. Trimmed global mean (TGM) with adaptive window is employed later for the filtering process. With the combination of these processes in the proposed method, a higher quality image is restored and thus becomes more suitable for extracting the desired information. Comparison with many state-of-the-art methodologies indicates that the proposed method is effective in the detection of corrupted pixels and the restoration of images. [ABSTRACT FROM AUTHOR]

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In general, raw image may suffer from various uncertain distortions arising due to many factors like low dynamic range of imaging device, low illumination, bad weather conditions. In this paper, a new idea of fuzzy peer group-based image denoising algorithm has been presented with a novel similarity measure function. Unlikely the Gaussian membership function based conventional similarity measure; here proposed method hired the local spatial information induced similarity measure together with Gaussian membership function by exploiting the significance of both. Using the proposed similarity measure, fuzzy peer group of similar pixels contrived and color image having contamination of mixed noise i.e., impulse noise and Gaussian noise is decontaminated with refashioned form of bilateral filter. The algorithm proposed has been compared with some state-of-the-art methods in studies and other techniques. The experimental performance shows that proposed approach is having leading filtering outcome with edge preserving and color information from existing methods. [ABSTRACT FROM AUTHOR]

This study rigorously investigates the effectiveness of nonlinear filters in CMOS for 2-D signal processing to enhance image quality. We comprehensively compare traditional linear filters' performance, which operate on the principle of linearity, with nonlinear filters, such as the median-median (Med-Med) approach, designed to handle nonlinear data. To ensure the validity of our findings, we use widely accepted metrics like normalized squared error (NSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) to quantify the differences. Our simulations and experiments, conducted under controlled conditions, demonstrate that nonlinear filters in CMOS outperform linear filters in removing impulse noise and enhancing images. We also address the challenges of implementing these algorithms at the hardware level, focusing on power consumption and chip area optimization. Additionally, we propose a new architecture for the Med-Med filter and validate its functionality through experiments using a 9-pixel image sensor array. Our findings highlight the potential of nonlinear filters in CMOS for real-time image quality enhancement and their applicability in various real-world imaging applications. This research contributes to visual technology by combining theoretical insights with practical implementations, paving the way for more efficient and adaptable imaging systems. [ABSTRACT FROM AUTHOR]

To restore images with clear edge details and no staircase artifacts from degraded versions, this paper incorporates the ℓ 2 plus ℓ 0 data fidelity and non-convex high-order total variation regularizer to establish an optimization model for eliminating mixed Gaussian-impulse noise. Among them, the ℓ 2 fidelity is adopted to suppress Gaussian noise, while the ℓ 0 -norm is more suitable for detecting and removing impulse noise. In addition, the non-convex regularization displays excellent performance in overcoming the staircase effect and preserving edge details. Computationally, by using the iteratively reweighted ℓ 1 algorithm and variable splitting method, this work designs a modified alternating minimization method to solve the optimization problem we construct. In theory, the convergence proof of our resulting algorithm is also presented. Finally, in the experimental section, we conduct extensive numerical experiments on degraded images and compare with other existing techniques. From the intuitive effects and restoration accuracy, it follows that our newly proposed method is effective and competitive for image deblurring and mixed noise removal. [ABSTRACT FROM AUTHOR]

In the past decade, the augmented complex-valued least mean square adaptive filtering algorithm based on the minimum mean square error criterion and widely-linear model has attracted much attentions due to its simplicity and applicability to non-circular signals. However, the system noise with impulse characteristics will dramatically affect the convergence of the augmented complex-valued least mean square algorithm. Recently, a robust widely-linear complex-valued least mean M-estimate algorithm has been proposed, which significantly improves the robustness of the augmented complex-valued least mean square algorithm to impulsive noise. But it should be noted that the widely-linear complex-valued least mean M-estimate algorithm with fixed step size faces the trade-off between convergence speed and steady-state accuracy. To overcome this drawback, a variable step size widely-linear complex-valued least mean M-estimate algorithm is presented in this paper, in which the optimal step size at each iteration is obtained by maximizing the difference between the mean square deviations of the WL-CLMM algorithm at adjacent moments. In order to reveal the statistical convergence behavior of the proposed variable-step-size widely-linear complex-valued least mean M-estimate algorithm to better guide the parameter selection in practical applications. Theoretical transient and steady-state mean-square deviation convergence behaviors of the variable-step-size widely-linear complex-valued least mean M-estimate algorithm are analyzed and the corresponding mean-square deviation expressions are also derived. Computer simulations on system identification and stereophonic acoustic echo cancellation in impulsive noise environments confirm the validity of the analysis results and the performance improvement from the variable step size strategy. [ABSTRACT FROM AUTHOR]

As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration. [ABSTRACT FROM AUTHOR]

The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Image noise removal is one of the important research directions in the field of image processing, and it can be transformed into a large scale unconstrained optimization problem. Based on the famous two-phase method, in this paper we propose an adaptive conjugate gradient method to remove the impulse noise from the corrupted images by solving the transformed unconstrained optimization problem, which is defined in terms of an edge-preserving potential function. The proposed method can generate a sufficient descent search direction at each step independent of line search condition, and it has global convergence under the Armijo line search condition. Experimental results show that the proposed method is efficient and robust for impulse noise removal. [ABSTRACT FROM AUTHOR]

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

Musical compositions are distinguished by their unique rhythmic patterns, determined by subtle differences in how regular beats are subdivided. Precise perception of these subdivisions is essential for discerning nuances in rhythmic patterns. While musical rhythm typically comprises sound elements with a variety of timbres or spectral cues, the impact of such spectral variations on the perception of rhythmic patterns remains unclear. Here, we show that consistency in spectral cues affects perceptual accuracy in discriminating subdivided rhythmic patterns. We conducted online experiments using rhythmic sound sequences consisting of band-passed noise bursts to measure discrimination accuracy. Participants were asked to discriminate between a swing-like rhythm sequence, characterized by a 2:1 interval ratio, and its more or less exaggerated version. This task was also performed under two additional rhythm conditions: inversed-swing rhythm (1:2 ratio) and regular subdivision (1:1 ratio). The center frequency of the band noises was either held constant or alternated between two values. Our results revealed a significant decrease in discrimination accuracy when the center frequency was alternated, irrespective of the rhythm ratio condition. This suggests that rhythm perception is shaped by temporal structure and affected by spectral properties. [ABSTRACT FROM AUTHOR]

A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of mixed noise removal approaches have been developed for HSI, and the methods based on spatial–spectral double factor and total variation (DFTV) regularization have achieved comparable performance. Additionally, the nonlocal low-rank tensor model (NLR) is often employed to characterize spatial nonlocal self-similarity (NSS). Generally, fully exploring prior knowledge can improve the denoising performance, but it significantly increases the computational cost when the NSS prior is employed. To solve this problem, this article proposes a novel DFTV-based NLR regularization (DFTVNLR) model for HSI mixed noise removal. The proposed model employs low-rank tensor factorization (LRTF) to characterize the spectral global low-rankness (LR), introduces 2-D and 1-D TV constraints on double-factor to characterize the spatial and spectral local smoothness (LS), respectively. Meanwhile, the NLR is applied to the spatial factor to characterize the NSS. Then, we developed an algorithm based on proximal alternating minimization (PAM) to solve the proposed model effectively. Particularly, we effectively controlled the computational cost from two aspects, namely taking small-sized double factor as regularization object and putting the time-consuming NLR model before the main loop with fewer iterations to solve it independently. Finally, considerable experiments on simulated and real noisy HSI substantiate that the proposed method is superior to the related state-of-the-art methods in balancing the denoising effect and speed. [ABSTRACT FROM AUTHOR]

Active noise reduction in the existential of impulsive noise poses a serious challenge in a distributed scenario. The conventional centralized multichannel active noise control (MANC) and diffusion filtered-x least mean square (F-xLMS) based distributed active noise control (DANC) systems exhibit deteriorated performance in the existence of impulse noise and outliers. In this regard, a diffusion least logarithmic hyperbolic cosine function (D-Llncosh) based algorithm for DANC system is developed with the goal of mitigating noise in such an environment. It utilizes natural logarithmic of hyperbolic cosine function integrating the mean square error (MSE) and mean absolute error (MAE) as the cost function depending upon the parameter value. The weight update rule of the introduced D-Llncosh-based DANC system is hereby derived. The convergence analysis of the suggested method is also carried out. The proposed system is validated for various types of impulsive noise under different scenarios. The extensive simulation results assert the efficacy of the posited DANC system in comparison with the existing techniques. The suggested method achieves quicker convergence and approximately a 1–5 dB improvement is the noise reduction for different noise scenarios as evident from the obtained results. [ABSTRACT FROM AUTHOR]

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach. [ABSTRACT FROM AUTHOR]

Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address these issues. We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combination of adaptive filters' step sizes, we propose a novel variable mixing-norm method, BC-SNQNLM-VMN, to accelerate the convergence of our BC-SNQNLM algorithm. Simulation results confirm that the proposed method significantly outperforms other comparative works regarding normalized mean-squared deviation (NMSD) in the steady state. [ABSTRACT FROM AUTHOR]

Introduction: Spatial hearing is most accurate using both ears, but accuracy decreases in persons with asymmetrical hearing between ears. In participants with deafness in one ear but normal hearing in the other ear (single-sided deafness [SSD]), this difference can be compensated by a unilateral cochlear implant (CI). It has been shown that a CI can restore sound localization performance, but it is still unclear to what extent auditory spatial discrimination can be improved. Methods: The present study investigated auditory spatial discrimination using minimum audible angles (MAAs) in 18 CI-SSD participants. Results were compared to 120 age-matched normal-hearing (NH) listeners. Low-frequency (LF) and high-frequency (HF) noise bursts were presented from 4°, 30°, and 60° azimuth on the CI side and on the NH side. MAA thresholds were tested for correlation with localization performance in the same participants. Results: There were eight good performers and ten poor performers. There were more poor performers for LF signals than for HF signals. Performance on the CI side was comparable to performance on the NH side. Most difficulties occurred at 4° and at 30°. Eight of the good performers in the localization task were also good performers in the MAA task. Only the localization ability at 4° on the CI side was positively correlated with the MAA at that location. Conclusion: Our data suggest that a CI can restore localization ability but not necessarily auditory spatial discrimination at the same time. The ability to discriminate between adjacent locations may be trainable during rehabilitation to enhance important auditory skills. [ABSTRACT FROM AUTHOR]

Blind deconvolution (BD) has proven to be an effective approach to detecting repetitive transients caused by bearing faults. However, BD suffers from instability issues including excessive sensitivity of kurtosis-guided BD methods to the single impulse and high computational time cost of the eigenvector algorithm-aided BD methods. To address these critical issues, this paper proposed a novel BD method maximizing negative entropy (NE), shortened as maximum negative entropy deconvolution (MNED). MNED utilizes NE instead of kurtosis as the optimization metric and optimizes the filter coefficients through the objective function method. The effectiveness of MNED in enhancing repetitive transients is illustrated through a simulation case and two experimental cases. A quantitative comparison with three existing BD methods demonstrates the advantages of MNED in fault detection and computational efficiency. In addition, the performance of the four methods under different filter lengths and external shocks is compared. MNED exhibits lower sensitivity to random impulse noise than the kurtosis-guided BD methods and higher computational efficiency than the BD methods based on the eigenvalue algorithm. The simulation and experimental results demonstrate that MNED is a robust and cost-effective method for bearing fault diagnosis and condition monitoring. [ABSTRACT FROM AUTHOR]

In this paper, we discuss two efficient three-term conjugate gradient methods (ECG) for impulse noise removal. The directions of ECG are first the direction of steepest descent and then spanned by the three terms: The steepest descent direction, the previous direction, and the gradient differences at the previous and current points. The second and third terms are scaled by two different step sizes called conjugate gradient parameters. Our goal is to generate and control these parameters such that they do not jointly dominate while preserving the effect of all terms, except near the optimizer where the first term dominates the other two terms. They are independent of the line search method and useful for finite precision arithmetic. The global convergence of ECG is proved. The efficiency (the lowest relative cost of function evaluations) and robustness (highest number of solved problems) of ECG compared to known conjugate gradient methods are shown in terms of PSNR (peak signal noise ratio) and time in seconds. [ABSTRACT FROM AUTHOR]

The impulse noise generated by a large volume of power electronics devices imposes a hazardous impact on information interaction reliability in low-voltage distribution grids. In this paper, we propose a two-stage collaborative information interaction reliability improvement algorithm to minimize the bit error rate (BER) of the information interaction under impulse noise. In the first stage, the transmission-side peak-to-average power ratio (PAPR) is reduced based on the adaptive particle swarm optimization (PSO)- enabled partial transmit sequence (PTS). In the second stage, the reception- side dual-signal blanking is proposed based on the transmission-side PAPR and reception-side useful signal power estimation and peak median ratio. The transmission-reception collaborative information interaction reliability improvement is realized through two aspects. First, transmission-side PAPR reduction improves the performance of reception-side signal blanking by making it easier to distinguish useful signals from impulse noise. Second, the transmission-side PAPR is utilized to improve the estimation accuracy of both coarse and precise thresholds in dual-signal blanking. Simulation results show that the proposed algorithm outperforms existing algorithms in both PAPR reduction and BER performances to achieve information interaction reliability improvement effectively. [ABSTRACT FROM AUTHOR]

In this article, with the help of Laplace transform, the existence of solution was established in a finite dimensional setting for nonlinear ψ-Hilfer fractional stochastic equation with both retarded and advanced arguments driven by multiplicative and fractional noises, with Hurst index H∈(1/2,1). At first, we obtained the existence and uniqueness results by using the Banach fixed point theorem (FPT). Second, the existence result was also obtained by applying Schaefer's fixed point theorem with less conservative conditions. Furthermore, we investigated the Hyers Ulam Rasisas stability for the aforementioned system. At the end, an example was illustrated to validate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Introduction Acute acoustic trauma (AAT) is characterized by cochlea–vestibular signs following intense noise exposure, often caused by impulse noise. French military faces a high risk of AAT because of the use of weapons with peak sound levels exceeding 150 dB. Hearing loss (HL) resulting from AAT can have a significant impact on quality of life and operational capacity. The aim of this study was to assess the prevalence of long-term hearing impairment after AAT. Materials and Methods The study involved a retrospective review of computer-based patient records from four military medical centers in Northeast France between January 2016 and December 2021. The inclusion criteria required the presence of cochlea–vestibular signs following impulse acoustic exposure and the absence of other causes. Sociodemographic and clinical data were collected, including audiometric data before and after exposure. The primary end point was the presence of a threshold elevation greater than 10 dB between reference and late audiograms. Results A total of 419 patients were included in the analysis, with a majority of males (n  = 419; 84.7%) and a mean age of 23.6 yrs. The most common causative agent was the 5.56-mm assault rifle (n  = 327; 78.0%). Tinnitus was the most frequent symptom (n  = 366; 87.4%), followed by hypoacusis (n  = 147; 35.1%) and earache (n  = 89; 21.2%). The initial audiograms showed no HL in 31.0% of cases, while the mean deficit across all frequencies was 15.4 dB. All patients received corticosteroid therapy, with a mean duration of 6.0 d. Late audiograms conducted at an average interval of 448.0 d after AAT revealed a prevalence of long-term HL exceeding 20%. Higher doses of corticosteroid therapy (>1 mg/kg) were associated with a reduced frequency of long-term HL. Conclusions This study highlights the prevalence of long-term hearing impairment after AAT in the French military. The findings emphasize the importance of preventive measures, including proper use of hearing protection devices, and the need for timely diagnosis and treatment. Further research is warranted to explore gender susceptibility to AAT and evaluate the impact of different weapons on AAT characteristics. The study also underscores the potential benefits of higher doses of corticosteroid therapy in reducing the risk of long-term hearing impairment. Overall, the findings contribute to a better understanding of AAT and can inform strategies for its prevention and management in military settings. [ABSTRACT FROM AUTHOR]

Gene expression comprises many asymmetric and complex processes. Transcriptional details revealed by the whole genome indicate that genes resort to transcriptional bursting and accumulate molecular memory. However, it is still unclear how the interplay of transcriptional bursting and memory regulates robustness and expression noise. Here, we consider a model of multiple coupled processes of protein polymerization to focus on decoding the effect of molecular memory. Using non-Markovian transformation technology, we first define the memory index to measure the correlation window of expression to decipher the mechanism of regulation. The results indicate that memory from synthesis can amplify expression noise, while memory originating from polymerization can reduce the lower bound of the noise of gene products; that is, the memory from different sources plays distinct regulatory roles to induce non-symmetry. Moreover, it is counterintuitive that the dual regulation from memory and bursting expression can directly suppress system noise, violating the principle that transcriptional bursting enhances noise. Our results not only provide a theoretical framework for investigating the function of memory but also imply that expression noise is not part of a half-power relationship with, nor mediated by, memory. [ABSTRACT FROM AUTHOR]

It is well known that the recursive least squares (RLS) algorithm is renowned for its rapid convergence and excellent tracking capability. However, its performance is significantly compromised when the system is sparse or when the input signals are contaminated by impulse noise. Therefore, in this paper, the minimum error entropy (MEE) criterion is introduced into the cost function of the RLS algorithm in this paper, with the aim of counteracting the interference from impulse noise. To address the sparse characteristics of the system, we employ a universally applicable convex function to regularize the cost function. The resulting new algorithm is named the convex regularization recursive minimum error entropy (CR-RMEE) algorithm. Simulation results indicate that the performance of the CR-RMEE algorithm surpasses that of other similar algorithms, and the new algorithm excels not only in scenarios with sparse systems but also demonstrates strong robustness against pulse noise. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a modified PRP-type conjugate gradient (CG) method for impulse noise removal in the second phase of the two-phase method. A nice property of the scheme is that the search direction at each iteration satisfies the sufficient descent condition independent of any line search. Under the Armijo-type line search, its global convergence result is proved. Numerical comparison is given to illustrate that the proposed method for removing impulse noise is promising. [ABSTRACT FROM AUTHOR]

Eye movements have been extensively studied with respect to visual stimulation. However, we live in a multisensory world, and how the eyes are driven by other senses has been explored much less. Here, we review the evidence on how audition can trigger and drive different eye responses and which cortical and subcortical neural correlates are involved. We provide an overview on how different types of sounds, from simple tones and noise bursts to spatially localized sounds and complex linguistic stimuli, influence saccades, microsaccades, smooth pursuit, pupil dilation, and eye blinks. The reviewed evidence reveals how the auditory system interacts with the oculomotor system, both behaviorally and neurally, and how this differs from visually driven eye responses. Some evidence points to multisensory interaction, and potential multisensory integration, but the underlying computational and neural mechanisms are still unclear. While there are marked differences in how the eyes respond to auditory compared to visual stimuli, many aspects of auditory‐evoked eye responses remain underexplored, and we summarize the key open questions for future research. [ABSTRACT FROM AUTHOR]

The efficacy of learning-based denoising techniques is heavily reliant on the quality of clean supervision. Unfortunately, acquiring clean images in many scenarios is a challenging task. Conversely, capturing multiple noisy frames of the same field of view is feasible and natural in real-life scenarios. Thus, it is imperative to explore the potential of noisy data in model training and avoid the limitations imposed by clean labels. In this paper, we propose a novel unsupervised learning strategy called one-pot denoising (OPD), which is the first unsupervised multi-frame denoising method. OPD differs from traditional supervision schemes, such as supervised Noise2Clean, unsupervised Noise2Noise, and self-supervised Noise2Void, as it employs mutual supervision among all the multiple frames. This provides learning with more diverse supervision and allows models to better exploit the correlation among frames. Notably, we reveal that Noise2Noise is a special case of the proposed OPD. We provide two specific implementations, namely OPD-random coupling and OPD-alienation loss, to achieve OPD during model training based on data allocation and loss refine, respectively. Our experiments demonstrate that OPD outperforms other unsupervised denoising methods and is comparable to non-transformer-based supervised N2C methods for several classic noise patterns, including additive white Gaussian noise, signal-dependent Poisson noise, and multiplicative Bernoulli noise. Additionally, OPD shows remarkable performance in more challenging tasks such as mixed-blind denoising, denoising random-valued impulse noise, and text removal. The source code and pre-trained models are available at https://github.com/LujiaJin/One-Pot_Multi-Frame_Denoising. [ABSTRACT FROM AUTHOR]

Neural tracking of spoken speech is considered a potential clinical biomarker for speech-processing difficulties, but the reliability of neural speech tracking is unclear. Here, younger and older adults listened to stories in two sessions while electroencephalography was recorded to investigate the reliability and generalizability of neural speech tracking. Speech tracking amplitude was larger for older than younger adults, consistent with an age-related loss of inhibition. The reliability of neural speech tracking was moderate (ICC ∼0.5–0.75) and tended to be higher for older adults. However, reliability was lower for speech tracking than for neural responses to noise bursts (ICC >0.8), which we used as a benchmark for maximum reliability. Neural speech tracking generalized moderately across different stories (ICC ∼0.5–0.6), which appeared greatest for audiobook-like stories spoken by the same person. Hence, a variety of stories could possibly be used for clinical assessments. Overall, the current data are important for developing a biomarker of speech processing but suggest that further work is needed to increase the reliability to meet clinical standards. • Neural speech tracking is a potential EEG biomarker for impaired speech processing. • However, reliability and generalizability of neural speech tracking are unclear. • Younger and older adults show moderate reliability of neural speech tracking. • Neural tracking also moderately generalized across speech materials. • Current results are critical for the development of a biomarker of speech processing. [ABSTRACT FROM AUTHOR]

Mitigating low-frequency noise in various industrial applications often involves the use of the filter-x least mean squares (FxLMS) algorithm, which relies on the mean square error criterion. This algorithm has demonstrated effectiveness in reducing noise induced by Gaussian noise within noise control systems. However, the performance of this algorithm experiences significant degradation and does not converge properly in the presence of impulsive noise. Consequently, to uphold the stability of the ANC system, several robust adaptive algorithms tailored to handle shock noise interference have been introduced. This paper systematically organizes and classifies robust adaptive algorithms designed for impulse noise based on algorithmic criteria, offering valuable insights for the research and application of pertinent active impact noise control methods. [ABSTRACT FROM AUTHOR]

In this article, the existence of solution is established in finite dimensional settings for nonlinear Hilfer fractional integrodifferential stochastic equations (HFISEs) driven by multiplicative and fractional noises with non-instantaneous (NI) impulses with Hurst index $ H \in (\frac{1}{2},1) $. At first, authors obtained the existence result by using the generalized fractional derivative (Hilfer), Banach fixed point theorem (FPT) and stochastic analysis approach. Secondly the existence result is obtained by applying Schaefer's type FPT for less conservative conditions. Then, we discuss the Ulam-Hyers-Rassias (UHR) stability for HFISEs. An example is illustrated to validate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a kernel fraction low-power adaptive filtering algorithm based on lncosh function (KFLP-LHCF) is proposed for chaotic time series prediction. The algorithm combines the lncosh function with the kernel fractional low-power algorithm. The nonlinear saturation characteristic of the lncosh function is used to achieve resistance to impulse noise, and the fractional low power is used to suppress the influence of error mutation on the performance of the algorithm. In addition, by adjusting the scale factor, a balance can be achieved between convergence speed as well as steady-state performance. In the alpha noise environment, the proposed algorithm is used to predict and simulate two typical chaotic time series of Lorenz and Mackey–Glass. Experimental results show that the algorithm has better steady-state performance and lower steady-state error than other kernel adaptive filtering algorithms, provided that the convergence speed is similar. [ABSTRACT FROM AUTHOR]

Background: Individuals with autism spectrum disorders (ASD) often exhibit altered sensory processing and deficits in language development. Prenatal exposure to valproic acid (VPA) increases the risk for ASD and impairs both receptive and expressive language. Like individuals with ASD, rodents prenatally exposed to VPA exhibit degraded auditory cortical processing and abnormal neural activity to sounds. Disrupted neuronal morphology has been documented in earlier processing areas of the auditory pathway in VPA-exposed rodents, but there are no studies documenting early auditory pathway physiology. Therefore, the objective of this study is to characterize inferior colliculus (IC) responses to different sounds in rats prenatally exposed to VPA compared to saline-exposed rats. Methods: In vivo extracellular multiunit recordings from the inferior colliculus were collected in response to tones, speech sounds, and noise burst trains. Results: Our results indicate that the overall response to speech sounds was degraded in VPA-exposed rats compared to saline-exposed controls, but responses to tones and noise burst trains were unaltered. Conclusions: These results are consistent with observations in individuals with autism that neural responses to complex sounds, like speech, are often altered, and lays the foundation for future studies of potential therapeutics to improve auditory processing in the VPA rat model of ASD. [ABSTRACT FROM AUTHOR]

Based on the convex combination technique, we propose a projection-based hybrid conjugate gradient algorithm for solving nonlinear equations with convex constraints in this paper. The conjugate parameter of the proposed algorithm is a convex combination of the modified Polak-Ribière-Polyak and Dai-Yuan type conjugate parameters, and the search direction has the sufficient descent property without the use of a line search strategy. The proposed hybrid algorithm's global convergence is established under appropriate assumptions. The numerical experiments demonstrate that the proposed algorithm is more efficient and competitive than existing methods under some benchmark test problems. Furthermore, it is also extended to solve the sparse signal and impulse noise image restoration problem that arises in compressive sensing. [ABSTRACT FROM AUTHOR]

Behavioral hearing thresholds and noise localization acuity were determined using a conditioned avoidance/suppression procedure for three Helmeted guineafowl (Numida meleagris). The guineafowl responded to frequencies as low as 2 Hz at 82.5 dB SPL, and as high as 8 kHz at 84.5 dB SPL. At a level of 60 dB SPL, their hearing range spanned 8.12 octaves (24.6 Hz–6.86 kHz). Like most birds, they do not hear sounds above 8 kHz. However, the guineafowl demonstrated good low-frequency hearing (frequencies below 32 Hz), showing thresholds that are more sensitive than both the peafowl and pigeon, both of which hear infrasound. It thus appears that infrasound perception may be more common than previously thought and may have implications for species that inhabit areas with wind energy facilities. The guineafowls' minimum audible angle for a 100-ms broadband noise burst was 13.8 °, at the median for birds and near the mean for mammals. Unlike in mammals, the small sample of bird species and limited representation of lifestyles do not yet allow for meaningful interpretations of the selective pressures or mechanisms that underlie their abilities to locate sound sources. [ABSTRACT FROM AUTHOR]

Background: Impact/impulse and continuous noise are two main causes of noise-induced hearing loss (NIHL) in workplaces. The aim of this study was to compare the effects of impulse/impact noise and continuous noise on hearing status. Methods: In this study, 259 workers referred to the occupational medicine clinic of Shahid Rahnemoun hospital, Yazd, Iran, entered the study and were divided into two groups: with exposure to impact/impulse noise and with exposure to continuous noise. Hearing thresholds were measured and compared between the two groups by pure-tone audiometry (PTA). The frequency of hearing loss and audiometric notch according to the results of PTA was compared between the two groups. Data were analyzed by SPSS (ver. 16) using Student's t-test, Chi-square test, and Mann-Whitney U test. Results: Hearing thresholds were significantly higher at all frequencies in the impact noise group. The hearing threshold at 6000 Hz was higher than other frequencies in both groups. The frequency of hearing loss at high frequencies was higher in the impact group. The frequency of audiometric notch was not significantly different between the two groups. Conclusions: The results of this study showed that hearing loss after exposure to impact/impulse noise is probably more frequent and more severe than exposure to continuous noise, but the pattern of hearing loss is similar in both types of noise exposure. [ABSTRACT FROM AUTHOR]