Gaussian Impulse Research Articles

Hider: A Hyperspectral Image Denoising Transformer With Spatial-Spectral Constraints for Hybrid Noise Removal.

Hyperspectral image (HSI) qualities are limited by a mixture of Gaussian noise, impulse noise, stripes, and deadlines during the sensor imaging process, resulting in weak application performance. To enhance HSI qualities, methods based on convolutional neural networks have been successively applied to restore clean data from the observed data. However, the architecture of these methods lacks spectral and spatial constraints, and the convolution operators have limited receptive fields and inflexible model inferences. Thus, in this study, we propose an efficient end-to-end transformer, named HSI denoising transformer (Hider), for mixed HSI noise removal. First, a U-shaped 3-D transformer architecture is built for multiscale feature aggregation. Second, a multihead global spectral attention module within the spectral transformer block is designed to excavate information in different spectral patterns. Finally, an additional locally enhanced cross-spatial attention module within the spatial-spectral transformer block is constructed to build the long-range spatial relationship to avoid the high computational complexity of global spatial self-attention. Through the imposition of global correlations along spectrum and spatial self-similarity constraints on the transformer, our proposed Hider aims to capture long-range spatial contextual information and cluster objects with the same spectral pattern for HSI denoising. To verify the effectiveness and efficiency of Hider, we conducted extensive simulated and real experiments. The denoising results on both simulated and real-world datasets show that Hider achieves superior evaluation metrics and visual assessments compared with other state-of-the-art methods.

Noise modelling and mitigation for broadband in‐door power line communication systems

AbstractCommunication 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.

Tunable temporal dynamics of dipole response in graphene-wrapped core–shell nanoparticles

The investigation on the temporal dynamics of graphene-wrapped core–shell nanoparticles under the illumination of a Gaussian impulse have been carried out. By altering the graphene layers and the aspect ratio of the core–shell structure, we can adjust the resonant modes into typical cases in regime of terahertz. Accordingly, different scenarios for the temporal evolution are detected, which include two kinds of ultrafast oscillation with exponential decay tendency, pure exponential decay, and Gaussian shape, when the pulse duration of the incident pulse is much shorter than, similar to, and much longer than the localized surface plasmon lifetime. To one's interest, when the coupling between two resonant modes exists, one predicts the long-periodic oscillation, whose period is just the difference between the frequencies of the resonant modes. Hence, the intrinsic properties of the ultrafast oscillation can be hardly influenced by the input signals. Further quantitative calculation demonstrate that the periods of the ultrafast oscillations can be tuned by different physical mechanisms, which are, respectively, based on the self-interacting correction of a single resonance and the strong coupling between the resonant modes in frequency domain. Our results may be applicable in the fields of optical sensors, optical information processing, and other nanophotonic devices.

A multi-task learning network based on the Transformer network for airborne electromagnetic detection imaging and denoising

Abstract 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.

Risk transference between climate variability and financial derivatives: Implications for global food security

We investigate the impacts of the El Niño-Southern Oscillation (ENSO) on global food security by using information embedded in financial derivatives. Using a state-of-the-art general circulation model (GCM) climate forecasting system that generates observations on the phase and magnitude of ENSO, we create novel indices that track its uncertainty throughout time. Together with the option implied volatilities of core food inputs (wheat, maize, rice, and soybean), we model the time-varying volatility spillover from ENSO to each commodity, capturing the direction and intensity of risk transference. Simulating Gaussian and non-Gaussian impulse responses that mimic an increase in climate variability show a persistent impact on the price uncertainty of the commodities lasting up to three months. We also show that shocks are contingent on the phase of ENSO, with warmer conditions in the Eastern Pacific (i.e., the El Niño phase) increasing risk transference for soybean and rice. In contrast, the La Niña phase has a material influence on the uncertainty of wheat and maize. In all, we reveal a volatility transmission channel of climate variability that affects financial markets, suggesting valuable inferences about the impact of climate shocks can be derived from the rich information embedded in commodity-linked derivatives.

Uncertainty quantification and stochastic response prediction for excavator systems under extreme operating conditions

The quantification of extreme operating conditions and their corresponding response predictions is a considerable challenge regarding the operation and maintenance of electromechanical equipment, such as dynamic excavator systems. This paper proposes a novel nonstationary modulation function model based on kinematic and dynamic analyses driven by on-site measurement data acquired from excavators. Extreme conditions are simulated through Gaussian white noise and impulse functions, and harmonic functions are overlaid on them to fit uncertain excitations, thus quantifying real random excitations. A key component of this method is its detailed statistical analysis of the probability density function (PDF) of the system response via Monte Carlo simulation. The results show that, compared with the measured results, the mean error of the system response PDFs predicted by this methodology is extremely low, and the developed approach can effectively capture the heavy-tailed characteristics of random responses under extreme loads. The proposed method utilizes a limited number of physical experiments to generate statistical results for large samples, providing the dual benefit of cost-effectiveness and high accuracy.

Sensing directional noise baths in levitated optomechanics

Optomechanical devices are being harnessed as sensors of ultraweak forces for applications ranging from inertial sensing to the search for the elusive dark matter. For the latter, there is a focus on detection of either higher energy single recoils or ultralight, narrow-band sources; a directional signal is expected. However, the possibility of searching for a stochastic stream of weak impulses, or more generally a directional broadband signal, need not be excluded; with this and other applications in mind, we apply Gaussian white noise impulses with a well defined direction Ψ to a levitated nanosphere trapped and 3D cooled in an optical tweezer. We find that cross-correlation power spectra offer a calibration-free distinctive signature of the presence of a directional broadband force and its orientation quadrant, unlike normal power spectral densities (PSDs). We obtain excellent agreement between theoretical and experimental results. With calibration we are able to measure the angle Ψ, akin to a force compass in a plane. We discuss prospects for extending this technique into the quantum regime and compare the expected behavior of quantum baths and classical baths. Published by the American Physical Society 2024

Semantic labeling of social big media using distributed online robust classification

Semantic labeling for image datasets is of significant importance in a wide range of social media. However, social datasets with massive amounts of data require effective technologies to increase the quality of classification. In this study, we propose a novel online robust classification using distributed learning method, in which the diffusion method is used over adaptive networks in order to the parallelization of the training process. The loss function of the suggested method is derived from the logarithm cosine function, being more suitable for impulsive noises. Also, the convergence of the proposed method is discussed theoretically. The Extensive experimental results show that the proposed method outperforms the other robust state-of-the-art classification algorithms in the presence of various scenarios, such as: free noise synthetic data sets, pure Gaussian noise, mixture of two Gaussian noises, impulse noise, and Alpha–Beta noise which are added to the synthetic data sets. In addition, the experiments were repeated for two different types of real data sets with real noises and outliers, i.e., UCI and 500PX social media data sets. The obtained results over UCI and 500px data sets on social media with real outliers and mislabeled samples confirmed the acceptable performance of the proposed method.

Reduced Complexity Approximation and Design of Gaussian Impulse Response Filters and Wavelets

Reduced Complexity Approximation and Design of Gaussian Impulse Response Filters and Wavelets

Hyperspectral image restoration based on color superpixel segmentation

Hyperspectral images (HSI) are often degraded by various types of noise during the acquisition process, such as Gaussian noise, impulse noise, dead lines and stripes, etc. Recently, there exists a growing attenrion on low-rank matrix/tensor-based methods for HSI data restoration, assuming that the overall data is low-rank. However, the assumption of overall low-rankness often proves inaccurate due to the spatially heterogeneous local similarity characteristics of HSI. Traditional cube-based methods involve dividing the HSI into fixed-size cubes. However, using fixed-size cubes does not provide flexible coverage of locally similar regions at varying scales. Inspired by superpixel segmentation, this paper proposes the Shrink Low-rank Super-tensor (SLRST) approach for HSI recovery. Instead of using fixed-size cubes, SLRST employs a size-adaptive super-tensor. The proposed approach is effectively solved using the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on HSI data verify that the proposed method outperforms other competing methods.

Modification of Non-local Mean Algorithm Using Parallel Calculation for Image Noise Reduction

Noise in digital image processing is a noise that occurs at pixel values due to random colour intensity. Several types of noise models include Gaussian noise, speckle noise, impulse noise, and Poisson noise. Before processing image data, a noise reduction process is required. One of the noise reduction algorithms used for gaussian noise models is Non-local Mean. This algorithm performs calculations sequentially on each pixel in the search block. Due to a large number of pixels and search block area in the image, the noise reduction process using the Non-local Mean algorithm is very slow. This study proposes the concept of parallel calculations for the Non-local Mean algorithm. This concept divides the search block into three parts and performs calculations on each part simultaneously. The experimental results show that the Non-local Mean algorithm with parallel calculations can reduce noise up to 30% faster if the noise standard deviation is above 30.

FastHyMix: Fast and Parameter-Free Hyperspectral Image Mixed Noise Removal.

The decrease in the widths of spectral bands in hyperspectral imaging leads to a decrease in signal-to-noise ratio (SNR) of measurements. The decreased SNR reduces the reliability of measured features or information extracted from hyperspectral images (HSIs). Furthermore, the image degradations linked with various mechanisms also result in different types of noise, such as Gaussian noise, impulse noise, deadlines, and stripes. This article introduces a fast and parameter-free hyperspectral image mixed noise removal method (termed FastHyMix), which characterizes the complex distribution of mixed noise by using a Gaussian mixture model and exploits two main characteristics of hyperspectral data, namely, low rankness in the spectral domain and high correlation in the spatial domain. The Gaussian mixture model enables us to make a good estimation of Gaussian noise intensity and the locations of sparse noise. The proposed method takes advantage of the low rankness using subspace representation and the spatial correlation of HSIs by adding a powerful deep image prior, which is extracted from a neural denoising network. An exhaustive array of experiments and comparisons with state-of-the-art denoisers was carried out. The experimental results show significant improvement in both synthetic and real datasets. A MATLAB demo of this work is available at https://github.com/LinaZhuang for the sake of reproducibility.

Anti-inference ability of CNN mainstream models based on face mask detection

Face Mask detection is important under epidemic condition, it influences prevention and controlling of epidemic. Picture recognition is mature technology with the development of convolution neural network (CNN). Camera with this function can be easily established for face mask detection, providing unmanned detection for safety concern. Picture noise occurs for long-term using of detection equipment. This research paper aims to explore behaviors of CNN mainstream models under face mask detection, observing their capability of resisting noise interference. AlexNet, visual geometry group (VGG), residual network (ResNet), EfficienNet and Vision Transformer (Vit) are selected as mainstream model. Gaussian (White) noise and Impulse (Salt and Pepper) noise are select to implement noise interference in pictures. Training methods and comparison of testing results are presented in this paper. Result of comparison shows Vit gives best performance for anti-interference ability.

Gaussian-impulse mixed noise image recovery based on AB fractional calculus and SNSS priors

Recovering images corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN) is a challenging difficulty. This paper for the first time proposes the definition of the higher-order Atangana-Baleanu (AB) fractional calculus and combines it with the simultaneous non-local self-similarity (SNSS) model organically to obtain the ABSNSS model for image denoising. The model can effectively restore images damaged by AWGN and IN, while preserving the texture and edge features of the images, and has strong denoising ability. Furthermore, the convergence of the iterative format of the model is demonstrated theoretically. Finally, numerical experiments show that the proposed ABSNSS model has significant advantages in removing the mixed noise and performs better than the state-of-the-art methods.

Weighted compact nonlinear hybrid scheme based on a family of mapping functions for aeroacoustics problem

The study of differential schemes with high accuracy and low dispersion is of great significance for the numerical simulation of aeroacoustics over complex geometries. The midpoint-and-node-to-node explicit finite difference is employed to solve the flux derivatives of the linear Euler equations for improving the robustness. To obtain the numerical flux at the center of the grid element, we adopted a hybrid interpolation method of center and upwind interpolations, combined with a symmetrical conservative metric method, to achieve high-resolution discretization of the acoustic field variables and geometric variables of the structural grid. To suppress spurious oscillations and improve the resolution of discontinuous regions, a family of mapping functions is developed to establish different smoothness indicators and applied to the sixth-order weighted compact nonlinear hybrid scheme (WCNHS), forming a new WCNHS scheme based on piecewise exponential mapping functions (WCNHS-Pe). The approximate dispersion relation shows that the dispersion error and numerical dissipation of WCNHS-Pe are smaller than those of WCNHS with a simple mapping function to the original weights in Jiang and Shu and other mapping function-weighted WCNHS schemes. We have applied various WCNHS schemes to the numerical simulation of the Shu–Osher problem, propagation of Gaussian impulses on two-dimensional wavy grids, sound transmission at discontinuous interfaces, propagation of Gaussian impulses on three-dimensional wavy grids, etc. Numerical results indicate that the WCNHS-Pe scheme has better discontinuity capture capability, higher resolution, and lower dispersion under the same differential stencil, and is suitable for computational aeroacoustics of complex geometries.

A Comprehensive Analysis of Various Delineation method for Exudates in Fundus Images using Miniaturized Pi Board

In current decades, employing clinical imagery analysis to automatically segregate exudates from color fundus pictures has proven to be a difficult endeavor. This paper compares the efficacy of several picture delineation techniques using a Raspberry Pi chip. By employing various techniques to regular publically accessible samples, the optimum delineation methodology is selected, while efficacy is measured using characteristics such as resemblance factors, implementation duration, sensitivity, as well as specificity. The source hue ocular pictures are initially obtained using publicly available resources. Gaussian distortion, impulse distortion, and speckle distortion could all be present in such pictures. As a result, a pre-processing approach is used to the source pictures in effort to reduce the distortion and boost brightness. After that, several delineation methods such as a thresholding technique, mean-shift algorithm, watershed algorithm, distance transform, K-means clustering, Fuzzy C-Means grouping approach and Active Contour Model are used to segment the normal and abnormal region in color fundus images. The Fuzzy C-Means grouping approach yields higher delineation precision yet requires longer execution time, according to the findings.

A serial attention module‐based deep convolutional neural network for mixed Gaussian‐impulse removal

AbstractThe removal of mixed noise is a challenging task because the attenuation of the noise distribution cannot be described precisely. The coupling of additive white Gaussian noise and impulse noise (IN) is a typical case. At present, most methods use a two‐phase strategy, that is, IN detection coupled with additive white Gaussian noise removal, often leading to poor denoising results with an increase in the ratio of IN. In this paper, an effective convolutional neural network (CNN) model is proposed, namely a serial attention module‐based CNN (SACNN), for mixed noise removal. In contrast to the existing two‐phase methods, SACNN unifies the denoising process into a single CNN framework. In SACNN, residual learning and batch normalization are used to train the model, which speeds up the convergence and improves the mixed noise removal performance. Meanwhile, the serial attention module is applied to better preserve the texture details. The experimental results reveal that SACNN achieves superior quality metrics and visual appearance when compared to several leading approaches.

A Novel Tensor-Based Hyperspectral Image Restoration Method With Low-Rank Modeling in Gradient Domains

The hyperspectral image (HSI) is easily contaminated by various kinds of mixed noise (such as Gaussian noise, impulse noise, stripes, and deadlines) during the process of data acquisition and conversion, which significantly affect the quality and applications of HSI. As an important and effective scheme for the quality improvement of HSI, the HSI restoration problem aims to recover a clean HSI from the noisy HSI with mixed noise. Thus, based on the tensor modeling of HSI, we propose a novel tensor-based HSI restoration model with low-rank modeling in gradient domains in a unified tensor representation framework in this article. First, for the spectral low-rank modeling of HSI in spectral gradient domain, we particularly exploit the low-rank property of spectral gradient, and propose the spectral gradient-based weighted nuclear norm low-rank prior term. Second, for the spatial-mode low-rank modeling of HSI in spatial gradient domain, we particularly exploit the low-rank property of spatial gradient tensors via the discrete Fourier transform, and propose the spatial gradient-based tensor nuclear norm low-rank prior term. Then, we use the alternative direction method of multipliers to solve the proposed model. Finally, the restoration results on both the simulated and real HSI datasets demonstrate that the proposed method is superior to many state-of-the-art methods in the aspects of visual and quantitative comparisons.

Improved Subcarrier Selection Technique for Power Line Communications

This paper proposes Power Line Communication (PLC) schemes that improve the data rate of PLC systems while combating impairments resulting from frequency disturbances like impulse noise. Firstly, a modified subcarrier selection technique that can be employed to transmit one or more M-ary Phase-Shift Keying (MPSK) or M-ary Quadrature Amplitude Modulated (MQAM) symbols within a subset of the subcarriers of an orthogonal frequency division multiplexing symbol. The proposed subcarrier selection technique employs the cyclic rotation of a set of subcarriers, chosen from a group of subcarriers to transmit the selected MPSK or MQAM symbols. A second scheme that employs the rotation of MPSK or MQAM symbols among a group of subcarriers, to increase the data rate of the PLC system is proposed. The proposed schemes show improvement in error performance when tested over Additive White Gaussian Noise (AWGN) and impulse noise of a PLC system despite the increase in the data rate.

Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity

In hyperspectral images (HSIs), mixed noise ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., Gaussian noise, impulse noise, stripe noise, and deadlines) contamination is a common phenomenon that greatly reduces the visual quality of the image. In recent years, methods combining global and non-local low-rankness have been widely used in the field of HSI denoising. However, most methods apply original space-based denoising strategies (low-rank tensor decomposition, total variation, and tensor sparse representation, etc.) directly to the modeling of non-local low-rank tensors in subspace, without fully exploiting the intrinsic and latent properties of the non-local similar tensors. In this paper, we propose a hybrid prior denoising method based on global tensor low-rankness and non-local SVD-aided group sparsity (GTL_NSGS). This method introduces a novel plug-and-play NSGS denoiser that uses singular value decomposition as assistance to successively explore self-similarity of spatial dimension, low-rankness of spectral dimension, and group sparsity of difference domain in subspace non-local similar tensors. Globally, we utilize the existing three-way log-based tensor nuclear norm (3DLogTNN) to approximate the HSI tensor fibered rank and introduce a difference continuity regularization to obtain a continuous smooth spectral basis. Finally, we combine the Alternating Direction Method of Multipliers (ADMM) with the Augmented Lagrangian Multiplication (ALM) algorithm to solve the proposed model effectively. Extensive experiments on simulated and real data sets demonstrate that the proposed method has superior performance in removing mixed noise compared to state-of-the-art denoisers.