Additive Noise Research Articles

Denoising of dual-VENC PC-MRI with large high/low VENC ratios.

Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocityencoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60)ms and noise levels (9,12)dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60ms, 9dB), the Dual-VENC (DV) error is reduced to: (Sign Correction); and (proposed techniques). The methods are found to be significantly different (p-value ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.

Double-layer data-hiding mechanism for ECG signals

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system.

Tailoring the magnetocaloric effect in doped GaAs quantum dot under the influence of noise

Present inspection thoroughly explores the magnetocaloric effect (MCE) of GaAs quantum dot (QD) doped with Gaussian impurity and under the influence of Gaussian white noise (GWHN). Two different routes for the application of GWHN viz. additive and multiplicative have been considered. The enquiry highlights extremely subtle and complex interplay between temperature, GWHN, mode of entry of GWHN and the given physical parameters concerned that finally designs the MCE profiles. The fact that under certain conditions the MCE maximizes comes out to be technologically relevant. Deviations in the MCE profiles from the noise-free state appear to be more profound under multiplicative noise than its additive analogue. As a general trend, additive and multiplicative noise reduces and augments the MCE, respectively, from that under the noise-free situation.

Simultaneous estimation of multiple order phase derivatives using deep learning method in digital holographic interferometry

For non-contact deformation testing, digital holographic interferometry is a prominent optical technique where the first and second order interference phase derivatives directly embed information about the strain and curvature distributions of a deformed object. Hence, reliable extraction of multiple order phase derivatives is of great practical significance; however, this problem is marred by several challenges such as the need of multiple differentiation operations, complex shearing operations and performance degradation due to noise. In this paper, we introduce a deep learning approach for the direct and simultaneous estimation of first and second order phase derivatives in digital holographic interferometry. Our method's performance is demonstrated via rigorous numerical simulations exhibiting wide range of additive white Gaussian noise and speckle noise. Moreover, we substantiate the practical efficacy of our proposed method for processing deformation fringes acquired via digital holographic interferometry.

Ecg signal watermarking using QR decomposition.

This study introduces a novel watermarking technique for electrocardiogram (ECG) signals. Watermarking embeds critical information within the ECG signal, enabling data origin authentication, ownership verification, and ensuring the integrity of research data in domains like telemedicine, medical databases, insurance, and legal proceedings. Drawing inspiration from image watermarking, the proposed method transforms the ECG signal into a two-dimensional format for QR decomposition. The watermark is then embedded within the first row of the resulting R matrix. Three implementation scenarios are proposed: one in the spatial domain and two in the transform domain utilizing discrete wavelet transform (DWT) for improved watermark imperceptibility. Evaluation on real ECG signals from MIT-BIH Arrhythmia database and comparison to existing methods demonstrate that the proposed method achieves: (1) higher Peak Signal-to-Noise Ratio (PSNR) indicating minimal alterations to the watermarked signal, (2) lower bit error rates (BER) in robustness tests against external modifications such as AWGN noise (additive white Gaussian noise), line noise and down-sampling, and (3) lower computational complexity. These findings emphasize the effectiveness of the proposed QR decomposition-based watermarking method, achieving a balance between robustness and imperceptibility. The proposed approach has the potential to improve the security and authenticity of ECG data in healthcare and legal contexts, while its lower computational complexity enhances its practical applicability.

MFR-Net: A multi-feature fusion phase unwrapping method for different speckle noises

Phase unwrapping is a crucial step in laser interferometry for obtaining accurate physical measurement of object. To reduce the impact of speckle noise on wrapped phase during actual measurement and improve the subsequent measurement accuracy, a multi-feature fusion phase unwrapping method for different speckle noises named MFR-Net is proposed in this paper. The network is composed of a front-end multi-module filter processing layer and a back-end network with dilated convolution and coordinate attention mechanism. By reducing random phase differences introduced by different levels of noise, the network enhances its capability to extract spatial features such as gradient information between pixels under speckle noise, so that it successfully unwraps the wrapped phase with different speckle noises and accurately recovers the real phase information. Taking the wrapped phases with multiplicative speckle noise and additive random noise as dataset, the results of ablation and comparison experiments show that the MFR-Net has superior unwrapped results. Under three different levels of speckle noise, the average values of MSE, SSIM, PSNR and AU for MFR-Net are at least improved by 84.80 %, 10.99 %, 29.00 % and 7.72 %, respectively, compared to PDVQG, TIE, DLPU and VURNet algorithms. When the standard deviation of speckle noise varies continuously in the range [1.0, 2.0], the average values of four indexes reaches 0.12 rad, 0.91, 31.80 dB and 99.96 %, respectively, indicating the stronger robustness of MFR-Net. In addition, the phase step unwrapping is performed by MFR-Net. Compared to DLPU and VURNet, MFR-Net method reduced MSE by 80 % and 87.35 %, respectively, demonstrating the outstanding generalization capability. The proposed MFR-Net can realize the correct phase unwrapping under different speckle noises. It may be applied in laser interferometry applications such as digital holography and interferometric synthetic aperture radar.

Use estimated signal and noise to adjust step size for image restoration

Image deblurring is a challenging inverse problem, especially when there is additive noise to the observation. To solve such an inverse problem in an iterative manner, it is important to control the step size for achieving a stable and robust performance. We designed a method that controls the progress of iterative process in solving the inverse problem without the need for a user-specified step size. The method searches for an optimal step size under the assumption that the signal and noise are two independent stochastic processes. Experiments show that the method can achieve good performance in the presence of noise and imperfect knowledge about the blurring kernel. Tests also show that, for different blurring kernels and noise levels, the difference between two consecutive estimates given by the new method tends to remain more stable and stay in a smaller range, as compared to those given by some existing techniques. This stable feature makes the new method more robust in the sense that it is easier to select a stopping threshold for the new method to use in different scenarios.

Machine-learning classification with additivity and diverse multifractal pathways in multiplicativity

Evidence of multifractal structures has spread to a wider set of physiological time series supporting the intricate interplay of biological and psychological functioning. These dynamics manifest as random multiplicative cascades, embodying nonlinear relationships characterized by recurring division, branching, and aggregation processes implicating noise across successive generations. This investigation focuses on how well the diversity of multifractal properties can be specific to the type of cascade relationship between generation (i.e., multiplicative, additive, or a mixture) as well as to the type of noise (i.e., including additive white Gaussian noise, fractional Gaussian noise, and various amalgamations) among 15 distinct types of binomial cascade processes. Cross-correlation analysis of multifractal spectral features confirms that these features capture nuanced aspects of cascading processes with minimal redundancy. Principal component analysis using 13 distinct multifractal spectral features shows that different cascade processes can manifest multifractal evidence of nonlinearity for distinct reasons. This transparency of multifractal spectral features to underlying cascade dynamics becomes less amenable to machine-learning strategies. Fully connected neural networks struggled to classify the 15 distinct types of cascade processes based on the respective multifractal spectral features (45.5% accuracy) yet demonstrated improved accuracy when addressing single categories of cross-generation relationships, that is, additive (91.6%), multiplicative (75.4%), or additomultiplicative (70.6%). While traditional principal component analysis reveals distinct loadings attributed to individual noise processes, multiplicative relationships between generations effectively make the constituent noise processes less discernible to neural networks. Neural networks may lack sufficient hierarchical depth required to effectively distinguish among nonadditive cascading processes, recommending either elaborating multifractal geometry or using alternate architectures for machine-learning classification of cascades with multiplicative relationships. Published by the American Physical Society 2024

Behavioural stochastic resonance across the lifespan.

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signalsin non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

Ultra-high-cardinality geometric shaping in the finite SNR regime.

Four-dimensional (4D) constellations with up to 131 072 points (17 bit/4D-sym) are designed for the first time using geometric shaping. The constellations are optimized in terms of mutual information (MI) and generalized MI (GMI) for the additive white Gaussian noise (AWGN) channel, targeting a forward error correction (FEC) rate of 0.8 at finite signal-to-noise ratios. The presented 15-17 bit constellations are currently the highest-performing constellations in the literature, having a gap to the AWGN capacity as low as 0.17 dB (MI) and 0.45 dB (GMI) at 17 bit/4D-sym. For lower cardinalities, our constellations match or closely approach the performance of previously published optimized constellations. We also show that (GMI-)optimized constellations with a symmetry constraint, optimized for a FEC rate of 0.8, perform nearly identical to their unconstrained counterparts for cardinalities above 8 bit/4D-sym. A symmetry constraint for MI-optimized constellations is shown to have a negative impact in general. The proposed procedure relies on a Monte-Carlo-based approach for evaluating performance and is extendable to other (nonlinear) channels. Stochastic gradient descent is used for the optimization algorithm for which the gradients are computed using automatic differentiation. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

A noncoherent chaotic communication system with multidimensional index modulation based on cyclic permutation grouping

With the surge in technological advancements and application demands, the study of high-data-rate communication systems has become increasingly significant. To enhance the data transmission rate and spectral efficiency of traditional DCSK systems, we have designed a differential chaos shift keying (DCSK) system with multidimensional index modulation based on cyclic permutation grouping (CPG-MIM-DCSK). Multidimensional index resources are efficiently combined by the CPG-MIM-DCSK system using the proposed cyclic permutation grouping method. In the CPG-MIM-DCSK system, both selected and unselected subcarriers, as well as selected and unselected time slots, are distinguished through different groups of cyclic permutations. Permutation, carrier, and time slot resources are fully utilized by this modulation method, thereby enhancing the data rate and spectral efficiency of traditional DCSK systems. The theoretical Bit Error Rate (BER) expressions of the CPG-MIM-DCSK system under Additive White Gaussian Noise (AWGN) and multipath Rayleigh fading channels are derived. The data rate, complexity, and spectral efficiency of the CPG-MIM-DCSK system are covered in further analysis. The simulation results highlight the superiority of the CPG-MIM-DCSK system in terms of data rate and spectral efficiency. Additionally, the proposed CPG-MIM-DCSK system exhibits better BER performance in both Gaussian and multipath Rayleigh fading channels compared to its comparative systems.

TRNG Entropy Model in the Presence of Flicker FM Noise

Flicker Frequency Modulated (FM) noise, which influences free-running Ring Oscillators (ROs), can make a substantial contribution to the entropy generated by RO-based True Random Number Generators (TRNGs). While current TRNG stochastic models predominantly concentrate on white FM noise, the addition of flicker FM noise could remarkably enrich the analysis of the TRNG entropy production rate. This paper introduces an entropy model for TRNGs, employing Gaussian processes, to estimate entropy generation from both white FM and flicker FM noise. We analytically derive the flicker FM noise Auto-Correlation Function (ACF), enabling assessment of entropy contributions conditioned on partial knowledge of the TRNG’s internal state. Utilizing the developed model with commonly reported noise magnitudes found in literature, it is determined that flicker FM noise holds the potential to substantially enhance the TRNG’s entropy rate. However, due to considerable variation in reported magnitudes across limited available research on flicker FM noise, it cannot yet be universally accepted as a dependable source of TRNG entropy.

The influence of jet on aerodynamic noise in the pantograph area at different sinking heights

The influence mechanism of jet on aerodynamic noise control in the pantograph region at different sinking heights was numerically studied using an Improved Delayed Detached Eddy Simulation (IDDES) model and the Ffowcs Williams-Hawkings (FW-H) equation. Active flow control was achieved by setting jet slots at the leading edge of the cavity to predict the noise generated by the jet itself. The results showed that in the pantograph region with a sinking height of 500 mm, the shear layer was lifted by the jet, which prevented high-speed turbulence caused by flow separation from entering the cavity. Therefore, the model with jet control device reduced the overall far-field noise on the side of the pantograph by 1.6 ∼ 3.9 dB. Through flow field analysis, for the pantograph region where flow separation occurs in the front of the cavity, the jet needs to lift the shear layer enough to cross the front of the pantograph and prevent flow separation, thereby reducing the aerodynamic noise. For the pantograph region without flow separation in the front of the cavity, the jet may introduce additional noise sources, deteriorating the aerodynamic noise in the pantograph region.

Efficient and Robust Arabic Automotive Speech Command Recognition System

The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive speech recognition system. Our system aims to enhance the driving experience to make it comfortable and safe while assisting individuals with disabilities. We created a speech dataset comprising 20 commonly used car commands. It consists of 5600 instances collected from Moroccan contributors and recorded in clean and noisy environments to increase its representativity. We used MFCC, weighted MFCC, and Spectral Subband Centroids (SSC) for feature extraction, as they demonstrated promising results in noisy settings. For classifier construction, we proposed a hybrid architecture, consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN). Training our proposed model with WMFCC and SSC features achieved an accuracy of 98.48%, outperforming all baseline models we trained and outperforming the existing solutions in the state-of-the-art literature. Moreover, it shows promising results in a clean and noisy environment and maintains resilience to additive Gaussian noise while using few computational resources.

The ergodicity and uniform large deviations for the 1D stochastic Landau-Lifshitz-Bloch equation

In this article, we consider two aspects asymptotic behaviors for the 1D stochastic Landau-Lifshitz-Bloch equation driven by additive noise including the unique ergodicity and the uniform large deviations. Using a deterministic argument relying on the exponential moment and exponential stability estimates, we first establish the unique ergodicity of invariant measure in H 1. Then, the uniform large deviations is established in C ( [ 0 , T ] ; H 1 ) by the uniform contraction principle.

Feedback Capacity of the Continuous-Time ARMA(1,1) Gaussian Channel

Feedback Capacity of the Continuous-Time ARMA(1,1) Gaussian Channel

Multifunctional Image Enhancement Tool with Image Processing Methods

The success of image-based machine learning and deep learning algorithms depends on the amount and variety of data sets. The richness and heterogeneity of the dataset has a direct impact on the capacity of the model to generalize to new, previously unknown data. Obtaining a large number and variety of images is a very difficult and laborious process, especially for practical applications to be developed in areas such as health, industry and agriculture. If this difficulty cannot be overcome, the performance of machine learning models may decrease or may not reach the desired level at all. This problem may lead to problems such as poor performance or overfitting of the developed models. To solve this problem, data augmentation techniques are used to increase the amount and variety of data sets. These strategies help to provide the necessary data set for the model to learn more successfully. In this publication, we present an image improvement tool that is specifically developed for academics working with limited data sets. This tool uses a variety of data augmentation techniques, including rotation, zooming, panning, noise addition, and blurring, to expand the scale and diversity of available data. This application offers a realistic method for dealing with the issues of limited data availability in machine learning research.

Data-Driven Approach to the Development of an Aeroelastic Flutter Precursor

This paper presents a novel algorithm for aeroelastic flutter early detection. Two new features for flutter onset detection are presented. Flutter early warning is accomplished using only measured signals, with essentially no prior knowledge needed about the aircraft or the flutter mechanism involved. The algorithm consists of three stages: 1) extraction of regularity features, 2) calibration by addition of white noise to nominal measurements, and 3) thresholding. Four types of datasets were used: a) synthetic data, b) simulated data generated using aeroelastic response simulations to stochastic gusts, c) measured data from a wind tunnel experiment, and d) flight test data including actual flutter onsets. The algorithm was shown to be able to flag an impending flutter event before critical onset occurs. (The Python code for paper is available at https://github.com/bmeivar/flutter.)

Framework for Image Denoising Employing Different Thresholding Techniques

Background: Noise represents a lack of data in the image which can be removed using Image denoising. Image denoising can be achieved by Gaussian filtering, anisotropic filtering, wavelet Thresholding, etc. Objective: In this paper, authors have used Wavelet-based denoising because it can effectively remove both additive and multiplicative noise from images, and preserve fine details and edges in the image. Methods: The different thresholding techniques like Visu Shrink and Bayes Shrink for Hard Thresholding (HT) and Soft Thresholding (ST) employing different standard deviations ranging from 0.05-0.3 with a difference of 0.05 is used. Results: The peak signal-to-noise ratio (PSNR) is evaluated as a performance parameter. For grayscale images, the maximum value of PSNR is obtained as 29.483 dB while for RGB images, 34.324dB using Bayes Shrink considering ST at 0.05 variance is achieved. 2.2% improvement is observed for grayscale images while 8.6% improvement is observed for RGB images considering Bayes Shrink ST over Bayes Shrink HT. Conclusion: While comparing PSNR values of other Thresholding techniques, ST results better over HT. The PSNR values for images produced by Bayes Shrink are high which therefore states that the quality of reconstructed images is better for Bayes Shrink than Visu Shrink.