White Noise Research Articles

A grey incidence-based algorithm for optimization of accelerated degradation test

PurposeThe purpose of this paper is to optimize the degradation test for products subject to multiple types of inherent stresses and external random shocks. The mechanism that shows how the variables to be optimized influence the considered multiple objectives is also aimed to be explored by using the grey incidence analysis (GIA) model.Design/methodology/approachThe Gamma process is employed to model the influences of different types of stresses and external random shocks. The GIA model is introduced to transfer multiple considered objectives as a comprehensive degree of grey incidence. The particle swarm optimization is integrated to search the globally optimal value of the characteristic variables to be optimized.FindingsThe acceleration of tested stresses and external random shocks both make the engineering systems become more vulnerable to the inherent degradation. And, the Kriging model can provide guidance of searching the optimal values of test characteristic variables and mitigate the computation burden. The grey incidence model can make the optimization focused and improve the optimality of objective values.Originality/valueThe proposed method can effectively overcome the drawbacks brought by the limitation of test data and can specify the dependence strength between the inherent degradation and external random shock. The computation cost and accuracy of optimization can be simultaneously ensured by the proposed model.

MITS-GAN: Safeguarding medical imaging from tampering with generative adversarial networks

The progress in generative models, particularly Generative Adversarial Networks (GANs), opened new possibilities for image generation but raised concerns about potential malicious uses, especially in sensitive areas like medical imaging. This study introduces MITS-GAN, a novel approach to prevent tampering in medical images, with a specific focus on CT scans. The approach disrupts the output of the attacker’s CT-GAN architecture by introducing finely tuned perturbations that are imperceptible to the human eye. Specifically, the proposed approach involves the introduction of appropriate Gaussian noise to the input as a protective measure against various attacks. Our method aims to enhance tamper resistance, comparing favorably to existing techniques. Experimental results on a CT scan demonstrate MITS-GAN’s superior performance, emphasizing its ability to generate tamper-resistant images with negligible artifacts. As image tampering in medical domains poses life-threatening risks, our proactive approach contributes to the responsible and ethical use of generative models. This work provides a foundation for future research in countering cyber threats in medical imaging. Models and codes are publicly available.11https://iplab.dmi.unict.it/MITS-GAN-2024/.

PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.

A Violet‐Light‐Responsive ReRAM Based on Zn2SnO4/Ga2O3 Heterojunction as an Artificial Synapse for Visual Sensory and In‐Memory Computing

AbstractDue to the imitation of the neural functionalities of the human brain via optical modulation of resistance states, photoelectric resistive random access memory (ReRAM) devices attract extensive attraction for synaptic electronics and in‐memory computing applications. In this work, a photoelectric synaptic ReRAM (PSR) of the structure of ITO/Zn2SnO4/Ga2O3/ITO/glass with a simple fabrication process is reported to imitate brain plasticity. Electrically induced long‐term potentiation/depression (LTP/D) behavior indicates the fulfillment of the fundamental requirement of artificial neuron devices. Classification of three‐channeled images corrupted with different levels (0.15–0.9) of Gaussian noise is achieved by simulating a convolutional neural network (CNN). The violet light (405 nm) illumination generates excitatory post synaptic current (EPSC), which is influenced by the persistent photoconductivity (PPC) effect after discontinuing the optical excitation. As an artificial neuron device, PSR is able to imitate some basic neural functions such as multi‐levels of photoelectric memory with linearly increasing trend, and learning‐forgetting‐relearning behavior. The same device also shows the emulation of visual persistency of optic nerve and skin‐damage warning. This device executes high‐pass filtering function and demonstrates its potential in the image‐sharpening process. These findings provide an avenue to develop oxide semiconductor‐based multifunctional synaptic devices for advanced in‐memory photoelectric systems.

AWDP-FL: An Adaptive Differential Privacy Federated Learning Framework

Data security and user privacy concerns are receiving increasing attention. Federated learning models based on differential privacy offer a distributed machine learning framework that protects data privacy. However, the noise introduced by the differential privacy mechanism may affect the model’s usability, especially when reasonable gradient clipping is absent. Fluctuations in the gradients can lead to issues like gradient explosion, compromising training stability and potentially leaking privacy. Therefore, gradient clipping has become a crucial method for protecting both model performance and data privacy. To balance privacy protection and model performance, we propose the Adaptive Weight-Based Differential Privacy Federated Learning (AWDP-FL) framework, which processes model gradient parameters at the neural network layer level. First, by designing and recording the change trends of two-layer historical gradient sequences, we analyze and predict gradient variations in the current iteration and calculate the corresponding weight values. Then, based on these weights, we perform adaptive gradient clipping for each data point in each training batch, which is followed by gradient momentum updates based on the third moment. Before uploading the parameters, Gaussian noise is added to protect privacy while maintaining model accuracy. Theoretical analysis and experimental results validate the effectiveness of this framework under strong privacy constraints.

Cyberactivism and Social and Political Participation Regarding the War in Ukraine: Analysis of Social Interactions in Spanish on Twitter

To evaluate and understand the dynamics of citizen participation in social networks concerning the war in Ukraine and delve into the phenomenon of cyberactivism, this study focuses on the conversation generated in Spanish around the conflict on Twitter. The research analyses 1,138,747 original tweets to investigate the general characteristics of the conversation, the user interaction patterns, and the creation and structure of communities and to determine the connecting factors. The study employs machine learning and artificial intelligence techniques via the Graphext data analytics tool. The conversation volume is very high, but the network of interactions is characterized by being unstructured and dominated by white noise, with disjointed interactions setting the tone of the conversation. Verified accounts and emotionality stand out as factors of connection and interconnection in the network. This case study highlights a form of social and political participation oriented towards visibility and information about the cause at a level of deliberation and debate that shows empathy and sympathy in a context close to slacktivism.

Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors

ABSTRACTAgricultural Internet of Things has become one of the most important data sources of agricultural big data. However, the wireless sensors equipped with agricultural Internet of Things is affected by many factors, and anomalous data inevitably exists during the data collection process. The existence of anomalous data leads to the deterioration of the agricultural data quality, which hinders the efficient development of agricultural data analysis. In order to detect anomalous data accurately for agricultural wireless sensors, in this article, we propose an anomaly detection model that combines multimodal fusion and error reconstruction. The model first adds Gaussian noise to the input sequence and converts it into standard time series and image data. Subsequently, it is processed by different modal encoders and fuses the image and sequence modal data using Temporal Cross‐modal Attention module to enhance the perception of anomalous modal information. Finally, the fused data is reconstructed, and anomalous data are identified by the image and sequence decoders. Comparison experiments with several baselines prove the validity of the model proposed in this article, ablation experiments prove the necessity of the key modules in the model, and multiple sets of experiments are also designed to discuss the effects of hyperparameters.

Recurrence resonance - noise-enhanced dynamics in recurrent neural networks

Understanding how neural networks process information is a fundamental challenge in neuroscience and artificial intelligence. A pivotal question in this context is how external stimuli, particularly noise, influence the dynamics and information flow within these networks. Traditionally, noise is perceived as a hindrance to information processing, introducing randomness and diminishing the fidelity of neural signals. However, distinguishing noise from structured input uncovers a paradoxical insight: under specific conditions, noise can actually enhance information processing. This intriguing possibility prompts a deeper investigation into the nuanced role of noise within neural networks. In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states, has been shown to increase by adding ongoing white noise of some optimal strength to each of the neurons. However, the precise conditions for and mechanisms of this phenomenon called ‘recurrence resonance’ (RR) remain largely unexplored. Using Boltzmann machines of different sizes and with various types of weight matrices, we show that RR can generally occur when a system has multiple dynamical attractors, but is trapped in one or a few of them. In probabilistic networks, the phenomenon is bound to a suitable observation time scale, as the system could autonomously access its entire attractor landscape even without the help of external noise, given enough time. Yet, even in large systems, where time scales for observing RR in the full network become too long, the resonance can still be detected in small subsets of neurons. Finally, we show that short noise pulses can be used to transfer recurrent neural networks, both probabilistic and deterministic, between their dynamical attractors. Our results are relevant to the fields of reservoir computing and neuroscience, where controlled noise may turn out a key factor for efficient information processing leading to more robust and adaptable systems.

Distortion of overlapping memories relates to arousal and anxiety.

Everyday experiences often overlap, challenging our ability to maintain distinct episodic memories. One way to resolve such interference is by exaggerating subtle differences between remembered events, a phenomenon known as memory repulsion. Here, we tested whether repulsion is influenced by emotional arousal, when resolving memory interference is perhaps most needed. We adapted an existing paradigm in which participants repeatedly studied object-face associations. Participants studied two different-colored versions of each object: a to-be-tested "target" and its not-to-be-tested "competitor" pair mate. The level of interference between target and competitor pair mates was manipulated by making the object colors either highly similar or less similar, depending on the participant group. To manipulate arousal, the competitor object-face associations were preceded by either a neutral tone or an aversive and arousing burst of white noise. Memory distortion for the color of the target objects was tested after each study round to examine whether memory distortions emerge after learning. We found that participants with greater sound-induced pupil dilations, an index of physiological arousal, showed greater memory attraction of target colors towards highly similar competitor colors. Greater memory attraction was also correlated with greater memory interference in the last round of learning. Additionally, individuals who self-reported higher trait anxiety showed greater memory attraction when one of the overlapping memories was associated with something aversive. Our findings suggest that memories of similar neutral and arousing events may blur together after repeated exposures, especially in individuals who show higher arousal responses and symptoms of anxiety.

Passive control and damage assessment with a pendulum tuned mass damper using mean square equations and second-order eigen-perturbation techniques

This paper proposes a novel framework for damage detection and retuning of a Pendulum Tuned Mass Damper (PTMD) addressing effects of stochasticity via mean-square equations and establishing rapid change detection. The framework was made possible via a Stochastic Differential Equation (SDE) formulation of the equations of motion considering a two degree of freedom base and structure system equipped with a PTMD. The system was assumed to be excited at the base with Gaussian white noise and the solutions were computed using a stochastic integration scheme. A Second Order Eigen-Perturbation (SOPS) estimate is used to carry out detection of sudden stiffness reduction in the base floor of the structure. Once damage is detected, mean square equations on monitored displacements were used to retune the PTMD by adapting the length of the pendulum. It was found that damage could be detected rapidly and successive retunings were able to minimize the mean square displacement of the damaged structure and stabilise it over time.

Harmonic Components Isolation in Vehicle Vibrations: Enhancing Quarter-Car Model Analysis with an Extended Kalman Filter Approach

<div>This article addresses the essential task of understanding vibrations produced by vehicles to enhance the design of authentic laboratory tests. The article focuses on two primary sources of vibrations: those arising from vehicle–road surface interaction, which is largely random, and those emanating from the drivetrain, characterized as a summation of harmonics with a time-varying fundamental frequency. The method involves the application of the extended Kalman filter (EKF) paired with robust nonlinear least-squares (NLS) initialization to isolate the harmonic components effectively. Through a comprehensive analysis involving mean-square-error (MSE) evaluation via Monte Carlo simulation, considering additive white Gaussian noise (AWGN) and a two-degrees-of-freedom quarter-car model’s simulation response to the road, the research demonstrates the EKF’s proficiency. The results indicate the EKF’s capability to accommodate AWGN with a signal-to-noise ratio (SNR) up to 0 dB and road-induced random background vibrations up to an SNR of −3 dB, maintaining an MSE order of approximately 10<sup>−3</sup>.</div>

Study on vehicle state and tire–road friction coefficient estimation based on maximum correntropy generalized high-degree cubature Kalman filter

To cope with the challenges of inadequate precision and weak robustness of tire–road friction coefficient (TRFC) and vehicle state estimation under mixed Gaussian noise circumstances, this research puts forward a method combining maximum correntropy criterion (MCC) and generalized high-degree cubature Kalman filter (GHCKF). A coupled lateral and longitudinal vehicle model and the Dugoff tire model are established. The vehicle mass is estimated using the recursive least squares approach based on a forgetting factor (FFRLS). Low-cost onboard sensors are utilized to design observers for vehicle state and TRFC. The observers’ effectiveness is tested by Carsim/Simulink co-simulation tests under the conditions of sine steering input and steering wheel angle step input. The research results indicate that in handling mixed Gaussian noise environments, maximum correntropy generalized high-degree cubature Kalman filter (MCGHCKF) method outperforms maximum correntropy cubature Kalman filter (MCCKF), GHCKF, and cubature Kalman filter (CKF) methods concerning robustness and estimation accuracy, providing stable and reliable support for systems of vehicle active safety control.

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm (Pf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_f$$\\end{document}) of 10% while still ensuring the probability of detection (Pd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_d$$\\end{document}) of over 90%.

Exploring the influence of refuges and additional foods on predator–prey interactions amidst environmental stochasticity and water level fluctuations

By considering that the predators have some additional foods apart from the focal prey and the prey take refuge, a predator–prey model with the effect of seasonality in a fluctuating environment is investigated in this study. Gaussian white noises, created by random fluctuation of water level, are introduced on the prey’s growth rate and predator’s mortality rate. Both the deterministic and stochastic systems are analyzed mathematically as well as numerically. Our analytical findings show that the intensity of environmental noise, additional foods and prey refuge play significant roles in survival as well as extinction of both prey and predator populations in the aquatic system. Our numerical results show that the effectual level of additional foods, depending on water level, has positive as well as negative effects on the predator and prey populations. We find that both species strongly persist at certain water level and for the low intensity of noise. However, for an increased value of noise intensity, they persist weakly and then go to extinction. Overall, our findings show that the variations of water level together with additional food and white noise plays crucial roles in persistence and extinction of species in the ecosystem.

Blind image authentication using singular value decomposition with enhanced robustness by augmenting hamming code

Singular value decomposition (SVD) based image authentication has widespread applications for digital media protection including forensic and medical domains due to the high stability and robustness of singular values under small perturbations. However, most of these techniques are non-blind and have high redundancies for the extraction of secret data. The proposed scheme employs a blind extraction strategy by utilizing the intrinsic relationship of coefficient pairs of the U matrix of the host image blocks. This relationship is utilized to hide a secret binary bit after computing a threshold value in constant time. The host image underwent block-wise decomposition and hash-based randomization of blocks, followed by block-wise SVD for embedding secret bits in the U matrix. Hamming coded watermark is embedded for high precision extraction under active attacks. High imperceptibility and strong robustness are observed under attacks when tested across general purpose as well as image datasets and the system outperforms many existing works. Experimental observation shows the average PSNR between the host and marked image and average NCC between embedded and extracted watermark to be 36.98 dB and 0.999 respectively for the SIPI dataset whereas 52.7 dB and 1 respectively for the X-Ray dataset. The system exhibited high robustness under Salt-and-pepper noise (SAPN), Gaussian Noise (GAUN), Cropping (CRP), Median Filtering (MF), Histogram Equalization (HEQ), and Jpeg Compression (JPC) with average NCC values of 0.998, 1, 0.932, 0.999 and 0.967 respectively for X-Ray dataset. The time complexity O (M × N) is observed for both embedding and extraction algorithms for a host image of size M × N.

A novel differential chaos shift keying system based on joint time slot and code index of carrier phase modulation

This paper proposes a novel differential chaos shift keying (DCSK) system that utilizes carrier phase modulation in combination with time slot and code index modulation, referred to as CP-TCIM-DCSK system, to achieve high data rate transmission. In the proposed CP-TCIM-DCSK system, the carrier modulated by the carrier phase is used to transmit the information-bearing signal. In addition to physically transmitted information bits, extra information bits are conveyed through time slot and Walsh code modulation, as well as carrier phase modulation, making full use of the benefits of multidimensional modulation. To successfully estimate carrier phase bits and code index bits, an effective joint detection algorithm for carrier phase and code index tailored for the CP-TCIM-DCSK system is introduced. The theoretical Bit Error Rate (BER) expressions for the CP-TCIM-DCSK scheme are derived for both Additive White Gaussian Noise (AWGN) and multipath Rayleigh fading channels. Furthermore, a comparison of data rates, energy efficiency, and complexity between the CP-TCIM-DCSK system and the most advanced systems in the same category at present is conducted. The results validate the accuracy of theoretical analysis through simulation and demonstrate that the proposed scheme outperforms its competitive systems in terms of BER performance.

Long-range cross-correlations between center of pressure velocity and colored noises provided during quiet standing

Unperceivable electrical noise stimulation has been applied to improve postural control through the enhancement of somatosensory feedback. It has been observed that stimulation with a pink noise (1/f) structure is more effective than stimulation with other noise structures. In addition, the 1/f structure embedded in the postural control system may have a superior effect on postural control stabilization. However, the direct relationship between the long-range correlations of the pink-noise signal applied to somatosensory receptors and those of the postural control system has not been elucidated. Thus, we aimed to explore a common long-range correlation factor shared in the time series of the provided noise and foot center of pressure (CoP) during quiet standing. Sixteen young adults stood quietly on the force platform for 65 s. Four noise conditions (no stimulation and stimulation of knee joints with white-, pink-, and red-noise-like signals) were employed during the standing trials. The detrending moving-average cross-correlation analysis revealed that in each of the anteroposterior and mediolateral directions, the CoP velocity time series displayed significant long-range cross-correlations with the white and pink noise signals provided at that time, whereas such an effect was not observed in the red noise signal. This result indicates that pink and white noise signals would alter the temporal behavior of the CoP during quiet standing, although the mechanism remains to be elucidated.

Comparing Kurtosis-adjusted weighted levels with other metrics to assess the risk of hearing loss from non-Gaussian noise exposures

Several studies have shown that impulsive noise can cause more damage to hearing than steady-state noise of equal energy. As a result, a large body of research has been devoted to evaluating the hazards of impulsive noise. However, work environments often have varying noise patterns, including Gaussian background noise combined with high-level transient noises contributing to the worker's daily dose. Thus, an energy-based noise metric underestimates the risk of hearing loss unless incorporating a temporal structure correction term. Kurtosis has been reported as an effective adjunct to energy for predicting hearing hazards from non-Gaussian noise exposures. In this work, recordings of real impulsive noise were conducted using a portable measuring system at a very high sampling frequency and using high-pressure microphones. Controlled synthetic non-Gaussian noise was generated using these impulsive signals and steady-state background noise. After exploring the detailed temporal structure of the noise, the daily exposure time has been determined using different metrics, including kurtosis-adjusted weighted levels, weighted peak levels, and auditory risk units. The examples presented here aim to demonstrate how kurtosis can help develop more accurate methods to prevent noise-induced hearing loss.

Fractal and Complex Patterns Existing in Music: Application to the Composition DIAPHONIES of Michael Paouris

This paper reports fractal patterns identified in the complex musical composition DIAPHONIES by Michael Paouris via power-law fractal analysis with sliding-windows of size 1024. From 7,647,232 analysed musical segments of DIAPHONIES, 3,222,832 (42.4%) are fractional Brownian motion (fBm) fractal segments and 4,424,400 (57.6%) are fractional Gaussian noise (fGn) stochastic ones. From the fBm fractal segments 295,294 (9.1%) exhibit strong persistency-P with power-law segments in the range of 2.3≤b≤3. These are the very strong fractal areas in DIAPHONIES. Numerous segments with strong antipersistency 1.7≤b<2 are reported together with segments with AP changes (1.7≤b<2.3). In DIAPHONIES continuous fractal fBm areas are dipped in non-fractal fGn areas of deterministic music. The results are within the fBm fractal areas reported in existing papers. Very importantly, the simple composition called Nocturnal-Angel by Michael Paouris, exhibited limited fBm areas of average b¯=1.98 (σ=0.3), namely of pure statistical, deterministic music, while simultaneously, the fractal analysis profile was completely different from the profiles of DIAPHONIES, hence reinforcing, the fractal findings of DIAPHONIES in relation to trivial music harmony.

Attention-based convolutional neural network for multi-channel active noise control

Multi-channel active noise control systems utilize multiple control units to cancel unwanted noise across different channels simultaneously. By employing adaptive filtering techniques, these systems can effectively reduce noise levels in complex environments, offering improved noise mitigation compared to single-channel active noise control approaches. The disadvantage of adaptive filtering in active noise control is its limited ability to accurately model nonlinearities present in real-world noise environments. In this work, we propose an attention-based convolutional neural network comprising convolutional, deconvolutional, and attention mechanism to approach multi-channel active noise control. The system simultaneously calculates multiple canceling signals to eliminate primary noises captured by error microphones. To evaluate the effectiveness of our proposed model, we conducted testing utilizing filtered white noise spanning various frequency bands. Experimental evaluations across various setups, including different loudspeaker and microphone configurations, as well as variations in secondary source positioning, are used to assess the performance of the proposed approach. Results demonstrate that the proposed attention-based convolutional neural network for multi-channel active noise control yields a noise reduction of 22.65 dB on wideband noise, while the conventional Filtered-x least mean square approach yields a noise reduction of 4.23 dB, indicating the effectiveness of the proposed approach.