White Noise Research Articles

Encoder decoder-based Virtual Physically Unclonable Function for Internet of Things device authentication using split-learning

Internet of Things (IoT) networks have been deployed widely making device authentication a crucial requirement that poses challenges related to security vulnerabilities, power consumption, and maintenance overheads. While current cryptographic techniques secure device communication; storing keys in Non-Volatile Memory (NVM) poses challenges for edge devices. Physically Unclonable Functions (PUFs) offer robust hardware-based authentication but introduce complexities such as hardware production and conservation expenses and susceptibility to aging effects. This paper’s main contribution is a novel scheme based on split learning, utilizing an encoder–decoder architecture at the device and server nodes to first create a Virtual PUF (VPUF) that addresses the shortcomings of the hardware PUF and secondly perform device authentication. The proposed VPUF reduces maintenance and power demands compared to the hardware PUF while enhancing security by transmitting latent space representations of responses between the node and the server. Also, since the encoder is placed on the node, while the decoder is on the server, this approach further reduces the computational load and processing time on the resource-constrained node. The obtained results demonstrate the effectiveness of the proposed VPUF scheme in modeling the behavior of the hardware-based PUF. Additionally, we investigate the impact of Gaussian noise in the communication channel between the server and the node on the system performance. The obtained results further reveal that the achieved authentication accuracy of the proposed scheme is 100%, as measured by the validation rate of the legitimate nodes. This highlights the superior performance of the proposed scheme in emulating the capabilities of a hardware-based PUF while providing secure and efficient authentication in IoT networks.

Asymptotic behavior of non-autonomous fractional stochastic lattice FitzHugh–Nagumo system driven by linear mixed white noise

This paper is concerned with the asymptotic behavior of the non-autonomous fractional stochastic lattice FitzHugh–Nagumo system driven by the linear mixed white noise, which simultaneously contains linear additive noise and multiplicative noise. For the sake of the long-term behavior of the system we considered, we need to utilize a different Ornstein–Uhlenbeck transformation than the general one. First, the existence and uniqueness of pullback random attractors are demonstrated. Then, we prove the upper semicontinuity of random attractors when the intensity of noise approaches zero.

Human foot force informs balance control strategies when standing on a narrow beam.

Despite the abundance of studies on the control of standing balance, insights about the roles of biomechanics and neural control have been limited. Previous work introduced an analysis combining the direction and orientation of foot-ground forces. The "intersection point" of the lines of actions of these forces exhibited a consistent pattern across healthy, young subjects when computed for different frequency components of the center of pressure signal. To investigate the control strategy of quiet stance, we applied this intersection point analysis to experimental data of 15 healthy, young subjects balancing in tandem stance on a narrow beam and on the ground. Data from the sagittal and frontal planes were analyzed separately. The task was modeled as a double-inverted pendulum controlled by an optimal controller with torque-actuated ankle and hip joints and additive white noise. To test our prediction that the controller that minimized overall joint effort would yield the best fit across the tested conditions and planes of analyses, experimental results were compared with simulation outcomes. The controller that minimized overall effort produced the best fit in both balance conditions and planes of analyses. For some conditions, the relative penalty on the hip and ankle joints varied in a way relevant to the balance condition or to the plane of analysis. These results suggest that unimpaired quiet balance in a challenging environment can be best described by a controller that maintains minimal effort through the adjustment of relative ankle and hip joint torques. NEW & NOTEWORTHY This study explored balance control in humans during a challenging task using the novel intersection point analysis, based on foot-ground force direction and point of application. Experimental data of subjects standing on a narrow beam in tandem stance were compared with modeling results of a double-inverted pendulum. The analysis showed that individuals minimized effort by adjusting ankle and hip torques, shedding light on the interplay of biomechanics and neural control in maintaining balance.

Frequency and damping factor estimation of damped sinusoid by using DFT and DTFT

An algorithm for estimating the frequency and damping factor of damped complex sinusoidal signal is presented. After the rough estimation of frequency by discrete Fourier transform (DFT), the fine estimation of frequency and damping factor is achieved by utilizing the DFT bin with the maximum magnitude and two arbitrarily spaced discrete time Fourier transform (DTFT) spectral lines located on the same side of the maximum DFT spectrum bin. Then the theoretical mean square error (MSE) in additive white noise is analyzed. Simulation results show that the proposed algorithm tracks the Cramer-Rao lower bound (CRLB), and the parameters estimation accuracy is higher than the competing algorithms in most situations. The computational requirements are almost the same as several existing methods.

Data augmentation using a 1D-CNN model with MFCC/MFMC features for speech emotion recognition

Speech emotion recognition (SER) is attractive in several domains, such as automated translation, call centres, intelligent healthcare, and human–computer interaction. Deep learning models for emotion identification need considerable labelled data, which is only sometimes available in the SER industry. A database needs enough speech samples, good features, and a better classifier to identify emotions efficiently. This study uses data augmentation to enhance the amount of input voice samples and address the data shortage issue. The database capacity increases by adding white noise to the speech signals by data augmentation. In this work, the Mel-frequency Cepstral Coefficient (MFCC) and Mel-frequency Magnitude Coefficient (MFMC) features, along with a one-dimensional convolutional neural network (1D-CNN), are used to classify speech emotions. The datasets utilized to estimate the model's enactment were AESDD, CAFE, EmoDB, IEMOCAP, and MESD. The data augmentation with the 1D-CNN (MFMC) model performed best, with an average accuracy of 99.2% for AESDD, 99.5% for CAFE, 97.5% for EmoDB, 92.4% for IEMOCAP and 96.9% for the MESD database. The proposed 1D-CNN (MFMC) with data augmentation outperforms the 1D-CNN (MFCC) without data augmentation in emotion recognition.

Time Series Methods to Assess the Impact of Regulatory Action: A Study of UK Primary Care and Hospital Data on the Use of Fluoroquinolones.

To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage. Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19-59 and ≥ 60 years old). Seasonality-adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively. Post-RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19-59) and 19.29% ( 60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19-59) and 41.47% ( 60) decreases. Slope analysis indicated increases for East Scotland (19-59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's 60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19-59 group. Both models showed no post-modelling autocorrelations across databases, yet Barts Health's residuals were non-normally distributed with non-constant variance. Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non-normal white noise using both methods.

Optical soliton solutions of the stochastic resonant nonlinear Schrödinger equation with spatio temporal and inter-modal dispersion under generalized Kudryashov's law non-linearity

This article investigates optical soliton solutions within the stochastic resonant nonlinear Schrödinger equation (SRNLSE). This equation incorporates both spatio-temporal dispersion (STD) and inter-modal dispersion (IMD), along with multiplicative white noise and generalized Kudryashov's law non-linearity. By applying the extended auxiliary equation method, we identify a range of soliton solutions, including bright, dark, and singular solitons. Additionally, we derive solutions that take the form of Jacobi elliptic functions, Weierstrass elliptic functions, and periodic wave functions. The study provides significant insights into soliton dynamics within nonlinear optical systems affected by stochastic influences and complex dispersion interactions. Specifically, it highlights how the interplay of STD and IMD, coupled with the presence of multiplicative noise, shapes the behavior of solitons. Moreover, we delve into the effects of multiplicative noise on the exact solutions of the NLSE using the Maple software. Our analysis reveals that multiplicative noise, interpreted in the Ito sense, plays a crucial role in stabilizing the soliton solutions, particularly maintaining their stability around the zero state. This finding underscores the importance of noise in influencing the stability and dynamics of solitons in optical systems.

Передача двоичных сообщений ортогонализованными частично перекрывающимися сигналами

Frequency efficiency, noise immunity and implementation complexity are the most important parameters for the transmission of binary messages. One of the directions for increasing frequency efficiency is the conscious or controlled introduction of intersymbol interference into the transmitted signal with an acceptable reduction in noise immunity and complication of implementation. The article analyzes the transmission of binary messages by orthogonalized narrow-band partially overlapping signals. The formation of the transmitted signal and processing of the received signal are considered. The probability of error when exposed to interference in the form of white noise is determined. The transmission noise immunity was assessed using simulation. The considered method of transmitting binary messages provides good frequency efficiency without the use of a spectrum shaper with high noise immunity.

Fluctuation-driven dynamics of liquid nano-threads with external hydrodynamic perturbations

Instability and rupture dynamics of a liquid nano-thread, subjected to external hydrodynamic perturbations, are captured by a stochastic lubrication equation (SLE) incorporating thermal fluctuations via Gaussian white noise. Linear instability analysis of the SLE is conducted to derive the spectra and distribution functions of thermal capillary waves influenced by external perturbations and thermal fluctuations. The SLE is also solved numerically using a second-order finite difference method with a correlated noise model. Both theoretical and numerical solutions, validated through molecular dynamics, indicate that surface tension forces due to specific external perturbations overcome the random effects of thermal fluctuations, determining both the thermal capillary waves and the evolution of perturbation growth. The results also show two distinct regimes: (i) the hydrodynamic regime, where external perturbations dominate, leading to uniform ruptures, and (ii) the thermal-fluctuation regime, where external perturbations are surpassed by thermal fluctuations, resulting in non-uniform ruptures. The transition between these regimes, modelled by a criterion developed from linear instability theory, exhibits a strong dependence on the amplitudes and wavenumbers of the external perturbations.

Multicarrier dual‐index permuted chaos shift keying system for efficiency improvement in chaotic communications

AbstractWe propose a multicarrier chaotic system that incorporates dual‐index and M‐ary modulation techniques. The proposed system improves the energy and spectrum efficiencies with a tradeoff in computational complexity. In addition, it applies index modulation to select the chaotic signal and permuted chaotic signals along with ‐ary modulated symbols. As a result, the spectral and energy efficiencies are increased in chaotic systems. Theoretical expressions are derived to calculate the bit error rate of the proposed system under additive white Gaussian noise and Rayleigh fading channels. The equations are validated through Monte Carlo simulations, whose results demonstrate a reduction in bit error rate for the proposed system. The data rate, spectral efficiency, energy efficiency, and computational complexity of the proposed system are analyzed and compared with those of existing multicarrier chaotic systems.

A Hybrid Approach for CT Image Noise Reduction Combining Method Noise-CNN and Shearlet Transform

The presence of gaussian noise commonly weakens the diagnostic precision of low-dose CT imaging. A novel CT image denoising technique that integrates the non-subsampled shearlet transform (NSST) with Bayesian thresholding, and incorporates a modern method noise Deep Convolutional neural network (DCNN) based post-processing operation on denoised images to strengthen low-dose CT imaging quality. The hybrid method commences with NSST and Bayesian thresholding to mitigate the initial noise while preserving crucial image features, such as corners and edges. The novel aspect of the proposed approach is its successive application of a DnCNN on initial denoised image, which learns and removes residual noise patterns from denoised images, thereby enhancing fine detail preservation. This dual-phase strategy addresses both noise suppression and image-detail preservation. The proposed technique is evaluated through the use of metrics, such as PSNR, SNR, SSIM, ED, and UIQI. The results demonstrate that the hybrid approach outperforms standard denoising techniques in preserving image quality and fine details.

Robust fusion filter for networked uncertain descriptor systems with colored noise and cyber-attacks

The robust fusion filtering problem of multi-sensor networked uncertain descriptor systems (NUDSs) with colored noise, uncertain noise variances and cyber-attacks is investigated. During data transmission in unreliable communication networks, the data can be maliciously attacked by attackers. In other words, the local filters (LFs) may receive false data or may not receive data because of the cyber-attacks. By adopting the singular value decomposition (SVD) method, the original NUDSs can be converted into two reduced-order subsystems with uncertain correlated fictitious white noises, and the cyber-attacks are transformed into the fictitious noises. Cross-covariance matrices between local filtering errors are derived. The robust LFs are obtained according to the minimax robust estimation principle. Under the linear unbiased minimum variance criterion, three weighted fusion algorithms are applied to fuse the LFs. For all allowable uncertainties of noise variances and cyber-attacks, the minimal upper bounds of covariance matrices of the local and distributed fusion filters are guaranteed. The proof of their robustness is established through the minimax estimation principle and Lyapunov equation method. Finally, the correctness and effectiveness of the proposed algorithms are verified by a circuit system example.

Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

Stationary distribution of a stochastic generalized SIRI epidemic model with reinfection and relapse

In this paper, we propose and investigate the stochastic SIRI epidemic model with two generalized incidence rate functions. We firstly study the existence and uniqueness of the globally positive solution to the stochastic SIRI model with positive initial value. Then we obtain sufficient conditions for the extinction of the disease in the stochastic epidemic model, and find that the large noise can make the disease die out exponentially. Meanwhile, we obtain that the solution to the stochastic model has a unique stationary distribution when R0s˜ is greater than one. Our results show that the intensity of white noise can affect the dynamical behaviors of the model. Finally, we use numerical simulation to illustrate theoretical results, and apply both the stochastic and deterministic models to analyze the outbreak of COVID-19 epidemic in Serbia.

Adaptive signal fusion for swashplate pump fault detection using bidirectional long short-term memory and wavelet scattering transform

In construction equipment, swashplate pumps are vital for uninterrupted operations. However, these pumps often experience multiple simultaneous faults. Current detection methods typically rely on a single signal, insufficient for accurately identifying multiple faults in this hydraulic system. To improve accuracy, we utilize multiple signals that provide comprehensive information, using an adaptive signal-level fusion technique. This method involves analyzing pressure, shaft torque, and swashplate torque signals to identify potential faults. The study employs a Bidirectional Long Short-Term Memory (BiLSTM) model to analyze each type of signal, assessing the model's ability to classify different faults. Once detection accuracy for each fault type is assessed, we propose using a weight matrix to adaptively fuse signals based on their assigned weights. Additionally, the Wavelet Scattering Transform (WST) is utilized for feature extraction from signals with low variance, and Bayesian optimization is applied to find the optimal settings for the BiLSTM model. The proposed approach is tested under various scenarios, including the impact of white Gaussian noise, to evaluate its effectiveness and stability. The results show that the adaptive signal-level fusion approach outperforms traditional methods and individual signal analyses in accurately and robustly detecting swashplate pump faults, highlighting its potential to significantly improve the reliability of construction equipment.

Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments.

Robustness evaluation of acceleration-based early rub detection methodologies with real fluid-induced noise

The early detection of rotor-casing rub allows to prevent permanent damage and even catastrophic failure of rotating machines, saving maintenance costs and decreasing downtime. Very often, aeroderivative gas turbines can be monitored only with accelerometers on the casing, which provide signals that contain both stochastic and deterministic noise that masks the vibration measurements. Although real vibration data from turbomachines present a background noise that is neither white nor Gaussian, the rule of thumb is to evaluate the robustness of fault detection under white signal noise, which may lead to estimation errors of such robustness. Here we evaluate and compare the performance of several signal processing methodologies for very early rub detection on accelerometer signals under fluid-induced signal noise due to turbulent fluid excitation. The independence between the fluid-induced vibrations and the rub and unbalance vibration was proved, and experimental data of fluid-induced noise were added as signal noise to clean rub signals. The robustness and sensitivity of rub detection under fluid-induced noise were calculated and compared. This study proves the superiority of reassigned time–frequency analysis methods such as the Wavelet Synchrosqueezed Transform for very early rub detection as compared with traditional time–frequency analysis methods and with the Fourier Transform. To the authors’ knowledge, this is the first time that non-Gaussian fluid-induced signal noise is used to evaluate fault detection performance.

World energy futures market efficiency and its determinants; evidence from white noise test based on block-wise wild bootstrap approach

ABSTRACT The study examines the time-varying efficiency of nine energy futures traded across different exchanges with varied trade settlement methods and pricing currency using the novel white noise test based on block-wsie wild bootstrap approach. The advantage of using this approach is that it relaxes the assumption of serial independence and the martingale difference sequence. Time-varying efficiency is observed for all nine energy futures consistent with the Adoptive Market Hypothesis (AMH). The three most (least) efficient energy futures based on efficiency ratio are Coal, Gasoline and NaturalGas (WTI, Brent and OmanCrude). Quantile regression analysis shows that the higher the level of illiquidity furthers the level of efficiency towards inefficiency (high level of efficiency) in the case of a less (more) efficient market. A mixed impact of volatility is observed across energy futures. No differential impact is found due to exchange, method of settlement and pricing currency. The robustness of the results is investigated, policy implications are discussed, and further research is recommended.

Protective impact of Betanin against noise and scrotal hyperthermia on testicular toxicity in Wistar rat: Role of apoptosis, oxidative stress and inflammation

The heat exposure and white noise can induce damage on reproductive organs. The main objective of this study is to observe, if betanin administration could ameliorate oxidative stress, apoptosis and inflammation in testis of rodents following noise and scrotal hyperthermia exposure. Wistar rats were divided into 6 groups; control, betanin, noise, hyperthermia and two treatment groups. Scrotal hyperthermia model was performed by heat exposure of rat testicular (43 °C) for 15 min and 3 times per weeks for 14 days. Noise induction model was done following exposure of rats with 100-dB noise level for 14 days and 8 h daily similar to real exposure condition in human. Betanin was administrated at the sub-effective dose (15 mg/kg) by gavage route for 4 weeks (5 times a week) to male rats. The animals were euthanized and testis were dissected and stored at −80 °C. Then, the oxidative stress biomarkers (MDA and GSH), apoptosis (cytochrome c & Annexin V), and inflammatory cytokines (TNF-α & IL-6) were measured by the real time polymerase chain reaction (RT-PCR) of testis collected samples. The data output demonstrates the impact of noise and hyperthermia in testicular toxicity induction by mitigating oxidative damage, apoptosis and inflammatory mediators. Following treatment with 15 mg/kg per day of betanin, lipid peroxidation and GSH content have been modulated, and TNF-α and IL-6 gene expression has been declined. Our results revealed that in Wistar rats, betanin displays protective effects against noise and scrotal hyperthermia-induced acute testicular toxicity through the inhibition of oxidative stress, apoptosis, and inflammation.

Research on a 3D Point Cloud Map Learning Algorithm Based on Point Normal Constraints.

Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local consistency and density of the point cloud normal vector. A feature map learning algorithm which integrates point normal constraints, Dirichlet energy, and coupled orthogonality bias terms is proposed. Specifically, the Dirichlet energy is employed to penalize the difference between neighboring normal vectors and combined with a coupled orthogonality bias term to enhance the orthogonality between the normal vectors and the subsurface, thereby enhancing the accuracy and robustness of the learned denoising of the feature maps. Additionally, to mitigate the effect of mixing noise, a point cloud density function is introduced to rapidly capture local feature correlations. In experimental findings on the anchor public dataset, the proposed method reduces the average mean square error (MSE) by 0.005 and 0.054 compared to the MRPCA and NLD algorithms, respectively. Moreover, it improves the average signal-to-noise ratio (SNR) by 0.13 DB and 2.14 DB compared to MRPCA and AWLOP, respectively. The proposed algorithm enhances computational efficiency by 27% compared to the RSLDM method. It not only removes mixed noise but also preserves the local geometric features of the point cloud, further improving computational efficiency.