Noise Variance Research Articles

SEISMONOISY: A Quasi-Real-Time Seismic Noise Network Monitoring System.

This paper introduces SEISMONOISY, an application designed for monitoring the spatiotemporal characteristic and variability of the seismic noise of an entire seismic network with a quasi-real-time monitoring approach. Actually, we have applied the developed system to monitor 12 seismic networks distributed throughout the Italian territory. These networks include the Rete Sismica Nazionale (RSN) as well as other regional networks with smaller coverage areas. Our noise monitoring system uses the methods of Spectral Power Density (PSD) and Probability Density Function (PDF) applied to 12 h long seismic traces in a 24 h cycle for each station, enabling the extrapolation of noise characteristics at seismic stations after a Seismic Noise Level Index (SNLI), which takes into account the global seismic noise model, is derived. The SNLI value can be used for different applications, including network performance evaluation, the identification of operational problems, site selection for new installations, and for scientific research applications (e.g., volcano monitoring, identification of active seismic sequences, etc.). Additionally, it aids in studying the main noise sources across different frequency bands and changes in the characteristics of background seismic noise over time.

Theory, validation, and improvement of four enhancement algorithms for repetitive impulses

Analyzing vibration of rotating machinery signals is a popular methodology derived on the potent tools provided by cyclostationary process theory. Among them, the autocorrelation function (ACF), the Fourier transform (FT), the short-time Fourier transform (STFT), and the time synchronous average (TSA) are widely used for enhancing and analyze random cyclostationary components, i.e., repetitive impulses. This paper aims to thoroughly investigate these techniques through a comprehensive scientific research process. Initially, theoretical analyses are conducted to discuss their noise suppression principles, on the foundation of mean and variance analysis. Results indicate that for Gaussian random noise, the variance of the noise linked to the ACF, the FT, and TSA almost decreases linearly with signal length, while the noise variance of the short-time Fourier transform does so with window length. Therefore, longer signal lengths for the ACF, the FT, and the TSA, or extended window lengths for the STFT, improve their denoising performance. Subsequently, a series of simulated signals are generated to validate these findings, complemented by public datasets for further verification. Finally, the paper discusses and concludes with enhancement approaches derived from these four methodologies.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

A Combined UWB/IMU Localization Method with Improved CKF.

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg-Marquardt algorithm (L-M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.

Noise reduction by bias cooling in gated Si/Six Ge1−x quantum dots

Silicon–germanium heterostructures are a promising quantum circuit platform, but crucial aspects, such as the long-term charge dynamics and cooldown-to-cooldown variations, are still widely unexplored quantitatively. In this Letter, we present the results of an extensive bias cooling study performed on gated silicon–germanium quantum dots with an Al2O3 dielectric. Over 80 cooldowns were performed in the course of our investigations. The performance of the devices is assessed by low-frequency charge noise measurements in the band of 200 μHz to 10 mHz. We measure the total noise power as a function of the applied voltage during cooldown in four different devices and find a minimum in noise at 0.7 V bias cooling voltage for all observed samples. We manage to decrease the total noise power median by a factor of 6 and compute a reduced tunneling current density using Schrödinger–Poisson simulations. Furthermore, we show the variation in noise from the same device in the course of eleven different cooldowns performed under the nominally same conditions.

Effect of the mixing of s-wave and chiral p-wave pairings on electrical shot noise properties of normal metal/superconductor tunnel junctions

Abstract We study theoretically the electrical shot noise properties of tunnel junctions between a normal metal and a superconductor with the mixture of singlet $s$-wave and chiral triplet $p$-wave pairing due to broken inversion symmetry. We investigate how the shot noise properties vary as the relative amplitude between the two parity components in the pairing potential is changed. It is demonstrated that some characteristics of the electrical shot noise properties of such tunnel junctions may depend sensitively on the relative amplitude between the two parity components in the pairing potential, and some significant changes may occur in the electrical shot noise properties when the relative amplitude between the two parity components is varied from the singlet $s$-wave pairing dominated regime to the chiral triplet $p$-wave pairing dominated regime. In the chiral triplet $p$-wave pairing dominated regime, the ratio of noise power to electric current is close to $2e$ both in the in-gap and in the out-gap region. In the singlet $s$-wave pairing dominated regime, the value of this ratio is close to $4e$ in the inner gap region but may reduce to about $2e$ in the outer gap region as the relative amplitude of the chiral triplet pairing component is increased. The variations of the differential shot noise with the bias voltage also exhibit some significantly different features in different regimes. Such different features can serve as useful diagnostic tools for the determination of the relative magnitude of the two parity components in the pairing potential.

Learning capabilities to resolve tilt-translation ambiguity in goldfish.

Spatial orientation refers to the perception of relative location and self-motion in space. The accurate formation of spatial orientation is essential for animals to survive and interact safely with their environment. The formation of spatial orientation involves the integration of sensory inputs from the vestibular, visual, and proprioceptive systems. Vestibular organs function as specialized head motion sensors, providing information regarding angular velocity and linear acceleration via the semicircular canals and otoliths, respectively. However, because forces arising from the linear acceleration (translation) and inclination relative to the gravitational axis (tilt) are equivalent, they are indistinguishable by accelerometers, including otoliths. This is commonly referred to as the tilt - translation ambiguity, which can occasionally lead to the misinterpretation of translation as a tilt. The major theoretical frameworks addressing this issue have proposed that the interpretation of tilt versus translation may be contingent on an animal's previous experiences of motion. However, empirical confirmation of this hypothesis is lacking. In this study, we conducted a behavioral experiment using goldfish to investigate how an animal's motion experience influences its interpretation of tilt vs. translation. We examined a reflexive eye movement called the vestibulo-ocular reflex (VOR), which compensatory-rotates the eyes in response to head motion and is known to reflect an animal's three-dimensional head motion estimate. We demonstrated that the VORs of naïve goldfish do not differentiate between translation and tilt at 0.5 Hz. However, following prolonged visual-translation training, which provided appropriate visual stimulation in conjunction with translational head motion, the VORs were capable of distinguishing between the two types of head motion within 3 h. These results were replicated using the Kalman filter model of spatial orientation, which incorporated the variable variance of process noise corresponding to the accumulated motion experience. Based on these experimental and computational findings, we discuss the neural mechanism underlying the resolution of tilt-translation ambiguity within a context analogous to, yet distinct from, previous cross-axis VOR adaptations.

Optimal lower bound on eigenvector overlaps for non-Hermitian random matrices

We consider large non-Hermitian N×N matrices with an additive independent, identically distributed (i.i.d.) noise for each matrix elements. We show that already a small noise of variance 1/N completely thermalises the bulk singular vectors, in particular they satisfy the strong form of Quantum Unique Ergodicity (QUE) with an optimal speed of convergence. In physics terms, we thus extend the Eigenstate Thermalisation Hypothesis, formulated originally by Deutsch [34] and proven for Wigner matrices in [23], to arbitrary non-Hermitian matrices with an i.i.d. noise. As a consequence we obtain an optimal lower bound on the diagonal overlaps of the corresponding non-Hermitian eigenvectors. This quantity, also known as the (square of the) eigenvalue condition number measuring the sensitivity of the eigenvalue to small perturbations, has notoriously escaped rigorous treatment beyond the explicitly computable Ginibre ensemble apart from the very recent upper bounds given in [7] and [45]. As a key tool, we develop a new systematic decomposition of general observables in random matrix theory that governs the size of products of resolvents with deterministic matrices in between.

Mixture models for simultaneous classification and reduction of three-way data

AbstractFinite mixture of Gaussians are often used to classify two- (units and variables) or three- (units, variables and occasions) way data. However, two issues arise: model complexity and capturing the true cluster structure. Indeed, a large number of variables and/or occasions implies a large number of model parameters; while the existence of noise variables (and/or occasions) could mask the true cluster structure. The approach adopted in the present paper is to reduce the number of model parameters by identifying a sub-space containing the information needed to classify the observations. This should also help in identifying noise variables and/or occasions. The maximum likelihood model estimation is carried out through an EM-like algorithm. The effectiveness of the proposal is assessed through a simulation study and an application to real data.

Learning a complex network representation for shape classification

Shape contour is a key low-level characteristic, making shape description an important aspect in many computer vision problems, with several challenges such as variations in scale, rotation, and noise. In this paper, we introduce an approach for shape analysis and classification from binary images based on representations learned by applying Randomized Neural Networks (RNNs) on feature maps derived from a Complex Network (CN) framework. Our approach models the contour in a complex network and computes their topological measures using a dynamic evolution strategy. This evolution of the CN provides significant information into the physical aspects of the shape’s contour. Therefore, we propose embedding the topological measures computed from the dynamics of the CN evolution into a matrix representation, which we have named the Topological Feature Map (TFM). Then, we employ the RNN to learn representations from the TFM through a sliding window strategy. The proposed representation is formed by the learned weights between the hidden and output layers of the RNN. Our experimental results show performance improvements in shape classification using the proposed method across two generic shape datasets. We also applied our approach to the recognition of plant leaves, achieving high performance in this challenging task. Furthermore, the proposed approach has demonstrated robustness to noise and invariance to transformations in scale and orientation of the shapes.

Forgered Image Perception System using CNN Algorithms

The progression and diversification of methodologies in digital image forensics and manipulation detection are evident in existing work, presenting innovative techniques tailored to specific aspects of forgery detection. These methodologies encompass various challenges such as copy-move forgery, manipulation detection, demosaicing artifacts, image splicing, edge detection, and compression analysis. Together, these studies exemplify ongoing efforts to enhance the accuracy, efficiency, and reliability of forgery detection methods within the constantly evolving landscape of digital image manipulation. The proposed image forgery detection system represents a pioneering advancement, surpassing the limitations of conventional methodologies. It integrates a comprehensive array of detection techniques, including copy-move analysis, color filter array scrutiny, noise variance inconsistency detection, and double JPEG artifact identification. These techniques are bolstered by Convolutional Neural Networks (CNNs). This innovative fusion not only addresses the limitations inherent in existing systems but also signifies a significant leap forward in functionality and efficacy. By synergistically leveraging the strengths of each detection method within a CNN framework, our approach promises heightened accuracy, robustness, and adaptability in discerning even the most sophisticated forms of image tampering. Keywords— Image Tampering, Double JPEG Compression, Demosaicing Artifacts, Recursive Edge Detection, Copy-Move Forgery Detection.

Heat wave time of emergence patterns: a matter of definition

Hot extremes, such as heatwaves, have been associated with health, economic, and ecosystem-wide impacts. The timing of emergence of changes in extremes due to anthropogenic climate change is a topic of broad scientific and societal importance. While various studies have estimated the timing and impacts of heatwaves, the definitional aspect of a heatwave in determining the relative time of emergence has not been addressed. We adopt two commonly used definitions of heatwave employed in different reports of the Intergovernmental Panel on Climate Change (IPCC) to evaluate the time at which the frequency of heatwaves becomes detectably different from the historical baseline using an ensemble of 10 GCMS from the CMIP6 archives forced by the SSP2–4.5 concentration pathway. For a heatwave definition of sustained temperatures exceeding 5 °C warmer above the historical climatology, time of emergence is earliest in high latitudes over land and displays correlation with the signal (amount of warming) and noise (variability). In contrast, for a heatwave definition of sustained temperatures exceeding the 90th percentile of historical climatology, time of emergence is earliest in low latitude regions and is correlated with the signal to noise ratio. This work underscores the importance of metric choice in estimating the timing of new climate regimes and that metric selection for informing adaptation timing should thus be tailored to the regional context.

Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering.

Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.

Identification and distributed fusion filter for multi-sensor networked systems with stochastic deception attacks

In the multi-sensor networked systems, each sensor produces its local state estimate based on its own observations, and then sends it to a fusion center (FC) for fusion estimation. During transmitting local state estimates to the fusion center, the data may suffer from stochastic deception attacks from malicious attackers, where the probabilities of attacks and the variances of attack noises are unknown. Using a correlation function method (CFM), the probabilities of attacks and the variances of attack noises in individual channels are identified in parallel. The possibly attacked local state estimate is filtered to reproduce local state estimate in the FC. Using the matrix-weighted fusion algorithm in the linear unbiased minimum variance (LUMV) criterion, a distributed fusion filter is performed based on the reproduced local state estimates. A simulation example illustrates the validity of the algorithms.

VIO-DualProNet: Visual-inertial odometry with learning based process noise covariance

Visual-inertial odometry (VIO) is a vital technique used in robotics, augmented reality, and autonomous vehicles. It combines visual and inertial measurements to accurately estimate position and orientation. Existing VIO methods assume a fixed noise covariance for the inertial uncertainty. However, accurately determining in real-time the noise variance of the inertial sensors presents a significant challenge as the uncertainty changes throughout the operation leading to suboptimal performance and reduced accuracy. To circumvent this, we propose VIO-DualProNet, a novel approach that utilizes deep learning methods to dynamically estimate the inertial measurement unit (IMU) noise uncertainty in real-time. By designing and training a deep neural network to predict inertial noise uncertainty using only inertial sensor measurements, and integrating it into the VINS-Mono algorithm, we demonstrate a substantial improvement in accuracy and robustness. Our system outperformed constant covariance methods in 9 out of 11 test sequences, with an average improvement of 25% compared to the baseline and a 12.5% improvement over the best constant covariance combination.

Fault Diagnosis of Rotating Machinery Bearings Based on Multi-Scale Attention Feature Fusion under Few Shot and Complex Working Conditions

Accidents caused by failures in rotating machinery bearings have heightened attention in bearing failure diagnosis. Scholars have explored methods to build fault diagnosis models to tackle the challenge of creating diagnostic models with few bearing failure samples available. However, many diagnostic models may suffer from overfitting issues under insufficient samples, affecting fault diagnosis performance. Additionally, rotating machinery operates under changing, complex conditions and noise interference, further deteriorating the effectiveness of fault diagnosis. This paper proposes a fault diagnosis model based on multi-scale attentional feature fusion (FD-MSAFF) to address the issue above. This model comprises a multi-scale feature extraction module, an attentional feature fusion module, and an MMD-based weighted prototype network. The FD-MSAFF model improves few-shot learning by using its multi-scale feature extraction and depth-wise separable attention modules to blend multi-scale features effectively with contextual information. It also tackles classification issues in few-shot sizes with its MMD-weighted prototype network, which is less susceptible to noise. Simulated tests under complex scenarios, such as changing conditions, noise variations, cross-bearing conditions, and comparisons with other algorithms, have proven the FD-MSAFF model's superior accuracy and generalization in diagnosing bearing faults in rotating machinery.

Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments

AbstractTo solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3‐FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused‐MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi‐damage recognition proficiency for concrete bridges across varying field‐of‐view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real‐time multi‐damage detection for concrete bridges under complex environments.

Two-Phase Speckle Noise Removal in US Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal Bayes Threshold

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance[Formula: see text]0.1.

Improvement and optimization of coal dust concentration detection technology: Based on the 3σ criterion and the kalman filtering composite algorithm

Due to the inherent time delay in dust concentration data transmission, dust reduction equipment is unable to respond according to the current moment's dust concentration. Hence, the focus of mine dust concentration detection technology extends beyond the current dust concentration to accurately predicting the next moment's dust concentration, allowing a response window for dust reduction equipment, leading to more optimal dust reduction effects. This necessitates the introduction of the Kalman filtering algorithm. Despite the traditional single Kalman filtering algorithm's ability to make certain dust concentration predictions, it is constrained by high-temperature, high-humidity underground environments and electromagnetic pulse signal interference from fan and coal mining machine operations, with an error rate reaching up to 40 %, seriously disrupting underground dust reduction equipment. However, the composite Kalman filtering algorithm incorporating the 3σ rule effectively eliminates bad values and removes instability from the underground environment and noise, rendering it applicable to most complex environments, providing accurate predicted concentrations for dust reduction equipment, and boosting overall universality. This study designed a fusion algorithm based on the Pauta criterion and Kalman filter to further optimize the dust concentration detection technology based on electrostatic induction. The sensor's model and measuring principle were first analyzed, and the initial calibration and precision testing of the sensor using the experimental system were completed. The dust concentration fluctuation characteristics were then modeled with Matlab, and the noise variance was estimated to process and filter the dust concentration data with the Pauta criterion, Kalman filter, and fusion algorithm respectively. The accuracy of the dust concentration sensor was tested again after applying the fusion algorithm, which proved to significantly enhance the stability and accuracy of the electrostatic induction-based dust concentration sensor. This study has critical implications for coal mine dust concentration monitoring and coal miner occupational health protection.

Comparative experimental study of different fetch distances, interpolation algorithms, source substrate noise, sampling point counts, and resolution for NF of BDFA

Noise figure (NF) is a key metric for Fiber amplifier (FA), which affects the performance of signal transmission. The main factors affecting the NF are related to the amplified spontaneous emission (ASE) characteristics of the active fiber itself. However, during our study of NF, we found some easily overlooked factors that can have a significant impact on the results, such as the fetch distances and interpolation algorithm for calculating the ASE where the signal wavelength is located. Further, to date, we have also found little relevant research on whether the sampling characteristics of the instrument and the substrate noise contribute to the NF. On the other hand, bismuth-doped fiber amplifiers (BDFA) have developed rapidly in recent years. In this work, we use BDFA as an example, using different fetch distances, interpolation algorithms, substrate noise, and sampling variables (resolution and point counts), to comprehensively compare and analyze the impact caused by NF. The experimental results suggest that the distance between the sampling locations on either side of the signal and the center wavelength must exceed the signal’s bandwidth to obtain the actual NF; Linear interpolation and cubic interpolation are more suitable for calculating the NF; Even using a high power input signal of 0 dBm, the NF is only degraded by about 1.8% compared to the case where the substrate noise is ignored; In addition, the effect of different sampling point counts and resolutions have less influence on the NF. We believe this work provides a reliable reference value for calculating the accuracy of NF and will be useful for practical applications of active-fiber amplifiers.