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In a lot of engineering applications, one has to recover the temperature of a heat body having a surface that cannot be measured directly. This raises an inverse problem of determining the temperature on the surface of a body using interior measurements, which is called the sideways problem. Many papers investigated the problem (with or without fractional derivatives) by using the Cauchy data u(x0,t),ux(x0,t)$$ u\left({x}_0,t\right),{u}_x\left({x}_0,t\right) $$ measured at one interior point x=x0∈(0,L)$$ x={x}_0\in \left(0,L\right) $$ or using an interior data u(x0,t)$$ u\left({x}_0,t\right) $$ and the assumption limx→∞u(x,t)=0$$ {\lim}_{x\to \infty }u\left(x,t\right)=0 $$. However, the flux ux(x0,t)$$ {u}_x\left({x}_0,t\right) $$ is not easy to measure, and the temperature assumption at infinity is inappropriate for a bounded body. Hence, in the present paper, we consider a fractional sideways problem, in which the interior measurements at two interior point, namely, x=1$$ x=1 $$ and x=2$$ x=2 $$, are given by continuous data with deterministic noises and by discrete data contaminated with random noises. We show that the problem is severely ill‐posed and further constructs an a posteriori optimal truncation regularization method for the deterministic data, and we also construct a nonparametric regularization for the discrete random data. Numerical examples show that the proposed method works well. [ABSTRACT FROM AUTHOR]

In this article, we study consensus robustness and performance problems for continuous‐time multi‐agent systems. We consider first‐order unstable agents coordinated by an output feedback protocol over a network subject to an unknown, uncertain constant delay. Our objectives are twofold. First, we seek to determine the largest range of delay permissible so that the agents may achieve robustly consensus despite variation of the delay length, herein referred to as the delay consensus margin. Second, we attempt to determine consensus error performance quantified under an ℋ2$$ {\mathscr{H}}_2 $$ norm criterion, which measures the disruptive effect of random nodal noises on consensus. We consider both undirected and directed graphs. For undirected graphs, we obtain analytical results for the delay consensus margin and the consensus error performance, while for directed graphs, we develop computational results and analytical bounds. The results provide conceptual insights and exhibit how the agents' unstable pole, nonminimum phase zero, as well as the network topology and network delay may limit fundamentally the consensus robustness and performance of first‐order agents. [ABSTRACT FROM AUTHOR]

Random noise attenuation is a fundamental task in seismic data processing aimed at improving the signal‐to‐noise ratio of seismic data, thereby improving the efficiency and accuracy of subsequent seismic data processing and interpretation. To this end, model‐based and data‐driven seismic data denoising methods have been widely applied, including f–x deconvolution, K‐singular value decomposition, feed‐forward denoising convolutional neural network and U‐Net (an improved fully convolutional neural network structure), which have received widespread attention for their effectiveness in attenuating random noise. However, they often struggle with low‐signal‐to‐noise ratio data and complex noise environments, leading to poor performance and leakage of effective signals. To address these issues, we propose a novel approach for random noise attenuation. This approach employs a multi‐model stacking structure, where the parameters governing this structure are optimized using a particle swarm optimizer. In the model‐based denoising method, we choose the f–x deconvolution method, whereas in the data‐driven denoising method, we choose K‐singular value decomposition for shallow learning and U‐Net for deep learning as components of the multi‐model stacking structure. The optimal parameters for the multi‐model stacking structure are obtained using a particle swarm optimizer, guided by the proposed novel hybrid fitness function incorporating weighted signal‐to‐noise ratio, structural similarity and correlation parameters. Finally, the effectiveness of the proposed method is verified with three synthetic and two real seismic datasets. The results demonstrate that the proposed method is effective in attenuating random noise and outperforms the benchmark methods in denoising both synthetic and real seismic data. [ABSTRACT FROM AUTHOR]

Motivated by an important geophysical application, we analyze the nonlinear dynamics of the number of earthquakes per unit time in a given Earth's surface area. At first, we consider a dynamical model of earthquakes describing their rhythmic behavior with time delays. This model comprises different earthquake scenarios divided into three types (A, B, and C) accordingly to various system dynamics. We show that the deterministic system contains stable equilibria and a limit cycle whose size drastically depends on the production rate α$$ \alpha $$ of earthquakes and their time delay effect. As this takes place, the frequency of earthquakes possesses an oscillatory behavior dependent on α$$ \alpha $$. To study the role of α$$ \alpha $$ in more detail, we have introduced a white Gaussian noise in the governing equation. First of all, we have shown that the dynamical system is stochastically excitable, that is, it excites larger‐amplitude noise‐induced fluctuations in the frequency of earthquakes. In addition, these large‐amplitude stochastic fluctuations can alternate with small‐amplitude fluctuations over time. In other words, the frequency of earthquakes can change its amplitude in an irregular manner under the influence of white noise. Another important effect is how close the current value of α$$ \alpha $$ is to its bifurcation point. The closer this value is, the less noise generates large‐amplitude fluctuations in the earthquake frequency. [ABSTRACT FROM AUTHOR]

Many practical dynamic models always suffer from complex nonlinearities, parameter uncertainties and random noises. Such kind of models are always characterized by uncertain stochastic nonlinear systems. Besides, many factors will lead to dead‐zone actuator, which further adds more challenges to control engineering. This paper studies stabilization issue of stochastic systems with polynomial nonlinear conditions, unknown parameters and dead‐zone actuator. An adaptive control approach is proposed by exploring homogeneous domination control and using dynamic‐gain‐based adaptive design strategy. It shows that all the signals are bounded almost surely and the system states converge to the origin almost surely. The presented method is successfully applied to the mass spring mechanical system. [ABSTRACT FROM AUTHOR]

Rank‐reduction‐based simultaneous random noise attenuation and three‐dimensional seismic‐data interpolation has recently become a hot topic in reflection seismology. However, the rank of traditional methods is fixed without considering the variation of signal‐to‐noise ratio on different frequency components, leading to serious residual noise and further affecting the following processing and interpretation tasks. In addition, traditional methods also heavily rely on the application of singular value decomposition technique for rank reduction, which is proven to be computationally expensive for large‐scale data. Thus, a fast‐adaptive rank‐reduction method is proposed in this study. First, the information entropy theory is introduced to adaptively select the optimal rank at various frequencies by calculating the increment of singular entropy. Second, we propose a fast Random Block Krylov algorithm and a subspace multiplexing technique to replace the singular value decomposition algorithm used in traditional methods. The proposed method can significantly improve computational efficiency and yield better seismic‐data reconstruction performance than traditional methods. Applications of the proposed approach on both synthetic and field seismic data demonstrate its superior performance over a well‐known rank‐reduction‐based method, that is the random multi‐channel singular spectrum analysis, in terms of recovered signal‐to‐noise ratio and visual view. [ABSTRACT FROM AUTHOR]

Memristor‐based electronic memory have recently started commercialization, although its market size is small (~0.5%). Multiple studies claim their potential for hardware implementation of artificial neural networks, advanced data encryption, and high‐frequency switches for 5G/6G communication. Application aside, the performance and reliability of memristors need to be improved to increase their market size and fit technology standards. Multiple groups propose novel nano‐materials beyond phase‐change, metal‐oxides, and magnetic materials as resistive switching medium (e.g., two‐dimensional, nanowires, perovskites). However, most studies use characterization setups that are blind to critical phenomena in understanding charge transport across the devices. Here an advanced setup with high temporal resolution is used to analyze current noise, dielectric breakdown growth, and ambipolar resistive switching in memristors based on multilayer hexagonal boron nitride (h‐BN), one of the most promising novel nano‐materials for memristive applications. The random telegraph noise in pristine memristors and its evolution as the devices degrade, covering ~7 orders of magnitude in current with consistent observation, is studied. Additionally, an ambipolar switching regime with very low resistance down to 50Ω and its connection with a telegraph behavior with high/low current ratios >100, linked to a thermally‐driven disruption of a metallic nanofilament, is shown. [ABSTRACT FROM AUTHOR]

While recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) and vision transformers in single‐image super‐resolution (SISR), the degradation assumptions are simple and usually bicubic downsampling. Thus, their performances will drop dramatically when the actual degradation does not match this assumption, and they lack the capability to handle multiple degradations (e.g. Gaussian noise, bicubic downsizing, and salt & pepper noise). To address the issues, in this paper, the authors propose a joint SR model (JIRSR) that can effectively handle multiple degradations in a single model. Specifically, the authors build the parallel Transformer and CNN branches that complement each other through bidirectional feature fusion. Moreover, the authors also adopt a random permutation of different kinds of noise and resizing operations to build the training datasets. Extensive experiments on classical SR, denoising, and multiple degradation removal demonstrate that the authors' JIRSR achieves state‐of‐the‐art (SOTA) performance on public benchmarks. Concretely, the authors' JIRSR outperforms the second‐best model by 0.23 to 0.74 dB for multiple degradations removal and is 0.20 to 0.36 dB higher than the SOTA methods on the Urban100 dataset under the ×4 SR task. [ABSTRACT FROM AUTHOR]

Background: High‐resolution magnetic resonance imaging (MRI) with excellent soft‐tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post‐processing algorithms. Purpose: This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR‐CDPM). The MAR‐CDPM aimed to remove motion artifacts from multicenter three‐dimensional contrast‐enhanced T1 magnetization‐prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. Materials and methods: This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2‐fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1‐weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k‐space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR‐CDPM was then implemented in k‐space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR‐CDPM was trained in two scenarios: one conditioning on the time step t$t$ of the diffusion process, and the other conditioning on both t$t$ and T2‐FLAIR images. The MAR‐CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2‐FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2‐FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS‐SSIM), peak signal‐to‐noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS‐GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p‐value <0.05$ < 0.05$ was considered statistically significant. Results: Qualitatively, MAR‐CDPM outperformed these methods in preserving soft‐tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR‐CDPM recovered motion‐free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p‐values <0.05$< 0.05$). In addition, our method conditioned on t and T2‐FLAIR outperformed (p‐values <0.05$< 0.05$) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS‐SSIM, SSIM, and MS‐GMSD. Moreover, our method conditioned on only t outperformed generative models (p‐values <0.05$< 0.05$) and had comparable performances compared with the supervised model (p‐values >0.05$> 0.05$) to remove real motion artifacts. Conclusions: The MAR‐CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high‐resolution MRI imaging with long acquisition times. [ABSTRACT FROM AUTHOR]

Due to subsurface viscosity and heterogeneity, the vertical resolution of observed seismic data is decreased after wave propagation, generating nonstationary seismic data with amplitude attenuation and phase distortion. Inverse Q filtering techniques are always used to enhance the vertical resolution of seismic data. However, the majority of inverse Q filtering methods treat attenuation compensation trace by trace, which may produce non‐robust compensation results with poor transverse continuity and amplify noise energy in noisy cases. Thus, we develop a novel sparsity‐promoting inversion‐based multichannel seismic data attenuation compensation approach by introducing a sparse constraint for curvelet coefficients of multichannel compensated data, which takes the transverse continuity of compensated data into account. Besides, the proposed method with a sparse constraint for curvelet coefficients has a better noise‐resistance property, which can attenuate the noise energy in noisy cases during attenuation compensation, improving compensation accuracy and robustness. To improve its computational efficiency, a fast iterative shrinkage–thresholding algorithm is adopted to solve the established lasso problem. Synthetic data examples with different noise levels and two post‐stack field data examples validate the effectiveness of the proposed multichannel method. Its compensation results have superior vertical resolution, transverse continuity and noise robustness in comparison to the conventional single‐channel compensation method using a damped least squares algorithm. [ABSTRACT FROM AUTHOR]

Background: Daily or weekly cone‐beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image‐guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. Purpose: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT. Methods: The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time‐embedded U‐net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head‐and‐neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR) and normalized cross‐correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model‐based sCT generation methods. Results: In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49 dB, and 0.99, respectively, compared to 40.63 HU, 27.87 dB, and 0.98 of the CBCT images. In the H&N patient study, the metrics were 32.56 HU, 27.65 dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis. Conclusions: The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT‐based organ segmentation and dose calculation for online ART. [ABSTRACT FROM AUTHOR]

This paper mainly studies a detection method of dynamic load altering attacks (D‐LAAs) in smart grids. First, communication factors are considered, and a smart grid discrete system model under D‐LAA attack is established. Second, for closed‐loop D‐LAAs, an adaptive fading Kalman filter (AFKF) is designed to estimate the states of smart grids with Gaussian noise in real time, and a Euclidean distance ratio detection algorithm based on AFKF is proposed to detect D‐LAAs. Moreover, the proposed detection algorithm can identify D‐LAAs even in the presence of noise in the measurement data, significantly enhancing the speed of attack detection. Finally, take a smart grid with three generators and six buses as an example. Its feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified by simulations. The simulations are carried out through the real‐time hardware‐in‐the‐loop simulation platform, which is mainly composed of StarSim and multi‐tasking devices. [ABSTRACT FROM AUTHOR]

Deep‐learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep‐learning full waveform inversion method that after the neural network has been trained can image CO2 saturation and its uncertainty in real time. Our deep‐learning inversion method is based on the U‐Net architecture with the neural network trained on pairs of synthetic seismic data and CO2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO2 saturation models and once fully trained directly estimates CO2 saturation as a function of subsurface location. We further quantify uncertainties of CO2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof‐of‐concept study, the CO2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep‐learning inversion experiments using noise‐free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep‐learning inversion can estimate 2D distributions of CO2 fairly well even in the presence of Gaussian random noise and (2) both CO2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep‐learning inversion method can serve as a robust real‐time monitoring tool for geological carbon storage and/or other time‐varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena. [ABSTRACT FROM AUTHOR]

We propose MMAe, a modified MMA based on penalized Mallow's Cp. A weighted elastic net penalty is added to handle the inevitable collinearity among models, which is beneficial to high‐dimensional data modelling. We proved the sparsity, the asymptotic optimality of its weight solution and also proved that its candidate model set can be exponentially enlarged under Gaussian noises. We further proved that an MMAe adjusted by generalized cross validation (GCV) has an asymptotically lower risk than MMA under more relaxed conditions. Our approach can be implemented efficiently by convex optimization algorithms. In simulation and real‐life analysis, MMAe achieves higher prediction accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises. [ABSTRACT FROM AUTHOR]

Summary: Different from the prior works, this paper presents the performance analysis of an intelligent reflecting surface (IRS)‐assisted communication link in a static and mobile scenario impaired by Rayleigh fading and additive white generalized Gaussian noise (AWGGN). Precisely, the IRS is configured as an intelligent access point, and the mobile behavior of the nodes is characterized by the random waypoint (RWP) model. To this end, closed‐form expressions of average bit error rate (BER), average channel capacity (ACC), and outage probability (OP) in both static and mobile scenarios are obtained. To gain further insight into the system performance at high signal‐to‐noise ratio (SNR), asymptotic expressions are obtained. Moreover, the effect of the number of reflective elements (N) and the shaping parameter (λ) on the system performance is thoroughly studied. The results indicate that introduction of IRS leads to significant improvement in the overall system performance. The derived results are corroborated with Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The present study thoroughly explores the time‐average population transfer rate (TAPTR) of impurity‐doped GaAs quantum dot (QD) pursuing the change in the spatial impurity spread (SIS). The said excitation rate is studied under the influence of Gaussian white noise (GWN). The rise of the ground‐state electronic density takes place due to different types of time‐changing fluctuations, viz. simple sinusoidal field, time‐dependent confinement potential, and time‐dependent magnetic field. GWN couples with the QD by additive and multiplicative modes. In this work, the joint influence of SIS and GWN and its pathway of inclusion and the nature of the time‐dependent perturbations are examined on the attributes of the TAPTR. The TAPTR curves are composed of steadfast rise, steadfast diminish, maximization (relevant to generation of large nonlinear optical properties), minimization, and saturation (suggesting dynamic freezing). The findings elucidate the means of fine‐tuning the TAPTR among the doped GaAs QD eigenstates in presence of noise, when the SIS undergoes a gradual change. [ABSTRACT FROM AUTHOR]

The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp ${l}_{p}$‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an lp ${l}_{p}$‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp ${l}_{p}$‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic susceptible‐infective‐recovered‐susceptible (SIRS) model with density‐dependent demographics is proposed to study the dynamics of transmission of infectious diseases under stochastic environmental fluctuations. We demonstrate that the position of the basic reproduction number R0s$R_0^s$ with respect to 1 is the threshold between extinction and persistence of the disease under mild extra conditions. That is, under mild extra conditions, when R0s<1$R_0^s<1$, the disease is eradicated with probability 1; when R0s>1$R_0^s>1$, the disease is persistent almost surely and the Markov process has a unique stationary distribution and is polynomial ergodic. As an application, we use the 2017 influenza A data from Western Asia to estimate the parameter values of the model and based on that investigate the effect of random noises on the dynamics of the model. Our study reveals that the basic reproduction number R0s$R_0^s$ is negatively correlated with the noise intensity for the infected but positively correlated with that for the susceptible population, which are different from the findings obtained in the existing literature. [ABSTRACT FROM AUTHOR]

This paper develops a calculus around a new β‐Pochhammer symbol of two variables, (a,b)m,n based on the Beta weighting ta−11−tb−1. The approach is a natural rising factorial formulation that offers a new way to express hypergeometric functions of two variables. The results are implemented to solve the problem of finding the phase error probability of a vector perturbed by Gaussian noise with an arbitrary phase threshold in closed form for the first time, in terms of the incomplete confluent hypergeometric function 1ℱ1. This has been an unresolved problem in angle modulation dating back to the 1950s. Closed‐form solutions are developed around the lower and upper incomplete Humbert second Φ2 confluent hypergeometric function of two variables using the new incomplete β‐Pochhammer calculus. [ABSTRACT FROM AUTHOR]

We consider inverse problems in Hilbert spaces under correlated Gaussian noise, and use a Bayesian approach to find their regularized solution. We focus on mildly ill‐posed inverse problems with fractional noise, using a novel wavelet‐based vaguelette–vaguelette approach. It allows us to apply sequence space methods without assuming that all operators are simultaneously diagonalizable. The results are proved for more general bases and covariance operators. Our primary aim is to study posterior contraction rate in such inverse problems over Sobolev classes and compare it to the derived minimax rate. Secondly, we study effect of plugging in a consistent estimator of variances in sequence space on the posterior contraction rate. This result is applied to the problem with error in forward operator. Thirdly, we show that empirical Bayes posterior distribution with a plugged‐in maximum marginal likelihood estimator of the prior scale contracts at the optimal rate, adaptively, in the minimax sense. [ABSTRACT FROM AUTHOR]

This paper presents a new time domain Zener diode compact model for transient noise simulation. SPICE2 and SPIC3 use piece‐wise linear time dependent sources for generating complex waveforms. This approach is not practical when applied to randomly generated noise. Today, through on‐going improvements to freely available circuit simulation tools, SPICE noise generation has moved to a new level. Ngspice, for example, computes white Gaussian noise 'on‐the‐fly' as transient simulation progresses. The proposed model has a simple behavioural structure that supports time domain shot, flicker, and thermal noise. The physical properties of the proposed model are introduced in the second section. This is followed by an evaluation of model performance in the third and fourth sections, including static d.c, dynamic charge, and transient noise characterisation. Finally, the fifth section summarises the conclusions of the research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a new and efficient computational method based on the hybrid of block‐pulse functions and shifted Legendre polynomials together with their exact operational vector of integration and stochastic operational matrix of integration with respect to the multifractional Brownian motion to approximate solutions of a class of nonlinear stochastic differential equations driven by multifractional Gaussian noise. The presented method transforms problems under consideration into systems of nonlinear equations which can be simply solved by the Newton method. Moreover, the convergence analysis of the presented method is investigated. Finally, the efficiency of the new method is illustrated by solving the stochastic logistic equation and three test problems. [ABSTRACT FROM AUTHOR]

Deblending technology aims at separating simultaneous source seismic data between adjacent shots by allowing multiple sources to be shot simultaneously. Conventional deblending methods based on sparse inversion assume that the primary source is coherent, and the secondary source is randomized. The L2‐norm minimization constraint can effectively minimize the Gaussian random noise while deblending in the transform domain. Nonetheless, the L2‐norm misfit function is highly sensitive to outliers, negatively influencing the deblending performance. An effective optimization strategy is developed with deblending in pre‐stack seismic data to eliminate outliers and enhance deblending accuracy. For this reason, we introduce the deblending algorithm in the morphological component analysis framework, modify the L2‐norm misfit function to outlier‐robust L1‐norm and provide the corresponding derivation in detail via the alternating direction method of multipliers. Applications to synthetic and field data sets prove the improved robustness and efficiency of our deblending method. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing technology is a new type of signal acquisition technology, and this technology has been widely used in obtaining vertical seismic profile data in recent years. Distributed acoustic sensing technology has the advantages of high sampling density and strong tolerance to a harsh environment. However, in the real distributed acoustic sensing–vertical seismic profile data, the effective signal will be annihilated by various noises, which significantly complicates data analysis and interpretation. Deep learning approaches have developed rapidly in the noise suppression field in recent years. In order to eliminate the noise in distributed acoustic sensing–vertical seismic profile data, based on traditional convolution neural network, we add channel attention and spatial attention modules to the network to enhance the feature extraction ability of the network and use extended convolution to increase the receptive field to build a more efficient denoising model. In addition, we use different indicators to evaluate the quality of denoising, including signal‐to‐noise ratio, mean absolute error, kurtosis and skewness. The experimental results show that our method can recover the uplink wave field and downlink wave field, remove horizontal noise, optical system noise, random noise and other noises and improve the overall signal‐to‐noise ratio before and after denoising by 22 dB, reflecting a good ability of denoising and recovering effective signals. [ABSTRACT FROM AUTHOR]

Considering log‐LFSN (log‐linear fractional stable noise) sequences {Yn=eδ·Xn+ε}n∈ℤ, driven by non‐Gaussian one‐sided LFSN {Xn}n∈ℤ with constant skewness intensity β0∈[−1,1], for any δ∈ℝ−{0} and ε∈ℝ, we show that the auto‐covariance function (ACVF) {γY(h)}h∈ℤ exists if and only if {Xn}n∈ℤ is persistent, with stability index α∈(1,2), Hurst exponent H∈(1/α,1) and extreme skewness β0=−1 (if δ>0) or β0=1 (if δ<0). Within that range of existence, 1/2<1/α

The unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi‐platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV‐UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV‐UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)‐follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non‐linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV‐UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust‐EKF and Robust‐Adaptive‐EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non‐Gaussian distribution. The results show that under the non‐Gaussian distribution conditions, the accuracy of the Robust‐Adaptive‐EKF algorithm is improved by about two to three times compared with the EKF and Robust‐EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self‐adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness. [ABSTRACT FROM AUTHOR]

The state estimation (SE) problem is addressed for state‐saturated complex networks (CNs) contaminated by the random mixed couplings. Such coupling phenomenon consists of the random inner coupling and the random outer coupling, where the random inner coupling is reflected by multiplicative noise and the random outer coupling is described by random variable obeying uniform distribution. In order to reduce resource waste and calculation cost, the additional auxiliary variable method is employed to construct the dynamic event‐triggered communication condition, which can more effectively represent the change of information demand. Accordingly, a novel partial‐nodes‐based (PNB) estimator is designed in the presence of general nonlinearity and state‐saturated nonlinearity, which can estimate the whole states of network nodes with the aid of the measurement information from partial nodes. The appropriate estimator gain is obtained to ensure that the upper bound of estimation error covariance (UBEEC) is minimized. Moreover, sufficient condition is given to guarantee that the trace of the UBEEC is uniformly bounded. Finally, the numerical example is presented to demonstrate the feasibility and effectiveness of the developed PNB estimation algorithm. [ABSTRACT FROM AUTHOR]

We model the incidence of the COVID‐19 disease during the first wave of the epidemic in Castilla‐Leon (Spain). Within‐province dynamics may be governed by a generalized logistic map, but this lacks of spatial structure. To couple the provinces, we relate the daily new infections through a density‐independent parameter that entails positive spatial correlation. Pointwise values of the input parameters are fitted by an optimization procedure. To accommodate the significant variability in the daily data, with abruptly increasing and decreasing magnitudes, a random noise is incorporated into the model, whose parameters are calibrated by maximum likelihood estimation. The calculated paths of the stochastic response and the probabilistic regions are in good agreement with the data. [ABSTRACT FROM AUTHOR]

Seismic attenuation values based on inverse Q‐filtering are useful in enhancing seismic data resolution for quantitative interpretation. However, as field attenuation estimations are often contaminated by noise and overburden effects, an inverse Q‐filter may reduce the signal‐to‐noise ratio. To help reservoir mapping with seismic resolution enhancement, we use well logs and surface seismic data to estimate the attenuation of a depth interval composed of several carbonate oil reservoirs onshore the Arabian Peninsula. From applying a prestack Q inversion on τ–p gathers from a large 3D seismic survey, the estimated effective attenuation values show spatially coherent patterns, likely corresponding to changes in the petrophysical and fluid properties. The patterns of the estimated effective attenuation values correlate considerably with the anticline geometry, with higher values on its crest than its flanks. The small percentage of the negative effective attenuation values indicates that the apparent attenuation and overburden effects are insignificant. At well locations, the effective attenuation values show a good correlation with the average porosity of the main reservoir, verifying that they are related to changes in petrophysical properties in the analysis interval. We propose an extended inverse Q‐filter with two additional parameters to make it more robust and flexible and test it with synthetic and field datasets. We estimate a multiple‐scattering attenuation value from well logs and use it in the proposed filter for the field dataset. The compensation for the multiple‐scattering effects suppresses the overprints of the data components unrelated to the target interval, hence benefitting the quantitative data interpretation. [ABSTRACT FROM AUTHOR]

Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment. This paper proposes a target adversarial attack algorithm discrete cosine transform‐mean target feature attack (DTFA) based on the target features and a limited‐area sampling method. The algorithm first examines the original image and a target image to generate an initial adversarial example. Then the disturbance is sampled from the low‐frequency region intercepted by Gaussian noise after discrete cosine transform. The authors determine the size of the disturbance according to the difference between the adversarial example and the original image with consideration of the number of iterations and the position of the target feature region. The disturbance is applied on the initial adversarial example to generate the new adversarial example with the difference from the original image reduced. To evaluate the proposed algorithm, based on the common image classification model InceptionV3, and with identical queries accessing the same target model, the authors conduct experiments to compare the attack effectiveness of DTFA and the benchmark algorithms on the same image and target datasets. Experimental results show that the generated adversarial examples by the proposed algorithm are superior to 94% of those by the similar attack algorithms with less than 10,000 access queries on the target model. [ABSTRACT FROM AUTHOR]

Accounting for an accurate noise model is essential when dealing with real data, which are noisy due to the effect of environmental noise, failures and limitations in data acquisition and processing. Quantifying the noise model is a challenge for practitioners in formulating an inverse problem, and usually, a simple Gaussian noise model is assumed as a white noise model. Here we propose a pragmatic approach to use an estimated seismic wavelet to capture the correlated noise model (coloured noise) for the processed reflection seismic data. We assess the proposed method through a direct inversion of post‐stack seismic data associated with a carbonate reservoir of an oil field in southwest Iran to porosity, using a probabilistic sampling‐based inversion algorithm. In the probabilistic formulation of the inverse problem, we assume eight different noise models with varying bandwidth and magnitude and investigate the corresponding posterior statistics. The results indicate that if the correlated nature of the noise samples is ignored in the noise covariance matrix, some unrealistic features are generated in porosity realizations. In addition, if the noise magnitude is underestimated, the inversion algorithm overfits the data and generates a biased model with low uncertainty. Furthermore, by considering an imperfect bandwidth for the noise model, the error is propagated to the posterior realizations. Assuming the correlated noise in a probabilistic inversion resolves these issues significantly. Therefore, for inverting real seismic data where the estimation of the magnitude and correlations of the noise is not straightforward, the wavelet, which is estimated from the real seismic data, provides a good proxy for describing the correlation of the noise samples or equivalently the bandwidth of the noise model. In addition, it might be better to overestimate the noise magnitude than to underestimate it. This is true especially for an uncorrelated noise model and to a lesser degree also for the correlated noise model. [ABSTRACT FROM AUTHOR]

Star detection is an important part of the spacecraft attitude determination performed by star trackers. The current advanced star detection algorithms can effectively extract star points in complex backgrounds. However, there are still two problems: first, the parameters of the star detection algorithm are not adaptive, which limits its reliability and robustness; second, the high performance and low computational time of algorithms usually cannot be achieved simultaneously. In this work, a rapid star detection algorithm utilizing dyadic wavelet transform (SDDWT) was developed based on the à Trous algorithm. The authors constructed a suitable dyadic wavelet basis and applied the dyadic wavelet transform to the star image. The maximum wavelet coefficients at a certain scale were binarized according to a threshold value to separate stars from the background. The threshold was adaptively determined according to the white Gaussian noise (WGN) power. Finally, a method for separating individual stars was developed. Experiments indicated that the proposed algorithm was able to ensure a low false alarm rate and high detection rate by adaptively threshold value. Moreover, for a star image of 1024×1024, the computational time of the SDDWT algorithm was stable within 0.3 s, which was smaller than other algorithms. [ABSTRACT FROM AUTHOR]

The study meticulously inspects the time‐average excitation rate (TAER)of lowest energy state electronic probability of GaAs quantum dot (QD)to the higher energy states. The excitation occurs as the anharmonic potential(a part of QD confinement) starts varying with time either periodicallyor randomly. The anharmonic potential is characterized by either evenor oddparity. Gaussian white noise (GWN)has also been introduced to the QD either via additiveor multiplicativemode. The study announces that the coupled impact of the given physical quantity, the symmetry of the anharmonicity, presence/absence of noise, the route of entrance of noise, and the nature of time progress of anharmonicity constant, ultimately regulates and fine‐tunes the attributes of the TAER diagrams. The TAER diagrams comprise of regular rise, regular fall, maximization (pertinent to production of high value of nonlinear optical (NLO) properties), minimization and saturation (pertinent to dynamic freezing). The outcomes of the study seem to be recognizable in various applications of devices containing QD. [ABSTRACT FROM AUTHOR]

This work scrutinizes the time‐average excitation rate (TAER) among the GaAs quantum dot (QD) eigenstates under the aegis of Gaussian white noise (GWN) and the parity of the anharmonic potential (odd/even). GWN connects with the system by additive or multiplicative mode. The said excitation of the ground state population has been triggered by an external field which may be a polychromatic radiation field (PRF), or pulsed field (PF) or chirped pulsed field (CPF). The study reveals the subtle nuances of the interplay between noise (additive or multiplicative), anharmonicity (odd or even) and the external field (PRF, PF or CPF) that finally govern the attributes of the TAER diagrams. The TAER profiles exhibit persistent growth, persistent decline, maximization (important in view of production of prominent nonlinear optical properties), minimization and saturation (relevant to the dynamic freezing). The findings appear useful for regulating the TAER among the GaAs QD eigenstates which have substantial technological relevance. [ABSTRACT FROM AUTHOR]

Interpolation techniques provide an effective method for recovery of missing traces. In recent years, many researchers have applied deep learning methods to seismic data interpolation. Generally, one can choose synthetic data as a training set; however, the features of synthetic data are always inconsistent with those of field data, which may lead to inaccurate interpolation. Meanwhile, U‐Net is a common network structure used in seismic data interpolation; however, the four downsampling and upsampling structures of U‐Net have limited adaptability for different data. In this study, the deep learning method based on U‐Net++ was proposed for seismic data interpolation, which contains U‐Net with different depths. The different depths were connected by skip pathways, and the best depth of the network was chosen for different seismic data by deep supervision. Furthermore, a new strategy for training sets was designed: frequency‐wavenumber (f‐k) bandpass filters were used to convert natural images into a natural seismic training set, which has a stronger generalization capability than synthetic data as the training set. The characteristics of the new training set can effectively improve the accuracy of missing data reconstruction. Compared with the conventional U‐Net and traditional interpolation techniques, for example, the Fourier Bregman method, the proposed method produces more accurate and reasonable interpolation results. Further, it can reconstruct both irregular and regular missing seismic data, even in the presence of strong random noise and aliasing. Synthetic and field data tests showed the effectiveness, robustness and generalization of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies the secure estimation problem for cyber‐physical systems (CPSs) with sparse attacks and noises. Instead of fixed attacked sensor channels, the malicious attack investigated in this work is s$$ s $$‐sparse and the set of attacked sensor channels is time‐varying. A novel l1$$ {l}_1 $$‐minimization based on QR decomposition, rather than minimizing the row support of the attack matrix directly, is designed to reconstruct malicious attacks. Furthermore, considering that the presence of Gaussian noises may produce estimation errors, a secure conservative estimation method based on Kalman filter is proposed to estimate the consolidated signal consisting of sparse attacks and measurement noises. Moreover, it is proved that robustness of the proposed estimation algorithm can be guaranteed with the upper bound of estimation error covariance. An illustrative example is utilized to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In quantum communication, the channel noise and the misalignment of the reference frames between the communication parties will lead to the failure of quantum state transmission. Here an alignment‐free spatial‐polarization hyperentanglement transmission scheme is provided for hyperentangled photons. In this scheme, before the spatial‐polarization hyperentanglement is transmitted through the fiber channel, it is first encoded as a time‐bin entanglement with the same polarization. After the photons pass through the noise channel, the polarization errors caused by reference frames misalignment and channel noise can be corrected by time‐bin entanglement. In principle, by implementing this scheme, the communication parties can share the original hyperentangled state, and the success probability can approach unity. The scheme is robust to random channel noise and reference frames misalignment, and the decoherence effect caused by the misalignment of the reference frames between the communication parties can be completely suppressed by implementing this scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose and assess several stochastic parametrizations for data‐driven modeling of the two‐dimensional Euler equations using coarse‐grid SPDEs. The framework of Stochastic Advection by Lie Transport (SALT) (Cotter et al., 2019, https://doi.org/10.1137/18m1167929) is employed to define a stochastic forcing that is decomposed in terms of a deterministic basis (empirical orthogonal functions, EOFs) multiplied by temporal traces, here regarded as stochastic processes. The EOFs are obtained from a fine‐grid data set and are defined in conjunction with corresponding deterministic time series. We construct stochastic processes that mimic properties of the measured time series. In particular, the processes are defined such that the underlying probability density functions (pdfs) or the estimated correlation time of the time series are retained. These stochastic models are compared to stochastic forcing based on Gaussian noise, which does not use any information of the time series. We perform uncertainty quantification tests and compare stochastic ensembles in terms of mean and spread. Reduced uncertainty is observed for the developed models. On short timescales, such as those used for data assimilation (Cotter et al., 2020a, https://doi.org/10.1007/s10955-020-02524-0), the stochastic models show a reduced ensemble mean error and a reduced spread. Particularly, using estimated pdfs yields stochastic ensembles which rarely fail to capture the reference solution on small time scales, whereas introducing correlation into the stochastic models improves the quality of the coarse‐grid predictions with respect to Gaussian noise. Plain Language Summary: Turbulent flows often contain small‐scale fluctuations that behave in a seemingly random way. Predicting the behavior of such a flow is challenging, since simulating the flow in full detail is computationally expensive. To reduce the computational costs, one can initially ignore the small‐scale fluctuations and subsequently try to include the effects of these scales by including an additional term into the equations that describe the flow. We propose and assess various models that represent the influence of the small‐scales through a stochastic (random) forcing term. We compare three types of stochastic processes that use information from high‐resolution data. It is found that using more information from the data leads to a reduced spread and ensemble mean error. Key Points: High‐resolution numerical simulation data are used to extract small‐scale features of the 2D Euler equationsAn empirical orthogonal function (EOF)‐based stochastic forcing is proposed, where the EOF time series serve to define data‐driven stochastic processes for each EOFThe data‐driven processes are found to produce ensembles with reduced mean error and spread, compared to using Gaussian noise [ABSTRACT FROM AUTHOR]

The attenuation of seismic waves propagating in reservoirs can be obtained accurately from the data analysis of vertical seismic profile in terms of the quality‐factor Q. The common methods usually use the downgoing wavefields in vertical seismic profile data. However, the downgoing wavefields consist of more than 90% energy of the spectrum of the vertical seismic profile data, making it difficult to estimate the viscoacoustic parameters accurately. Thus, a joint viscoacoustic waveform inversion of velocity and quality‐factor is proposed based on the multi‐objective functions and analysis of the difference between the results inverted from the separated upgoing and downgoing wavefields. A simple separating step is accomplished by the reflectivity method to obtain the individual wavefields in vertical seismic profile data, and then a joint inversion is carried out to make full use of the information of the individual wavefields and improve the convergence of viscoacoustic full‐waveform inversion. The sensitivity analysis of the different wavefields to the velocity and quality‐factor shows that the upgoing and downgoing wavefields contribute differently to the viscoacoustic parameters. A numerical example validates our method can improve the accuracy of viscoacoustic parameters compared with the direct inversion using full wavefield and the separate inversion using upgoing or downgoing wavefield. The application on real field data indicates our method can recover a reliable viscoacoustic model, which helps reservoir appraisal. [ABSTRACT FROM AUTHOR]

In neuromorphic computing networks, a flexible synaptic memristor with high recognition accuracy is highly desired. In this study, ZnO nanosheets (ZnO NS) embedded within a polymethyl methacrylate host material are used as the intermediate layer to prepare flexible synaptic memristor at a low‐temperature of 80 °C. The device shows excellent switching characteristics with low SET/RESET voltages (−0.4 V/0.4 V) and stable retention characteristic (104 s). By modulating the conductance continuously, the flexible synaptic memristor simulates typical synaptic plasticities, including excitation post‐synaptic current, paired‐pulse facilitation, and spike‐timing dependent plasticity. Especially, the neuromorphic system built from flexible ZnO NS‐based memristors achieves a high recognition accuracy up to 97.7% for handwriting digit. Under the influence of 5% Uniform noise and 5% Gaussian noise, recognition accuracies are maintained at 94.6% and 93.7%, respectively. These properties are well maintained even when bending 1000 times at a radius of 5 mm. The flexible ZnO NS‐based memristor shows great prospects in wearable devices and neural morphology calculation. [ABSTRACT FROM AUTHOR]

In this short note, we prove an asymptotic expansion for the ratio of the Dirichlet density to the multivariate normal density with the same mean and covariance matrix. The expansion is then used to derive an upper bound on the total variation between the corresponding probability measures and rederive the asymptotic variance of the Dirichlet kernel estimators introduced by Aitchison and Lauder (1985) and studied theoretically in Ouimet (2020). Another potential application related to the asymptotic equivalence between the Gaussian variance regression problem and the Gaussian white noise problem is briefly mentioned but left open for future research. [ABSTRACT FROM AUTHOR]

The current world condition is dire due to epidemics and pandemics as a result of novel viruses, such as influenza and the coronavirus, causing acute respiratory syndrome. To overcome these critical situations, the current research seeks to generate a common surveillance system with the assistance of a controlled Internet of Things operated under a Gaussian noise channel. To create the model system, a study with an analysis of H1N1 influenza virus determination on an interdigitated electrode (IDE) sensor was validated by current–volt measurements. The preliminary data were generated using hemagglutinin as the target against gold‐conjugated aptamer/antibody as the probe, with the transmission pattern showing consistency with the Gaussian noise channel algorithm. A good fit with the algorithmic values was found, displaying a similar pattern to that output from the IDE, indicating reliability. This study can be a model for the surveillance of varied pathogens, including the emergence and reemergence of novel strains. [ABSTRACT FROM AUTHOR]

Most crystalline materials present a highly heterogeneous response at the microscale, which can be affected by both internal factors (such as microstructural parameters) and external factors (such as loading). Relating microscale inhomogeneities to the macroscale response of a material requires the use of homogenisation techniques, usually based on the concept of a representative volume element (RVE)—the smallest volume of material that represents the global average response. In this work, we present a new and robust experimental method of measuring the size of a strain‐based RVE from high‐resolution grain‐scale strain fields obtained using digital image correlation (DIC). The proposed method is based on the statistical (stereological) nature of the RVE, which has been widely adopted in numerical studies, and involves dividing a strain field into randomly selected regions of varying sizes and statistically analysing the distributions of average strains within them. To validate the new method, we generate a large number of synthetic strain fields from a fractional Gaussian noise algorithm. The proposed stereological method is shown to be capable of producing reliable RVE measurements from a very large range of possible microscale strain fields while at the same time being robust in that it can produce RVE measurement results even in cases where other existing methods may be unable to do so. The proposed method has a low field‐of‐view requirement, only needing a field‐of‐view about 1.2 times as large as the RVE to produce reliable measurements. In addition, the stereological method offers significant flexibility since its statistical nature allows for control over how strict the RVE measurement should be in each case. [ABSTRACT FROM AUTHOR]

A direct approach is described to determine the elastic modulus distribution in a nominally heterogeneous material subject to tensile/compression loading and primarily experiencing deformations in the axial direction. The formulation is developed for uniaxial applications using basic theoretical constructs, resulting in a computational framework that has a matrix form [A] {E} = {R}, where the [A] matrix components are known functions of measured axial strains and axial positions, {R} components are known functions of axial body forces, applied loads and reactions and {E} components are the unknown elastic moduli at discrete locations along the length of the specimen. For a series of one‐dimensional (1D) material property identification procedure with known axial strains at discrete locations and various levels of random noise, results are presented to demonstrate the accuracy and noise sensitivity of the methodology. Finally, experimental measurements for a heterogeneous bone specimen are compared to our 1D model predictions, demonstrating that the predictions are in very good agreement with independent estimates at each load level of interest along the length of the bone specimen. [ABSTRACT FROM AUTHOR]

This letter presents an accuracy enhancement technique utilized in 16 bit fully differential successive‐approximation‐register analog‐to‐digital converters (SAR ADC). For noise performance improvement to obtain a higher ENOB, the residue measurement technique which statistically estimates the input residual error of comparator with Gaussian noise fitting is presented. The ADC digital output is compensated by the residue measurement results. It is verified in the 0.18‐μm 1P5M 5 V CMOS process. Finally, a 93.1 dB SNR and a 110.5 dB SFDR are measured operating at 1 MS/s with a 10‐kHz input tone and larger than 1‐dB SNR improvement is obtained in different chips. [ABSTRACT FROM AUTHOR]

Current study focuses on realizing the role of Gaussian white noise (GWN) and external sinusoidal polychromatic radiation field (PRF) on the time‐average excitation rate (TAER) of impurity doped quantum dot (QD). The PRF induces the transfer of electronic population from the ground state to the excited states in course of time‐evolution. The PRF has been envisaged to be composed of four component fields that maintain either a prime or a non‐prime frequency ratio. GWN couples with the system additively and multiplicatively. The study highlights the role of various physical parameters, presence of noise, mode of application of noise and the features of PRF (amplitude, phase and type of frequency ratio of the components) in shaping the TAER profiles. The TAER profiles have been found to be enriched with steady growth, steady decline, maximization (useful for obtaining large nonlinear optical response), minimization and saturation (the dynamic freezing). The study reveals promising scope of obtaining desired TAER of doped QD by judicious adjustment/regulation of several control parameters which may bear serious technological implications. [ABSTRACT FROM AUTHOR]

Present investigation rigorously explores the time‐average excitation rate (TAER) of ground‐state electronic population of GaAs quantum dot (QD) to the excited eigenstates. The QD confinement potential contains Gaussian impurity (the dopant) and Gaussian white noise (GWN). GWN can be categorized as either additive or multiplicative depending upon its way of attachment to the doped QD. The excitation occurs as soon as the noise strength begins fluctuating with time in periodic or random manner. The study highlights the coupled influence of the varying physical quantity, presence/absence of noise, the mode of attachment of noise to QD, and the nature of time‐fluctuation of the noise strength (periodic/random), and finally harnesses and tailors the features of the TAER profiles. The TAER plots unveil steady rise, steady fall, maximization (linked with the emergence of large nonlinear optical response), minimization, and saturation (linked with dynamic freezing). The findings appear to be significant in the practical usage of QD‐based devices. [ABSTRACT FROM AUTHOR]

Diffractions carry out important information about subsurface features. These features include small‐scale objects, fracture zones and faults. There have been several robust pre‐ and post‐stack diffraction imaging workflows in the literature to attribute diffraction locations and properties. Most of the traditional workflows are not fully capable of dealing with polarity reversals in the case of polarity reversed diffracted wavefields. This challenge causes null measures at the location of such diffractions. To overcome this issue, which is an ongoing subject of research, we propose to implement local semblance analysis along moveout curves. To do so, the global scanning window is subdivided into smaller windows followed by semblance analysis over each window. The final coherency measure in each image point is computed by averaging the semblance measures from all the subdivided windows. We demonstrated the proposed workflow on synthetic as well as field recorded datasets in the post‐stack domain. The results prove the capability of the proposed method in circumventing polarity reversals without any need to conduct polarity correction prior to imaging. At the end, we studied the seismic imaging resolution in the presence of white noise through the proposed approach. [ABSTRACT FROM AUTHOR]

Most deep‐learning‐based multi‐view stereo series studies are concerned with improving the depth prediction accuracy of noise‐free images. However, it is difficult to obtain off‐the‐set clean images in practice and 3D convolutional neural networks require a lot of computing resources. To make full use of its computing power, different types of information can be processed simultaneously in the network. For these two issues, this paper proposes a novel multi‐stage network architecture to address depth inference and denoising simultaneously. Specifically, 2D feature maps are first converted into 3D cost volumes containing pixel information and depth information through differentiable homography and Gaussian probability mapping. Then, the cost volume is input into the regularisation module in each network stage to obtain the predicted probability volumes. Furthermore, simple static weights lead to training failure, and it is necessary to dynamically adjust the loss function by gradient normalisation. The proposed method can dispose of pixel information and depth information simultaneously and both reach an excellent level. Extensive experimental results show that the authors' work surpasses the state‐of‐the‐art denoising on the DTU dataset (adding Gaussian–Poisson noise) and is more robust to noise images in depth inference. [ABSTRACT FROM AUTHOR]