Noise Correlation Research Articles

Stochastic Approach to the Quantum Noise of a Single-Emitter Nanolaser.

It is shown that the quantum intensity noise of a single-emitter nanolaser can be accurately computed by adopting a stochastic interpretation of the standard rate equation model. The only assumption made is that the emitter excitation and photon number are stochastic variables with integer values. This extends the validity of rate equations beyond the mean-field limit and avoids using the standard Langevin approach, which is shown to fail for few emitters. The model is validated by comparison to full quantum simulations of the relative intensity noise and second-order intensity correlation function, g^{(2)}(0). Surprisingly, even when the full quantum model displays vacuum Rabi oscillations, which are not accounted for by rate equations, the intensity quantum noise is correctly predicted by the stochastic approach. Adopting a simple discretization of the emitter and photon populations, thus, goes a long way in describing quantum noise in lasers. Besides providing a versatile and easy-to-use tool for modeling emerging nanolasers, these results provide insight into the fundamental nature of quantum noise in lasers.

Optimal Linear Filter Based on Feedback Structure for Sensing Network with Correlated Noises and Data Packet Dropout.

This paper is concerned with the estimation of correlated noise and packet dropout for information fusion in distributed sensing networks. By studying the problem of the correlation of correlated noise in sensor network information fusion, a matrix weight fusion method with a feedback structure is proposed to deal with the interrelationship between multi-sensor measurement noise and estimation noise, and the method can achieve optimal estimation in the sense of linear minimum variance. Based on this, a method is proposed using a predictor with a feedback structure to compensate for the current state quantity to deal with packet dropout that occurs during multi-sensor information fusion, which can reduce the covariance of the fusion results. Simulation results show that the algorithm can solve the problem of information fusion noise correlation and packet dropout in sensor networks, and effectively reduce the fusion covariance with feedback.

Globally optimal distributed and sequential state fusion filters for multi-sensor systems with correlated noises

The problem concerning the multi-sensor fusion filtering for systems with correlated system and measurement noises is studied in this paper. Each sensor produces a local predictor by using its own measurements and then sends it to a fusion center. In the fusion center, by applying the projection theory and difference technology, the globally optimal distributed state fusion filter without feedback and sequential state fusion filter with recursive estimators of noises are presented in the sense of linear minimum variance by using all received local predictors, respectively. Under the condition that local prediction gain matrices are of full column rank, the proposed distributed and sequential state fusion filters can achieve the same estimation accuracy as the centralized fusion filter, i.e., they both have global optimality. The optimality is strictly proved. The corresponding steady-state fusion filters are also explored. A sufficient condition for the convergence of the proposed fusion filters is given. A target tracking example verifies the effectiveness of the proposed algorithms.

Robust superdirective beamforming for arbitrary sensor arrays

This paper presents a method of designing robust superdirective beamformers for arbitrary sensor arrays. First, the optimal weighting vector is transformed into an equivalent form based on the eigen–decomposition of the noise correlation matrix in an isotropic uniform noise field, and its entries will be indexed using mode orders instead of element numbers. Second, a parameter is weighted at the maximum–order entry in the modified weighting vector. The beampattern, directivity factor, and error sensitivity function can be expressed as functions of the parameter. Adjusting the parameter will achieve arbitrary–order superdirective beampatterns with different directivity factors or error sensitivity functions. Third, the closed–form expression of the parameter can be derived given a desired directivity factor or error sensitivity function. The final robust broadband superdirective beampatterns can then be readily synthesized. The proposed method, which is an extension of that previously proposed for circular sensor arrays, provides flexible schemes to designing robust broadband superdirective beamformers for arbitrary arrays in simulations and experiments.

SDR-Implementation of a Support Vector Machine-Assisted Covariance-Based Spectrum Sensing Algorithm in the Presence of Correlated Noise

Cognitive radio (CR) is an emergent technology that is considered for more efficient spectrum utilization. Spectrum Sensing (SS), i.e. detection of primary user (PU) in the frequency band, is one of the key tasks of cognitive radio systems. Recently, various works on the support vector machine (SVM)-assisted covariance-based spectrum sensing (CSS) in multi-receiver systems were presented and evaluated with computer simulations. However, the real environment is different, especially since the noise is often assumed to be spatially uncorrelated, while it is correlated in reality. Therefore, we propose a four-receivers software-defined radio (SDR)-based test platform for experimental evaluation of SVM-assisted CSS techniques. Furthermore, we implement an SVM-assisted CSS technique in the proposed platform to assess its performance when two and four receivers are activated. The measurements showed that increasing the number of receiving channels does indeed enhances the detection probability. Moreover, we evaluated the algorithm in both white and correlated noise scenarios. The evaluation demonstrates how the presence of correlated noise deteriorated the performance of the SVM-assisted CSS compared with the case where only white noise is present. More specifically, the simulations showed that to reach 99% detection probability when noise correlation is present, the implemented algorithm requires an SNR value that is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{10}~dB$</tex-math></inline-formula> greater than the one required when white noise only is present.

DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver

In this paper, we propose a novel deep learning-based spectrum sensing scheme using a multi-antenna receiver. Our main idea is constructing a correlation matrix composed of not only auto-correlation functions per each antenna but also cross-correlation functions between antennas. By using the rich informative matrix, with a simple convolutional neural network (CNN) structure, our model, DS2MA (Deep Spectrum Sensing with Multiple Antennas), can efficiently learn to detect the presence of a primary user (PU). In our extensive simulation results, DS2MA using only auto-correlation functions for each antenna can outperform previous related works. By adding cross-correlation functions together, DS2MA can significantly improve the detection performance. We also show the impact of impulsive noise and correlation coefficient. The simulation results show that the proposed DS2MA provides a higher detection probability compared to the existing works regardless of the impulsive noise impact and correlation coefficient.

GaiaData Release 3

Context.Blue (BP) and Red (RP) Photometer low-resolution spectral data are one of the exciting new products inGaiaData Release 3 (GaiaDR3). These data have also been used to derive astrometry and integrated photometry inGaiaEarly Data Release 3 and astrophysical parameters and Solar System object reflectance spectra inGaiaDR3.Aims.In this paper, we give an overview of the processing techniques that allow raw satellite data of multiple transits per source to be converted into combined spectra calibrated to an internal reference system, resulting in low-resolution BP and RP mean spectra. We describe how we overcome challenges due to the complexity of the on-board instruments and to the various observation strategies. Furthermore, we show highlights from our scientific validation of the results. This work covers the internal calibration of BP/RP spectra to a self-consistent mean instrument, while the calibration of the BP/RP spectra to the absolute reference system of physical flux and wavelength is covered by one of the accompanyingGaiaDR3 papers.Methods.We calibrate about 65 billion individual transit spectra onto the same mean BP/RP instrument through a series of calibration steps, including background subtraction, calibration of the CCD geometry, and an iterative procedure for the calibration of CCD efficiency as well as variations of the line-spread function and dispersion across the focal plane and in time. The calibrated transit spectra are then combined for each source in terms of an expansion into continuous basis functions. We discuss the configuration of these basis functions.Results.Time-averaged mean spectra covering the optical to near-infrared wavelength range [330, 1050] nm are published for approximately 220 million objects. Most of these are brighter thanG = 17.65 but some BP/RP spectra are published for sources down toG = 21.43. Their signal-to-noise ratio (S/N) varies significantly over the wavelength range covered, and with magnitude and colour of the observed objects, with sources aroundG = 15 having a S/N above 100 in some wavelength ranges. The top-quality BP/RP spectra are achieved for sources with magnitudes 9 &lt; G &lt; 12, with S/N reaching 1000 in the central part of the RP wavelength range. Scientific validation suggests that the internal calibration was generally successful. However, there is some evidence for imperfect calibrations at the bright endG &lt; 11, where calibrated BP/RP spectra can exhibit systematic flux variations that exceed their estimated flux uncertainties. We also report that, due to long-range noise correlations, BP/RP spectra can exhibit wiggles when sampled in pseudo-wavelength.Conclusions.TheGaiaDR3 data products are the expansion coefficients and corresponding covariance matrices for BP and RP separately. Users are encouraged to work with the data in this format, with full covariance information showing that correlations between coefficients are typically very low. Documentation and instructions on how to access and use BP/RP spectral data from the archive are also provided.

GAWBS Noise in Digital Coherent Transmission

This paper presents recent progress on guided acoustic wave Brillouin scattering (GAWBS) noise in single-core fiber (SCF), multi-core fiber (MCF), and few-mode fiber (FMF). First, we describe theoretical analyses of GAWBS phase noise generated in these various types of fiber. By using three types of SCF, namely ultra-large area fiber (ULAF), standard single-mode fiber (SSMF), and dispersion-shifted fiber (DSF), we show that the GAWBS phase noise depends strongly on the effective core area A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eff</sub> . We also describe the GAWBS phase noise in four-core fiber (4CF), where it is shown that GAWBS noise in the off-center core is quite different from that in SCF. Furthermore, we describe the GAWBS phase noise in FMF supporting two LP modes. Then, we compare the GAWBS noise powers induced in SCF, MCF, and FMF. Next, we describe experiments on GAWBS phase noise in various fibers, which agree well with the theory. We also measured and analyzed the GAWBS phase noise correlation between the cores in a 4CF, where it is shown that the correlation between cores strongly depends on the core position. Finally, we describe the influence of GAWBS phase noise on digital coherent transmission. We formulate the GAWBS phase noise based on the Gaussian distribution and evaluate its influence on the BER characteristics in a coherent QAM transmission. We found that it is difficult to realize a transoceanic-class QAM transmission with a QAM multiplicity of higher than 64 even without other impairments. Furthermore, we evaluate the SNR penalty induced by the GAWBS noise in a long-haul transmission by taking account of the amplified spontaneous emission (ASE) and nonlinear interference noise, which are unavoidable in actual systems. The calculated results show that the influence of the GAWBS noise is smaller than the other impairments, but it cannot be neglected in system evaluations.

Car Door Assembly FE Analysis - A Review for NVH Performance Research

When talking about the car door and its importance, all aspects should be considered. Main applications of the door is the protection of passengers from outside weather variations such as air flow, temperatures, dust, side impact, etc. As a part of the car, it is connected to the car body by means of hinges, latch, and weather seals. It is excited by all excitation sources which excite to the vehicle plus vehicle accessories. Car door design evaluation is nothing but simulation of real application of door with different loading and boundary connections. An extensive literature survey and study of technical papers have been done to understand the amount of work done on Car door design evaluation during 2002 - 2022. A mainly top-level study was done to find the number of analysis papers published, the number of papers published per year, and the number of papers published of each type of analysis, and a detailed study was carried out of the papers published on NVH analysis. NVH analysis of a Car door itself is a broad area for study and research. To make it simple and streamline NVH analysis papers are sub-divided into further classes that are modal analysis, vibration and noise analysis, correlation of modal, vibration, noise, and other NVH analysis results. A thorough review of published papers provided an inside picture of the subject, the current status of Car door NVH, and the scope of future research on Car door.

Representational Geometries Reveal Differential Effects of Response Correlations on Population Codes in Neurophysiology and Functional Magnetic Resonance Imaging.

Two sensory neurons usually display trial-by-trial spike-count correlations given the repeated representations of a stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has become the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations among voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex (five males, one female) and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in empirical neurophysiological studies. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist within the primate visual system. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high-dimensional representational space. Interestingly, response correlations simultaneously reduce and enhance information on higher- and lower-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effects of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.SIGNIFICANCE STATEMENT Despite the vast research interest in the effect of spike-count noise correlations on population codes in neurophysiology, it remains unclear how the response correlations between voxels influence MVPA in human imaging. We used an information-theoretic approach and showed that unlike the detrimental effects of response correlations reported in neurophysiology, voxelwise response correlations generally improve sensory coding. We conducted a series of in-depth analyses and demonstrated that neuronal and voxel response correlations can coexist within the visual system and share some common computational mechanisms. These results shed new light on how the population codes of sensory information can be evaluated via different neural measurements.

Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation

This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm.

Time delay feedbacks enhanced bifurcation in the birhythmic fractional self-sustained system subjected to correlated noise

The response of the birhythmic fractional Van der Pol (VdP) self-sustained oscillator driven both by time delay feedbacks and correlated noise is considered. We mainly focus on the effects of time-delay feedback parameter and fractional damping on stochastic properties of the birhytmic system, such as P-bifurcation. The minimum mean-square error is used to reduce the system to its equivalent integer-order nonlinear stochastic equation. An analytical approximation is adopted to obtain the system behavior and it is compared with numerical simulation. The delay self-control feedbacks are transformed into state variables without delay by means of the harmonic approximation. The maximum extension of the control parameters to have a birhythmic solution can be thus determined by an appropriated choice of the fractional order and delay time parameters. In particular, when the fractional damping is lowered below 2, the birhythmic region disappears. However, the influence of the fractional damping changes as a function of the correlation noise parameter. Instead, the delay feedback and the fractional derivative are not so effective on the transition. The input delay and fractional derivative can be greatly enhanced by the nonlinear interaction. Specifically, the amplitude of the induced oscillations can be much larger than that of the input fractional with the cooperation of an appropriated time delay and nonlinear terms. It is then shown that the time delay feedback parameters and the fractional damping also act as bifurcation parameters.

Tunable neural networks for CT image formation.

Optimization of CT image quality typically involves balancing variance and bias. In traditional filtered back-projection, this trade-off is controlled by the filter cutoff frequency. In model-based iterative reconstruction, the regularization strength parameter often serves the same function. Deep neural networks (DNNs) typically do not provide this tunable control over output image properties. Models are often trained to minimize the expected mean squared error, which penalizes both variance and bias in image outputs but does not offer any control over the trade-off between the two. We propose a method for controlling the output image properties of neural networks with a new loss function called weighted covariance and bias (WCB). Our proposed method uses multiple noise realizations of the input images during training to allow for separate weighting matrices for the variance and bias penalty terms. Moreover, we show that tuning these weights enables targeted penalization of specific image features with spatial frequency domain penalties. To evaluate our method, we present a simulation study using digital anthropomorphic phantoms, physical simulation of CT measurements, and image formation with various algorithms. We show that the WCB loss function offers a greater degree of control over trade-offs between variance and bias, whereas mean-squared error provides only one specific image quality configuration. We also show that WCB can be used to control specific image properties including variance, bias, spatial resolution, and the noise correlation of neural network outputs. Finally, we present a method to optimize the proposed weights for a spiculated lung nodule shape discrimination task. Our results demonstrate this new image quality can control the image properties of DNN outputs and optimize image quality for task-specific performance.

Square-wave optoelectronic oscillators: Effects of noise in bit generators

Optoelectronic oscillators delivering optical square waves may potentially serve as sources of optical or microwave bits. Noise analysis and prediction of eye diagrams and various figures of merit can involve intensive numerical simulation specific to a given problem at hand. Here we present an analytically tractable model of the effects of amplitude noise and timing jitter on the use of such a square-wave generator as an optical binary bit train. The approach depends on a small number of empirical parameters characterizing the temporal correlations of noise in the system. We compute in closed form the time-averaged signal, autocorrelation function, power spectral density, eye diagram, quality factor, bit-error rate, and Allan variance. While the model exploits simplification as the cost of tractability, the availability of such a model is expected to be of interest more broadly in digital communications to provide a simple tool for analysis and characterization.

Cross-correlating radial peculiar velocities and CMB lensing convergence

We study, for the first time, the cross correlation between the angular distribution of radial peculiar velocities (PV) and the lensing convergence of cosmic microwave background (CMB) photons. We derive theoretical expectations for the signal and its covariance and assess its detectability with existing and forthcoming surveys. We find that such cross-correlations are expected to improve constraints on different gravitational models by partially breaking degeneracies with the matter density. We identify in the distance-scaling dispersion of the peculiar velocities the most relevant source of noise in the cross correlation. For this reason, we also study how the above picture changes assuming a redshift-independent scatter for the PV, obtained for example using a reconstruction technique. Our results show that the cross correlation might be detected in the near future combining PV measurements from DESI and the convergence map from CMB-S4. Using realistic direct PV measurements we predict a cumulative signal-to-noise ratio of approximately 3.8σ using data on angular scales 3 ≤ ℓ ≤ 200. For an idealized reconstructed peculiar velocity map extending up to redshift z = 0.15 and a smoothing scale of 4 Mpc h -1 we predict a cumulative signal-to-noise ratio of approximately 27σ from angular scales 3 ≤ ℓ ≤ 200. We conclude that currently reconstructed peculiar velocities have more constraining power than directly observed ones, even though they are more cosmological-model dependent.

Bridge weigh-in-motion considering dynamic response in observation noise with application to multiple driving conditions

In addition to causing a static response when traversing a bridge, moving vehicles also produce a dynamic response, which is thought to be one of the factors adversely affecting the accuracy of bridge weigh-in-motion approaches. This paper proposes a novel method for improving the accuracy of such approaches. Rather than modeling the dynamic response directly, the proposed model considers it as noise with autocorrelation. A Bayesian inference approach is considered as it allows a rational way of assimilating data into the model. Here, the dynamic response is considered in the form of a covariance matrix of observation noise, composed of the residuals of the measured and calculated responses, in a Bayesian framework. In a validation study using actual measured data, it was confirmed that the estimation of axle weights in the presence of multiple vehicles was significantly improved. From the result, it can be concluded that proper modeling of the dynamic response in observation data leads to more accurate weigh-in-motion estimates of axle weights and GVW, particularly when complicated vehicle travel is involved. A comparison of static and dynamic influence lines is also discussed using actual measured data. It is shown that the weights estimated by the dynamic influence line are generally more accurate than those by the static influence line when correlation of noise is not considered. The dynamic component in influence line can be interpreted as a regularization term. The proposed method, which uses static influence line and covariance matrix considering correlation of noise, is more accurate than the method with dynamic influence line and also, it does not cause any bias.

An iterative BP-CNN decoder for optical fiber communication systems.

The conventional belief propagation (BP) of the low-density parity-check (LDPC) is designed based on additive white Gaussian noise (AWGN) close to the Shannon limit; however, the correlated noise due to chromatic dispersion or square-law detection results in a performance penalty in the intensity modulation and direct-detection (IM/DD) system. We propose an iterative BP cascaded convolution neural network (CNN) decoder to mitigate the correlated channel noise. We use a model of correlated Gaussian noise to verify that the noise correlation can be identified by the CNN and the decoding performance is improved by the iterative processing. We successfully demonstrate the proposed method in a 50-Gb/s 4-ary pulse amplitude modulation (PAM-4) IM/DD system. The simulation results show that the proposed decoder can achieve a BER performance improvement which is robust to transmission distance and launch optical power. The experimental results show that the iterative BP-CNN decoder outperforms the standard BP decoder by 1.2 dB in received optical power over 25-km SSMF.

A Design of Differential-Low Earth Orbit Opportunistically Enhanced GNSS (D-LoeGNSS) Navigation Framework

Considering the problem of GNSS service interruption caused by the insufficient number of available satellites in complex environments, Low Earth Orbit (LEO) satellites can supplement GNSS effectively. To eliminate the unknown satellite clock error and the atmospheric delay error with spatial correlation in LEO observations, a Differential-Low Earth Orbit opportunistically enhancing GNSS (D-LoeGNSS) navigation framework is proposed. Firstly, because of the uncertainty of the LEO orbit, we derive the effect of the LEO orbit error on the differential measurement model. Secondly, aiming at the noise amplification and correlation in double-difference (DD), we propose a Householder-Based D-LoeGNSS (HB-DLG) algorithm, which suppresses noise by introducing an orthogonal matrix. Thirdly, in D-LoeGNSS, the typical measurement of LEO is Doppler, which is heterogeneous with the GNSS pseudorange, rendering the Dilution of Precision (DOP) evaluation method unsuitable. Given the unbiasedness of differential measurements, the Cramer Rao Lower Bound (CRLB) is derived as a metric to characterize the positioning accuracy and satellite spatial distribution. Finally, a field experiment using Orbcomm (ORB) and GPS is conducted. The experimental results show that the performance of the HB-DLG algorithm is superior to DD. Especially when the number of satellites is insufficient or the measurement redundancy is poor; the D-LoeGNSS framework has advantages of rapid convergence and high accuracy compared with a single constellation.

Relation between the noise correlations and the spin structure factor for Mott-insulating states in SU(N) Hubbard models

It is well established that the noise correlations measured by time-of-flight imaging in cold-atom experiments, which correspond to the density-density correlations in the momentum space of trapped atomic gases, can probe the spin structure factor deep in the Mott-insulating regime of SU(2) Hubbard models. We explicitly derive the mathematical relation between the noise correlations and the spin structure factor in the strong-interaction limit of SU$(N)$ Hubbard models at any integer filling $\rho$. By calculating the ground states of one-dimensional SU$(N)$ Fermi-Hubbard models for $2\leq N\leq 6$ with use of the density-matrix renormalization-group method, we confirm the relation numerically in the regime of strong interactions $U \gg t$, where $U$ and $t$ denote the onsite interaction and the hopping energy. We show that the deviation between the actual noise correlations and those obtained from the spin structure factor scales as approximately $(t/U)^2$ for $\rho=1$ at intermediate and large lattice sizes on the basis of numeric and semi-analytic arguments.

Weighted stacking of ambient noise correlations based on the coherency of 3C data

Seismic interferometry (SI) is widely used in imaging subsurface structures at different scales by extracting empirical Green's functions (EGFs) of surface wave propagation from ambient noise. The EGFs may be misestimated, however, because the anisotropic distribution of noise sources violates the SI's basic assumption of energy equipartitioning. A common solution is to stack all short-duration noise cross-correlation functions (NCFs) over a long period of time to attenuate the effect of anisotropic noise sources. The effectiveness of the common method is limited by the time of observation, and it may even be counterproductive. The influence of anisotropic noise sources may be reinforced if these sources possess strong energy. To obtain reliable EGFs, we develop a new weighted stack method named the components-coherency-weighted stack (CCWS). We calculate the coherency of different components of noise recorded by a single station to scan the azimuth of the main source in one segment. We select the segments according to the azimuth for NCF computation and then stack the NCFs with a weight relevant to the coherency. We first test the method on simulated data containing strong azimuthal noise sources and show that our method yields high quality EGFs. The method is then used for two real data examples at a regional scale of hundreds of kilometers and an engineering scale of tens of meters. Two examples confirm the ability of our approach to improve the EGFs and its applicability at different scales.