Noise Correlation Research Articles

Electric field and SAR reduction in high-impedance RF arrays by using high permittivity materials for 7T MR imaging.

In the field of ultra-high field MR imaging, the challenges associated with higher frequencies and shorter wavelengths necessitate rigorous attention to multichannel array design. While the need for such arrays remains, and efforts to increase channel counts continue, a persistent impediment-inter-element coupling-constantly hinders development. This coupling degrades current and field distribution, introduces noise correlation between channels, and alters the frequency of array elements, affecting image quality and overall performance. The goal of optimizing ultra-high field MRI goes beyond resolving inter-element coupling and includes significant safety considerations related to the design changes required to achieve high-impedance coils. Although these coils provide excellent isolation, the higher impedance needs special design changes. However, such changes pose a significant safety risk in the form of strong electric fields across low-capacitance lumped components. This process may raise Specific Absorption Rate (SAR) values in the imaging subject, increasing power deposition and, as a result, the risk of tissue heating-related injury. To balance the requirement of inter-element decoupling with the critical need for safety, we suggest a new solution. Our method uses high-dielectric materials to efficiently reduce electric fields and SAR values in the imaging sample. This intervention tries to maintain B1 efficiency and inter-element decoupling within the existing array design, which includes high-impedance coils. Our method aims to promote the full potential of ultra-high field MRI by alleviating this critical safety concern with minimal changes to the existing array setup.

Crustal structure beneath the Western Dharwar Craton segment of Western Ghats: Insights from ambient noise correlation technique

The Western Ghats (WG) comprise three major tectonic blocks: the Deccan Volcanic Province (DVP), Western Dharwar Craton (WDC) and Southern Granulite Terrain (SGT) from north to south. Among these, the WDC region encompasses the oldest basement rocks, including greenstone belts and calc-alkaline plutons dating back to ∼3.0 to 3.4 Ga. While the crustal structure of the DVP and SGT segments of the WG has been extensively studied, knowledge regarding the crustal structure beneath the WDC segment of the WG is limited due to the scarcity of seismic stations. To address this gap, we utilized continuous waveform data from a newly established network of seismic stations by the National Centre for Earth Science Studies (NCESS), in addition to other available stations and deciphered the shallow crustal shear-wave velocity structure along a profile beneath the WDC segment of the WG using ambient noise cross-correlation and surface wave dispersion. The results reveal a continuous upper layer characterized by a high shear wave velocity of ∼3.6 km/s with notable variation in thickness along the profile. Furthermore, a thick middle layer with velocities reaching up to ∼3.9 km/s and a lower layer with velocities of ≥4.2 km/s are observed. Intriguingly, variations in the thickness of the upper layer, ranging from 5 km to 20 km, are noted beneath the study region. The thickness of this layer exhibits changes in relation to topographical variations, with this dependency diminishing for deeper layers. Notably, high shear wave velocity perturbations (∼10 %) are observed in the upper layer beneath low-elevation regions, possibly indicating the presence of Tonalite–Trondhjemite–Granodiorite (TTG)-type gneiss − a significant rock type in the WDC region, alongside greenstone belts and late calc-alkaline to potassic plutons. These findings suggest that the uneven topography of the WG may result from long-term erosion, exposing deeper and higher-density rocks and leading to higher shear wave velocities at shallow depths. This study serves as a foundation for further exploration into the influence of the upper crustal layer and its correlation with the evolution of the WG.

Repeated exposure to multiple concurrent stressors alters visual processing in the adult posterior parietal cortex

Chronic stress is well known to erode cognitive functions. Yet, our understanding of how repeated stress exposure impacts one of the fundamental bases of cognition: sensory processing, remains limited. The posterior parietal cortex (PPC) is a high order visual region, known for its role in visually guided decision making, multimodal integration, attention, and working memory. Here, we used functional measures to determine how repeated exposure to multiple concurrent stressors (RMS) affects sensory processing in the PPC in adult male mice. A longitudinal experimental design, repeatedly surveying the same population of neurons using in vivo two-photon imaging, revealed that RMS disrupts the balanced turnover of visually responsive cells in layer 2/3 of the PPC. Across the population, RMS-induced changes in visual responsiveness followed a bimodal distribution suggesting idiosyncratic stress effects. In cells that maintained their responsiveness across recording sessions, we found that stress reduced visual response magnitudes and feature selectivity. While we did not observe stress-induced elimination of excitatory synapses, noise correlation statistics indicated that RMS altered visual input to the neuronal population. The impact of RMS was restricted to visually evoked responses and was not evident in neuronal activity associated with locomotion onset. Together, our results indicate that despite no apparent synaptic reorganization, stress exposure in adulthood can disrupt sensory processing in the PPC, with the effects showing remarkable individual variation.

Noise correlations behind superdiffusive quantum walks.

We study how discrete-time quantum walks behave under short-range correlated noise. By considering noise as a source of inhomogeneity of quantum gates, we introduce a primitive relaxation in the assumption of uncorrelated stochastic noise: binary pair correlations manifesting in the random distribution. Using different quantum gates, we examined the transport properties for both spatial and temporal noise regimes. For spatial inhomogeneities, we unveil noise correlations driving quantum walks from the well-known exponentially localized regime to superdiffusive spreading. This scenario displays an intriguing performance in which the superdiffusive exponent is almost invariant to the degree of inhomogeneity. The time-asymptotic regime and the finite-size scaling also unveil an emergent superdiffusive behavior for quantum walks undergoing temporal noise correlation, replacing the diffusive regime exhibited when noise is random and uncorrelated. However, some quantum gates are insensitive to correlations, contrasting with the spatial noise scenario. Numerical and analytical results provide valuable insights into the underlying mechanism of superdiffusive quantum walks, including those arising from deterministic aperiodic inhomogeneities.

Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement

We develop and demonstrate a trainable temporal postprocessor (TPP) harnessing a simple but versatile machine learning algorithm to provide optimal processing of quantum measurement data subject to arbitrary noise processes for the readout of an arbitrary number of quantum states. We demonstrate the TPP on the essential task of qubit state readout, which has historically relied on temporal processing via matched filters in spite of their applicability for only specific noise conditions. Our results show that the TPP can reliably outperform standard filtering approaches under complex readout conditions, such as high-power readout. Using simulations of quantum measurement noise sources, we show that this advantage relies on the TPP’s ability to learn optimal linear filters that account for general quantum noise correlations in data, such as those due to quantum jumps, or correlated noise added by a phase-preserving quantum amplifier. Furthermore, we derive an exact analytic form for the optimal TPP weights: this positions the TPP as a linearly scaling generalization of matched filtering, valid for an arbitrary number of states under the most general readout noise conditions, all while preserving a training complexity that is essentially negligible in comparison with that of training neural networks for processing temporal quantum measurement data. The TPP can be autonomously and reliably trained on measurement data and requires only linear operations, making it ideal for field-programmable gate array implementations in circuit QED for real-time processing of measurement data from general quantum systems. Published by the American Physical Society 2024

Semiconductor laser systems excited by multiplicative Ornstein-Uhlenbeck noise and additive sine-Wiener noise in relation to real and imaginary parts.

In this paper we study a semiconductor laser system excited by multiplicative Ornstein-Uhlenbeck (OU) noise and additive sine-Wiener (SW) noise with a related real part and imaginary part. The two-dimensional Langevin equation(LE) with cross-correlating complex OU noise and complex SW noise is equivalent to the six-dimensional LE. Based on functional methods and spatial dimension extension methods, the six-dimensional Fokker-Planck equation(FPE) is achieved. Through Taylor expansion approximation and linear transformation, the dimensionality of FPE is reduced, and the exact expression of the probability distribution (SPD) in steady state is obtained. The impacts of noise parameters on laser intensity on SPD are further analyzed. Moreover, based on the reduced FPE, the information entropy of the system is obtained. This calculation is useful for us to understand further the influence of system parameters and noise correlation coefficient on the system. The main work lies in obtaining an alternative method for deriving a FPE corresponding to the two-dimensional stochastic semiconductor laser model, which is more applicable than methods in previous literature. An equivalent expression for cross-correlated OU noise and cross-correlated SW noise as well as a method for approximate solutions of a high-dimensional FPE are also provided.

Correlated non-Gaussian and Gaussian noises induced transition and mean first-passage time in a fractional-order bistable system

The transition and mean first-passage time(MFPT) in a fractional-order bistable system, excited by multiplicative non-Gaussian noise and additive Gaussian white noise, are investigated. Utilizing the fractional-order minimum mean square error criterion and the path integral approach, we derive approximate expressions for both the steady-state probability distribution function and the MFPT. The results show that the noise correlation strength, non-Gaussian parameter, Gaussian noise strength and fractional order can induce phase transitions. The MFPT exhibits a peak as a function of non-Gaussian noise intensity, demonstrating the noise-enhanced stability phenomenon, while the MFPT displays a consistent, monotonic relationship as a function of Gaussian white noise intensity. Numerical simulations are conducted to validate theoretical results, which show a reasonably high degree of consistency.

Perceptual Learning of Fine Contrast Discrimination Under Non-roving, Roving-Without-Flanker, and Roving-with-Flanker Conditions and its Relation to Neuronal Activity in Macaque V1

AbstractPerceptual learning refers to an improvement in perceptual abilities with training. Neural signatures of visual perceptual learning have been demonstrated mostly in mid- and high-level cortical areas, while changes in early sensory cortex were often more limited. We recorded continuously from multiple neuronal clusters in area V1 while macaque monkeys learned a fine contrast categorization task. Monkeys performed the contrast discrimination task initially when a constant-contrast sample stimulus was followed by a test stimulus of variable contrast, whereby they had to indicate whether the test was of lower or higher contrast than the sample. This was followed by sessions where we employed stimulus roving; i.e. the contrast of the sample stimulus varied from trial to trial. Finally, we trained animals, under ‘stimulus roving-with-flanker’ conditions, where the test stimuli to be discriminated were flanked by ‘flanking stimuli’. Perceptual discrimination abilities improved under non-roving conditions and under roving-with-flanker conditions as training progressed. Neuronal discrimination abilities improved with training mostly under non-roving conditions, but the effect was modest and limited to the most difficult contrast. Choice probabilities, quantifying how well neural activity is correlated with choice, equally increased with training during non-roving, but not during either of the roving conditions (with and without flankers). Noise correlations changed with training in both monkeys, but the changes were not consistent between monkeys. In one monkey, noise correlations decreased with training for non-roving and both roving conditions. In the other monkey, noise correlations changed for some conditions, but lacked a systematic pattern. Thus, while perceptual learning occurred under non-roving and roving-with-flanker conditions, the changes in neural activity in V1 were overall modest and were essentially absent under the different roving conditions.

Dynamic of seismic noise sources in the Mediterranean Sea: implication for monitoring using noise correlations

Dynamic of seismic noise sources in the Mediterranean Sea: implication for monitoring using noise correlations

Sparse sensor selection for distributed systems: An [formula omitted]-relaxation approach

We study the problem of sensor selection for distributed systems, where a large number of sensors are located spatially in many different locations. Specifically, we consider both perfect and packet-dropping communication channels. While the original problem is NP-hard, by adopting a sparse design, we can solve the problem via convex optimization and reduce the computation cost significantly. Our method not only handles correlated measurement noise but also can be easily extended to actuator selection or sensor-and-actuator (SaA) selection problems. Simulation shows that our sparsity-based approach performs similarly to the brute force optimal strategy while consuming significantly less computation time. Additionally, our method is shown to outperform the state-of-art method notably.

Quantification and mitigation of the effect of resynchronization errors in ultrasound sensor network for passive imaging in elastic plates.

The problem of signal desynchronization in passive imaging based on noise correlation for defect detection in elastic plates is investigated. Although a post-processing resynchronization process relying on the symmetry of noise correlation functions can be applied prior to the imaging algorithm, perfect synchronization might not be achieved experimentally. Effect of such residual synchronization errors on the defect detection performance is quantified as a function of their probability density function. A mathematical regularization process is then proposed to reduce the standard deviation of the resynchronization errors by a factor of N-1/N (N is the number of sensors), which results in a significant improvement in the detection performance. Finally, these theoretical results are validated through a simple flexural-wave propagation simulation.

A Reciprocity‐Based Efficient Method for Improved Source Parameter Estimation of Submarine Earthquakes With Hybrid 3‐D Teleseismic Green's Functions

AbstractAccurate source parameters of global submarine earthquakes are essential for understanding earthquake mechanics and tectonic dynamics. Previous studies have demonstrated that teleseismic P coda waveform complexities due to near‐source 3‐D structures are highly sensitive to source parameters of marine earthquakes. Leveraging these sensitivities, we can improve the accuracy of source parameter inversion compared to traditional 1‐D methods. However, modeling these intricate 3‐D effects poses significant computational challenges. To address this issue, we propose a novel reciprocity‐based hybrid method for computing 3‐D teleseismic Green's functions. Based on this method, we develop a grid‐search inversion workflow for determining reliable source parameters of moderate‐sized submarine earthquakes. The method is tested and proven on five Mw5+ earthquakes at the Blanco oceanic transform fault (OTF) with ground truth locations resolved by a local ocean bottom seismometer array, using ambient noise correlation and surface‐wave relocation techniques. Our results show that fitting P coda waveforms through 3‐D Green's functions can effectively improve the source location accuracy, especially for the centroid depth. Our improved centroid depths indicate that all the five Mw5+ earthquakes on the Blanco transform fault ruptured mainly above the depth of 600°C isotherm predicted by the half‐space cooling model. This finding aligns with the hypothesis that the rupture zone of large earthquakes at OTFs is confined by the 600°C isotherm. However, it is noted that the Blanco transform fault serves as a case study. Our 3‐D source inversion method offers a promising tool for systematically investigating global oceanic earthquakes using teleseismic waves.

Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion.

How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.

Long Period Rayleigh Wave Focal Spot Imaging Applied to USArray Data

AbstractWe demonstrate the effectiveness of seismic dense array surface wave focal spot imaging using USArray data from the western‐central United States. We study dispersion in the 60–310 s period range and assess the image quality of fundamental mode Rayleigh wave phase velocity maps. We apply isotropic spatial autocorrelation models to the time domain zero lag noise correlation wavefield data at distances of about one wavelength. Local estimates of the phase velocity, its uncertainty, and the regression quality imply overall better ZZ relative to ZR or RZ results. The extension of the depth resolution compared to passive surface wave tomography is demonstrated by the inversion of three clustered dispersion curves from different tectonic units. We observe anisotropic surface wave energy flux and the influence of body wave energy, but sensitivity tests at 60 s targeting the data range, correlation component, and processing choices show that the ZZ focal spots yield consistent high‐quality images compared to regional tomography results in the 60–150 s period range. In contrast, at 200–300 s the comparatively small scales of the imaged structures and the imperfect agreement with low‐resolution global tomography results highlight the persistent challenge to reconcile imaging results based on different data sources, theories, and techniques. Our study shows that surface wave focal spot imaging is an accurate, robust, local imaging approach. Better control over clean autocorrelation fields can further improve applications of this seismic imaging tool for increased resolution of the elastic structure below dense seismic arrays.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation.

The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs). Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.

Adaptive nonlinear Kalman filters based on credibility theory with noise correlation

To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods, we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility Kalman Filter (KF) theory considering noise correlation. This method first extends credibility to the Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) based on the credibility theory. Further, an optimization model for nonlinear credibility under noise related conditions is established considering noise correlation. A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm, further expanding its application scenarios, and the derivation process of the formula theory is provided. Finally, the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives, and a comparative analysis conducted on specific experimental data. The simulation and experimental results show that the proposed credibility EKF and credibility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods, indicating that the proposed algorithm has stable estimation performance and good real-time performance.

Correlated noise enhancement of coherence and fidelity in coupled qubits

ABSTRACT It is generally assumed that environmental noise arising from thermal fluctuations are detrimental to preserving coherence and entanglement in a quantum system. In the simplest sense, dephasing and decoherence are tied to energy fluctuations driven by coupling between the system and the normal modes of the bath. Here, we explore the role of noise correlation in an open-loop model quantum communication system whereby the ‘sender’ and the ‘receiver’ are subject to local environments with various degrees of correlation or anti-correlation. We introduce correlation within the spectral density by solving multidimensional stochastic differential equations and introduce these into the Redfield equations of motion for the system density matrix. We find that correlation can enhance both the fidelity and purity of a maximally entangled (Bell) state. Moreover, by comparing the evolution of different initial Bell states, we show that one can effectively probe the correlation between two local environments. These observations may be useful in the design of high-fidelity quantum gates and communication protocols.

Control of working memory by phase–amplitude coupling of human hippocampal neurons

Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta–gamma phase–amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3–5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.

Laboratory study of aberration calculation in underwater turbulence using Shack-Hartmann wavefront sensor and Zernike polynomials

This paper presents a laboratory study of the aberrations calculation in underwater turbulence using the Shack-Hartmann wavefront sensor. The wavefront decomposition method and Zernike polynomials determine the aberration parameters. In our experimental setup, the turbulent phase screen generator is located in two locations: near the transmitter and therefore far from the receiver, and near the receiver and consequently far from the transmitter. Additionally, we investigate the impact of aperture diameter on turbulence-induced aberrations in the optical receiver system. However, it is essential to note that the coefficients of Zernike polynomials obtained using this method are subject to errors caused by receiver sensor noise and correlation between the polynomials. To address this, we first calculate the coefficients in different arrangements and then correct measurement errors arising from sensor noise and polynomial coefficient correlation.

Insights into fluid migration during the 2021 La Palma eruption using seismic noise interferometry

Ambient noise correlation analyses are largely used to monitor temporal medium changes generally associated with stress field variations and/or fluid movement. Here, we analyze the 2021 eruption of La Palma, the most active island in the Canary archipelago, to study its effects on the structure in a post-eruptive stage. To date, most of the studies, whether in volcanic environments or other geological systems, focus on determining seismic velocity changes that can be associated with precursory signals. In our study, we are interested in localizing the medium changes that permit constraining the depth of the most affected structure and suggesting possible mechanisms capable of inducing such alterations. The auto- and cross-correlation functions were computed using the phase cross-correlation strategy. The correlations were linearly stacked using a 3-day sliding window. The combination of these two approaches proved to render the best results. The analysis of 3 years of data resulted in the detection of occasional decorrelation before the eruption, followed by a well-defined decoherence period after the eruption. In addition, the relationship between the waveform correlation and lag time, using autocorrelations from before and after the eruption, permits identifying phase shifts and waveform distortion, which are sensitive to different parameters and, thus, have great importance in inferring the possible mechanism. Phase shifts occur when there is only velocity change without changing the structure, whereas waveform distortion is caused by a structural (geological) change. We also inferred the depths at which the most significant medium alterations occur. We observed that the decorrelation occurs at lag times corresponding to changes localized in depth. The surface structure appears to not have undergone significant medium changes for depths until approximately 8 km, either before or after the eruption.