Noise Correlation Research Articles

Frequency-separated principal component analysis of cortical population activity.

A hallmark of neocortical activity is the presence of low-dimensional fluctuations in firing rate that are coordinated across neurons. However, the impact of these fluctuations on sensory processing remains unclear. Here, we examined fluctuations in populations of orientation-selective neurons from anesthetized macaque primary visual cortex (V1) during stimulus viewing as well as spontaneous activity. We introduce a novel approach termed frequency-separated principal component analysis (FS-PCA) to characterize these fluctuations. This method unveiled a distribution of components with a broad range of frequencies whose eigenvalues and variance followed an approximate power law. During stimulus viewing, subpopulations of V1 neurons correlated either positively or negatively with low-dimensional fluctuations. These two subpopulations displayed distinct activation properties and noise correlations in response to sensory input. Together, results suggest that slow, low-dimensional fluctuations in V1 population activity shape the response of individual neurons to oriented stimuli and may impact the transmission of sensory information to downstream regions of the primary visual system.NEW & NOTEWORTHY A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

Pairing patterns in one-dimensional spin- and mass-imbalanced Fermi gases

We study spin- and mass-imbalanced mixtures of spin-1/2 fermions interacting via an attractive contact potential in one spatial dimension. Specifically, we address the influence of unequal particle masses on the pair formation by means of the complex Langevin method. By computing the pair-correlation function and the associated pair-momentum distribution we find that inhomogeneous pairing is present for all studied spin polarizations and mass imbalances. To further characterize the pairing behavior, we analyze the density-density correlations in momentum space, the so-called shot noise, which is experimentally accessible through time-of-flight imaging. At finite spin polarization, the latter is known to show distinct maxima at momentum configurations associated with the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) instability. Besides those maxima, we find that additional features emerge in the noise correlations when mass imbalance is increased, revealing the stability of FFLO-type correlations against mass imbalance and furnishing an experimentally accessible signature to probe this type of pairing.

Relative Time Corrections for Historical Analog Seismograms Using the Single-Day Ambient Noise Correlation Function

ABSTRACTThis study examines analog seismograms that were generated when most seismic stations had their own clock for timing, making precise comparison of time between different stations difficult. Availability of accurate relative timing facilitates differential travel-time analyses, such as seismic tomography and local earthquake relocations, to be performed using data originally recorded on paper or other physical media. These analyses allow for the investigation of longer-term processes like the earthquake cycle or climate change. We take advantage of the continuous nature of seismic noise to determine the relative time correction between two stations by leveraging the symmetry of the noise correlation function. This procedure is applied to two Global Positioning System-timed stations in the Hawaiian Volcano Observatory network demonstrating subsecond time accuracy. The technique is then applied to analog records from comparable stations between 7 and 10 August in 1988, and relative time corrections of up to about 6 s are obtained. These corrections are confirmed by the relative arrival times of teleseismic P waves of earthquake doublets.

Multi-site benchmarking of clinical 13C RF coils at 3T

A quality assurance protocol for RF coils is proposed, which can be used for volume (Tx/Rx) and surface (Rx) coils. Following this protocol, a benchmarking of seven coils (from three different MR sites) dedicated to 13C MRI at 3T is reported. Coil performance is particularly important for 3T MRI at the 13C frequency, since the coil-to-sample noise ratio is typically high. The coils are evaluated experimentally using the proposed protocol based on MR spectroscopic imaging performed with two different phantoms: one head-shaped, and one with cylindrical shape and nearly twice the volume of the first one. To achieve an unbiased SNR comparison of volume and array coils, coil combination was done using sensitivity profiles extracted from the data. SNR, noise correlation matrices and example g-factor maps are reported. For globally calibrated, equal excitation angles, the measured SNR shows large differences for the volume coils of up to 115% at the phantom center for a head phantom. The arrays show lower differences in superficial SNR. The sample surface depth at which the volume coils outperform the arrays is estimated to 7 cm, and SNR furthest away from the coil surface is 28% lower for the best array compared to the best volume coil. A broad set of coils for 13C at 3T have been benchmarked. The results reported, and the method used to benchmark them, should guide the 13C community to choose the most suitable coil for a given experiment.

Comprehensive system based on a DNN and LSTM for predicting sinter composition

Because of the lag in sinter composition detection, fluctuations in production conditions are not conducive to making timely adjustments to sintering. In this paper, the characteristics of sintering production data are studied, and three core conclusions are drawn. We find that these data have (1) noise, (2) high dimensionality, and (3) time correlation. Based on these findings, an integrated model based on a deep neural network (DNN) and a long short-term memory (LSTM) network is proposed; using a DNN and an LSTM network solves the problem of developing a system model according to the given input and output data to predict the chemical composition of sinter. Specifically, first, we use a box plot and an isolated forest (iForest) algorithm to detect and filter noise in the data preparation stage and then propose using key feature selection and the Pearson correlation coefficient to reduce the high dimensionality of the data. Then, both an online component monitoring model based on a DNN and an advance component prediction model based on an LSTM are proposed to help the field operators to control the change of the sinter composition in real time. The results of many experiments show that the proposed method performs better than the current methods: the goodness of fit (R2) score of the better model is above 0.92, and both the mean square error (MSE) and mean absolute error (MAE) are approximately zero. The deep neural network-based method proposed in this paper is more suitable for the online monitoring and advance prediction of sinter components.

Low frequency noise-dark current correlations in HgCdTe infrared photodetectors.

In this paper, low frequency noise and dark current correlation is investigated as a function of reverse bias and temperature for short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) HgCdTe homo-junction photodetectors. Modelling of dark current-voltage characteristics shows that the detectors have ohmic-behavior under small reverse bias, thus enabling further analysis of 1/f noise-current dependences with the empirical square-law relation (SI ∼ I2) at different temperature regions. It is found that for the SWIR and MWIR devices, the total 1/f noise spectral density at arbitrary temperatures can be modelled by the sum of shunt and generation-recombination noise as SI(T,f)=[αSHISH2(T)+αG-RIG-R2(T)]/f, with no contribution from the diffusion component observed. On the other hand, for the LWIR device the diffusion component induced 1/f noise that cannot be overlooked in high temperature regions, and a 1/f noise-current correlation of SI(T,f)={αs[IDIFF2(T)+IG-R2(T)]+αSHISH2(T)}/f is proposed, with a shared noise coefficient of αs ≅ 1 × 10-9 which is close to that calculated for shunt noise. The 1/f noise-current correlation established in this work can provide a powerful tool to study the low frequency noise characteristics in HgCdTe-based photodetectors and to help optimizing the "true" detectivity of devices operating at low frequency regime.

Ambient-Noise Tomography of the Baiyun Gold Deposit in Liaoning, China

Abstract Ambient-noise tomography (ANT) has become an effective method for determining the fine velocity structure of the shallow crust. However, studies on metal mines using this method are rarely reported. To investigate the tectonic background and prospecting of the deep mine in the Baiyun gold deposit (BYGD) of eastern Liaoning Province, China, we use ANT to determine a 3D S-wave velocity structure model of the BYGD. A total of 21 broadband seismic stations were installed in an area of 15×14 km, centered at the BYGD. Continuous observations for approximately three months were made. After single-station preprocessing, cross correlation of ambient noise, and phase-weighted stacking, the empirical Green’s function for the Rayleigh waves between stations was recovered. Next, group-velocity dispersion with 0.8–3 s periods was measured. A direct inversion method of surface-wave dispersion based on raytracing was then adopted to determine a 3D S-wave velocity structure of the BYGD from the ground surface to a depth of 1.8 km. The results show that the distribution of S-wave velocities in the study area well reflected the geological characteristics of the surface. The velocities were significantly low within the “ore field” and the regional ore-controlling Jianshanzi fault. Combining this with the fact that a large number of magmatic veins were visible inside both structures, it was deduced that both structures had experienced large-scale magmatic intrusion activities, thus confirming that BYGD was a magmatic hydrothermal deposit. The significantly low S-wave velocities beneath the gold deposit extended to a depth of 1.8 km. This might imply the occurrence of blind ore bodies at that depth. The fine velocity structure of the BYGD reconstructed by this study provided a direction for subsequent prospecting of deep regions and demonstrated that ANT has good potential in metal mine exploration.

Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water

Array gain (AG) is significant in evaluating the detection performance of the vertical line array, which is directly determined by the correlation of signal and noise, respectively. In this paper, we analyze the vertical correlation for a 16-element vertical line array experimented in the deep ocean in 2016. The ray interference theory is utilized to interpret the mechanism of the vertical correlation of the sound field in different zones. In the direct-arrival zone, the direct rays and once-surface-reflected rays are two dominated components, whose arrival time difference for each element are nearly the same, and the vertical correlation is high. In the shadow zone, the sound field is mainly dominated by bottom-reflected rays and the vertical correlation decreases due to different grazing angles and arrival times of each ray. Different from the previous assumption of noise independence, the effect of noise correlation on the AG is analyzed through the measured marine environmental noise. Results indicate that the noise correlation coefficients in two zones are low but not 0. In the direct-arrival zone, AG is about 10 dB, very close to the ideal value of 10 log M . AG even exceeds it when NG is negative. Moreover, AG in the direct-arrival zone is higher than the one in the shadow zone.

Low-dose x-ray CT simulation from an available higher-dose scan

In computed tomography (CT)-imaging an optimal compromise between the radiation burden and the image quality for the imaging task is needed. Lower-dose CT is desirable, however, lowering the dose results in a lower signal-to-noise ratio and therefore in a reduced image quality. In this research, we aim to develop a tool to simulate lower-dose scans from an existing standard-dose scan. The main application of this tool is to determine the lowest possible radiation dose that still produces sufficient clinical information. The x-ray tube current reduction is modeled by estimating the noise equivalent number of photons in the high exposure scan and applying a thinning technique to reduce that number. The proposed method accounts for the bowtie filter, for the electronic system noise, for the noise correlation between neighboring detector elements, for the beam hardening effect, and for the non-linear smoothing filter in very low-dose scans. Several phantom studies with different acquisition protocols were performed to evaluate the accuracy of the proposed framework. The results demonstrate a close agreement between the noise magnitude and texture of the measured and the simulated lower-dose scans. For instance, the standard deviation of noise in the simulation of lower-dose scans with 90% tube current reduction matches the reconstructions from the real scans with less than 1% and 3% error for sequential and helical scans, respectively. The noise texture was also assessed by analyzing the noise power spectrum of the simulated lower-dose images which matches those from the real scans. Furthermore, the relation between the measured and predicted noise in projection domain is very close to the line of identity which confirms the accuracy of the model.

Inverse Techniques for Efficient Corneal Image Restoration

This paper presents two proposed approaches for digital restoration of corneal images. The first algorithm is based on Wiener Restoration approach. The second algorithm depends on regularized image restoration. As corneal images are usually acquired with confocal microscopes. Hence if the corneal layer is outside the focus of the microscopes, the image will be blurred. To solve this problem, the restoration process can be applied on the corneal image. Both Linear Minimum Mean Square Error (LMMSE) and regularized restoration are implemented. The evaluation metrics used to test the performance of the proposed restoration approaches are mean square error (MSE), peak signal to noise ratio (PSNR) and correlation coefficient. Simulations results reveal good success in restoration of corneal images refer to the mentioned evaluation metrics and appearance view.

Increasing excitation versus decreasing inhibition in auditory cortex: consequences on the discrimination performance between communication sounds.

Enhancing cortical excitability can be achieved by either reducing intracortical inhibition or by enhancing intracortical excitation. Here we compare the consequences of reducing intracortical inhibition and of enhancing intracortical excitation on the processing of communication sounds in the primary auditory cortex. Local application of gabazine and of AMPA enlarged the spectrotemporal receptive fields and increased the responses to communication to the same extent. The Mutual Information (an index of the cortical neurons' ability to discriminate between natural sounds) was increased in both cases, as were the noise and signal correlations. Spike-timing reliability was only increased after gabazine application and post-excitation suppression was affected in the opposite way: it was increased when reducing the intracortical inhibition but was eliminated by enhancing the excitation. A computational model suggests that these results can be explained by an additive effect vs. a multiplicative effect ABSTRACT: The level of excitability of cortical circuits is often viewed as one of the critical factors controlling perceptive performance. In theory, enhancing cortical excitability can be achieved either by reducing inhibitory currents or by increasing excitatory currents. Here, we evaluated whether reducing inhibitory currents or increasing excitatory currents in auditory cortex similarly affects the neurons' ability to discriminate between communication sounds. We attenuated the inhibitory currents by application of gabazine (GBZ), and increased the excitatory currents by applying AMPA in the auditory cortex while testing frequency receptive fields and responses to communication sounds. GBZ and AMPA enlarged the receptive fields and increased the responses to communication sounds to the same extent. The spike-timing reliability of neuronal responses was largely increased when attenuating the intracortical inhibition but not after increasing the excitation. The discriminative abilities of cortical cells increased in both cases but this increase was more pronounced after attenuating the inhibition. The shape of the response to communication sounds was modified in the opposite direction: reducing inhibition increased post-excitation suppression whereas this suppression tended to disappear when increasing the excitation. A computational model indicates that the additive effect promoted by AMPA vs. the multiplicative effect of GBZ on neuronal responses, together with the dynamics of spontaneous cortical activity, can explain these differences. Thus, although apparently equivalent for increasing cortical excitability, acting on inhibition vs. on excitation impacts differently the cortical ability to discriminate natural stimuli, and only modulating inhibition changed efficiently the cortical representation of communication sounds.

Bounds on Phase, Frequency, and Timing Synchronization in Fully Digital Receivers With 1-bit Quantization and Oversampling

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard receiver algorithms cannot be applied. Thus, we derive performance bounds for phase, timing, and frequency estimation in order to gain a deeper insight into the impact of 1-bit quantization and oversampling. We identify uniform phase and sample dithering as crucial to combat the effect of the non-linearity introduced by 1-bit quantization. Since oversampling results in noise correlation, a closed form of the likelihood function is not available. Thus, we study a system model with white noise by adapting the receive filter bandwidth to the sampling rate. Considering the aforementioned dithering, we obtain very tight closed form lower bounds on the Cramér-Rao lower bound (CRLB) in the large sample regime. We show that with uniform phase and sample dithering, all large sample properties of the CRLB of the unquantized receiver are preserved under 1-bit quantization, except for an signal-to-noise ratio (SNR) dependent performance loss that can be decreased by oversampling. Numerical computations show that the properties of the CRLB for white noise still hold for colored noise except that the performance loss due to 1-bit quantization is reduced.

Improved Spectrum Sensing Schemes Using Prewhitening and Weights Under Spatially Correlated Noise

Conventional spectrum sensing (SS) schemes in a multiantenna cognitive radio utilize matrix-inverse based prewhitening to decorrelate the spatially correlated received signals. However, the improved SS schemes can be proposed by efficiently exploiting the spatial correlation information. In this paper, two detectors—weighted cross-correlation absolute value detector (WCCAVD) and weighted energy detector (WED), are proposed by exploiting the spatial correlation of noise. It is shown that in the presence of high spatial correlation and low signal-to-noise ratio, the proposed WCCAVD and WED outperform the conventional cross-correlation absolute value detector and energy detector (ED), respectively, by more than 1 dB. For the spatial correlation below 0.5, both the proposed detectors are shown to have comparable performance with respect to the corresponding conventional detectors. The analytical expressions for the decision threshold, the false-alarm and the detection probabilities of the proposed detectors are derived. The analytical results are validated by Monte-Carlo simulations.

Spatial noise correlations in a Si/SiGe two-qubit device from Bell state coherences

We study spatial noise correlations in a Si/SiGe two-qubit device with integrated micromagnets. Our method relies on the concept of decoherence-free subspaces, whereby we measure the coherence time for two different Bell states, designed to be sensitive only to either correlated or anti-correlated noise respectively. From these measurements, we find weak correlations in low-frequency noise acting on the two qubits, while no correlations could be detected in high-frequency noise. A theoretical model and numerical simulations give further insight into the additive effect of multiple independent (anti-)correlated noise sources with an asymmetric effect on the two qubits. Such a scenario is plausible given the data and our understanding of the physics of this system. This work is highly relevant for the design of optimized quantum error correction codes for spin qubits in quantum dot arrays, as well as for optimizing the design of future quantum dot arrays.

Predicted signatures of topological superconductivity and parafermion zero modes in fractional quantum Hall edges

The authors derive the current and noise correlation that will be measured in a setup consisting of two counter-propagating fractional quantum Hall edge modes, strongly coupled to a superconductor by proximity. These reveal signatures of parafermion zero modes, fractionalized generalizations of Majorana zero modes. These values are obtained using both perturbative calculations and mapping onto an exact solution.

Virtual Sources of Body Waves from Noise Correlations in a Mineral Exploration Context

Abstract The extraction of body waves from passive seismic recordings has great potential for monitoring and imaging applications. The low environmental impact, low cost, and high accessibility of passive techniques makes them especially attractive as replacement or complementary techniques to active-source exploration. There still, however, remain many challenges with body-wave extraction, mainly the strong dependence on local seismic sources necessary to create high-frequency body-wave energy. Here, we present the Marathon dataset collected in September 2018, which consists of 30 days of continuous recordings from a dense surface array of 1020 single vertical-component geophones deployed over a mineral exploration block. First, we use a cross-correlation beamforming technique to evaluate the wavefield each minute and discover that the local highway and railroad traffic are the primary sources of high-frequency body-wave energy. Next, we demonstrate how selective stacking of cross-correlation functions during periods where vehicles and trains are passing near the array reveals strong body-wave arrivals. Based on source station geometry and the estimated geologic structure, we interpret these arrivals as virtual refractions due to their high velocity and linear moveout. Finally, we demonstrate how the apparent velocity of these arrivals along the array contains information about the local geologic structure, mainly the major dipping layer. Although vehicle sources illuminating array in a narrow azimuth may not seem ideal for passive reflection imaging, we expect this case will be commonly encountered and should serve as a good dataset for the development of new techniques in this domain.

Scaling laws and dynamics of hashtags on Twitter.

In this paper, we quantify the statistical properties and dynamics of the frequency of hashtag use on Twitter. Hashtags are special words used in social media to attract attention and to organize content. Looking at the collection of all hashtags used in a period of time, we identify the scaling laws underpinning the hashtag frequency distribution (Zipf's law), the number of unique hashtags as a function of sample size (Heaps' law), and the fluctuations around expected values (Taylor's law). While these scaling laws appear to be universal, in the sense that similar exponents are observed irrespective of when the sample is gathered, the volume and the nature of the hashtags depend strongly on time, with the appearance of bursts at the minute scale, fat-tailed noise, and long-range correlations. We quantify this dynamics by computing the Jensen-Shannon divergence between hashtag distributions obtained τ times apart and we find that the speed of change decays roughly as 1/τ. Our findings are based on the analysis of 3.5×109 hashtags used between 2015 and 2016.

Accelerating linear system solvers for time-domain component separation of cosmic microwave background data

Component separation is one of the key stages of any modern cosmic microwave background data analysis pipeline. It is an inherently nonlinear procedure and typically involves a series of sequential solutions of linear systems with similar but not identical system matrices, derived for different data models of the same data set. Sequences of this type arise, for instance, in the maximization of the data likelihood with respect to foreground parameters or sampling of their posterior distribution. However, they are also common in many other contexts. In this work we consider solving the component separation problem directly in the measurement (time-) domain. This can have a number of important benefits over the more standard pixel-based methods, in particular if non-negligible time-domain noise correlations are present, as is commonly the case. The approach based on the time-domain, however, implies significant computational effort because the full volume of the time-domain data set needs to be manipulated. To address this challenge, we propose and study efficient solvers adapted to solving time-domain-based component separation systems and their sequences, and which are capable of capitalizing on information derived from the previous solutions. This is achieved either by adapting the initial guess of the subsequent system or through a so-called subspace recycling, which allows constructing progressively more efficient two-level preconditioners. We report an overall speed-up over solving the systems independently of a factor of nearly 7, or 5, in our numerical experiments, which are inspired by the likelihood maximization and likelihood sampling procedures, respectively.

When Clocks Are Not Working: OBS Time Correction

AbstractOcean-bottom seismographs (OBSs) are used to obtain seismic recordings offshore and are an increasingly important tool for investigating the globe. However, because OBS data cannot be time stamped using Global Positioning System (GPS) during deployment, correction for drift of the internal clock is required. This time drift is typically derived by synchronizing the clock before and after deployment. Linear correction is then applied using the timing deviation between GPS and the instrument’s internal clock at recovery, that is, the skew measurement. If synchronization measurements are missing, ambient noise cross-correlation functions (CCFs) are commonly used for time correction. When investigating recordings from a small-scale OBS network located on the Mohn’s mid-ocean ridge, we observed a remaining drift on the skew-corrected data. After recalculating the drift of the raw data using CCFs, we found that the skew-based time correction was incorrect. This was also verified with the observation of teleseismic P-wave arrivals. We describe a method to obtain properly time-corrected data and discuss the OBS timing issues in detail. The results shown were obtained using a software package that we developed for this specific purpose and made available as open-source software. Although we cannot explain the technical reason for the failure of skew correction, this study shows that skew corrections should not be trusted alone, and OBS timing should always be verified by either ambient noise correlations or P-wave arrival times.

Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.