Cross-correlation Model Research Articles

Correlation of instantaneous fading in bidirectional channels of atmospheric laser communication

The instantaneous fading correlation of the bidirectional channel is an important characteristic of the atmospheric random channel. The instantaneous channel state of the transmitting end can be directly obtained through the instantaneous channel state of the receiving end, reducing the overhead of the additional feedback branch. In this paper, an autoregressive (AR) model and an inverse transformation method are used to generate time-dependent optical signals obeying an APD exponential Weibull distribution. Based on the channel reciprocity function, a channel cross-correlation model on the laser communication transmission path is established, and the influence of factors such as transmitting and receiving apertures, zenith angle, transmission distance, altitude, wavelength, and detector responsivity on the cross-correlation coefficient is studied. Based on theoretical research, an experimental system was built to verify the heterogeneous reciprocity model, and the experimental results were basically consistent with the simulation results. A channel state information prediction and evaluation are achieved through a relevant reciprocity theory, providing a basis for the development of adaptive technology in atmospheric laser communications.

Quantile correlation between fintech stocks and crypto-assets

ABSTRACT This research explores the dependence, directional predictability and dynamic co-movement between fintech and cryptocurrency markets from July 2016 to March 2021 using a series of quantile-based coherency techniques. The causality-in-quantiles results show a considerable difference between causality-in-mean and in-variance under different market conditions. For cross-quantilogram analysis, we observe minimal directional predictability between cryptocurrencies and fintech both in the short-run and in the long-run under bearish and bullish market states. From wavelet multiple cross-correlation models, we show that cryptocurrencies maximize multiple correlation compared to fintech across all time scales, denoting that cryptocurrencies are most dependent on fintech for all wavelet scales.

Peering above the clouds of the warm Neptune GJ 436 b with CRIRES+

Context. Exoplanets with masses between Earth and Neptune are amongst the most commonly observed, yet their properties are poorly constrained. Their transmission spectra are often featureless, which indicate either high-altitude clouds or a high atmospheric metallicity. The archetypical warm Neptune GJ 436 b is such a planet showing a flat transmission spectrum in observations with the Hubble Space Telescope (HST). Aims. Ground-based high-resolution spectroscopy (HRS) effectively probes exoplanet atmospheres at higher altitudes and can therefore be more sensitive to absorption coming from above potential cloud decks. In this paper we aim to investigate this for the exoplanet GJ 436b. Methods. We present new CRIRES+ HRS transit data of GJ 436 b. Three transits were observed, but since two were during bad weather conditions, only one transit was analyzed. The radiative transfer code petitRADTRANS was used to create atmospheric models for cross-correlation and signal-injection purposes, including absorption from H2O, CH4, and CO. Results. No transmission signals were detected, but atmospheric constraints can be derived. Injection of artificial transmission signals indicate that if GJ 436 b would have a cloud deck at pressures P > 10 mbar and a <300× solar metallicity, these CRIRES+ observations should have resulted in a detection. Conclusions. We estimate that the constraints presented here from one ground-based HRS transit are slightly better than those obtained with four HST transits. Combining HRS data from multiple transits is an interesting avenue for future studies of exoplanets with high-altitude clouds.

Copy-move detection method based on Decoupled Edge Supervision and multi-domain cross correlation modeling

Copy-move detection method based on Decoupled Edge Supervision and multi-domain cross correlation modeling

Dynamics of Chlorophyll-a Concentration in Ternate Island Waters and Its Effect on Yellowfin Tuna Production

This research was conducted from May to August 2023, with the aim of assessing the distribution of chlorophyll-a concentration and its effect on yellowfin tuna production in Ternate Island waters. The use of experimental fishing methods in collecting research data and data analysis in the form of abundance analysis, exponential regression, cross correlation, and General Aditive Models is expected to answer the research objectives. The results showed that the fluctuation of yellowfin tuna fish catch was similar to the abundance of fish stocks with the highest catch in June (14 229 kg) followed by July (11 142 kg), August (10 764 kg) and May (8 001 kg). The catch of yellowfin tuna fish is spread over a range of chlorophyll-a concentrations between 0.06 mg m-3 to 0.32 mg m-3, with an average monthly chlorophyll-a concentration of 0.22 mg m-3. Chlorophyll-a conditions in Ternate Island waters are quite fluctuating and significantly affect the catch of yellowfin tuna fish with a very strong correlation coefficient of 0.87. The results of General Aditive Models analysis found that the chlorophyll-a concentration value for potential yellowfin tuna fishing areas is > 0.01 mg m-3 with a correlation distance or time leg is in week 15.

Localizing concurrent sound sources with binaural microphones: A simulation study

The human auditory system can localize multiple sound sources using time, intensity, and frequency cues in the sound received by the two ears. Being able to spatially segregate the sources helps perception in a challenging condition when multiple sounds coexist. This study used model simulations to explore an algorithm for localizing multiple sources in azimuth with binaural (i.e., two) microphones. The algorithm relies on the “sparseness” property of daily signals in the time-frequency domain, and sound coming from different locations carrying unique spatial features will form clusters. Based on an interaural normalization procedure, the model generated spiral patterns for sound sources in the frontal hemifield. The model itself was created using broadband noise for better accuracy, because speech typically has sporadic energy at high frequencies. The model at an arbitrary frequency can be used to predict locations of speech and music that occurred alone or concurrently, and a classification algorithm was applied to measure the localization error. Under anechoic conditions, averaged errors in azimuth increased from 4.5° to 19° with RMS errors ranging from 6.4° to 26.7° as model frequency increased from 300 to 3000 Hz. The low-frequency model performance using short speech sound was notably better than the generalized cross-correlation model. Two types of room reverberations were then introduced to simulate difficult listening conditions. Model performance under reverberation was more resilient at low frequencies than at high frequencies. Overall, our study presented a spiral model for rapidly predicting horizontal locations of concurrent sound that is suitable for real-world scenarios.

A model for multiphase flow velocity calculation in pipelines based on ultrasonic sensors

In the petroleum and natural gas industry, a wide variety of multiphase fluids are prevalent, and precise measurement of their flow velocity in pipelines holds significant importance for different stages of well drilling and construction. However, due to the presence of large solid particles and the corrosive nature of the liquid phase in multiphase fluids within the petroleum industry, invasive measurement methods struggle to maintain long-term acceptable detection accuracy. Therefore, the non-contact fluid flow velocity measurement method based on ultrasonic sensors exhibits substantial research value. Nonetheless, when employing this approach for pipeline multiphase fluid flow velocity measurement, the abundance of background interference noise at the site poses challenges in Doppler echo signal reconstruction and results in lower precision for frequency shift extraction, leading to considerable errors in flow velocity calculation results. To address this issue, the present study utilizes a transmit-receive separated continuous wave ultrasonic sensor. First, a mathematical model is developed for the superimposed signal of ultrasonic Doppler echoes within the pipeline. Next, a novel signal reconstruction method is proposed by employing Chebyshev polynomials for interpolation computation of the sampled discrete signals. Subsequently, a Doppler shift model is introduced, leading to the formulation of a new model for multiphase flow velocity calculation in pipelines based on ultrasonic sensors. Finally, a comparison experiment for full-pipe multiphase flow velocity detection is conducted to validate the computational performance of the new model. The experimental results show that, compared with the FFT model and the conventional cross correlation model, the comprehensive meter factor of the ultrasonic flow measurement system with the new model is reduced by 0.024 445, the accuracy is reduced by 2.98%, the nonlinear error is reduced by 2.4405%, the average relative error is reduced by 0.646%, the standard deviation is reduced by 0.045 175, and the root mean squared error is reduced by 0.029 615.

Coupling habitat-specific temperature scenarios with tolerance landscape to predict the impacts of climate change on farmed bivalves

Due to climate change, heatwaves are likely to become more frequent, prolonged and characterized by higher peak values, compared with climatological averages. However, the thermal tolerance of organisms depends on the actual exposure, which can be modulated by environmental context and microhabitat characteristics. This study investigated the frequency of occurrence of mass mortality events in the next decades for two species of farmed bivalves, the mussel Mytilus galloprovincialis and the clam Ruditapes philippinarum, in a shallow coastal lagoon, characterised by marked diurnal oscillations of water temperature. The effect of heatwaves was estimated by means of tolerance landscape models, which predict the occurrence of 50% mortality based on the exposure intensity and duration. Scenarios of water temperature up to the year 2100 were modelled by combining two mechanistic components, namely: 1) monthly mean water temperatures, simulated using a hydrodynamic model including the heat budget; 2) daily oscillations, estimated from the harmonic analysis of a twenty year-long site-specific time series of water temperature. Scenarios of mean daily sediment temperature were estimated by means of a cross-correlation model, using as input the water temperature one: the model parameters were estimated based on a comprehensive set of site-specific water and sediment temperature observations. The results indicate that for both species the risk of mass mortality rapidly increases starting from the 2060s. Furthermore, the daily patterns of water temperature seemed to be relevant, as overnight it falls below the predicted mortality thresholds for a few hours. These findings suggest that further studies should address: 1) the improvement of tolerance landscape models, in order to take into account the integrated effect of repeated non-lethal stress events on mortality rate; 2) the prediction of environmental temperature in specific habitat, by means of both process-based and data driven models.

Neural coding of dichotic pitches in auditory midbrain.

Dichotic pitches such as the Huggins pitch (HP) and the binaural edge pitch (BEP) are perceptual illusions whereby binaural noise that exhibits abrupt changes in interaural phase differences (IPDs) across frequency creates a tonelike pitch percept when presented to both ears, even though it does not produce a pitch when presented monaurally. At the perceptual and cortical levels, dichotic pitches behave as if an actual tone had been presented to the ears, yet investigations of neural correlates of dichotic pitch in single-unit responses at subcortical levels are lacking. We tested for cues to HP and BEP in the responses of binaural neurons in the auditory midbrain of anesthetized cats by varying the expected pitch frequency around each neuron's best frequency (BF). Neuronal firing rates showed specific features (peaks, troughs, or edges) when the pitch frequency crossed the BF, and the type of feature was consistent with a well-established model of binaural processing comprising frequency tuning, internal delays, and firing rates sensitive to interaural correlation. A Jeffress-like neural population model in which the behavior of individual neurons was governed by the cross-correlation model and the neurons were independently distributed along BF and best IPD predicted trends in human psychophysical HP detection but only when the model incorporated physiological BF and best IPD distributions. These results demonstrate the existence of a rate-place code for HP and BEP in the auditory midbrain and provide a firm physiological basis for models of dichotic pitches.NEW & NOTEWORTHY Dichotic pitches are perceptual illusions created centrally through binaural interactions that offer an opportunity to test theories of pitch and binaural hearing. Here we show that binaural neurons in auditory midbrain encode the frequency of two salient types of dichotic pitches via specific features in the pattern of firing rates along the tonotopic axis. This is the first combined single-unit and modeling study of responses of auditory neurons to stimuli evoking a dichotic pitch.

High-Precision Characterization of Seismicity from the 2022 Hunga Tonga-Hunga Ha'apai Volcanic Eruption

AbstractThe earthquake swarm accompanying the January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate-magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera-forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface-wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate-period Rayleigh- and Love-wave cross-correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time-dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude (Mw) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an Mw 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. Our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface-wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.

Impact localization with a weighted spectral cross correlation method

The impact localization is a typical inverse problem to identify the damage source of structures. To overcome the positioning problems of the low resolution caused by sparse reference points, a weighted spectral cross-correlation (WSCC) method is proposed. First, a mathematical model with a series of Fiber Bragg Grating (FBG) sensors is built to generate a database of transient frequency responses of each reference point when random shock happens. Second, to identify the spectrum difference between the impact point and the reference point, a cross correlation model is built by mean and normalization operations on the frequency domain. Third, to figure out the impact point among the adjacent reference points, bicubic spline interpolation is realized by subdividing the grid of the WSCC. To validate the accuracy of our suggested method, an impact localization system is designed with a multi FBG sensor network and a composite laminate. The experimental result shows that the error of our suggested method is almost ten times lower than that of other traditional methods. The weighted spectral cross-correlation has advantages in the field of structural health monitoring.

Evaluation of Turbulent Jet Characteristic Scales Using Joint Statistical Moments and an Adaptive Time-Frequency Analysis

This paper presents an analysis of turbulence characteristic scales and eddy convection velocity of jet flows computed using joint statistical moments, digital filters, and a modified version of the empirical mode decomposition (EMD). The ongoing aim of this study is to develop semi-empirical space-time cross-correlation models based on stationary statistics and jet physical lengths. Multivariate statistics are used to correlate jet properties to one-dimensional time series. The data available to this study were recorded from single-point and two-point hot-wire anemometry experiments carried out for a range of jet Mach numbers (0.2≤M≤0.8). Firstly, the jet eddy convection velocity, turbulence length, and time scales are computed using space-time cross-correlation functions. Isotropic flow and frozen turbulence hypothesis are then used to estimate the joint moments from single-point statistics in the fully developed turbulence region. An EMD-based decomposition method is presented and assessed in both the Gaussian and non-Gaussian signal regions. It is demonstrated that the artificially filtered signal reconstructs the physical properties of single and multi-point jet statistics. The relationship between central moments and joint moments presented here focuses on the region of high turbulence levels, which generates the vast majority of jet mixing noise produced by turbofan engines. Further analysis is required to extend this investigation to intermittent zones and other jet noise sources, such as jet-surface installation noise.

High-resolution monitoring of debris-covered glacier mass budget and flow velocity using repeated UAV photogrammetry in Iran

Iran's glaciers are commonly debris-covered. Due to climate changes, the frequency of glacial hazards such as glacial lake outbursts floods is increasing, and consequently, the mass of Iran's glaciers is decreasing. Fieldwork in high-altitude mountains is difficult, thus, the dynamics of debris–covered glaciers and their associated driving factors are poorly studied. In this study, changes in the elevation and the surface flow speed of the largest and most dynamic debris-covered glacier in Iran (Alamkouh glacier) are evaluated during 2018 to 2020 for the first time. For this purpose, images from a high-resolution unmanned aerial vehicle (UAV) are used. Additionally, the spatial distribution of glacier ice thickness is estimated based on the principles of ice-flow and glacier surface velocity data. The distribution of ice thickness is evaluated with in-situ Ground Penetration Radar (GPR) data. The acquired high-resolution images from UAV are processed into a digital elevation model (with a spatial resolution of about 15 cm) and orthoimages (with a spatial resolution of about 8 cm), and glacier ice thickness change and surface flow velocity are derived. In order to estimate glacier surface velocity, a frequency cross-correlation model was applied using the COSI-Corr module. Our results show that the mean glacier ice thinning during the study period 2018–2020 is about −0.23 ± 0.03 m yr−1 and it reaches about −5 ± 0.65 m yr−1 at the terminus and accumulation areas. The observed variations in ice thinning are best explained by the existence of debris cover (and their corresponding insulating effects), the existence of ice cliffs, and their associated supraglacial lakes. We found that ice cliffs and supraglacial lakes show mass losses that can be an order of magnitude higher than average. Moreover, the mean surface velocity of Alamkouh glacier is about 1.1 ± 0.06 m yr−1 from 2018 to 2020 with considerable spatial variation in surface velocities. The maximum surface velocity (about 4.5 ± 0.27 m yr−1) belongs to its upper regions, and lower and marginal areas are nearly stagnant. The slow movement of the glacier, along with a continuous increase in debris and ice thinning, provides a suitable condition for supraglacial pond development, which decreases glacier mass. Total volume of glacier was estimated to be ~102 × 106 m3 in 2020, corresponding to an average ice thickness of ~31.3 ± 7.5 m. Moreover, the average ice thinning rate is 0.66% yr−1 in 2018 to 2020. According to this reduction rate, it is expected that more than 51.7% of the glacier will be lost by the end of the 21st century.

A parallel test of the SCRAM-CAM transdermal monitors ensuring reliability.

Previous studies validating the transdermal alcohol concentration (TAC) as measured by the Secure Continuous Remote Alcohol Monitors Continuous Alcohol Monitoring (SCRAM-CAM) have tested the monitor against self-reports or breath alcohol concentration (BrAC). This study aims to provide further evidence of the reliability of the SCRAM-CAM testing two monitors in parallel. Participants (N=21) received four standard drinks in a laboratory session while wearing SCRAM-CAMs simultaneously on their left and right ankles. The SCRAM-CAMs sampled TAC every 30 min and participants were monitored for at least 2-3h after their BrAC levels reached zero. Weight and height measures were taken to calculate body mass index (BMI). There was a positive correlation between the TAC measurements from the left and right SCRAM-CAM (r=0.718), a cross-correlation model revealed that this correlation was not significantly different for sex or BMI. Area under the TAC curve (AUC) and peak TAC values as measured by the left and right SCRAM-CAM also show positive correlations (r=0.554 and r=0.579, respectively). Cross-correlation models show a significant effect of BMI on the relationship between left and right peak TAC values, which may be due to outlier effects. No further effects were significant for on both peak and AUC values. Results show that TAC measured by SCRAM-CAMs worn on the left and right showed a good correlation, with correlations between AUC and peak TAC values considered to be fair. TAC monitors show promise for use in research settings; however, work is needed testing the reliability of TAC as measured by two TAC monitors.

PIV measurements using refraction at a solid–fluid interface

Laser light sheet refraction at the fluid flow–window interface is proposed to perform fluorescent particle image velocimetry (PIV) measurements, in particular for cases in which optical access limits the possibilities of illuminating a flow with a planar collimated light sheet. The study of laser sheet propagation in the present measurement method is led by ray tracing to quantify the illumination intensity. To describe the limitations of this developed PIV version (refracted PIV (R-PIV)), we proposed a cross-correlation model (CCM) for analyzing image pairs that is an extension of the models used in the cases of µ-PIV or planar PIV (P-PIV) techniques. This CCM accounts for the R-PIV optical constraints of (1) in-depth and longitudinal inhomogeneities of the light sheet refraction at the location of the object plane or (2) integration in depth of the fluorescence from seeding particles. This R-PIV and the P-PIV techniques are further compared in the case of an academic pipe flow for which the analytic solution is known. Our extended CCM function is finally applied, given the theoretical flow velocity, the flow illumination and the seeding flow parameters, to analyze error levels in determining the real flow velocity field.

Developing a customized system for generating near real time ground motion ShakeMap of Iran’s earthquakes

ABSTRACT This paper provides a review of the procedure of customizing ShakeMap V4.0 provided by U.S. Geological Survey based on seismic characteristics of Iran. Selecting appropriate GMPEs, adopting a suitable spatial cross-correlation model and proper modeling of site condition are important factors in the context of the ShakeMap algorithm. The present paper technically reviews the aforementioned parameters. In addition, a number of statistical tests were performed to provide the optimum configuration. The initial prototype of the configured ShakeMap has been used to provide the ground motion shaking map in the aftermath of a great earthquake in Iran (since Aug, 2021).

Modeling spatial cross-correlation of multiple ground motion intensity measures (SAs, PGA, PGV, Ia, CAV, and significant durations) based on principal component and geostatistical analyses

Ground motion intensity measures (IMs) were observed to be spatially correlated during past earthquakes. In this article, a new spatial cross-correlation model for a vector-IM, which consists of spectral acceleration (SA) ordinates at 17 periods and six non-SA IMs (e.g. peak ground velocity, Arias intensity, cumulative absolute velocity, and significant durations), is proposed using principal component analysis (PCA) and geostatistical analysis. A total of 3797 ground motion records are selected from the NGA-West2 database for such analyses. PCA is used to transform the spatially correlated within-event residuals into uncorrelated principal components; a permissible function is then proposed to fit the empirical semivariograms calculated by the principal components. It is evident that the proposed model performs well in capturing the spatial variability characteristics of the multiple ground motion IMs. A simple example is presented to illustrate the use of the proposed model in realizing spatially correlated ground motion residuals of multiple IMs. The model developed enables one to simulate spatially cross-correlated IMs over a large area in a rapid way.

Spatial Cross-Correlation Models for Absolute and Relative Spectral Input Energy Parameters Based on Geostatistical Tools

ABSTRACTIn recent years, energy-based seismic design methodology has received increasing attention because it takes into account not only the force and displacement behavior of a structure but also the cumulative damage effect caused by seismic loading. Specifically, as a fundamental parameter, input energy parameters (both absolute and relative measures) are directly related to the cumulative damage potential; therefore, they are commonly used in energy-based seismic design and seismic risk assessment. This study thus proposes new spatial cross-correlation models for absolute and relative elastic input energy parameters, using 2219 ground-motion records selected from 12 earthquake events. The normalized within-event residuals for both absolute and relative measures are first calculated. Semivariogram analysis is then conducted to quantify the spatial correlation of residuals for the input energy parameters at multiple sites and multiple periods. The linear model of coregionalization (LMC) approach is adopted to fit the empirical data; it is observed that the proposed LMC-based function performs reasonably well in capturing the spatial variability of the input energy measures. The influence of regional site conditions on the spatial cross correlation of input energy parameters is also investigated, and generic models are proposed using the averaged standardized coregionalization matrices of 12 events. The spatial cross-correlation models developed for input energy parameters can be used in regional seismic risk assessment within an energy-based framework.

A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image.

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

Impairment of cyclopean surface processing by disparity-defined masking stimuli.

Binocular disparity signals allow for the estimation of three-dimensional shape, even in the absence of monocular depth cues. The perception of such disparity-defined form depends, however, on the linkage of multiple disparity measurements over space. Performance limitations in cyclopean tasks thus inform us about errors arising in disparity measurement and difficulties in the linkage of such measurements. We used a cyclopean orientation discrimination task to examine the perception of disparity-defined form. Participants were presented with random-dot sinusoidal modulations in depth and asked to report whether they were clockwise or counter-clockwise rotated. To assess the effect of different noise structures on measurement and linkage processes, task performance was measured in the presence of binocular, random-dot masks, structured as either antiphase depth sinusoids, or as random distributions of dots in depth. For a fixed number of surface dots, the ratio of mask-to-surface dots was varied to obtain thresholds for orientation discrimination. Antiphase masks were found to be more effective than random depth masks, requiring a lower mask-to-surface dot ratio to inhibit performance. For antiphase masks, performance improved with decreased cyclopean frequency, increased disparity amplitude, and/or an increase in the total number of stimulus dots. Although a cross-correlation model of disparity measurement could account for antiphase mask performance, random depth masking effects were consistent with limitations in relative disparity processing. This suggests that performance is noise-limited for antiphase masks and complexity-limited for random masks. We propose that use of differing mask types may prove effective in understanding these distinct forms of impairment.