scholarly journals Bandwidth control of the biphoton wavefunction exploiting spatio-temporal correlations

2022 ◽  
Vol 504 ◽  
pp. 127461
Author(s):  
J.J. Miguel Varga ◽  
Jon Lasa-Alonso ◽  
Martin Molezuelas-Ferreras ◽  
Nora Tischler ◽  
Gabriel Molina-Terriza
Keyword(s):  
Temporal Correlations ◽  
Bandwidth Control ◽  
Spatio Temporal

scholarly journals Stereo-PIV measurements of spatio-temporal turbulence correlations in an axisymmetric jet

2015 ◽  
Vol 778 ◽  
pp. 216-252 ◽  
Author(s):  
C. D. Pokora ◽  
J. J. McGuirk
Keyword(s):  
Fourth Order ◽  
Single Point ◽  
Aerodynamic Noise ◽  
Filter Method ◽  
Frequency Noise ◽  
Turbulent Structures ◽  
Validation Data ◽  
Piv Measurements ◽  
Temporal Correlations ◽  
Spatio Temporal

Stereoscopic three-component particle image velocimetry (3C-PIV) measurements have been made in a turbulent round jet to investigate the spatio-temporal correlations that are the origin of aerodynamic noise. Restricting attention to subsonic, isothermal jets, measurements were taken in a water flow experiment where, for the same Reynolds number and nozzle size, the shortest time scale of the dynamically important turbulent structures is more than an order of magnitude greater that in equivalent airflow experiments, greatly facilitating time-resolved PIV measurements. Results obtained (for a jet nozzle diameter and velocity of 40 mm and $1~\text{m}~\text{s}^{-1}$, giving $\mathit{Re}=4\times 10^{4}$) show that, on the basis of both single-point statistics and two-point quantities (correlation functions, integral length scales) the present incompressible flow data are in excellent agreement with published compressible, subsonic airflow measurements. The 3C-PIV data are first compared to higher-spatial-resolution 2C-PIV data and observed to be in good agreement, although some deterioration in quality for higher-order correlations caused by high-frequency noise in the 3C-PIV data is noted. A filter method to correct for this is proposed, based on proper orthogonal decomposition (POD) of the 3C-PIV data. The corrected data are then used to construct correlation maps at the second- and fourth-order level for all velocity components. The present data are in accordance with existing hot-wire measurements, but provide significantly more detailed information on correlation components than has previously been available. The measured relative magnitudes of various components of the two-point fourth-order turbulence correlation coefficient ($R_{ij,kl}$) – the fundamental building block for free shear flow aerodynamic noise sources – are presented and represent a valuable source of validation data for acoustic source modelling. The relationship between fourth-order and second-order velocity correlations is also examined, based on an assumption of a quasi-Gaussian nearly normal p.d.f. for the velocity fluctuations. The present results indicate that this approximation shows reasonable agreement for the measured relative magnitudes of several correlation components; however, areas of discrepancy are identified, indicating the need for work on alternative models such as the shell turbulence concept of Afsar (Eur. J. Mech. (B/Fluids), vol. 31, 2012, pp. 129–139).

SPATIO-TEMPORAL CORRELATIONS FROM fMRI TIME SERIES BASED ON THE NN-ARx MODEL

2010 ◽  
Vol 09 (04) ◽  
pp. 381-406 ◽  
Author(s):  
J. BOSCH-BAYARD ◽  
J. RIERA-DIAZ ◽  
R. BISCAY-LIRIO ◽  
K. F. K. WONG ◽  
A. GALKA ◽  
...  
Keyword(s):  
Time Series ◽  
Arx Model ◽  
Temporal Correlations ◽  
Fmri Time Series ◽  
Spatio Temporal

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

scholarly journals Chromatin and transcriptome changes in human myoblasts show spatio-temporal correlations and demonstrate DPP4 inhibition in differentiated myotubes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tomasz J. Kolanowski ◽  
Natalia Rozwadowska ◽  
Agnieszka Zimna ◽  
Magdalena Nowaczyk ◽  
Marcin Siatkowski ◽  
...  
Keyword(s):  
Temporal Correlations ◽  
Spatio Temporal ◽  
Dpp4 Inhibition

scholarly journals Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

2019 ◽  
Vol 33 ◽  
pp. 2556-2563 ◽  
Author(s):  
Abhay M S Aradhya ◽  
Aditya Joglekar ◽  
Sundaram Suresh ◽  
M. Pratama
Keyword(s):  
Resting State ◽  
Cognitive Disorders ◽  
Feature Space ◽  
Resting State Fmri ◽  
Transformation Method ◽  
Brain Network ◽  
Fmri Data ◽  
Functional Activities ◽  
Temporal Correlations ◽  
Spatio Temporal

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

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