Multivariate analysis of intermediate periodicities of the green corona

2018 ◽  
Vol 13 (S340) ◽  
pp. 57-58
Author(s):  
S. Mancuso ◽  
T. S. Lee ◽  
C. Taricco ◽  
S. Rubinetti
Keyword(s):  
Singular Spectrum Analysis ◽  
Coronal Emission ◽  
Multivariate Technique ◽  
Modes Of Variability ◽  
Temporal Correlations ◽  
Period Distribution ◽  
Wide Range ◽  
Coronal Emission Line ◽  
Data Adaptive ◽  
South Asymmetry

AbstractSolar activity is observed to fluctuate with time, undergoing a wide range of periodicities from minutes up to thousands of years as evinced from proxies based on cosmogenic isotopes. In this work, we apply Multichannel Singular Spectrum Analysis (MSSA), a data-adaptive, multivariate technique that simultaneously exploits the spatial and temporal correlations of the input data to extract common modes of variability to investigate the intermediate quasi-periodicities of the green coronal emission line at 530.3 nm for the period between 1944 and 2008. A preliminary MSSA analysis confirms the presence of significant quasi-biennial oscillations in the data with amplitude varying significantly with time and latitude. On the other hand, a clear North-South asymmetry is observed both in their intensity and period distribution.

scholarly journals Data-adaptive spatio-temporal filtering of GRACE data

2019 ◽  
Vol 219 (3) ◽  
pp. 2034-2055
Author(s):  
Paoline Prevost ◽  
Kristel Chanard ◽  
Luce Fleitout ◽  
Eric Calais ◽  
Damian Walwer ◽  
...  
Keyword(s):  
Parametric Method ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Temporal Variations ◽  
Frequency Noise ◽  
Modes Of Variability ◽  
Processing Strategies ◽  
Grace Data ◽  
Spatio Temporal ◽  
Data Adaptive

SUMMARY Measurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements. Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.

Rotational Characteristics of the Green Solar Corona: 1947-1991

1994 ◽  
Vol 144 ◽  
pp. 139-141 ◽  
Author(s):  
J. Rybák ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Solar Corona ◽  
World Wide ◽  
Rotation Period ◽  
Original Data ◽  
Coronal Emission ◽  
Time Intervals ◽  
Data Set ◽  
The World ◽  
Coronal Emission Line ◽  
Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

Coronal emission line polarization

Solar Physics ◽  
1972 ◽  
Vol 23 (1) ◽  
pp. 103-119 ◽  
Author(s):  
Lewis L. House
Keyword(s):  
Emission Line ◽  
Coronal Emission ◽  
Coronal Emission Line

scholarly journals A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

2015 ◽  
Vol 471 (2184) ◽  
pp. 20150309 ◽  
Author(s):  
Tyler Lee ◽  
Frédéric Theunissen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
State Of The Art ◽  
Reduction Algorithm ◽  
Temporal Correlations ◽  
Temporal Features ◽  
Wide Range ◽  
Noise Reduction Algorithm

Animals throughout the animal kingdom excel at extracting individual sounds from competing background sounds, yet current state-of-the-art signal processing algorithms struggle to process speech in the presence of even modest background noise. Recent psychophysical experiments in humans and electrophysiological recordings in animal models suggest that the brain is adapted to process sounds within the restricted domain of spectro-temporal modulations found in natural sounds. Here, we describe a novel single microphone noise reduction algorithm called spectro-temporal detection–reconstruction (STDR) that relies on an artificial neural network trained to detect, extract and reconstruct the spectro-temporal features found in speech. STDR can significantly reduce the level of the background noise while preserving the foreground speech quality and improving estimates of speech intelligibility. In addition, by leveraging the strong temporal correlations present in speech, the STDR algorithm can also operate on predictions of upcoming speech features, retaining similar performance levels while minimizing inherent throughput delays. STDR performs better than a competing state-of-the-art algorithm for a wide range of signal-to-noise ratios and has the potential for real-time applications such as hearing aids and automatic speech recognition.

scholarly journals Towards Reliable Velocities of Permanent GNSS Stations

2016 ◽  
Vol 100 (1) ◽  
pp. 17-26
Author(s):  
Janusz Bogusz ◽  
Anna Klos ◽  
Marta Gruszczynska ◽  
Maciej Gruszczynski
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
North East ◽  
Temporal Correlations ◽  
The North ◽  
Spatio Temporal ◽  
Permanent Station

Abstract In the modern geodesy the role of the permanent station is growing constantly. The proper treatment of the time series from such station lead to the determination of the reliable velocities. In this paper we focused on some pre-analysis as well as analysis issues, which have to be performed upon the time series of the North, East and Up components and showed the best, in our opinion, methods of determination of periodicities (by means of Singular Spectrum Analysis) and spatio-temporal correlations (Principal Component Analysis), that still exist in the time series despite modelling. Finally, the velocities of the selected European permanent stations with the associated errors determined following power-law assumption in the stochastic part is presented.

scholarly journals Temporal correlations among functionally specialized striatal neural ensembles in reward-conditioned mice

2016 ◽  
Vol 115 (3) ◽  
pp. 1521-1532 ◽  
Author(s):  
Konstantin I. Bakhurin ◽  
Victor Mac ◽  
Peyman Golshani ◽  
Sotiris C. Masmanidis
Keyword(s):  
Large Scale ◽  
Network Dynamics ◽  
Projection Neurons ◽  
Striatal Neurons ◽  
Neural Recordings ◽  
Neural Ensembles ◽  
Temporal Correlations ◽  
Cross Correlation Analysis ◽  
Wide Range ◽  
Specific Outcome

As the major input to the basal ganglia, the striatum is innervated by a wide range of other areas. Overlapping input from these regions is speculated to influence temporal correlations among striatal ensembles. However, the network dynamics among behaviorally related neural populations in the striatum has not been extensively studied. We used large-scale neural recordings to monitor activity from striatal ensembles in mice undergoing Pavlovian reward conditioning. A subpopulation of putative medium spiny projection neurons (MSNs) was found to discriminate between cues that predicted the delivery of a reward and cues that predicted no specific outcome. These cells were preferentially located in lateral subregions of the striatum. Discriminating MSNs were more spontaneously active and more correlated than their nondiscriminating counterparts. Furthermore, discriminating fast spiking interneurons (FSIs) represented a highly prevalent group in the recordings, which formed a strongly correlated network with discriminating MSNs. Spike time cross-correlation analysis showed the existence of synchronized activity among FSIs and feedforward inhibitory modulation of MSN spiking by FSIs. These findings suggest that populations of functionally specialized (cue-discriminating) striatal neurons have distinct network dynamics that sets them apart from nondiscriminating cells, potentially to facilitate accurate behavioral responding during associative reward learning.

How bright is the [Si X] 1431 nm coronal emission line?

Solar Physics ◽  
1994 ◽  
Vol 151 (1) ◽  
pp. 51-56 ◽  
Author(s):  
M. J. Penn ◽  
J. R. Kuhn
Keyword(s):  
Emission Line ◽  
Coronal Emission ◽  
Coronal Emission Line

scholarly journals On Estimating the Hurst Parameter from Least-Squares Residuals. Case Study: Correlated Terrestrial Laser Scanner Range Noise

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 674 ◽  
Author(s):  
Gaël Kermarrec
Keyword(s):  
Simple Procedure ◽  
Laser Scanner ◽  
Hurst Parameter ◽  
Low Frequencies ◽  
Input Noise ◽  
Temporal Correlations ◽  
Wide Range ◽  
Hurst Coefficient ◽  
Correct Determination ◽  
Two Parameters

Many signals appear fractal and have self-similarity over a large range of their power spectral densities. They can be described by so-called Hermite processes, among which the first order one is called fractional Brownian motion (fBm), and has a wide range of applications. The fractional Gaussian noise (fGn) series is the successive differences between elements of a fBm series; they are stationary and completely characterized by two parameters: the variance, and the Hurst coefficient (H). From physical considerations, the fGn could be used to model the noise of observations coming from sensors working with, e.g., phase differences: due to the high recording rate, temporal correlations are expected to have long range dependency (LRD), decaying hyperbolically rather than exponentially. For the rigorous testing of deformations detected with terrestrial laser scanners (TLS), the correct determination of the correlation structure of the observations is mandatory. In this study, we show that the residuals from surface approximations with regression B-splines from simulated TLS data allow the estimation of the Hurst parameter of a known correlated input noise. We derive a simple procedure to filter the residuals in the presence of additional white noise or low frequencies. Our methodology can be applied to any kind of residuals, where the presence of additional noise and/or biases due to short samples or inaccurate functional modeling make the estimation of the Hurst coefficient with usual methods, such as maximum likelihood estimators, imprecise. We demonstrate the feasibility of our proposal with real observations from a white plate scanned by a TLS.

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