scholarly journals Analysis of Rhythmic Patterns Produced by Spinal Neural Networks

2007 ◽  
Vol 98 (5) ◽  
pp. 2807-2817 ◽  
Author(s):  
Y. Mor ◽  
A. Lev-Tov
Keyword(s):  
Time Series ◽  
Statistical Significance ◽  
Wavelet Coherence ◽  
Motor Patterns ◽  
Experimental Conditions ◽  
Wide Range ◽  
Continuous Frequency ◽  
Pattern Generators ◽  
The Fourier Transform ◽  
Short Time

A network of spinal neurons known as central pattern generator (CPG) produces the rhythmic motor patterns required for coordinated swimming, walking, and running in mammals. Because the output of this network varies with time, its analysis cannot be performed by statistical methods that assume data stationarity. The present work uses short-time Fourier (STFT) and wavelet-transform (WT) algorithms to analyze the nonstationary rhythmic signals produced in isolated spinal cords of neonatal rats during activation of the CPGs. The STFT algorithm divides the time series into consecutive overlapping or nonoverlapping windows and repeatedly applies the Fourier transform across the signal. The WT algorithm decomposes the signal using a family of wavelets varying in scale, resulting in a set of wavelet coefficients presented onto a continuous frequency range over time. Our studies revealed that a Morlet WT algorithm was the tool of choice for analyzing the CPG output. Cross-WT and wavelet coherence were used to determine interrelations between pairs of time series in time and frequency domain, while determining the critical values for statistical significance of the coherence spectra using Monte Carlo simulations of white-noise series. The ability of the cross-Morlet WT and cross-WT coherence algorithms to efficiently extract the rhythmic parameters of complex nonstationary output of spinal pattern generators over a wide range of frequencies with time is demonstrated in this work under different experimental conditions. This ability can be exploited to create a quantitative dynamic portrait of experimental and clinical data under various physiological and pathological conditions.

scholarly journals Application of the cross wavelet transform and wavelet coherence to geophysical time series

2004 ◽  
Vol 11 (5/6) ◽  
pp. 561-566 ◽  
Author(s):  
A. Grinsted ◽  
J. C. Moore ◽  
S. Jevrejeva
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Statistical Significance ◽  
Wavelet Coherence ◽  
The Arctic ◽  
Arctic Oscillation Index ◽  
Time Frequency ◽  
Cross Wavelet Transform ◽  
The Cross ◽  
Cross Wavelet

Abstract. Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/).

A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Fang Zhang ◽  
Ang Shan ◽  
Yihui Luan
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
Permutation Test ◽  
Statistical Significance ◽  
Theoretical Method ◽  
Similarity Analysis ◽  
Type I ◽  
Local Similarity ◽  
Biological Studies ◽  
Wide Range

Abstract In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim. However, all these methods require the assumption that the time series are independent and identically distributed. In this paper, we propose a new approach based on moving block bootstrap to approximate the statistical significance of local similarity scores for dependent time series. Simulations show that our method can control the type I error rate reasonably, while theoretical approximation and the permutation test perform less well. Finally, our method is applied to human and marine microbial community datasets, indicating that it can identify potential relationship among operational taxonomic units (OTUs) and significantly decrease the rate of false positives.

scholarly journals Time Series Chaos Detection and Assessment via Scale Dependent Lyapunov Exponent

2016 ◽  
Vol 5 (6) ◽  
pp. 1
Author(s):  
Livio Fenga
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Market Price ◽  
Real Life ◽  
Quantitative Information ◽  
Small Sample ◽  
Experimental Conditions ◽  
Practical Applications ◽  
Chaos Detection ◽  
Wide Range

Many dynamical systems in a wide range of disciplines -- such as engineering, economy and biology -- exhibit complex behaviors generated by   nonlinear components which might result in deterministic chaos. While in  lab--controlled setups its detection and level estimation  is in general a doable task, usually the same  does not hold for many   practical applications. This is because experimental conditions imply facts like low signal--to--noise ratios, small sample sizes and not--repeatability  of the experiment, so that the performances of the tools commonly employed for chaos detection can be seriously affected.  To tackle this problem, a combined approach based on wavelet and chaos theory is proposed. This is a procedure designed to provide the analyst with qualitative and quantitative information, hopefully conducive to a better understanding of the dynamical system the time series under investigation is generated from. The chaos detector considered is the well known Lyapunov Exponent. A real life application, using the Italian Electric Market price index, is employed to corroborate the validity of the proposed approach.

scholarly journals Statistical Time-Series Analysis of Interferometric Coherence from Sentinel-1 Sensors for Landslide Detection and Early Warning

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6799
Author(s):  
Marios Tzouvaras
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Statistical Significance ◽  
Early Warning Systems ◽  
Time Of Day ◽  
Landslide Occurrence ◽  
Geotechnical Investigations ◽  
Wide Range ◽  
Sar Imagery

Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.

scholarly journals Multivariate Estimations of Equilibrium Climate Sensitivity from Short Transient Warming Simulations

2021 ◽  
Author(s):  
Robbin Bastiaansen ◽  
Henk Dijkstra ◽  
Anna von der Heydt
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Equilibrium Temperature ◽  
Global Climate Models ◽  
Equilibrium Climate Sensitivity ◽  
Wide Range ◽  
Short Time

<p>One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long-term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks – spanning a wide range of temporal scales – it is hard to extract long-term behaviour from short-time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long-term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multi-component linear regression model. This way, not only the dominant but also the next-dominant eigenmodes of the climate system are captured, leading to better long-term estimates from short, non-equilibrated time series.</p>

scholarly journals Multiscale Complexity Analysis of Rainfall in Northeast Brazil

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3213
Author(s):  
Antonio Samuel Alves da Silva ◽  
Ikaro Daniel de Carvalho Barreto ◽  
Moacyr Cunha-Filho ◽  
Rômulo Simões Cezar Menezes ◽  
Borko Stosic ◽  
...  
Keyword(s):  
Complexity Analysis ◽  
Statistical Significance ◽  
Entropy Method ◽  
Multiscale Methods ◽  
Northeastern Brazil ◽  
Multiscale Entropy ◽  
Temporal Scales ◽  
Wide Range ◽  
One Year ◽  
Short Time

In this work, we analyze the complexity of monthly rainfall temporal series recorded from 1962 to 2012, at 133 gauge stations in the state of Pernambuco, northeastern Brazil. To this end, we employ the modified multiscale entropy method (MMSE), which is well suited for short time series, to analyze the rainfall regularity across a wide range of temporal scales, from one month to one year. We identify the temporal scales that distinguish rainfall regularity in the inland semiarid Sertão region, the transitional inland Agreste region, and the coastal, tropical humid Zona da Mata region, by comparing the results for stations across the study area and performing statistical significance tests. Our work contributes to the establishment of multiscale methods based on information theory in climatological studies.

Strongly Coupled Redox-Linked Conformational Switching at the Active Site of the Non-Heme Iron-Dependent Dioxygenase, TauD

2019 ◽  
Author(s):  
Christopher John ◽  
Greg M. Swain ◽  
Robert P. Hausinger ◽  
Denis A. Proshlyakov
Keyword(s):  
Active Site ◽  
Structural Model ◽  
Heme Iron ◽  
Chemical Transformations ◽  
Experimental Conditions ◽  
Strongly Coupled ◽  
Non Heme Iron ◽  
Reversible Redox ◽  
Wide Range ◽  
Complex Structural

2-Oxoglutarate (2OG)-dependent dioxygenases catalyze C-H activation while performing a wide range of chemical transformations. In contrast to their heme analogues, non-heme iron centers afford greater structural flexibility with important implications for their diverse catalytic mechanisms. We characterize an <i>in situ</i> structural model of the putative transient ferric intermediate of 2OG:taurine dioxygenase (TauD) by using a combination of spectroelectrochemical and semi-empirical computational methods, demonstrating that the Fe (III/II) transition involves a substantial, fully reversible, redox-linked conformational change at the active site. This rearrangement alters the apparent redox potential of the active site between -127 mV for reduction of the ferric state and 171 mV for oxidation of the ferrous state of the 2OG-Fe-TauD complex. Structural perturbations exhibit limited sensitivity to mediator concentrations and potential pulse duration. Similar changes were observed in the Fe-TauD and taurine-2OG-Fe-TauD complexes, thus attributing the reorganization to the protein moiety rather than the cosubstrates. Redox difference infrared spectra indicate a reorganization of the protein backbone in addition to the involvement of carboxylate and histidine ligands. Quantitative modeling of the transient redox response using two alternative reaction schemes across a variety of experimental conditions strongly supports the proposal for intrinsic protein reorganization as the origin of the experimental observations.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

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