scholarly journals Spike detection for calcium activity

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hermine Biermé ◽  
Camille Constant ◽  
Anne Duittoz ◽  
Christine Georgelin
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Statistical Test ◽  
Biological Data ◽  
Spike Detection ◽  
Calcium Activity ◽  
Experimental Time Series ◽  
Set Up ◽  
Simple Stochastic Model ◽  
Biological Context

Abstract We present in this paper a global methodology for the spike detection in a biological context of fluorescence recording of GnRH-neurons calcium activity. For this purpose we first propose a simple stochastic model that could mimic experimental time series by considering an autoregressive AR(1) process with a linear trend and specific innovations involving spiking times. Estimators of parameters with asymptotic normality are established and used to set up a statistical test on estimated innovations in order to detect spikes. We compare several procedures and illustrate on biological data the performance of our procedure.

scholarly journals Research of behaviors of continuous GNSS station by signal analysis methods

2018 ◽  
Vol 22 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Osman Oktar ◽  
Hediye Erdoğan
Keyword(s):  
Time Series ◽  
Plate Tectonics ◽  
Linear Trend ◽  
Developed Countries ◽  
Maximum Energy ◽  
Central Anatolia ◽  
Reference Station ◽  
International Studies ◽  
Single Station ◽  
Set Up

Continuously Operating Reference Station (CORS) networks consisting of multiple GNSS systems have been set up and operating in many developed countries. In Turkey, CORS-TR has been operating. The aim of CORS-TR is a fast, correct, and reliable collection of all kinds of geographic data, thus, speeding up the activities of cadastre, assuring organized urbanization, constituting the spatial infrastructure for relevant works of e-government, and monitoring plate tectonics. Therefore, in this paper, we investigated 788 days of linear and periodic of AKHR, BEYS, CIHA, KAMN, YUNA, AKSI and KNYA CORS-TR stations by linear trend analysis and Wavelet Transform in located in the Central Anatolia Region of Turkey. As a result of trend analyses of time series, it was determined that except a single station (KAMN), other stations were moving in the Southwest direction (17.78 mm/year). This finding is consistent with the region’s tectonic plate movements. In the periodical analyses, stations’ daily, monthly, seasonal, annual and even biennial movements resulting from atmospheric and hydrologic loading, climatic and other effects were revealed. Moreover, Detail (D) component, which protects the largest percentage of energy, was determined by calculating the energy spectrum. Energies of detail components are similar in horizontal, and especially D7-D8 have the highest energy. However, the components obtained the maximum energy was varied in Up coordinate time series. These results indicate that stations of CORS-TR network located in Central Anatolia were rather stable and able to be providing reliable, accurate and continuous data for national and international studies to be conducted in many fields.

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

scholarly journals Benchmarking homogenization algorithms for monthly data

2012 ◽  
Vol 8 (1) ◽  
pp. 89-115 ◽  
Author(s):  
V. K. C. Venema ◽  
O. Mestre ◽  
E. Aguilar ◽  
I. Auer ◽  
J. A. Guijarro ◽  
...  
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Linear Trend ◽  
Simulated Data ◽  
Integrated Approach ◽  
Global Network ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Simulated Network ◽  
The Individual

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Characterising experimental time series using local intrinsic dimension

1995 ◽  
Vol 202 (2-3) ◽  
pp. 183-190 ◽  
Author(s):  
Thorsten M. Buzug ◽  
Jens von Stamm ◽  
Gerd Pfister
Keyword(s):  
Time Series ◽  
Intrinsic Dimension ◽  
Experimental Time ◽  
Experimental Time Series

scholarly journals Long-term monitoring programme of the hydrological variability in the Mediterranean Sea: a first overview of the HYDROCHANGES network

Ocean Science ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 301-324 ◽  
Author(s):  
K. Schroeder ◽  
C. Millot ◽  
L. Bengara ◽  
S. Ben Ismail ◽  
M. Bensi ◽  
...  
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sampling Interval ◽  
Global Changes ◽  
Hydrological Variability ◽  
Long Term Monitoring ◽  
The Mediterranean ◽  
Set Up ◽  
Term Monitoring

Abstract. The long-term monitoring of basic hydrological parameters (temperature and salinity), collected as time series with adequate temporal resolution (i.e. with a sampling interval allowing the resolution of all important timescales) in key places of the Mediterranean Sea (straits and channels, zones of dense water formation, deep parts of the basins), constitute a priority in the context of global changes. This led CIESM (The Mediterranean Science Commission) to support, since 2002, the HYDROCHANGES programme (http//www.ciesm.org/marine/programs/hydrochanges.htm), a network of autonomous conductivity, temperature, and depth (CTD) sensors, deployed on mainly short and easily manageable subsurface moorings, within the core of a certain water mass. The HYDROCHANGES strategy is twofold and develops on different scales. To get information about long-term changes of hydrological characteristics, long time series are needed. But before these series are long enough they allow the detection of links between them at shorter timescales that may provide extremely valuable information about the functioning of the Mediterranean Sea. The aim of this paper is to present the history of the programme and the current set-up of the network (monitored sites, involved groups) as well as to provide for the first time an overview of all the time series collected under the HYDROCHANGES umbrella, discussing the results obtained thanks to the programme.

scholarly journals Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

2012 ◽  
Vol 19 (6) ◽  
pp. 675-683 ◽  
Author(s):  
K. Moghtased-Azar ◽  
A. Mirzaei ◽  
H. R. Nankali ◽  
F. Tavakoli
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Salt Lake ◽  
Linear Trend ◽  
Groundwater Resources ◽  
Time Interval ◽  
Gps Measurements ◽  
Lake Urmia ◽  
North West ◽  
Continuous Gps

Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena.

scholarly journals Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

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