scholarly journals The Procyon Campaign: Observations from Kitt Peak

1998 ◽  
Vol 185 ◽  
pp. 319-320
Author(s):  
C. A. Pilachowski ◽  
S. Barden ◽  
F. Hill ◽  
J. W. Harvey ◽  
C. U. Keller ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelength Range ◽  
International Collaboration ◽  
Sampling Rate ◽  
Resolving Power ◽  
National Observatory ◽  
Sample Spectrum ◽  
Solar Type

Time series spectra of the F5IV star Procyon (α CMi) were obtained at the Kitt Peak National Observatory during a 35-night observing run in January-February 1997. The observations were obtained as part of an international collaboration to detect and study acoustic p-mode oscillations in solar-type stars. Spectra covered the wavelength range from 4000 to 5300 Å, with a resolving power of approximately 3500 (1.3 Å resolution). The sampling rate was one observation per minute, and the typical S/N ratio per pixel after averaging along columns is in excess of 1000. We obtained 12,888 spectra. A sample spectrum is shown in Figure 1

scholarly journals Improving the Resolving Power of InSAR for Earthquakes Using Time Series: A Case Study in Iran

2021 ◽  
Author(s):  
F. Liu ◽  
J. R. Elliott ◽  
T. J. Craig ◽  
A. Hooper ◽  
T. J. Wright
Keyword(s):  
Time Series ◽  
Resolving Power

scholarly journals The Pulsation of M-type Miras: Multi-Epoch ISO-SWS Observations and Dynamical Models

2002 ◽  
Vol 185 ◽  
pp. 538-541 ◽  
Author(s):  
B. Aringer ◽  
U.G. Jørgensen ◽  
F. Kerschbaum ◽  
J. Hron ◽  
S. Höfner
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Wavelength Range ◽  
Infrared Range ◽  
Dynamical Models ◽  
Long Period ◽  
Mira Variables ◽  
Long Period Variables ◽  
Hydrostatic Model ◽  
Additional Assumptions

AbstractWe present time series of observed and synthetic ISO-SWS spectra of oxygen-rich Mira variables covering the wavelength range between 2.36 and 7.75 μm. The calculations are based on new dynamical models, which have been computed with a non-grey radiative transfer taking into account all relevant molecular opacities. It turns out that many features in the ISO spectra of cool long period variables which could not be reproduced within the framework of classical hydrostatic model atmospheres nor with grey dynamical calculations can now be understood without any additional assumptions. This is especially true for the water bands, which dominate the opacity in the infrared range of M-type Miras.

Automatic Detection of Manufacturing Equipment Cycles Using Time Series

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Jan-Peter Seevers ◽  
Kristina Jurczyk ◽  
Henning Meschede ◽  
Jens Hesselbach ◽  
John W. Sutherland
Keyword(s):  
Time Series ◽  
Energy Demand ◽  
Manufacturing Industry ◽  
Electrical Power ◽  
Sampling Rate ◽  
Real Life ◽  
Recognition Algorithm ◽  
Sensor Data ◽  
Data Set ◽  
Manufacturing Equipment

Abstract Manufacturing industry companies are increasingly interested in using less energy in order to enhance competitiveness and reduce environmental impact. To implement technologies and make decisions that lead to less energy demand, energy/power data are required. All too often, however, energy data are either not available, or available but too aggregated to be useful, or in a form that makes information difficult to access. Attention herein is focused on this last point. As a step toward greater energy information transparency and smart energy-monitoring systems, this paper introduces a novel, robust time series-based approach to automatically detect and analyze the electrical power cycles of manufacturing equipment. A new pattern recognition algorithm including a power peak clustering method is applied to a large real-life sensor data set of various machine tools. With the help of synthetic time series, it is shown that the accuracy of the cycle detection of nearly 100% is realistic, depending on the degree of measurement noise and the measurement sampling rate. Moreover, this paper elucidates how statistical load profiling of manufacturing equipment cycles as well as statistical deviation analyses can be of value for automatic sensor and process fault detection.

scholarly journals CIRCADA: Shiny Apps for Exploration of Experimental and Synthetic Circadian Time Series with an Educational Emphasis

2020 ◽  
Vol 35 (2) ◽  
pp. 214-222
Author(s):  
Lisa Cenek ◽  
Liubou Klindziuk ◽  
Cindy Lopez ◽  
Eleanor McCartney ◽  
Blanca Martin Burgos ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Locomotor Activity ◽  
Body Temperature ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Data Set ◽  
Maximum Entropy Spectral Analysis ◽  
Noise Characteristics ◽  
Synthetic Time Series

Circadian rhythms are daily oscillations in physiology and behavior that can be assessed by recording body temperature, locomotor activity, or bioluminescent reporters, among other measures. These different types of data can vary greatly in waveform, noise characteristics, typical sampling rate, and length of recording. We developed 2 Shiny apps for exploration of these data, enabling visualization and analysis of circadian parameters such as period and phase. Methods include the discrete wavelet transform, sine fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, giving a sense of how well each method works on each type of data. The apps also provide educational overviews and guidance for these methods, supporting the training of those new to this type of analysis. CIRCADA-E (Circadian App for Data Analysis–Experimental Time Series) allows users to explore a large curated experimental data set with mouse body temperature, locomotor activity, and PER2::LUC rhythms recorded from multiple tissues. CIRCADA-S (Circadian App for Data Analysis–Synthetic Time Series) generates and analyzes time series with user-specified parameters, thereby demonstrating how the accuracy of period and phase estimation depends on the type and level of noise, sampling rate, length of recording, and method. We demonstrate the potential uses of the apps through 2 in silico case studies.

PLANE WAVES AT SMALL ARRAYS

Geophysics ◽  
1973 ◽  
Vol 38 (6) ◽  
pp. 1023-1041 ◽  
Author(s):  
John W. Woods ◽  
Paul R. Lintz
Keyword(s):  
Maximum Likelihood ◽  
White Noise ◽  
Plane Waves ◽  
Sampling Rate ◽  
Resolving Power ◽  
Time Gate ◽  
High Sampling Rate ◽  
Power Spectral ◽  
Short Time ◽  
Background White Noise

The resolving power of a seismic array is defined in terms of the array response function and via the classical uncertainty principle. Using the theory of maximum likelihood wavenumber spectra (Capon, 1969), we show for the case of two correlated plane waves that arbitrarily high resolution is achievable in the limit as the background white noise tends to zero. This extends Barnard’s (1969) result to the case of correlated plane waves. The increased resolution arises from the additional assumption that the data are plane waves over all space, and not zero off the array as the classical result assumes. It is found that a sample rate (in time) large compared to the Nyquist rate, is needed in the case of a short time gate at a small array. Cross‐power spectral matrices are estimated at 4 hz from 1 sec of computer generated data consisting of two correlated plane waves in white noise. These spectral matrices are then used to generate maximum likelihood wavenumber spectra. The two plane waves are resolved at various signal‐to‐noise ratios and at correlations up to ρ=0.8. The need for using a high sampling rate is demonstrated. Results are compared with conventional wavenumber spectra, where the classical resolution results hold. The use of a 1‐sec window provides improved resolution of the wavenumber structure as it changes in time, resulting in better separation of any time‐overlapping phases and multipathed waves that arise from one event.

scholarly journals The potential of many-line inversions of photospheric spectropolarimetric data in the visible and near UV

2019 ◽  
Vol 622 ◽  
pp. A36 ◽  
Author(s):  
T. L. Riethmüller ◽  
S. K. Solanki
Keyword(s):  
Wavelength Range ◽  
Solar Physics ◽  
Solar Observatory ◽  
Spectral Lines ◽  
Resolving Power ◽  
Short Wavelength Range ◽  
Photon Noise ◽  
Infrared Spectral ◽  
The Many

Our knowledge of the lower solar atmosphere is mainly obtained from spectropolarimetric observations, which are often carried out in the red or infrared spectral range and almost always cover only a single or a few spectral lines. Here we compare the quality of Stokes inversions of only a few spectral lines with many-line inversions. In connection with this, we have also investigated the feasibility of spectropolarimetry in the short-wavelength range, 3000 Å−4300 Å, where the line density but also the photon noise are considerably higher than in the red, so that many-line inversions could be particularly attractive in that wavelength range. This is also timely because this wavelength range will be the focus of a new spectropolarimeter in the third science flight of the balloon-borne solar observatory SUNRISE. For an ensemble of state-of-the-art magneto-hydrodynamical atmospheres we synthesize exemplarily spectral regions around 3140 Å (containing 371 identified spectral lines), around 4080 Å (328 lines), and around 6302 Å (110 lines). The spectral coverage is chosen such that at a spectral resolving power of 150 000 the spectra can be recorded by a 2K × 2K detector. The synthetic Stokes profiles are degraded with a typical photon noise and afterward inverted. The atmospheric parameters of the inversion of noisy profiles are compared with the inversion of noise-free spectra. We find that significantly more information can be obtained from many-line inversions than from a traditionally used inversion of only a few spectral lines. We further find that information on the upper photosphere can be significantly more reliably obtained at short wavelengths. In the mid and lower photosphere, the many-line approach at 4080 Å provides equally good results as the many-line approach at 6302 Å for the magnetic field strength and the line-of-sight (LOS) velocity, while the temperature determination is even more precise by a factor of three. We conclude from our results that many-line spectropolarimetry should be the preferred option in the future, and in particular at short wavelengths it offers a high potential in solar physics.

WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (04) ◽  
pp. 509-526 ◽  
Author(s):  
R. FALTERMEIER ◽  
A. ZEILER ◽  
A. M. TOMÉ ◽  
A. BRAWANSKI ◽  
E. W. LANG
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Sampling Rate ◽  
Computational Effort ◽  
Intrinsic Mode Functions ◽  
Weighted Version ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Reconstruction Quality ◽  
Spectral Transform

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality.

scholarly journals Estimating Sand Bedload in Rivers by Tracking Dunes: a comparison of methods based on bed elevation time-series

2019 ◽  
Author(s):  
Kate C. P. Leary ◽  
Daniel Buscombe
Keyword(s):  
Time Series ◽  
Sampling Rate ◽  
Bedload Transport ◽  
Single Beam ◽  
Bed Elevation ◽  
Sonar Systems ◽  
Optimal Beam ◽  
Field Methodology ◽  
Spatio Temporal ◽  
Bed Material

Abstract. Quantifying bedload transport is paramount to the effective management of rivers with sand or gravel-dominated bed material. However, a practical and scalable field methodology for reliably estimating bedload remains elusive. A popular approach involves calculating transport from the geometry and celerity of migrating bedforms, extracted from time-series of bed elevation profiles acquired using echosounders. Various echosounder sampling methodologies of how to extract bed elevations profiles exist. Using two sets of repeat multibeam sonar surveys with large spatio-temporal resolution and coverage, we compute bedload using three field techniques (one actual and two simulated) for acquiring bed elevation profiles: repeat multi-, single-, and multiple single-beam sonar. Significant differences in flux arise between repeat multibeam and single beam sonar. Mulitbeam and multiple single beam sonar systems can potentially yield comparable results, but the latter relies on knowledge of bedform geometries and flow that collectively inform optimal beam spacing and sampling rate. These results serve to guide design of optimal sampling, and for comparing transport estimates from different sonar configurations.

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