Time series prediction with signal-to-noise ratio maps and high performance computing

2011 ◽  
Author(s):  
L. Bucur ◽  
S. Petrescu
Keyword(s):  
Time Series ◽  
High Performance Computing ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Time Series Prediction ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Performance Computing

A computational prototyping environment interface for DoD CREATE™-AV Helios simulations

2021 ◽  
Author(s):  
Robert Haehnel ◽  
Scott Christensen ◽  
J. Whitlow ◽  
Andrew Bauer ◽  
Ari Meyer ◽  
...  
Keyword(s):  
Time Series ◽  
Modeling And Simulation ◽  
High Performance Computing ◽  
Department Of Defense ◽  
High Performance ◽  
Data Input ◽  
Web Based ◽  
Simulation Tools ◽  
Parametric Studies ◽  
Performance Computing

Computational Prototyping Environment (CPE) is a web-based portal designed to simplify running Department of Defense (DoD) modeling and simulation tools on the DoD Supercomputing Resource Center’s (DSRC) High Performance Computing (HPC) systems. The first of these tools to be deployed in the CPE is an application (app) to conduct parametric studies and view results using the CREATE-AV Helios CFD software. Initial capability includes hover (collective sweep) and forward flight (speed sweep) performance calculations. The CPE Helios app allows for job submission to a DSRC’s HPC system and for the viewing of results created by Helios, i.e., time series and volumetric data. Example data input and results viewing are presented. Planned future functionality is also outlined.

Gisola: A High-Performance Computing Application for Real-Time Moment Tensor Inversion

2021 ◽  
Author(s):  
Nikolaos Triantafyllis ◽  
Ioannis E. Venetis ◽  
Ioannis Fountoulakis ◽  
Erion-Vasilis Pikoulis ◽  
Efthimios Sokos ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Moment Tensor ◽  
Time Moment ◽  
Parallel Execution ◽  
Waveform Data ◽  
Notification System ◽  
Performance Computing

Abstract Automatic moment tensor (MT) determination is essential for real-time seismological applications. In this article, Gisola, a highly evolved software for MT determination, oriented toward high-performance computing, is presented. The program employs enhanced algorithms for waveform data selection via quality metrics, such as signal-to-noise ratio, waveform clipping, data and metadata inconsistency, long-period disturbances, and station evaluation based on power spectral density measurements in parallel execution. The inversion code, derived from ISOLated Asperities—an extensively used manual MT retrieval utility—has been improved by exploiting the performance efficiency of multiprocessing on the CPU and GPU. Gisola offers the ability for a 4D spatiotemporal adjustable MT grid search and multiple data resources interconnection to the International Federation of Digital Seismograph Networks Web Services (FDSNWS), the SeedLink protocol, and the SeisComP Data Structure standard. The new software publishes its results in various formats such as QuakeML and SC3ML, includes a website suite for MT solutions review, an e-mail notification system, and an integrated FDSNWS-event for MT solutions distribution. Moreover, it supports the ability to apply user-defined scripts, such as dispatching the MT solution to SeisComP. The operator has full control of all calculation aspects with an extensive and adjustable configuration. MT’s quality performance, for 531 manual MT solutions in Greece between 2012 and 2021, was measured and proved to be highly efficient.

Ground surface elevation changes estimated using multiple GNSS signal-to-noise ratio observations over permafrost area

2020 ◽  
Author(s):  
Yufeng Hu
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Model Fitting ◽  
Ground Surface ◽  
Composite Model ◽  
Surface Elevation ◽  
Signal To Noise ◽  
Frozen Ground ◽  
Elevation Changes ◽  
Noise Ratio

<p>The ground surface over permafrost area subsides and uplifts annually due to the seasonal thawing and freezing of active layer. GPS Interferometric Reflectometry (GPS-IR) has been successfully applied to the signal-to-noise ratio (SNR) observations to retrieve elevation changes of frozen ground surface at Barrow, Alaska. In this study, the method is extended to include GLONASS and Galileo SNR observations. Based on the multiple SNR observations collected by SG27 in Barrow, the multiple GNSS-IR time series of ground surface elevation changes during snow-free days from late June to middle October in year 2018 are obtained at daily intervals. All the three time series show a similar pattern that the ground subsided in thaw season followed by uplifts in freezing season, which is well characterized by the previous composite physical model using thermal indexes. Fitted with the composite model, the amplitude of the GPS-derived elevation changes during the snow-free days is suggested to be 3.3 ± 0.2 cm. However, the time series of GLONASS-IR and Galileo-IR measurements are much noisier than that of GPS-IR due to their inconsistent daily satellite tracks. Applied with a specific strategy in the composite model fitting, the amplitudes of GLONASS- and Galileo-derived elevation changes are estimated to be 4.0 ± 0.3 cm and 3.9 ± 0.5 cm, respectively. Then, GLONASS-IR and Galileo-IR time series are reconstructed in turn with the fitting coefficients. Moreover, the occurrences of the short-term variations in time series of GNSS-IR measurements are found to coincidence with the precipitation events, indicating the hydrologic control on the movements of frozen ground surface. The results presented in this study show the feasibility to combine multiple GNSS to densely monitor frozen ground surface deformations, and provide an insight to understand the impacts of both thermal and hydrologic forces on the frozen ground dynamics.</p>

Brain activity detection by estimating the signal-to-noise ratio of fMRI time series using dynamic linear models

2015 ◽  
Vol 47 ◽  
pp. 205-211
Author(s):  
Alicia Quirós ◽  
Simon P. Wilson ◽  
Raquel Montes Diez ◽  
Ana Beatriz Solana ◽  
Juan Antonio Hernández Tamames
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Activity Detection ◽  
Signal To Noise ◽  
Dynamic Linear Models ◽  
Fmri Time Series ◽  
Noise Ratio

Homogeneity of the 18 QuickSIN™ Lists

2006 ◽  
Vol 17 (03) ◽  
pp. 157-167 ◽  
Author(s):  
Rachel A. McArdle ◽  
Richard H. Wilson
Keyword(s):  
Hearing Loss ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Normal Hearing ◽  
Signal To Noise ◽  
Speech In Noise ◽  
Performance Variability ◽  
Noise Test ◽  
Noise Ratio ◽  
Db Difference

The purpose of this study was to determine the list equivalency of the 18 QuickSIN™ (Quick Speech in Noise test) lists. Individuals with normal hearing (n = 24) and with sensorineural hearing loss (n = 72) were studied. Mean recognition performances on the 18 lists by the listeners with normal hearing were 2.8 to 4.3 dB SNR (signal-to-noise ratio), whereas the range was 10.0 to 14.3 dB SNR for the listeners with hearing loss. The psychometric functions for each list showed high performance variability across lists for listeners with hearing loss but not for listeners with normal hearing. For listeners with hearing loss, Lists 4, 5, 13, and 16 fell outside of the critical difference. The data from this study suggest nine lists that provide homogenous results for listeners with and without hearing loss. Finally, there was an 8.7 dB difference in performances between the two groups indicating a more favorable signal-to-noise ratio required by the listeners with hearing loss to obtain equal performance.

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