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21 results for “AEGIS”
Smart Home Sensor and HVAC Control Dataset from the SHAL Demonsrtator of the Aegis Project
<p>The example dataset was produced within the Smart Home and Assisted Living demonstrator of the AEGIS project. IT contains measurements of indoor temperature and HVAC control actions (ON/OFF status and setpoint values), which were be used to extract comfort profiles.</p>
Smart Insurance datasets subset of the AEGIS project
<p>The example dataset was produced within the Smart Insurance demonstrator of the AEGIS project. It contains a sample of the SYNTHETIC data that have been created for the demonstrator purposes. </p>
Automotive CAN bus data: An Example Dataset from the AEGIS Big Data Project
<p>Here you find an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected during trips conducted by three drivers driving the same vehicle in Austria.</p> <p>The dataset contains 20Hz sampled CAN bus data from a passenger vehicle, e.g. WheelSpeed FL (speed of the front left wheel), SteerAngle (steering wheel angle), Role, Pitch, and accelerometer values per direction.</p> <p>GPS data from the vehicle (see signals 'Latitude_Vehicle' and 'Longitude_Vehicle' in h5 group 'Math') and GPS data from the IMU device (see signals 'Latitude_IMU', 'Longitude_IMU' and 'Time_IMU' in h5 group 'Math') are included. However, as it had to be exported with single-precision, we lost some precision for those GPS values.</p> <p> </p> <p>For data analysis we use R and R Studio (https://www.rstudio.com/) and the library h5.</p> <p>e.g. check file with R code:</p> <p>library(h5)</p> <p>f <- h5file("file path/20181113_Driver1_Trip1.hdf")</p> <p>summary(f["CAN/Yawrate1"][,])</p> <p>summary(f["Math/Latitude_IMU"][,])</p> <p>h5close(f)</p>
Automotive Sensor Data. An Example Dataset from the AEGIS Big Data Project
<p>This is an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria.</p>
Anticoagulation With Enhanced Gastrointestinal Safety (AEGIS) Trial
ClinicalTrials.gov study NCT05085405. IPD Sharing: NO. Countries: 1. Publications: 1.
FIGURE 15. Goniodiscaster aegis n in New genera, species and occurrence records of Goniasteridae (Asteroidea; Echinodermata) from the Indian Ocean
FIGURE 15. Goniodiscaster aegis n. sp. Holotype, USNM E51097. R=4.7, r=1.9 cm. A. Abactinal surface. Scale bar=1.0 cm. B. Closeup of abactinal surface with white box around interradial paired plates and "fold". Scale bar=0.3 cm. C. Interradius showing superomarginal and inferomarginal plates.Scale bar=0.5 cm. D. Actinal surface. Scale bar=1.0 cm. E. Closeup of furrow and adambulacral spines. Scale bar =0.3 cm.
NEOadjuvant Trial in Adenocarcinoma of the oEsophagus and oesophagoGastric Junction International Study (Neo-AEGIS)
ClinicalTrials.gov study NCT01726452. IPD Sharing: Not stated. Countries: 5. Publications: 2.
Airway Epithelium Gene Expression in the Diagnosis of Lung Cancer: AEGIS CLIA
ClinicalTrials.gov study NCT01309087. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Safety and Efficacy Study of Oral Ferric Iron To Treat Iron Deficiency Anaemia in Quiescent Ulcerative Colitis (AEGIS-1)
ClinicalTrials.gov study NCT01340872. IPD Sharing: NO. Countries: 0. Publications: 2.
Safety and Efficacy Study of Oral Ferric Iron To Treat Iron Deficiency Anaemia in Quiescent Crohn's Disease (AEGIS-2)
ClinicalTrials.gov study NCT01352221. IPD Sharing: NO. Countries: 0. Publications: 2.
Airway Epithelium Gene Expression: AEGIS IDE
ClinicalTrials.gov study NCT00746759. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Adherence to Electrical Glossal In Situ Stimulation for Sleep Apnea (AEGIS Study)
ClinicalTrials.gov study NCT04974515. IPD Sharing: NO. Countries: 1. Publications: 0.
Prospective Clinical Evaluation of the New Aegis Plate for Anterior Interbody Fusions
ClinicalTrials.gov study NCT00555659. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development and Validation of the Automated Evaluation of Gastrointestinal Symptoms (AEGIS) Platform
ClinicalTrials.gov study NCT02436057. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluating the Impact of Automated Evaluation of Gastrointestinal Symptoms (AEGIS) on Clinical Outcomes
ClinicalTrials.gov study NCT02530216. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Alpha Ketoglutarate Enhances Geroprotection In Surgery (AEGIS)
ClinicalTrials.gov study NCT07031128. IPD Sharing: NO. Countries: 1. Publications: 0.
Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways (AEGIS nasal brushings)
GEO Series GSE210271. Homo sapiens. 505 samples. Type: Expression profiling by array.
All-Wavelength Extended Groth Strip Int. Survey (AEGIS) VLA 20-cm Source Catalog
This table contains results from AEGIS20, a radio survey of the Extended Groth Strip (EGS) conducted with the Very Large Array (VLA) at a frequency of 1.4 GHz. The resulting catalog contains 1122 emitters (HEASARC Note: The abstract of the original reference paper said 1123, but as noted by Willner et al. (2012, ApJ, 756, 72: footnote 10, one entry ('EGS20 J142303.7+532224.5') was listed twice in the original catalog), and it is sensitive to ultraluminous (10<sup>12</sup> solar luminosities) starbursts to z <= 1.3, well matched to the redshift range of the DEEP2 spectroscopic survey in this region. The authors use stacking techniques to explore the microJansky-level emission from a variety of galaxy populations selected via conventional criteria - Lyman break galaxies (LBGs), distant red galaxies (DRGs), UV-selected galaxies, and extremely red objects (EROs) - determining their properties as a function of color, magnitude, and redshift and their extinction-free contributions to the history of star formation. This study confirms the familiar pattern that the star formation rate (SFR) density, increases by at least a factor of ~ 5 from z = 0 to 1, although the authors note highly discrepant UV- and radio-based SFR estimates. Their radio-based SFRs become more difficult to interpret at z > 1 where correcting for contamination by radio-loud active galactic nuclei (AGNs) comes at the price of rejecting luminous starbursts. While stacking radio images is a useful technique, accurate radio-based SFRs for z >> 1 galaxies require precise redshifts and extraordinarily high fidelity radio data to identify and remove accretion-related emission. Data were obtained at 1.4 GHz during 2003 to 2005 with the VLA in its B configuration, acquiring seven 3.125 MHz channels every 5 s at each of four intermediate frequencies. Data were obtained at six positions, spaced by 15 arcminutes, concentrating in the northern half of the EGS because of the proximity of 3C 295 (a 23 Jy source at 1.4 GHz). Around 18 hours of data were acquired for each of the field positions. Calibrated visibilities and associated weights were used to generate mosaics of 37 x 512<sup>2</sup> x 0.8 arcsec<sup>2</sup> pixel images to quilt the VLA's primary beam in each EGS field position. CLEAN boxes were placed tightly around all sources, and a series of IMAGR and CALIB tasks were run, clipping the UV data after subtracting CLEAN components generated by the third iteration of IMAGR. The central images from each of the pointings were then knitted together using FLATN, ignoring data beyond the primary beam's half-power point, to produce a large mosaic. The synthesized beam is circular, with a FWHM of ~ 3.8 arcseconds. To define a sample of radio sources, the authors searched signal-to-noise ratio (S/N) images using the SAD detection algorithm, emulating the technique described by Biggs & Ivison (2006, MNRAS, 371, 963). Sources with >= 4-sigma peaks were fitted with two-dimensional Gaussians using JMFIT, and those with >- 5-sigma peaks that survived were fitted in total intensity. Sources with sizes equal to or smaller than the restoring beam were considered unresolved. No correction is made for bandwidth smearing in the catalog; this is a small effect (~ 5%) given the mosaicking strategy and the use of the B configuration. 38, 79, 171, 496, and 1123 sources are detected with 1.4 GHz flux densities >= 2000, >= 800, >= 320, >= 130 and >= 50 microJansky (µJy) [including the duplicate source mentioned above], where the 5-sigma detection limits at 130 and 50 uJy cover 0.73 and 0.04 deg<sup>2</sup>, respectively. Confusion is not an issue; the source density on an arcmin<sup>2</sup> scale is < 0.01 beam<sup>-1</sup>. This table was created by the HEASARC in July 2013 based on an electronic versions of the catalog described in the reference paper which was obtained as a FITS file from the first author's web site at <a href="http://www.roe.ac.uk/~rji/aegis20/">http://www.roe.ac.uk/~rji/aegis20/</a>. This is a service provided by NASA HEASARC .
AEGIS-X Chandra Extended Groth Strip X-Ray Point Source Catalog
This table contains the X-ray sources detected in the AEGIS-X survey, a series of deep Chandra ACIS-I observations of the Extended Groth Strip (EGS). The survey comprises pointings at eight separate positions, each with nominal exposure of 200 ks, covering a total area of approximately 0.67 deg<sup>2</sup> in a strip of length 2 degrees. In their paper, the authors describe in detail an updated version of the data reduction and point-source-detection algorithms used to analyze these data. A total of 1325 band-merged sources have been found to a Poisson probability limit of 4 x 10<sup>-6</sup>, with limiting fluxes of 5.3 x 10<sup>-17</sup> erg cm<sup>-2</sup> s<sup>-1</sup> in the soft (0.5 - 2 keV) band and 3.8 x 10<sup>-16</sup> erg cm<sup>-2</sup> s<sup>-1</sup> in the hard (2 - 10 keV) band. They present simulations verifying the validity of their source-detection procedure and showing a very small, <1.5%, contamination rate from spurious sources. Optical/NIR counterparts have been identified from the DEEP2, CFHTLS, and Spitzer/Infrared Array Camera (IRAC) surveys of the same region. Using a likelihood ratio method, they find optical counterparts for 76% of their sources, complete to R<sub>AB</sub> = 24.1, and, of the 66% of the sources that have IRAC coverage, 94% have a counterpart to a limit of 0.9 uJy at 3.6 um (m<sub>AB</sub> = 23.8). After accounting for (small) positional offsets in the eight Chandra fields, the astrometric accuracy of Chandra positions is found to be 0.8 arcseconds rms; however, this number depends both on the off-axis angle and the number of detected counts for a given source. This table was created by the HEASARC in February 2009 based on the electronic versions of Tables 9, 10 and 11 from the paper which were obtained from the Astrophysical Journal web site. It is also available from the CDS at <a href="https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/180/102">https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/180/102</a>. This is a service provided by NASA HEASARC .
All-Wavelength Extended Groth Strip Int. Survey (AEGIS) 20-cm Fully Id-ed Sample
Infrared 3.6 - 8.0 micron (µm) images of the Extended Groth Strip (EGS) yield plausible counterpart identifications for all but one of 510 radio sources in the AEGIS20 S(1.4 GHz) > 50 microJansky (µJy) sample (Ivison et al. 2007, ApJ, 660, L77, available at the HEASARC as the AEGIS20 database table). This is the first such deep sample that has been effectively 100% identified. Achieving the same identification rate at R band would require observations reaching R<sub>AB</sub> > 27. Spectroscopic redshifts are available for 46% of the sample and photometric redshifts for an additional 47%. Almost all of the sources with 3.6-um AB magnitudes brighter than 19 have spectroscopic redshifts z < 1.1, while fainter objects predominantly have photometric redshifts with 1 <~ z <~ 3. Unlike more powerful radio sources that are hosted by galaxies having large stellar masses within a relatively narrow range, the AEGIS20 counterparts have stellar masses spanning more than a factor of 10 at z ~ 1. The sources are roughly 10% - 15% starbursts at z <~ 0.5 and 20%-25% active galactic nuclei mostly at z > 1, with the remainder of uncertain nature. Throughout this study, magnitudes are in the AB system, and the notation [w] means the AB magnitude at wavelength w in um. Source distances are based on standard Lambda-CDM cosmology with H<sub>0</sub> = 71 km s<sup>-1</sup> Mpc<sup>-1</sup> and Omega<sub>M</sub> = 0.27. Practical calculation of luminosity distances was based on the program ANGSIX (Kayser et al. 1997, A&A, 318, 680). This table was created by the HEASARC in July 2013 based on the electronic versions of Tables 1, 3, 4, and 5 from the reference paper which were obtained from the ApJ web site. This is a service provided by NASA HEASARC .
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