scholarly journals Analysis of 21-cm tomographic data

2017 ◽  
Vol 12 (S333) ◽  
pp. 26-29
Author(s):  
Garrelt Mellema ◽  
Sambit Giri ◽  
Raghunath Ghara
Keyword(s):  
Radio Telescope ◽  
Data Sets ◽  
Noise Levels ◽  
Tomographic Images ◽  
The Future ◽  
Tomographic Data

AbstractThe future SKA1-Low radio telescope will be powerful enough to produce tomographic images of the 21-cm signal from the Epoch of Reionization. Here we address how to identify ionized regions in such data sets, taking into account the resolution and noise levels associated with SKA1-Low. We describe three methods of which one, superpixel oversegmentation, consistently performs best.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals Weather and Stochastic Forecasting Method for Generated Discharge Level Data at Sathanur Dam

2019 ◽  
Vol 8 (10) ◽  
pp. 3797-3802
Keyword(s):  
Trend Analysis ◽  
Error Estimates ◽  
Data Sets ◽  
Forecasting Models ◽  
Level Data ◽  
Forecasting Method ◽  
The Future ◽  
Current Scenario ◽  
Stochastic Forecasting

This article forecasts the future values using stochastic forecasting models for specified fitted values by using downscaling data, which are collected from Sathanoor Dam gauging site. Due to the demand of the water in this current scenario, this study analyzed the perdays Discharge level data collected from Sathanoor Dam where the outcome is predicted in a downscaling data sets in hydrology, extended Thomas –Fiering, ARIMA, MLE models, is used to estimate perdays discharge level data of each month. The error estimates RMSE, MAE of forecasts from above models is compared to identify the most suitable approaches for forecasting trend analysis.

Optimal Values for Calculation of Premium in Life Insurance

2013 ◽  
Vol 2 (3) ◽  
pp. 68-85
Author(s):  
Cvetko Andreeski
Keyword(s):  
Developing Countries ◽  
Life Insurance ◽  
Data Sets ◽  
Mortality Trend ◽  
Republic Of Macedonia ◽  
The Future ◽  
Optimal Values ◽  
Mortality Forecast ◽  
The Republic

Life insurance is very challenging sector in developing countries. Life insurance makes contribute at the investments in every country, so the more developed life insurance, more investments one should expect. One of the main aspects in calculation of risk in life insurance is using updated tables of mortality and forecast of the future values of mortality. There are many functions and models for mortality forecast calculation. Lee-Carter and Azbel Model for mortality trend calculation are used in this paper. In order to evaluate the results, data sets with the mortality in the Republic of Macedonia are used.

PhaseQuant: A tool for quantifying tomographic data sets of geological specimens

2012 ◽  
Vol 48 ◽  
pp. 323-329 ◽  
Author(s):  
Premkumar Elangovan ◽  
Dominik C. Hezel ◽  
Lauren Howard ◽  
Robin Armstrong ◽  
Richard L. Abel
Keyword(s):  
Data Sets ◽  
Tomographic Data

scholarly journals Artificial intelligence in oral and maxillofacial radiology: what is currently possible?

2020 ◽  
pp. 20200375
Author(s):  
Min-Suk Heo ◽  
Jo-Eun Kim ◽  
Jae-Joon Hwang ◽  
Sang-Sun Han ◽  
Jin-Soo Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Open Data ◽  
Data Sets ◽  
Radiographic Images ◽  
Data Set ◽  
Actual Clinical Practice ◽  
Area Of Interest ◽  
The Future ◽  
Treatment Plans ◽  
Image Quality Improvement

Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.

Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data

BioScience ◽  
2020 ◽  
Author(s):  
Corey T Callaghan ◽  
Alistair G B Poore ◽  
Thomas Mesaglio ◽  
Angela T Moles ◽  
Shinichi Nakagawa ◽  
...  
Keyword(s):  
Citizen Science ◽  
Secondary Data ◽  
Science Research ◽  
Single Species ◽  
Data Sets ◽  
Biodiversity Data ◽  
Science Data ◽  
Biodiversity Research ◽  
Research Areas ◽  
The Future

Abstract Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science's unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.

scholarly journals Quantification of volume flaw sizes in ceramics by image processing of 3D x-ray tomographic data sets

1994 ◽  
Vol 27 (2) ◽  
pp. 102
Author(s):  
M.C. Stinson ◽  
D.A. Holloway ◽  
W.A. Ellingson
Keyword(s):  
Image Processing ◽  
Data Sets ◽  
X Ray ◽  
Tomographic Data

scholarly journals Antenna design and implementation for the future space Ultra-Long wavelength radio telescope

2018 ◽  
Vol 45 (2) ◽  
pp. 231-253 ◽  
Author(s):  
Linjie Chen ◽  
Amin Aminaei ◽  
Leonid I. Gurvits ◽  
Marc Klein Wolt ◽  
Hamid Reza Pourshaghaghi ◽  
...  
Keyword(s):  
Radio Telescope ◽  
Antenna Design ◽  
Long Wavelength ◽  
Design And Implementation ◽  
Future Space ◽  
The Future

Enhancement of first arrivals using the τ-p transform on energy-ratio seismic shot records

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. V101-V111 ◽  
Author(s):  
Wail A. Mousa ◽  
Abdullatif A. Al-Shuhail
Keyword(s):  
Conventional Method ◽  
Noise Level ◽  
Energy Ratio ◽  
Data Sets ◽  
Robust Method ◽  
Noise Levels ◽  
High Noise ◽  
Seismic Records ◽  
Better Than

We propose a robust method of first-break picking for data sets with high noise levels through the use of the [Formula: see text] transform on energy-ratio seismic records. Using synthetic shot records with various noise levels, we showed that the performance of this proposed method enhances first arrivals, which helps in picking them. This was particularly true when the noise level was high where picking on raw amplitudes completely fails. We also applied the method on two published real shot records, for which first-break picking was difficult. We showed that for one of the shot records our method succeeds in determining first breaks that are consistent with manual picks and better than those obtained from a conventional method. The method can be used to guide better the subsequent careful picking of first arrivals and requires one forward and one inverse [Formula: see text] transform operations. In contrast to methods based on trace-by-trace picking that often fail to pick some traces, the proposed method automatically interpolates missing picks.

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