Big Data in Astronomy: Scientific Data Processing for Advanced Radio Telescopes
Editat de Linghe Kong, Tian Huang, Yongxin Zhu, Shenghua Yuen Limba Engleză Paperback – 16 iun 2020
- Bridges the gap between radio astronomy and computer science
- Includes coverage of the observation lifecycle as well as data collection, processing and analysis
- Presents state-of-the-art research and techniques in big data related to radio astronomy
- Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)
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Specificații
ISBN-13: 9780128190845
ISBN-10: 0128190841
Pagini: 438
Ilustrații: Approx. 120 illustrations
Dimensiuni: 191 x 235 mm
Greutate: 0.75 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128190841
Pagini: 438
Ilustrații: Approx. 120 illustrations
Dimensiuni: 191 x 235 mm
Greutate: 0.75 kg
Editura: ELSEVIER SCIENCE
Public țintă
Practitioners and researchers working in data processing for astronomy; students studying data in astronomyCuprins
Part A: Fundamentals
Chapter 1: Introduction of Radio Astronomy
Chapter 2: Fundamentals of Big Data in Radio Astronomy
Part B: Big Data Processing
Chapter 3: Pre-processing Pipeline on FPGA
Chapter 4: Real-time stream processing in radio astronomy
Chapter 5: Digitization, Channelization and Packeting
Chapter 6: Processing Data of Correlation on GPU
Chapter 7: Data Calibration for single dish radio telescope
Chapter 8: Imaging Algorithm Optimization for Scale-out Processing
Part C: Computing Technologies
Chapter 9: Execution Framework Technology
Chapter 10: Application Design For Execution Framework
Chapter 11: Heterogeneous Computing Platform for Backend Computing Tasks
Chapter 12: High Performance Computing for Astronomical Big Data
Chapter 13: Spark and Dask Performance Analysis Based on ARL Image Library
Chapter 14: Applications of Artificial Intelligence in Astrnomical Big Data
Part D: Future Developments
Chapter 15: Mapping the Universe with 21cm Observations
Chapter 1: Introduction of Radio Astronomy
Chapter 2: Fundamentals of Big Data in Radio Astronomy
Part B: Big Data Processing
Chapter 3: Pre-processing Pipeline on FPGA
Chapter 4: Real-time stream processing in radio astronomy
Chapter 5: Digitization, Channelization and Packeting
Chapter 6: Processing Data of Correlation on GPU
Chapter 7: Data Calibration for single dish radio telescope
Chapter 8: Imaging Algorithm Optimization for Scale-out Processing
Part C: Computing Technologies
Chapter 9: Execution Framework Technology
Chapter 10: Application Design For Execution Framework
Chapter 11: Heterogeneous Computing Platform for Backend Computing Tasks
Chapter 12: High Performance Computing for Astronomical Big Data
Chapter 13: Spark and Dask Performance Analysis Based on ARL Image Library
Chapter 14: Applications of Artificial Intelligence in Astrnomical Big Data
Part D: Future Developments
Chapter 15: Mapping the Universe with 21cm Observations