Pulsar Search on Mobile Devices

সংরক্ষণ করুন:
গ্রন্থ-পঞ্জীর বিবরন
প্রকাশিত:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 104-109
প্রধান লেখক: Peng, Shilin
অন্যান্য লেখক: Hu, Qianchen, Wang, Layue, Lu, Jiang, Wang, Jian, Peng, Zhicheng
প্রকাশিত:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
বিষয়গুলি:
অনলাইন ব্যবহার করুন:Citation/Abstract
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MARC

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024 7 |a 10.1109/ICAIBD64986.2025.11082002  |2 doi 
035 |a 3246661695 
045 2 |b d20250101  |b d20251231 
084 |a 228229  |2 nlm 
100 1 |a Peng, Shilin  |u Sui Ning Vocational College,School of Information Engineering,Suining,China 
245 1 |a Pulsar Search on Mobile Devices 
260 |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  |c 2025 
513 |a Conference Proceedings 
520 3 |a Conference Title: 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD)Conference Start Date: 2025 May 23Conference End Date: 2025 May 26Conference Location: Chengdu, ChinaThis paper present an edge computing model for pulsar search on mobile devices, to reduce the computing load of server nodes, make full use of distributed computing nodes. Using edge devices for dedispersion processing and acceleration searching, which makes the previously time-consuming and challenging to achieve parallel computing dedispersion algorithm divided into multiple small computing tasks in time domain and frequency domain, and a large number of computing tasks can be distributed into various edge devices. Based on that, a multilayer directed connection distributed search model is designed to reduce the amount of computation on the server side in the search process. Raspberry Pi 3B and 4B and smart phones are used as distributed computing nodes to test the data processing capability of mobile devices with different performance and the energy consumption in computing. With the characteristics of volunteer computing nodes, the network bandwidth requirements of different performance mobile computing nodes are discussed according to the test results. Experiments have shown that those devices can afford such computing tasks, especially mobile phones with GPGPU computing, which can carry such computing tasks well under the low power consumption. 
653 |a Energy consumption 
653 |a Search process 
653 |a Data processing 
653 |a Big Data 
653 |a Servers 
653 |a Multilayers 
653 |a Smartphones 
653 |a Electronic devices 
653 |a Edge computing 
653 |a Nodes 
653 |a Artificial intelligence 
653 |a Power consumption 
653 |a Pulsars 
653 |a Distributed processing 
653 |a Economic 
700 1 |a Hu, Qianchen  |u Sui Ning Vocational College,School of Information Engineering,Suining,China 
700 1 |a Wang, Layue  |u Sui Ning Vocational College,School of Information Engineering,Suining,China 
700 1 |a Lu, Jiang  |u Sui Ning Vocational College,School of Information Engineering,Suining,China 
700 1 |a Wang, Jian  |u Sui Ning Vocational College,School of Information Engineering,Suining,China 
700 1 |a Peng, Zhicheng  |u Sui Ning Vocational College,School of Information Engineering,Suining,China 
773 0 |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings  |g (2025), p. 104-109 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3246661695/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch