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Performance of gaussian waveform in OvTDM

The Journal of China Universities of Posts and Telecommunications 2009年第1期16卷 32-37页

作者： YANG Xun WANG Jian LI Dao-ben Key Laboratory of Information Processing and Intelligent Technology Beijing University of Posts and Telecommunications Beijing 100876 China

waveforms play a key role in overlapping time division multiplexing （OvTDM） system. The OvTDM has been proved to be a novel transmission technique that can utilize time diversity to improve the bit error ratio （BER） performance in Rayleigh channel. According to the resulted law, the BER performance of gaussian waveform in OvTDM also improves with the increase in the waveform parameter monotonically, and the rectangular waveform is the worst case of gaussian waveform. In contrast, the spectrum effectiveness of gaussian waveform OvTDM decreases with the increase in the waveform parameter. In the study, it is obtained that waveforms can achieve the best performance when the smallest correlation coefficient between neighbor symbols is e^-1 .

关键词： OvTDM inter symbol interference (ISI) gaussian waveform rectangular waveforrn BER spectrum effectiveness

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Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow

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Earthquake Research Advances 2024年第1期4卷 59-66页

作者： Kang Wang Jie Zhang Ji Zhang Zhangyu Wang Huiyu Zhu School of Earth and Space Sciences University of Science and Technology of ChinaHefei230026China

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and *** this study,we propose an automatic workflow based on machine learning(ML)to monitor seismicity in the southern Sichuan Basin of *** workflow includes coherent event detection,phase picking,and earthquake location using three-component data from a seismic *** combining Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local *** approach allows us to use synthetic samples covering the entire study area to train Phase Loc,addressing the problems of insufficient data samples,imbalanced data distribution,and unreliable labels when training with observed *** apply the trained model to observed data recorded in the southern Sichuan Basin,China,between September 2018 and March *** results show that the average differences in latitude,longitude,and depth are 5.7 km,6.1 km,and 2 km,respectively,compared to the reference *** Loc combines all available phase information to make fast and reliable predictions,even if only a few phases are detected and *** proposed workflow may help real-time seismic monitoring in other regions as well.

关键词： Earthquake monitoring Machine learning Local seismicity gaussian waveform Sparse stations

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