Seismic Time-Frequency Analysis Based on Entropy-Optimized Paul Wavelet Transform

Luan, Yi; Huang, Yu‐Cheng; Zheng, Xiaodong; Cheng, Jixing

doi:10.1109/lgrs.2019.2919756

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…This family behaves like a Ricker wavelet at low order with respect to its real part, while it approaches a Morlet wavelet for high orders [21]. Moreover, the peak frequency in the spectral domain just equals the wavelet order n [29]. Turbulence causes kinetic and magnetic energy, mostly contained in the large scales, to cascade via non-linear interactions to smaller scales, where they are eventually dissipated [19,30].…”

Section: Methodsmentioning

confidence: 99%

Modulation of Solar Wind Impact on the Earth’s Magnetosphere during the Solar Cycle

et al. 2022

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The understanding of extreme geomagnetic storms is one of the key issues in space weather. Such phenomena have been receiving increasing attention, especially with the aim of forecasting strong geomagnetic storms generated by high-energy solar events since they can severely perturb the near-Earth space environment. Here, the disturbance storm time index Dst, a crucial geomagnetic activity proxy for Sun–Earth interactions, is analyzed as a function of the energy carried by different solar wind streams. To determine the solar cycle activity influence on Dst, a 12-year dataset was split into sub-periods of maximum and minimum solar activity. Solar wind energy and geomagnetic activity were closely correlated for both periods of activity. Slow wind streams had negligible effects on Earth regardless of their energy, while high-speed streams may induce severe geomagnetic storming depending on the energy (kinetic or magnetic) carried by the flow. The difference between the two periods may be related to the higher rate of geo-effective events during the maximum activity, where coronal mass ejections represent the most energetic and geo-effective driver. During the minimum period, despite a lower rate of high energetic events, a moderate disturbance in the Dst index can be induced.

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Section: Methodsmentioning

confidence: 99%

Modulation of Solar Wind Impact on the Earth’s Magnetosphere during the Solar Cycle

et al. 2022

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“…Its principle is similar to a zoom camera [24]. Define the wavelet transform of a signal sptq as [25] CWT s pa, bq"…”

Section: A Linear and Bilinear Time-frequency Transformsmentioning

confidence: 99%

Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System

Xu¹,

Ren

et al. 2022

Preprint

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It is known that, interference classification plays an important role in protecting the authorized communication system and avoiding its performance degradation in the hostile environment. In this paper, the interference classification problem for the frequency hopping communication system is discussed. Considering the possibility of presence multiple interferences in the frequency hopping system, in order to fully extract effective features of the interferences from the received signals, the linear and bilinear transform based composite time-frequency analysis method is adopted. Then the time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input to the deep neural network for classification. In particular, the Siamese neural network is used as the classifier, where the paired spectrograms are input into the two sub-networks of the deep networks, and these two sub-networks extract the features of the paired spectrograms for interference type classification. The simulation results confirm that the proposed algorithm can obtain higher classification accuracy than both traditional single time-frequency representation based approach and the AlexNet transfer learning or convolutional neural network based methods.

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“…Its principle is similar to a zoom camera [23]. Define the wavelet transform of a signal sptq as [24] CW T s pa, bq "…”

Section: A Linear and Bilinear Time-frequency Transformsmentioning

confidence: 99%

Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System

Xu¹,

Ren²,

Yu³

et al. 2021

Preprint

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In a hostile environment, interference identification plays an important role in protecting the authorized communication system and avoiding its performance degradation. In this paper, the interference identification problem for the frequency hopping communication system is discussed. Considering presence of multiple and compound interference in the frequency hopping system, in order to fully extracted effective features of the interferences from the received signals, a composite time-frequency analysis method based on both the linear and bilinear transform is proposed. The time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input into the deep neural network for identification. In particular, the Siamese neural network is adopted as the classifier to perform the interference identification. That is, the paired spectrograms are input into the two subnetworks of the Siamese neural network to extract the features of the paired spectrograms. The Siamese neural network is trained and tested by calculating the gap between the generated features, and the interference type identification is realized by the trained Siamese neural network. The simulation results confirm that the proposed algorithm can obtain higher identification accuracy than both traditional single time-frequency representation based approach and the AlexNet transfer learning or convolutional neural network based methods.

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Seismic Time-Frequency Analysis Based on Entropy-Optimized Paul Wavelet Transform

Cited by 13 publications

References 25 publications

Modulation of Solar Wind Impact on the Earth’s Magnetosphere during the Solar Cycle

Modulation of Solar Wind Impact on the Earth’s Magnetosphere during the Solar Cycle

Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System

Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System

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