The Earth's natural pulse electromagnetic fields for earthquake time-frequency characteristics: Insights from the EEMD-WVD method

Island Arc ◽  
2018 ◽  
Vol 27 (4) ◽  
pp. e12256 ◽  
Author(s):  
Guo-Cheng Hao ◽  
Yu-Xiao Bai ◽  
Hui Liu ◽  
Juan Zhao ◽  
Zuo-Xun Zeng
Keyword(s):  
Electromagnetic Fields ◽  
Frequency Characteristics ◽  
Time Frequency ◽  
Pulse Electromagnetic Fields ◽  
Natural Pulse

Influence of Pulse Electromagnetic Fields on Integrated Memory Chips

2007 ◽  
Vol 66 (19) ◽  
pp. 1799-1804
Author(s):  
L. N. Akhramovich ◽  
M. P. Gribskii ◽  
Ye. V. Grigor'ev ◽  
S. A. Zuev ◽  
V. V. Starostenko ◽  
...  
Keyword(s):  
Electromagnetic Fields ◽  
Pulse Electromagnetic Fields

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

Application of Joint-Time Frequency Analysis Methods for Nondestructive Evaluation

1999 ◽  
Author(s):  
Ki-Woo Nam ◽  
Kun-Chan Lee ◽  
Jeong-Hwan Oh
Keyword(s):  
Crack Propagation ◽  
Nondestructive Evaluation ◽  
Frequency Analysis ◽  
Cyclic Fatigue ◽  
Frequency Characteristics ◽  
Time Frequency Analysis ◽  
Ultrasonic Signals ◽  
Time Frequency ◽  
Analysis Methods ◽  
Ae Signals

Abstract Application of signal processing techniques to nondestructive evaluation (NDE) in general and acoustic emission (AE) studies in particular has become a standard tool in determining the frequency characteristics of the signals and relating these characteristics to the integrity of the structure under consideration. Recent studies have shown that the frequency characteristics of ultrasonic signals from evolving damage during cyclic (fatigue) and dynamic loads change with time; in other words, the signals are nonstationary, and that these changes can be related to the nature of the damage taking place during loading. A joint time-frequency analysis such as Short Time Fourier Transform (STFT) and Wigner-Ville distribution (WVD), can in principle be used to determine the time dependent frequency characteristics of nonstationary signals in presence of background noise. In this study these techniques are applied to analyze AE signals from fatigue crack propagation in 5083 aluminum alloys and ultrasonic signals in degraded austenitic 316 stainless steels, to study the evolution of damage in these materials. It is demonstrated that the nonstationary characteristics of both AE and ultrasonic signals could be analyzed effectively by these methods. STFT was found to be more effective in analyzing AE signals, and WVD was more effective for analyzing the attenuation and frequency characteristics of degraded materials through ultrasonics. It is indicated that the time-frequency analysis methods should also be useful in evaluating crack propagation and final fracture process resulting from various damages and defects in structural members.

On-line monitoring of WEDM-LS surface roughness by utilizing time-frequency characteristics of AE signal

2017 ◽  
Vol 25 (8) ◽  
pp. 2090-2097
Author(s):  
王 艳 WANG Yan ◽  
王健波 WANG Jian-bo ◽  
王 强 WANG Qiang ◽  
熊 巍 Xiong Wei ◽  
贺独醒 HE Du-xing
Keyword(s):  
Surface Roughness ◽  
Frequency Characteristics ◽  
Time Frequency ◽  
On Line ◽  
Ae Signal

scholarly journals Identifying Users Based on Time-Frequency Characteristics

2019 ◽  
Vol 1302 ◽  
pp. 042035
Author(s):  
Yaqiong Gao ◽  
Wanghu Chen ◽  
Bo Yang ◽  
Yuxiang Mu ◽  
Jing Li
Keyword(s):  
Frequency Characteristics ◽  
Time Frequency
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