Using Auto-Associative Neural Networks for Signal Recognition Technology on Sky Screen

2013 ◽  
pp. 71-79
Author(s):  
Yan Lou ◽  
Zhipeng Ren ◽  
Yiwu Zhao ◽  
Yugui Song
Keyword(s):  
Neural Networks ◽  
Signal Recognition

scholarly journals Review of Neural Networks in the EEG Signal Recognition

2020 ◽  
Author(s):  
Ildar Rakhmatulin
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Processing Technique ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Signal Processing Technique ◽  
Comprehensive Overview ◽  
The Past ◽  
Eeg Signal Processing ◽  
Physical Principles

In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn’t change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

EMR Interference Signal Recognition in Coal Rock Mining Based on Recurrent Neural Networks

Geophysics ◽  
2021 ◽  
pp. 1-59
Author(s):  
yangyang Di ◽  
Enyuan Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Rock Burst ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Underground Mining ◽  
Interference Signal ◽  
Signal Recognition ◽  
Interference Signals ◽  
Coal Rock

The electromagnetic radiation (EMR) method is a promising geophysical method for monitoring and providing early warnings about coal rock burst disasters. In the underground mining process, personnel activities and electromechanical equipment produce EMR interference signals that affect the accuracy of EMR monitoring. Current methods for identifying the EMR interference signals mainly use their time and amplitude characteristics. However, these methods of EMR interference signal recognition and filtering need to be further improved. The advancements in the deep learning technique provide an opportunity to develop a new method for their identification and filtering. A method for EMR interference signal recognition based on deep learning algorithms is proposed. The proposed method uses bidirectional long short-term memory recurrent neural networks and Fourier transform to analyze numerous EMR interference signals along with other signals to intelligently identify and filter EMR signal sequences. The results showed that the proposed method can respond positively to EMR interferences and accurately eliminate EMR interference signals. This method can significantly improve the reliability of EMR monitoring data and effectively monitor rock burst disasters.

scholarly journals Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex

2019 ◽  
Vol 216 ◽  
pp. 02003
Author(s):  
D. Shipilov ◽  
P.A. Bezyazeekov ◽  
N.M. Budnev ◽  
D. Chernykh ◽  
O. Fedorov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Antenna Arrays ◽  
Signal Reconstruction ◽  
Cosmic Ray ◽  
Matched Filtering ◽  
Background Suppression ◽  
Signal Recognition ◽  
Air Showers ◽  
Matched Filters ◽  
Prospects Of Application

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km2. In the present workwe discuss the improvements of the signal reconstruction applied for Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performanceof matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised traces, i.e. removes all signal-unrelated amplitudes. We present the comparison between the standard method of signal reconstruction, matched filtering and the autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.

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