Underwater non-cooperative communication signal recognition with deep learning

2017 ◽  
Vol 142 (4) ◽  
pp. 2732-2732 ◽  
Author(s):  
Cheng LI ◽  
Qiming Zhou ◽  
Xiao Han ◽  
Jingwei Yin ◽  
Mengqi Shao
Keyword(s):  
Deep Learning ◽  
Cooperative Communication ◽  
Signal Recognition ◽  
Communication Signal

scholarly journals Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang
Keyword(s):  
Deep Learning ◽  
Network Structure ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Real World Data ◽  
Data Set ◽  
Practical Applications ◽  
Epilepsy Diagnosis ◽  
Single Data ◽  
Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

scholarly journals Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3424
Author(s):  
Xujia Liang ◽  
Zhonghua Huang ◽  
Liping Lu ◽  
Zhigang Tao ◽  
Bing Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Detection Methods ◽  
Signal Recognition ◽  
Echo Signal ◽  
Deep Learning Algorithm ◽  
Recognition Criterion

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

scholarly journals Lionfish predators use flared fin displays to initiate cooperative hunting

2014 ◽  
Vol 10 (6) ◽  
pp. 20140281 ◽  
Author(s):  
Oona M. Lönnstedt ◽  
Maud C. O. Ferrari ◽  
Douglas P. Chivers
Keyword(s):  
Success Rate ◽  
Cooperative Communication ◽  
Test Chamber ◽  
Pectoral Fins ◽  
Communication Signal ◽  
Cooperative Hunting ◽  
Hunting Success ◽  
Dendrochirus Zebra ◽  
Per Capita ◽  
Group Hunting

Despite considerable study, mystery surrounds the use of signals that initiate cooperative hunting in animals. Using a labyrinth test chamber, we examined whether a lionfish, Dendrochirus zebra , would initiate cooperative hunts with piscine partners. We found that D. zebra uses a stereotyped flared fin display to alert conspecific and heterospecific lionfish species Pterois antennata to the presence of prey. Per capita success rate was significantly higher for cooperative hunters when compared with solitary ones, with hunt responders assisting hunt initiators in cornering the prey using their large extended pectoral fins. The initiators would most often take the first strike at the group of prey, but both hunters would then alternate striking at the remaining prey. Results suggest that the cooperative communication signal may be characteristic to the lionfish family, as interspecific hunters were equally coordinated and successful as intraspecific hunters. Our findings emphasize the complexity of collaborative foraging behaviours in lionfish; the turn-taking in strikes suggests that individuals do not solely try to maximize their own hunting success: instead they equally share the resources between themselves. Communicative group hunting has enabled Pteroine fish to function as highly efficient predators.

scholarly journals Large-scale real-world radio signal recognition with deep learning

2021 ◽  
Author(s):  
Ya Tu ◽  
Yun Lin ◽  
Haoran Zha ◽  
Ju Zhang ◽  
Yu Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Large Scale ◽  
Radio Signal ◽  
Signal Recognition

scholarly journals Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhijun Guo ◽  
Shuai Liu
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Recognition Performance ◽  
Image Transmission ◽  
Recognition System ◽  
Interference Signal ◽  
Signal Recognition ◽  
Interference Signals ◽  
The Time Domain ◽  
Wireless Image Transmission

In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

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.

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