Bispectral Estimation of Fluorescent Objects

2015 ◽  
Author(s):  
Shoji Tominaga ◽  
Keita Hirai ◽  
Takahiko Horiuchi
Keyword(s):  
Bispectral Estimation

Parametric Bispectral Estimation of EEG Signals in Disorders of Consciousness

2019 ◽  
Author(s):  
Xile Wei ◽  
Wenli Wang ◽  
Lihui Cai ◽  
Meili Lu ◽  
Guosheng Yi ◽  
...  
Keyword(s):  
Disorders Of Consciousness ◽  
Eeg Signals ◽  
Bispectral Estimation

The relationship between AR-modelling bispectral estimation and the theory of linear prediction

1994 ◽  
Vol 37 (3) ◽  
pp. 381-388 ◽  
Author(s):  
Antolino Gallego ◽  
María C. Carrión ◽  
Diego P. Ruiz ◽  
Abdellatif Medouri
Keyword(s):  
Linear Prediction ◽  
Bispectral Estimation ◽  
The Relationship

scholarly journals Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4320
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Short Wave ◽  
Radio Station ◽  
Communication Behavior ◽  
Communication Behaviors ◽  
Training Time ◽  
Wave Radio ◽  
Bispectral Estimation ◽  
Model Algorithm

This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

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