Research on voltage characteristic of the third generation low-light-level image intensifier tube's output signal to noise ratio

2012 ◽  
Author(s):  
Xiaofeng Bai ◽  
Lei Yin ◽  
Wen Hu ◽  
Feng Shi ◽  
Zhipeng Hou ◽  
...  
Keyword(s):  
Output Signal ◽  
Signal To Noise Ratio ◽  
Image Intensifier ◽  
Light Level ◽  
Voltage Characteristic ◽  
Third Generation ◽  
Signal To Noise ◽  
Low Light ◽  
The Third ◽  
Noise Ratio

Analysis of output signal to noise ratio’s uniformity for low light level image intensifier assembly

2013 ◽  
Author(s):  
Xiaofeng Bai ◽  
Lei Yin ◽  
Wen Hu ◽  
Yingping He ◽  
Fangcheng Wang
Keyword(s):  
Output Signal ◽  
Image Intensifier ◽  
Light Level ◽  
Signal To Noise ◽  
Low Light

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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