Combination of finite impulse response neural network technique with FDTD method for simulation of electromagnetic problems

1996 ◽  
Vol 32 (12) ◽  
pp. 1112 ◽  
Author(s):  
C. Wu ◽  
A. Navarro ◽  
J. Litva
Keyword(s):  
Neural Network ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Fdtd Method ◽  
Electromagnetic Problems

scholarly journals Intelligent LED Certification System in Mass Production

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2891
Author(s):  
Galina Malykhina ◽  
Dmitry Tarkhov ◽  
Viacheslav Shkodyrev ◽  
Tatiana Lazovskaya
Keyword(s):  
Neural Network ◽  
Impulse Response ◽  
Mass Production ◽  
Adaptive Filters ◽  
Finite Impulse Response ◽  
Statistical Moments ◽  
Signal Extraction ◽  
Infinite Impulse Response ◽  
Cooling Curves ◽  
Generalized Moments

It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.

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