The Fourier transform has been widely applied in the optical signal processing, yet it is just fit for analyzing the stationary signals. By extending the Fourier transform into wavelet transform, a new type of filter is proposed and its analogy to neural networks is developed. Optical neural networks (ONNs) are the new type networks, which possess good capacity of super parallel processing, signal transmission and high-density connecting lines. Although neural networks' implementations have been limited by the availability of high-resolution optical devices, by virtue of simple optical architectures for the wavelet transforms, the new neural network is easy to implement in large-scale by applying photoelectric technology. In this paper, the basic principles of ONNs and optical wavelet transform (OWT) are presented respectively, and the principle and structure of their combination—optical neural networks based on the wavelet transform are also proposed. For the optical neural networks and optical wavelet transforms, their optical implementations have many unique superiority, yet theirs combination takes on characteristics better than such structures just using neural networks or wavelet transform. Furthermore, their application perspectives are predicted in the paper.
In this paper, the characteristics of wavelength scanning micro-displacement measurement signal is analyzed, and it is pointed out to be uncontinuous and unstable, so using Fourier transform to process must bring about error. Because of wavelet transform having good localization property in time-frequency domain and strong effects for the filtering of white noise and sharp pulse, it can overcome shortcomings of original signal's edge fuzzy caused by average filtering and median filtering. In this paper, wavelet transform is applied to process detected displacement signal in the wavelength scanning Fabry-Perot interferometer. Theory and test have shown that using this method is more accordant with reality of optical measurement and can eliminate noise. The measurement resolution and precision can be improved.
A neural-fuzzy network applied in fire detection system is presented in this paper. New Back-Propagation algorithm with optimization-based architecture revised automatically is proposed to train network structure. Experimental results show this system can detect china standard experimental fire correctly and lower false alarm.
The Fabry-Perot interferometer (FPI) has been traditionally used to examine either small spectral ranges or relatively simple spectra. Recently, however, the studies have shown that the FPI can be competitive with the Michelson interferometer over extended spectral ranges. A relatively new FPI is described based on two gratings. In order to measure angular micro-displacement, a novel grating angular displacement transducer using a multiplex Fabry-Perot interference technology has been developed in this paper.
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