The key technology and main difficulty for optical fiber perimeter system is the extraction and recognition of intrusion signals, vibration signals normally consist of noises, intrusion and disturb signals. Firstly, a new detection method combining constant false alarm rate (CFAR) method and Level Crossing (LC) method was proposed to distinguish the intrusion and no-intrusion signal before recognition. The former can produce adaptive thresholds to eliminate noise and disturb signals according to the background homogeneity, the later can ensure the integrity of the intrusion signal and further reduce disturb signal. Second, multi-feature parameters including traditional timedomain features, wavelet packet energy Shannon entropy and wavelet packet energy, energy proportion, kurtosis, skewness are accurately extracted from the intrusion signal. Finally, use support vector machine (SVM) identify multi-feature vectors of different types of vibration signals. The proposed method was experimented on Sagnac optical fiber pre-warning system. The result show that the method can extract vibration signal effectively form sensing signals, improve the system recognition rate.
Porous silicon has many advantages, such as biodegradability, biocompatibility, tunable pore size and active covalent and non-covalent surface chemical properties. One-dimensional porous silicon photonic crystal microcavity structure has the characteristics of porous silicon and optical microcavity, it is compatible with existing silicon micromachining technology and can be embedded into the sensitive chip so as to realize the function of micro-nano detection devices and integration. At present, there are many biosensors based on existing porous silicon microcavity, through controlling the pore size of porous silicon microcavities, the biological target molecules penetrate into the porous silicon microcavity structure, leading to increases of refractive index of porous silicon layers. In the practical test, we found that the penetration of biological molecules in the microcavity is not uniform, it is difficult to enter into the deeper porous silicon layers, according to this, the paper will explore the distributional characteristics of different biological molecules in the microcavity, and the variation of the sensing efficiency under the circumstance of nonuniform increase in refractive index. This study will be helpful to the accurate design and theoretical development of high efficiency porous silicon microcavity biosensor.
Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.