A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH4 and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH4 and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.

We discuss the refractive-index measurement of biological tissues by total internal reflection. The methodology of the measurement is illuminated comprehensively, and an experimental setup, combined with a data processing program, is developed correspondingly. Refractive indices of typical tissue samples are measured by use of the developed methodology. The agreement of our measurements with the reported results shows the validity of our scheme, which has the potential for being a simple, quick, and low-cost practical means for determining the refractive index of a turbid medium. Moreover, an empirical formula for evaluating the refractive index of Intralipid suspensions with different concentrations is also presented according to experimental measurements.

To apply reflection ellipsometry to determine the real and imaginary parts of the refractive index of biological tissues simultaneously, we combine reflection ellipsometry with total internal reflection to warrant minimal influences by the strong scattering and absorption of biological tissues. A K9 glass prism with refractive index 1.51468 at wavelength 632.8 nm and a Glan prism polarizer with an angular sampling interval of 0.1 degrees were used in our experimental setup. Using the setup, the complex refractive indices of some typical mammalian tissues were measured under the wavelength of 632.8 nm. The results show that the indices of porcine muscle, liver, pancreas, and dermis tissues were 1.3713+0.062i, 1.3791+0.0087i, 1.3517+0.0113i, and 1.3818+0.0049i, respectively.

We propose a single layer all-dielectric metasurface lens to simultaneously convert and focus an incident linear polarization into a radial beam with high efficiency and high numerical aperture (NA). It shows a better focusing property compared with the linearly polarized metasurface lens for high NA. A tight spot size (0.502λ) is achieved for the NA = 0.94. Additionally, the emergent polarization can in principle be switched flexibly between radially and azimuthally polarized beams by the adjustment of incident polarization direction. It is expected that our scheme may have potential value in microscopy, material processing, medicine, particles accelerating and trapping, and so on.

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