We propose a continuous Stokes imaging system with a refresh rate of several seconds, instead of a traditional Mueller measurement setup, to quickly track the microstructural changes of tissues during the optical clearing process. The effectiveness of this fast Stokes imaging applied in monitoring the dynamic process is first validated by three designed experiments with a polarization state that changes continuously and rapidly, and is further confirmed by gradual changes in polarization image contrast and resolution with clearing. By comparison with experiments from different tissue samples with the same agent, the fast Stokes response curve can improve the analysis ability of photon polarization behavior connected with the complicated changes of tissue characteristics.
In this study, we employ our developed instrument to obtain high-throughput multi-angle single-particle polarization scattering signals. Based on experimental results of a variety of samples with different chemical composition, particle size, morphology, and microstructure, we trained a deep convolutional network to identify the polarization signal characteristics during aerosol scattering processes, and then investigate the feasibility of multi-dimensional polarization characterization applied in the online and real-time fine and accurate aerosol recognition. Our model shows a high classification accuracy rate (>98%) and can achieve aerosol recognition at a very low proportion (<0.1%), and shows well generalization ability in the test set and the sample types not included in the training set. The above results indicate that that the time series pulses from multi-angle polarization scattering contain enough information related with microscopic characteristics of an individual particle, and the deep learning model shows its capability to extract features from these synchronous multi-dimensional polarization signals. Our investigations confirm a good prospect of aerosol attribute retrieval and identifying and classifying individual aerosols one by one by the combination of multi-dimensional polarization scattering indexes with deep learning method.
This paper presents a real-time IP position controller realized by neural network for permanent magnet linear synchronous motor (PMLSM) servo system. In the paper, the proposed neural network's configuration is simple and reasonable and weight has definite physical meaning and rapidly adjustable character in order to obtain real-time control. The mover mass, damping coefficient and disturbance force are estimated by the proposed estimator, which is composed of a recursive least-square (IUS) estimator and a disturbance observer. The observed disturbance force is fed forward, to increase the robustness of PMLSM drive system.
In this Letter, we report a dual-wavelength Mueller matrix imaging system for polarization phase unwrapping, allowing simultaneous acquisition of the polarization images at 633 nm and 870 nm. After phase unwrapping, the relative error of linear retardance is controlled to be 3% and the absolute error of birefringence orientation is about 6°. We first show that polarization phase wrapping occurs when the samples are thick or present obvious birefringence effects, and further analyze the effect of phase wrapping on anisotropy parameters via Monte Carlo simulations. Then, experiments on porous alumina with different thicknesses and multilayer tapes are performed to verify the feasibility of phase unwrapping by a dual-wavelength Mueller matrix system. Finally, by comparing the temporal characteristics of linear retardance during tissue dehydration before and after phase unwrapping, we emphasize the significance of the dual-wavelength Mueller matrix imaging system not only for anisotropy analysis in static samples, but also for determining the trend in polarization properties of dynamic samples.
In this work, we propose a high-throughput online identification method of bioaerosols based on multi-angle polarization index system (MAPIS). In the study, four categories and 10 subclasses of aerosol samples from biological and non-biological sources are detected under three incident polarization mode. Then their measured MAPIS shows that bioaerosols like pollen can be easily distinguished from other types of aerosols. Not only that, experimental results also indicate the feasibility of fine identification between different kinds of bioaerosols based on MAPIS in P and R modes. To further extract simple and optimized polarization characterization parameters suitable for bioaerosols, we analyze the multidimensional data of MAPIS by PCA then validate the aerosol recognition accuracy using the first two principal components by multiple groups of randomly mixed aerosol datasets. The comparison with PCA components based on only scattering intensity demonstrate that MAPIS can be not only applied in the specific identification of bioaerosols but also suitable for the distinction between different kinds of bioaerosols.
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