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As an important electromagnetic field in experiment, Gaussian beams have non-vanishing longitudinal electric and magnetic components that generate significant energy fluxes on transverse directions. We focus on the transverse energy flux and derive the theoretical propagation properties. Unlike the longitudinal energy flux, the transverse energy flux has many unique physical behaviors, such as the odd symmetry on propagation, slower decay rate on resonant condition. By means of the characteristics of transverse energy flux, it is feasible to find the suitable regions where the information of coherent lights could be extracted exactly. With the typical laser parameters, we simulate the energy fluxes on receiver surface and analyze the corresponding distribution for the coherent light beams. Especially for coherent lights, the transverse energy flux on the y–z plane with x = 0 and x–z plane with y = 0, contains pure coherent information. Meanwhile, in the transverse distance |y| < 2W0 (W0 is the waist radius) and |x| < W0/3 the coherent information could also be extracted appropriately. [ABSTRACT FROM AUTHOR]

Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are "hand-crafted", which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency. [ABSTRACT FROM AUTHOR]