In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

In this paper, we describe the design, modeling, fabrication, and optical characterization of the first micropolarimeter array enabling full Stokes polarization imaging in visible spectrum. The proposed micropolarimeter is fabricated by patterning a liquid-crystal (LC) layer on top of a visible-regime metal-wire-grid polarizer (MWGP) using ultraviolet sensitive sulfonic-dye-1 as the LC photoalignment material. This arrangement enables the formation of either micrometer-scale LC polarization rotators, neutral density filters or quarter wavelength retarders. These elements are in turn exploited to acquire all components of the Stokes vector, which describes all possible polarization states of light. Reported major principal transmittance of 75% and extinction ratio of 1100 demonstrate that the MWGP's superior optical characteristics are retained. The proposed liquidcrystal micropolarimeter array can be integrated on top of a complementary metal-oxide-semiconductor (CMOS) image sensor for real-time full Stokes polarization imaging.

Division of focal plane (DoFP) polarization image sensors capture polarization properties of light at every imaging frame. However, these imaging sensors capture only partial polarization information, resulting in reduced spatial resolution output and a varying instantaneous field of overview (IFoV). Interpolation methods are used to reduce the drawbacks and recover the missing polarization information. In this paper, we propose residual interpolation as an alternative to normal interpolation for division of focal plane polarization image sensors, where the residual is the difference between an observed and a tentatively estimated pixel value. Our results validate that our proposed algorithm using residual interpolation can give state-of-the-art performance over several previously published interpolation methods, namely bilinear, bicubic, spline and gradient-based interpolation. Visual image evaluation as well as mean square error analysis is applied to test images. For an outdoor polarized image of a car, residual interpolation has less mean square error and better visual evaluation results.

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