Abstract:A mobile phone camera with an innovative electrically tunable liquid crystal lens (TLCL) concept is demonstrated. We first report the comparative theoretical and experimental analyses of the performance of a traditional "modal control" TLCL versus a TLCL using a floating (unpowered) transparent electrode (FTE). It is shown that the appropriate choice of voltage and frequency values of the driving electric signal may improve significantly (almost twice) the optical quality of the lens using the FTE. Exceptional… Show more
“…It needs to identify each commodity with barcode printed on the outer packaging. However, the printed bar codes of different commodities are not the same, so it is necessary to find the location of bar codes manually to assist machine recognition, so the degree of automation is relatively low [ 2 , 3 ]. In recent years, automated retail stores have appeared successively at home and abroad.…”
In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.
“…It needs to identify each commodity with barcode printed on the outer packaging. However, the printed bar codes of different commodities are not the same, so it is necessary to find the location of bar codes manually to assist machine recognition, so the degree of automation is relatively low [ 2 , 3 ]. In recent years, automated retail stores have appeared successively at home and abroad.…”
In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.
“…For instance, tunable spherical lenses are very much in demand for applications such as virtual- and augmented-reality displays 24 . Moreover, they can find direct use in cameras, telescopes and optical zooming devices 25 , 26 . Axicons are a special kind of optical components with a cone-shaped phase profile, which generates a field distribution proportional to the zero-order Bessel function (non-diffracting Bessel beam).…”
there is an increasing need to control light phase with tailored precision via simple means in both fundamental science and industry. one of the best candidates to achieve this goal are electro-optical materials. In this work, a novel technique to modulate the spatial phase profile of a propagating light beam by means of liquid crystals (Lc), electro-optically addressed by indium-tin oxide (ito) grating microstructures, is proposed and experimentally demonstrated. A planar Lc cell is assembled between two perpendicularly placed ito gratings based on microstructured electrodes. By properly selecting only four voltage sources, we modulate the LC-induced phase profile such that non-diffractive Bessel beams, laser stretching, beam steering, and 2D tunable diffraction gratings are generated. In such a way, the proposed Lc-tunable component performs as an all-in-one device with unprecedented characteristics and multiple functionalities. the operation voltages are very low and the aperture is large. Moreover, the device operates with a very simple voltage control scheme and it is lightweight and compact. Apart from the demonstrated functionalities, the proposed technique could open further venues of research in optical phase spatial modulation formats based on electro-optical materials.
“…The measured minimum focal length ~ 26 cm, close to the estimated f . The high operation voltage of HLC lens can be further reduced with application of a high resistivity film for practical application 24 – 26 . Figure 3 ( a ) Voltage-dependent focal lengths and ( b ) phase retardations of the HLC lenses at various voltages.…”
In this study, the response time of a 4 mm-aperture hole-patterned liquid crystal (HLC) lens has been significantly improved with doping of N-benzyl-2-methyl-4-nitroaniline (BNA) and rutile titanium dioxide nanoparticle (TiO2 NP) nanocomposite. The proposed HLC lens provides the focus and defocus times that are 8.5× and 14× faster than the pristine HLC lens, respectively. Meanwhile, the focus and defocus times of the proposed HLC lens reach the order of millisecond. Result shows that the synergistic effect of BNA and TiO2 NP induces a 78% decrement in the viscosity of pristine LC mixture that significantly shortens the focus and defocus times of HLC lens. The remarkable decrement in viscosity is mainly attributed to spontaneous polarization electric fields from the permanent dipole moments of the additives. Besides, the strengthened electric field surrounding TiO2 NP assists in decreasing the focus time of HLC lens. The focus and defocus times of HLC lens are related to the wavefront (or phase profile) bending speed. The time-dependent phase profiles of the HLC lenses with various viscosities are calculated. This result shows the decrease in wavefront bending time is not simply proportional to viscosity decrement. Furthermore, the proposed HLC lens emerges a larger tunable focus capability within smaller voltage interval than the pristine HLC lens.
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