Today, various nanomaterials have shown remarkable potential in liquid crystal display and non-display applications. Among them, carbon nanotubes and liquid crystal molecules have the same anisotropic geometry, which is easier to disperse into the liquid crystal and has more substantial stability. This review briefly reviews the carbon nanotubes/liquid crystal composite materials in recent years.Firstly, the basic concept and classification of carbon nanotubes and liquid crystal materials are introduced. Then, three common methods of dispersing carbon nanotubes into liquid crystal are described, and the effects of carbon nanotubes on physical properties of liquid crystal materials such as phase transition temperature, optical properties, electrical conductivity, dielectric properties and mechanics properties are discussed. Finally, the applications of carbon nanotubes/liquid crystal composites in actuators, sensors, handwriting tablets, high strength materials, and other fields are introduced. The understanding of the preparation, properties, and application of carbon nanotubes/liquid crystal composites will help to explore the exact mechanism of the interaction between carbon nanotubes and liquid crystal molecules leading to the improvement of liquid crystal thermal, dielectric, electro-optical, mechanical, and other physical properties, and lay a foundation for the realization of related applications in the future.

Micro-LED (Micro-LED) display is a revolutionary and promising technology that has a significant impact on display technology. Micro-LED has high brightness, low power consumption, extended life, short response time, and good stability. It has good development potential in the display area and may be applied to virtual reality (VR), augmented reality (AR), mobile phone micro-displays, as well as household TV, conference wall, and other big and medium-sized displays. Although Micro-LED display has broad application prospects, its technology is still not mature, driving technology is facing high production costs, resource dispersion and other issues, limiting its industrialization process. This study examines the development process and research outcomes of Micro-LED display technology since 2000, analyzes array preparation and flip chip integration technology, and concentrates on the driving technology of Micro-LED displays. Micro-LED display driving technology is classified into two categories: passive driving and active driving. The circuit theory of passive driving, complementary metal oxide semiconductor driven by active driving, thin film transistor driving technology, and active pixel driving circuit are primarily introduced.

Beam steering is widely used in aerospace, laser communication, vehicle-mounted ridar, optical information processing, biomedicine and military confrontation. Non-mechanical beam steering has received intensive attention. Liquid-crystal-polymer-based polarization grating can realize high-efficient, large-angle beam steering with merits of simple and low-cost fabrication, and thus has been gradually adopted in non-mechanical beam steering. In this paper, two circularly polarized holographic optical paths are used to fabricate the liquid-crystal-polymer-based polarization grating of different pitches, respectively. The measured diffraction efficiency reaches over 99.3%. Multiple liquid-crystal-polymer-based gratings are cascaded and then stacked with liquid crystal polymer/ferroelectric liquid crystal/liquid crystal polymer waveplates to achieve fast response (70 μs) and large angle 4-channel beam deflections.

When the OLED emitting layers are produced by using the injet printing method, the film thickness of different pixel pits can cause Mura defects in the light-emitting devices. The main reason for the film thickness variation is the difference of ink volume in different pixel pits caused by the inconsistent droplet volume of different nozzles and the wrong deposition of droplets. To solve the problem of film thickness consistency, a film forming control method for OLED emitting layer based on droplet volume control and deposition position control is proposed. Firstly, the driving waveform is adjusted so that the droplets with appropriate volume can be generated from nozzles. Then, the droplet landing position of each nozzle is detected, and the deposition position of droplets is controlled by the offset compensation and abnormal nozzles screening. Finally, the ink volume in the pixel pit is controlled by droplets mixing method. In the experiment, the deviation of droplet landing position is controlled within ±10 μm, and the volume difference of the ink is controlled within ±4% by droplets mixing method. The film thickness of the emitting layer after drying is measured. The experimental results show that the consistency of different pixel pit film thickness is more than 95%, and the OLED device with uniform light emission is obtained. The film thickness consistency control method can meet the manufacturing requirements of OLED functional layer.

In the long distance laser communication system, it is necessary to control the dispersion angle of laser beam in real time in order to suppress the influence of complicated atmospheric channel environment. However, the traditional optical zoom system has low precision in adjusting the laser beam scatter angle, which requires mechanical movement of the distance between the lens groups, resulting in poor flexibility and real-time performance. In this paper, a method of controlling laser beam scatter angle using liquid crystal on spatial light modulator (LC-SLM) is proposed. LC-SLM was used to simulate the zoom lens, and the phase gray images with different focal lengths were generated by computer and imported into LC-SLM. The applied voltage of LC-SLM was controlled by the gray information of different phase gray images to realize the function of the zoom lens. Then, the control system of beam scattering angle was composed of the zoom lens and the matching lens with known focal length. The distance between the zoom lens and the matching lens was constant, and the beam scattering angle was controlled by changing the focal length of the zoom lens. The results show that the error between the variation value and the theoretical value is within 5%, and the RMSE of the beam divergence angle is 0.017 5. The root mean square error of beam divergence angle is reduced by 22% compared with the traditional method of controlling beam divergence angle by mechanically moving the distance between lens groups, which provides an important technical support for dynamic adjustment of laser parameters.

In order to improve the electrical and optical properties of thin film transistors (TFTs), the tungsten-doped ZnO thin films (WZO) were deposited on p-type silicon substrates by RF magnetron sputtering, and the thin film transistors (TFTs) were fabricated. The effects of sputtering power of tungsten (W) on the electrical and optical properties of WZO thin film transistors were investigated. The results show that the sputtering power of W affects the crystallization quality of the films. With the increase of the sputtering power of W, the crystalline quality of the films is gradually improved, and when the sputtering power of W reaches a certain value, the crystalline quality of the films is not greatly improved. The WZO films studied in this paper were grown on (002) crystal plane. When the sputtering power of W is 2 W, the comprehensive properties of WZO thin film transistors are the best, the switching ratio is 5.24×105, the threshold voltage is 16.89 V, and the average transmissivity is 90% between 400 nm and 1 400 nm.

The flexible screen module is a composite structure of different screen film layers and OCA (Optical Clear Adhesive) stacking which has good display effect and flexibility. As a new type of extending a flexible screen, its mechanical behavior remains to be studied. This paper establishes a numerical model based on the finite element method for the double scroll structure applicable to the flexible slidable screen, and numerically analyzes the effects of different scroll radii, stacking structure order and thickness shapes of the adhesive layer on the mechanical properties of the screen during sliding process. The results show that the stress of the flexible screen module during the sliding process increases with the decrease of the scroll radius, the maximum stress appears in the support film layer, and the optimal scroll radius is between 3 mm to 3.5 mm. Interchanging the stacking order of the OLED layer and the backplane layer so that the OLED layer is located in the middle of the entire module can not optimize the stress of the OLED layer. The thickness shape of the adhesive layer can offset the position of each film layer, where the stress of the OLED layer is mainly influenced by its own offset and less influenced by the offset of other film layers.

Underwater wireless optical transmission adopts blue-green laser with the advantage of fast and real-time, playing an important role in ocean exploration and communication. However, the divergence of the beam itself is affected by the absorption and scattering of water, resulting in energy loss and weak signal at the receiving end. As a result, a beam shaping module based on Hadamard encoding algorithm is proposed in this paper to improve the transmission efficiency of beam in water. The wavefront phase of incident beam is modulated by liquid crystal on silicon to find the optimal hologram in multiple iterative process and reduce the loss of light energy after passing through the underwater channel. The beam is divided into reference beam and modulation beam and the angle of the two beams can be calculated by four-step phase shift. The results show that the energy of the receiver at different distances can be improved, reaching a 2.3 times increase in the energy of the receiver at 15 m. The finer the beam is modulted, the better it focuses.

In order to realize SDI transmission of any resolution video source, a multi-resolution adaptive SDI transmission system with programmable logic device (FPGA) as the core processor is designed. The video resolution detection, multi-resolution processing and other related methods involved in the system are deeply studied. Firstly, on the premise of realizing a 4K (3 840×2 160P@60) transmission system, a 4K video SDI transmission system is realized by using the SQD-square division method. Then, a resolution detection module is added to realize the multi-resolution video adaptive receiving function. Then, a pixel filling module and a cropping module are designed so that the videos with a resolution lower than 1 920×1 080P@60 can be unified into a resolution of 1 920×1 080P@60 and transmitted on the SDI interface. It has been verified by experiments that the system can realize adaptive transmission of videos of various resolutions. Video source types include but are not limited to 4K (3 840×2 160P@60), 1 920×1 080P@60, 1 360×768P@60, 1 280×720P@60, 1 024×768P@60 and other resolutions so that the 4K SDI transmission system has better compatibility, breaking through the limitation that the SDI interface can only transmit limited resolution video.

Image engine optimizes the image signal through a variety of specific algorithms, which plays an extremely important role in the display system. The traditional color optimization algorithm of image engine is composed of various image optimization algorithms, which can not optimize images adaptively and easy to amplifies the noise. Therefore, a full convolution neural network based on dilated convolution is proposed to construct the optimization algorithm, which focuses on optimizing images from the perspective of subjective perception. At the same time, a large-scale dataset is constructed to improve the generalization ability of the algorithm and prevent overfitting. The experiment results show that the proposed algorithm can effectively enhance the color of original images. Compared with the traditional method, the average peak signal-to-noise ratio is improved by 4.01 dB and the average structural similarity is improved by 0.04. The subjective comparison experiment shows that the proposed algorithm has a significant impact on the subjective perception quality of the image, with an average improvement of 61%.

Fourier Ptychography (FP) is a technology of achieving high-resolution, large field-of-view imaging of optical system. However, the high-resolution reconstruction based on traditional FP methods requires a high aperture overlap ratio, resulting in a large number of captured images and low sampling efficiency. In addition, the FP reconstruction algorithm has high complexity and long reconstruction time. Aiming at solving these problems of the FP, this paper proposes a deep learning algorithm based on multi-scale feature fusion network. Through the improved feature pyramid module, the feature information can be extracted from multiple low-resolution images captured by the FP imaging system, and the information is fused to achieve super-resolution reconstruction. Experimental results show that compared with traditional methods, the deep learning algorithm proposed in this paper improves the quality of image reconstruction, reduces the reconstruction time by 90%, and is more robust to Gaussian noise. In addition, the proposed method can reduce the overlap ratio between sub-apertures from 50% to 25% in frequency domain, and reduce the number of captured images by 50%, greatly improving the sampling efficiency.

In the traditional dark channel defogging algorithm, there is blocking effect in the transmittance estimation, which is easy to cause the white edge phenomenon. At the same time, the dark channel principle is not applicable to the bright areas such as sky and white clouds in the image, and results in defogging image distortion.In this paper, a defogging algorithm based on multi-scale dark channel and adaptive tolerance is proposed, which can effectively avoid the above problems. Firstly, the rough transmittance estimates under three windows with different sizes are calculated, and the results are fused effectively. Then, the transmittance is refined by guided filtering to obtain a multi-scale transmittance map with better robustness and accuracy, and the adaptive tolerance strategy is introduced to correct the transmittance of the bright areas. Finally, the overall brightness of the defogged results is dark, so the brightness and contrast of the defogged image are compensated. Experimental results indicate that the information entropy increases by 0.2 bit/symbol, the average gradient increases by 0.5, and the PSNR increases by about 8 dB when different algorithms are used to the image without or with a small amount of sky. For processing more and a large number of sky region images, PSNR and SSIM are improved by about 3 dB and 0.1, respectively. The requirements of clear image details, reliable color and good defogging effect in bright areas are better realized.

Aiming at the problems of traditional remote sensing landslide recognition methods based on convolutional neural network, such as too many model parameters, insufficient attention in areas of interest, and difficulty in capturing long-term (global) dependency, an automatic landslide recognition algorithm of remote sensing images based on improved self-attention and convolution block attention is proposed. The algorithm is based on the encoder-decoder target recognition framework. In order to enhance the model’s attention to local features of landslide areas, the convolution block attention mechanism is applied to the extraction of shallow features, and the landslide target feature association information is obtained from the spatial and channel dimensions. The improved self-attention mechanism is applied to the extraction of deep features, so that the model can capture global feature information within and between feature maps, which effectively distinguishes landslide targets from background areas. Experimental results show that the landslide recognition precision of this method is 96.81%, and the average accuracy of pixel segmentation is 90.11%. The proposed method can effectively improve the accuracy of landslide identification while keeping the model lightweight in comparison with FCN, DeeplabV3+and other algorithms.