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Photovoltaic cells for indoor light harvesting have recently attracted considerable attention for low power consumption electronic products due to the rapid growth of the Internet of Things. Dye-sensitized cells (DSC) show tremendous promise in delivering a low-cost, high-manufacturability, and excellent conversion efficiency for various artificial lights to combine with electronics achieving the energy autonomous system. Commercial DSC technology in terms of the reproducibility on cell efficiency and lifetime prediction was discussed to meet requirements of photovoltaic cell market for various applications. The DSC automated pilot line towards industrially relevant manufacturing volumes was described, providing an average efficiency of 13.8% at 1000 lux white LED light and an overall yield of 98.7%. Furthermore, an approach to obtain the database of degradation rate constant for various environmental stresses was proposed according to Arrhenius plots and can be used to predict DSC lifetime for a variety of situations. The lifetime was evaluated to 10.9 years for the indoor situation using the corresponding degradation rate constant.

We propose a novel depth maps refinement algorithm and generate multi-view video sequences from two-view video sequences for modern autostereoscopic display. In order to generate realistic contents for virtual views, high-quality depth maps are very critical to the view synthesis results. Therefore, refining the depth maps is the main challenging problem in the task. We propose an iterative depth refinement algorithm, including error detection and error correction, to correct errors in depth map. Error detection aims at two types of error: across-view color-depth-inconsistency error and local color-depth-inconsistency error. Then, error pixels are corrected based on sampling local candidates. A trilateral filter that considers intensity, spatial and temporal terms into the filter weighting is applied to enhance the spatial and temporal consistency across frames. So the virtual views can be better synthesized according to the refined depth maps. To combine both warped images, disparity-based view interpolation is introduced to alleviate the translucent artifacts. Finally, a directional filter is applied to reduce the aliasing around the object boundaries to generate multiple high-quality virtual views between the two views. We demonstrate the superior image quality of the synthesized virtual views by using the proposed algorithm over the state-of-the-art view synthesis methods through experiments on benchmarking image and video datasets.

A cruciform cavity is presented for multi-wavelength laser generation. On the basis of considering the optimal power ratio and good spatial overlap of the two fundamental beams, the maximum output power of 589 nm laser reaches 3.5 W when the pumping power of Nd:YAG A and Nd:YAG B are 311.5 W and 261.8 W, respectively. At the same time, the other wavelength lasers are also obtained with the output power distribution of 2.5 W at 660nm, 15W at 532nm, 100m W at 1319nm and 240mW at 1064nm. The corresponding beam quality factors are Mx2 = 4.93, My2 = 5.01 at 589 nm, Mx2 = 4.51, My2 = 4.85 at 660 nm, and Mx2 = 4.12, My2 = 3.96 at 532nm, respectively. The instabilities of the three visible lights are measured, which are also less than 2% within three hours.

We propose a novel depth maps refinement algorithm and generate multi-view video sequences from two-view video sequences for modern autostereoscopic display. In order to generate realistic contents for virtual views, high-quality depth maps are very critical to the view synthesis results. We propose an iterative depth refinement approach of a joint error detection and correction algorithm to refine the depth maps that can be estimated by an existing stereo matching method or provided by a depth capturing device. Error detection aims at two types of error: across-view color-depth-inconsistency error and local color-depth-inconsistency error. Subsequently, the detected error pixels are corrected by searching appropriate candidates under several constraints to amend the depth errors. A trilateral filter is included in the refining process that considers intensity, spatial and temporal terms into the filter weighting to enhance the consistency across frames. In the proposed view synthesis framework, it features a disparity-based view interpolation method to alleviate the translucent artifacts and a directional filter to reduce the aliasing around the object boundaries. Experimental results show that the proposed algorithm effectively fixes errors in the depth maps. In addition, we also show the refined depth maps along with the proposed view synthesis framework significantly improve the novel view synthesis on several benchmark dataseis.

Operation in minimally invasive surgery is more difficult since the surgeons perform operations without haptic feedback or depth perception. Moreover, the field of view perceived by the surgeons through endoscopy is usually quite limited. The goal of this paper is to allow surgeons to see wide-angle images from endoscopy without the drawback of lens distortion. The proposed distortion correction process consists of lens calibration and real-time image warping. The calibration step is to estimate the parameters in the lens distortion model. We propose a fully automatic Hough-entropy-based calibration algorithm, which provides calibration results comparable to the previous manual calibration method. To achieve real-time correction, we use graphics processing unit to warp the image in parallel. In addition, surgeons may adjust the focal length of a lens during the operation. Real-time distortion correction of a zoomable lens is impossible by using traditional calibration methods because the tedious calibration process has to repeat again if focal length is changed. We derive a formula to describe the relationship between the distortion parameter, focal length, and image boundary. Hence, we can estimate the focal length for a zoomable lens from endoscopic images online and achieve real-time lens distortion correction.

In this paper we present a novel view synthesis method for mobile platform. A disparity-based view interpolation is proposed to synthesize the virtual view from a left and right image pair. This makes to full use of the available information of both images to give accurate interpolation results and greatly decreases the pixel number in the disoccusion region, thus reducing the errors introduced by the patch-based image inpainting. Two boundary refining schemes considering gradient and color coherence and applying directional filters on boundaries are proposed to improve the synthesized results. Experimental results show that the proposed method is effective and suitable for mobile environment.

Touch screen as a kind of simple and convenient input device has been already used in consumer electronics and industrial control areas. S3C2410 is a embedded processor, which is with the ARM920T core produced by Samsung Corp. This paper introduces the architecture of touch screen and its driver under Linux based on ADS7843 connected with S3C2410 platform. Both hardware architecture and software design of the system are described.

Wide-angle lens provides a broad field of view which benefits some applications, such as video surveillance or endoscopic imaging. However, it also induces lens distortion, especially radial distortion, which may impede further video analysis or perceptual interpretation. For some applications, such as minimally invasive surgery and visual surveillance, real-time correction of image distortion is required. Traditional CPU-centric machines are difficult to achieve the requirement of real-time computation because of a large amount of computation. In this paper, we propose to achieve real-time correction of wide-angle lens distortion of images on several target platforms. In the GPGPU platform, we achieve real-time correction at full-HD resolution by using CUDA. For middle-end devices, an error-controllable mesh is used and the system is implemented by industry standard, OpenGL. We also implement it with OpenGL ES on embedded GPUs for mobile devices. Experiments show using our error-controllable mesh greatly outperforms those using regularly downsampled meshes.