All-inorganic cesium lead-halide perovskites exhibit a great development prospect in optoelectronic devices owing to their stability and remarkable optoelectronic properties. Herein, we investigate the solution-processed synthesis of perovskite CsPb2Br5 nanosheets by using aqueous and ethanol as solvents. The results show that the aqueous environment ensures the phase formation of CsPb2Br5 and that the supersaturated solution in ethanol boosts nucleation of the nanosheets. The substrate temperature is the key factor for the evolution of morphology and the variation of the thickness of CsPb2Br5 nanosheets. Lower substrate temperature (<35 °C) is conducive to the formation of evenly distributed nanosheets with less stacking. The spatial and time-resolved fluorescence spectra indicate the heterogeneity of the defect density and the recombination process in different nanosheet regions. The photodetector based on the prepared CsPb2Br5 nanosheet displays an excellent switching current ratio (9 × 102), a short rise and decay time (43 and 83 ms, respectively), and good stability (75% of the initial current after 90 days in air). In addition, the mechanical stability and flexibility of the photodetector on the flexible substrate are investigated for 500 bending cycles.

The segmentation and classification of retinal arterioles and venules play an important role in the diagnosis of various eye diseases and systemic diseases. The major challenges include complicated vessel structure, inhomogeneous illumination, and large background variation across subjects. In this study, we employ a fully convolutional network to simultaneously segment arterioles and venules directly from the retinal image, rather than using a vessel segmentation-arteriovenous classification strategy as reported in most literature. To simultaneously segment retinal arterioles and venules, we configured the fully convolutional network to allow true color image as input and multiple labels as output. A domain-specific loss function was designed to improve the overall performance. The proposed method was assessed extensively on public data sets and compared with the state-of-the-art methods in literature. The sensitivity and specificity of overall vessel segmentation on DRIVE is 0.944 and 0.955 with a misclassification rate of 10.3% and 9.6% for arteriole and venule, respectively. The proposed method outperformed the state-of-the-art methods and avoided possible error-propagation as in the segmentation-classification strategy. The proposed method was further validated on a new database consisting of retinal images of different qualities and diseases. The proposed method holds great potential for the diagnostics and screening of various eye diseases and systemic diseases.

As a potential candidate for modern optoelectronics, two-dimensional PbI 2 nanosheets have received much attention. In this paper, we report a simple, low-cost cooling supersaturated aqueous solution method for the controllable morphology growth of high-quality PbI 2 single crystals. The fluorescence kinetics of PbI 2 nanosheets was investigated by the temporally and spatially resolved spectrum measurements. The body center region of as-prepared PbI 2 nanosheets showed a higher density of defect states than that at the edges or corners. In addition, the as-prepared PbI 2 nanosheet-based photodetectors exhibit stable and efficient performance, including the large on/off ratio (1.371 × 10 3 ), high light response (40 mA/W), and high detection sensitivity (3.31 × 10 10 J).

The movement state of obstacle including position, velocity, and yaw angle in the real traffic scenarios has a great impact on the path planning and decision-making of autonomous vehicle. Aiming at how to get the obstacle’s movement state in the real traffic scenarios, an approach is proposed to detect and track obstacle based on three-dimensional Light Detection And Ranging (LiDAR). Firstly, the point-cloud data produced by three-dimensional LiDAR after the road segmentation is rasterized, and the reuse of useful non-obstacle cells is carried out on the basis of the rasterized point-cloud data. The proposed eight-neighbor cells clustering algorithm is used to cluster the obstacle. Based on the clustering result, static obstacle detection of multi-frame fusion is worked out by combining real-time kinematic global positioning system data and inertial navigation system data of autonomous vehicle. And we further use the static obstacle detection result to detect moving obstacle located in the travelable area. After that, an improved dynamic tracking point model and Kalman filter are applied to track moving obstacle stably, and we finally get the moving obstacle’s stable movement state. A large amount of experiments on the autonomous vehicle developed by us show that the method has a high degree of reliability.

Pathological diagnosis plays an important role in the diagnosis and treatment of hepatocellular carcinoma (HCC). The traditional method of pathological diagnosis of most cancers requires freezing, slicing, hematoxylin and eosin staining, and manual analysis, limiting the speed of the diagnosis process. In this study, we designed a one‐dimensional convolutional neural network to classify the hyperspectral data of HCC sample slices acquired by our hyperspectral imaging system. A weighted loss function was employed to promote the performance of the model. The proposed method allows us to accelerate the diagnosis process of identifying tumor tissues. Our deep learning model achieved good performance on our data set with sensitivity, specificity, and area under receiver operating characteristic curve of 0.871, 0.888, and 0.950, respectively. Meanwhile, our deep learning model outperformed the other machine learning methods assessed on our data set. The proposed method is a potential tool for the label‐free and real‐time pathologic diagnosis. © 2019 International Society for Advancement of Cytometry