Unsupervised flood detection in large areas using Synthetic Aperture Radar (SAR) data always faces the challenge of automatic thresholding, because the histograms of large-scale images are unimodal, which thus makes it difficult to determine the threshold. In this paper, an iteratively multi-scale chessboard segmentation-based tiles selection method is introduced. This method includes a robust search procedure for tiles which obey bimodal Gaussian distribution, and a non-parametric histogram-based thresholding algorithm for thresholds identifying water areas. Then, the thresholds are integrated into the region-growing algorithm to obtain a consistent flood map. In addition, a classification refinement technique using multiresolution segmentation is proposed to address the omission in a heterogeneous flood area caused by water surface roughening due to weather factors (e.g., wind or rain). Experiments on the flooded area of Jialing River on July 2018 using Sentinel-1 images show a high classification accuracy of 99.05% through the validation of Landsat-8 data, indicating the validity of the proposed method.

In this paper, we fabricate a graphene film heater through laser reduction on graphene oxide, which is a two-step process. The electrothermal performance of the graphene heater can be adjusted by the laser energy density. While the applied voltage is 18 V, the graphene heater reaches a steady-state temperature of 247.3 °C within 20 s. After the graphene heater is folded in half 100 times, its output temperature remains to be precisely controlled by the input power and the temperature distribution is uniform. In addition, the flexibility of the graphene heater is superior to a heater based on a commercial indium tin oxide film. It's worth noting that the graphene heater can be fabricated with desired shapes directly and easily, which is rare among the reported film heaters. In consideration of the high performance of the graphene film heater, we demonstrate its three application scenarios: portable warmers applied in medical infusion apparatus, flexible custom-shaped heaters for special requirements and displays.

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Molybdenum disulfide is a promising channel material for field effect transistors (FETs). In this paper, monolayer MoS2 grown by chemical vapor deposition (CVD) was used to fabricate top-gate FETs through standard optical lithography. During the fabrication process, charged impurities and interface states are introduced, and the photoresist is not removed cleanly, which both limit the carrier mobility and the source-drain current. We apply a SiO2 protective layer, which is deposited on the surface of MoS2, in order to avoid the MoS2 directly contacting with the photoresist and the ambient environment. Therefore, the contact property between the MoS2 and the electrodes is improved, and the Coulomb scattering caused by the charged impurities and the interface states is reduced. Comparing MoS2 FETs with and without a SiO2 protective layer, the SiO2 protective layer is found to enhance the characteristics of the MoS2 FETs, including transfer and output characteristics. A high mobility of ∼42.3 cm2/V s is achieved, which is very large among the top-gate CVD-grown monolayer MoS2 FETs.

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In this paper, we propose a novel method for constructing developable surfaces using generalized C-Bézier bases with shape parameters. Based on the duality between points and planes in 3D projective space, the generalized developable C-Bézier surfaces, whose shape can be adjusted by changing multiple shape parameters, are designed using control planes with extensional C-Bézier basis functions. With the shape parameters taking different values, a family of developable surfaces can be constructed, which keeps most of characteristics of classic developable Bézier surfaces. Furthermore, some interesting properties of the new developable surfaces, as well as the geometric continuity conditions between two adjacent generalized developable C-Bézier surfaces, are investigated. Finally, we illustrate the convenience and efficiency of the proposed methods by several convictive and representative numerical examples.

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Abstract:To obtain accurate information in a timely manner on built-up areas (BAs) is essential for urban planning and natural hazard (e.g., earthquakes) response strategies. In this paper, a new method for BAs extraction using the Sentinel-1 SAR is proposed, which includes two steps: (1) Candidate BAs are first selected as seeds from images that show high backscattering and obvious textural patterns, as characterized by image intensity, Getis-Ord index, and the variogram texture features; (2) region growing is iteratively implemented from these seed pixels to extract the BAs. Sentinel-1 data, with 5 × 20 m 2 resolution, are selected over eight cities with various environmental settings around China, to validate the robustness of the proposed method. The results show that the proposed method achieves higher detection accuracy and fewer commission errors compared with the intensity-based region growing and thresholding methods. An averaged accuracy of 96.5% in validation points of eight cities was achieved, which outperforms the GlobCover urban product in both urban and rural area, while fewer commission errors were achieved compared to Landsat data-based methods. Moreover, two polarizations (VV/VH) and the averaged channel are compared for BAs extraction in areas with various environments. It turns out that improved results can be achieved using the averaged image of two polarizations in north China, while the VV image is better suited for BAs extraction in south. These findings indicate that operational BAs mapping over China, and even globally, is possible, since the Sentinel-1 data can provide images with global coverage.