Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Single-pixel three-dimensional imaging with time-based depth resolution

Abstract: A three-dimensional imaging system which distributes the optical illumination over the full field-of-view is sought after. Here, the authors demonstrate the capability of reconstructing 128 × 128 pixel resolution three-dimensional scenes to an accuracy of 3 mm as well as real-time video with a frame-rate up to 12 Hz.

Robust Anti-Icing Performance of a Flexible Superhydrophobic Surface

Abstract: A material with superhydrophobic and anti-ice/de-icing properties, which has a micro-/nanostructured surface, is produced by a straightforward method. This material comprises a poly(dimethylsiloxane) (PDMS) microstructure with ZnO nanohairs and shows excellent water and ice repellency even at low temperatures (-20 °C) and relatively high humidity (90%) for over three months. These results are expected to be helpful for designing smart, non-wetting materials that can be adapted to low-temperature environments for the development of anti-icing systems.

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

Abstract: In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers. The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23db to 36.39db on the SOTS indoor test dataset. Code has been made available at GitHub.

Facile approach to prepare nickel cobaltite nanowire materials for supercapacitors

Abstract: Excellent electrochemical performance results from the coexistence of nickel and cobalt ions, with mesoporous characteristics and nanocrystal structure. Nickel cobalt nanowire is prepared by hydrothermal and thermal decomposition processes. High capacitance of 722 F g(-1) can be obtained at 1 A g(-1) in 6 M KOH, with a capacitance retention ratio of ca. 79% at 20 A g(-1) .