Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. 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.

Recent developments in bio-inspired special wettability

Abstract: Nature is a school for scientists and engineers. After four and a half billion years of stringent evolution, some creatures in nature exhibit fascinating surface wettability. Biomimetics, mimicking nature for engineering solutions, provides a model for the development of functional surfaces with special wettability. Recently, bio-inspired special wetting surfaces have attracted wide scientific attention for both fundamental research and practical applications, which has become an increasingly hot research topic. This Critical Review summarizes the recent work in bio-inspired special wettability, with a focus on lotus leaf inspired self-cleaning surfaces, plants and insects inspired anisotropic superhydrophobic surfaces, mosquito eyes inspired superhydrophobic antifogging coatings, insects inspired superhydrophobic antireflection coatings, rose petals and gecko feet inspired high adhesive superhydrophobic surfaces, bio-inspired water collecting surfaces, and superlyophobic surfaces, with particular focus on the last two years. The research prospects and directions of this rapidly developing field are also briefly addressed (159 references).

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Abstract: Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

Abstract: The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

OFDM and Its Wireless Applications: A Survey

Abstract: Orthogonal frequency-division multiplexing (OFDM) effectively mitigates intersymbol interference (ISI) caused by the delay spread of wireless channels. Therefore, it has been used in many wireless systems and adopted by various standards. In this paper, we present a comprehensive survey on OFDM for wireless communications. We address basic OFDM and related modulations, as well as techniques to improve the performance of OFDM for wireless communications, including channel estimation and signal detection, time- and frequency-offset estimation and correction, peak-to-average power ratio reduction, and multiple-input-multiple-output (MIMO) techniques. We also describe the applications of OFDM in current systems and standards.

Two-Dimensional Graphene Bridges Enhanced Photoinduced Charge Transport in Dye-Sensitized Solar Cells

Abstract: As a novel two-dimensional (2D) material, graphene shows great benefits in electric and material science. Compared to 1D nanomaterials, it may show more excellent properties. Here, we introduced graphene as 2D bridges into the nanocrystalline electrodes of dye-sensitized solar cells, which brought a faster electron transport and a lower recombination, together with a higher light scattering. On the basis of these advantages, the short-circuit current density was increased by 45% without sacrificing the open-circuit voltage, and the total conversion efficiency was 6.97%, which was increased by 39%, comparing with the nanocrystalline titanium dioxide photoanode, and it was also much better than the 1D nanomaterial composite electrode.