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.

A Multi-Wall Sn/SnO 2 @Carbon Hollow Nanofiber Anode Material for High-Rate and Long-Life Lithium-Ion Batteries

Abstract: Multi-wall Sn/SnO2 @carbon hollow nanofibers evolved from SnO2 nanofibers are designed and programable synthesized by electrospinning, polypyrrole coating, and annealing reduction. The synthesized hollow nanofibers have a special wire-in-double-wall-tube structure with larger specific surface area and abundant inner spaces, which can provide effective contacting area of electrolyte with electrode materials and more active sites for redox reaction. It shows excellent cycling stability by virtue of effectively alleviating pulverization of tin-based electrode materials caused by volume expansion. Even after 2000 cycles, the wire-in-double-wall-tube Sn/SnO2 @carbon nanofibers exhibit a high specific capacity of 986.3 mAh g-1 (1 A g-1 ) and still maintains 508.2 mAh g-1 at high current density of 5 A g-1 . This outstanding electrochemical performance suggests the multi-wall Sn/SnO2 @ carbon hollow nanofibers are great promising for high performance energy storage systems.

Adaptive foveated single-pixel imaging with dynamic supersampling

Abstract: In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom—a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements.