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The mid-infrared absorber is an important device for effective capture of high by atmospheric infrared transparent window, and has been a research hotspot in military, medical, biological and other fields. In this paper, a kind of ultra-thin mid-infrared ultra-broadband absorber with background insensitive is realized by stacking four metal/dielectric films (Si/Ag/Si3N4/SiO2) on silver substrate and periodically arranged cross-anchor metal pattern in a method of finite-difference time domain. The results show that the absorption rate is more than 90% and the relative absorption bandwidth is 95.5% in the wavelength ranging from 9.1 μm to 24.1 μm. The average absorptivity is 92% in the range of background refractive index (n = 1∼1.8), A proof of the device's insensibility to background refractive index. At a large incidence angle (θ ≤ 50°), the lowest absorption rate is up to 80%. The thickness of the optimized device is only 1.7 μm. The research results have important application value in infrared remote sensing, pollution monitoring, disease diagnosis and other fields. [ABSTRACT FROM AUTHOR]

This paper presents a new surveillance video synopsis method which performs much better than previous approaches in terms of both compression ratio and artifact. Previously, a surveillance video was usually compressed by shifting the moving objects of that video forward along the time axis, which inevitably yielded serious collision and chronological disorder artifacts between the shifted objects. The main observation of this paper is that these artifacts can be alleviated by changing the speed or size of the objects, since with varied speed and size the objects can move more flexibly to avoid collision points or to keep chronological relationships. Based on this observation, we propose a video synopsis method that performs object shifting, speed changing, and size scaling simultaneously. We show how to integrate the three heterogeneous operations into a single optimization framework and achieve high-quality synopsis results. Unlike previous approaches that usually use alternative optimization strategies to solve synopsis optimizations, we develop a Metropolis sampling algorithm to find the solution for our three-variable optimization problem. A variety of experiments demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]