An infrared small target detection algorithm based on high-speed local contrast method

Small-target detection in infrared imagery with a complex background is always an important task in remote sensing fields. It is important to improve the detection capabilities such as detection rate, false alarm rate, and speed. However, current algorithms usually improve one or two of the detection capabilities while sacrificing the other. In this letter, an Infrared (IR) small target detection algorithm with two layers inspired by Human Visual System (HVS) is proposed to balance those detection capabilities. The first layer uses high speed simplified local contrast method to select significant information. And the second layer uses machine learning classifier to separate targets from background clutters. Experimental results show the proposed algorithm pursue good performance in detection rate, false alarm rate and speed simultaneously.     

Scale-space point spread function based framework to boost infrared target detection algorithms

Small target detection is one of the major concern in the development of infrared surveillance systems. Detection algorithms based on Gaussian target modeling have attracted most attention from researchers in this field. However, the lack of accurate target modeling limits the performance of this type of infrared small target detection algorithms. In this paper, signal to clutter ratio (SCR) improvement mechanism based on the matched filter is described in detail and effect of Point Spread Function (PSF) on the intensity and spatial distribution of the target pixels is clarified comprehensively. In the following, a new parametric model for small infrared targets is developed based on the PSF of imaging system which can be considered as a matched filter. Based on this model, a new framework to boost model-based infrared target detection algorithms is presented. In order to show the performance of this new framework, the proposed model is adopted in Laplacian scale-space algorithms which is a well-known algorithm in the small infrared target detection field. Simulation results show that the proposed framework has better detection performance in comparison with the Gaussian one and improves the overall performance of IRST system. By analyzing the performance of the proposed algorithm based on this new framework in a quantitative manner, this new framework shows at least 20% improvement in the output SCR values in comparison with Laplacian of Gaussian (LoG) algorithm.     

A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain

The aim of infrared and visible image fusion is to enhance the feature in infrared image and preserve abundant detail information in visible image. Based on the fact that the human sense system accepts external stimulation only when the stimulus intensity is greater than a certain value and the reaction of neuronal cells have obvious regional characters, an image fusion algorithm based on region dual-channel unit-linking pulse coupled neural networks (RDU-PCNN) and independent component analysis (ICA) bases in non-subsampled shearlet transform (NSST) domain for infrared and visible images is proposed. RDU-PCNN we constructed has obvious regional characters and much lower computational costs. We trained ICA-bases using a number of images that the content and statistical properties are similar with the fusion images but applied it as low-frequency ICA-bases, which can reduce calculation complexity. Experimental results demonstrate that the proposed method can significantly improved the fusion quality and need less computational costs.     

On the photon-drag effect of photocurrent of surface states of topological insulators

The photocurrent of surface states of topological insulator due to photon-drag effect is computed, being based on pure Dirac model of surface states. The scattering by disorder is taken into account to provide a relaxation mechanism for the photocurrent. The Keldysh–Schwinger formalism has been employed for the systematic calculation of photocurrent. The helicity dependent photocurrent of sizable magnitude transverse to the in-plane photon momentum is found, which is consistent with experimental data. Other helicity independent photocurrents with various polarization states are also calculated.     

WSANs中基于蜂巢结构的移动容错恢复算法

旨在研究无线传感器与执行器网络(WSANs)中节点失效情况下恢复执行器(actor)节点服务的算法. 首先说明了WSANs中的实时覆盖模型, 证明WSANs覆盖恢复问题是NP难问题, 给出了近似求解方案. 在此基础上, 提出了一种基于六边形蜂巢结构的移动容错算法HMFR用于恢复失效actor节点, HMFR 算法在限制网络初始部署的条件下拥有很好的性能. 通过实验与现有的恢复算法进行比较, 发现HMFR算法在actor覆盖sensor节点数和移动距离方面有更好的性能.     

Enhanced room temperature ferromagnetism and photoluminescence behavior of Cu-doped ZnO co-doped with Mn

Cu, Mn co-doped ZnO nanoparticles were successfully synthesized by the sol–gel technique. XRD pattern described that Mn-doping did not affect the hexagonal wurtzite structure of the samples and no secondary phases were found. The reduced crystallite size at Mn=2% is due to the suppression of grain surface growth by foreign impurity. The enhancement of crystal size after Mn=2% is due to the expansion of lattice volume produced by the distortion around the dopant ion. The better dielectric constant and conductivity noticed at Mn=2% are explained by charge carrier density and crystallite size. The suppression of broad UV band by Mn-doping is discussed based on the generation of non-radiative recombination centers. Hysteresis loop showed the clear room temperature ferromagnetism in all the samples and the magnetization increased with Mn-doping. Better electrical and magnetic behavior of Zn_0.94Cu_0.04Mn_0.02O sample is suggested for effective opto-magnetic devices.