人眼启发的图像非均匀映射变换模型构造算法

人眼的视觉信息采集是非均匀的。这种特征和注意力机制确保了人类视觉系统在信息处理方面的优先性。首先介绍了对数极坐标映射，然后基于人类视觉系统的构造，提出了非均匀映射模型的构造规则。基于此规则，以线性模型为例进行了应用性研究。扩展了非均匀映射模型的范围，促进了空间变分辨率视觉理论的进一步发展。     

基于Contourlet区域对比度的遥感图像融合算法

为综合利用多传感器遥感图像之间的信息，提出了一种基于contourlet变换的遥感图像融合算法。Contourlet是近年发展起来的一种多尺度几何分析工具，比小波变换更适合对图像等二维信号进行处理。算法的思想是将源图像分别进行contourlet变换，在contourlet域完成图像信息的融合，最后通过逆变换得到融合结果。根据分解系数特性，首次提出了contourlet域的区域对比度作为高频子带的融合规则，系数选择更符合人类视觉系统的要求，使得融合图像更有意义。低频子带采用基于区域能量的加权平均作为系数融合规则，在充分保留图像主要信息的前提下进一步增强了算法的稳定性。实验结果表明该算法在性能上明显优于PCA及小波图像融合等传统算法，且能够有效增强源图像的对比度和细节信息。     

应用联合变换相关器的成像制导系统

提出了一种光电混合的成像制导系统，它采用光学联合变换相关器作为其主要的图像识别单元，利用计算机对识别结果进行判决和显示，并发出跟踪信号给导引头，从而实现制导。联合变换相关器是对光学图像信号进行并行处理的器件，实时识别性能强，精度高。此成像制导系统克服了传统的红外制导系统易受外界因素影响，实时处理能力差，误判率较高的缺点，具有并行处理速度快、信息吞吐量大、体积小等优点，应用前景广阔。     

基于AT89C52的离散信号测量电路设计

介绍了离散信号测量与控制在反舰导弹雷达导引头自动测控系统中的重要性 ,给出了基于AT89C5 2单片机控制的离散信号测量电路的具体构成和工作原理     

二维小波变换及其在医学图像处理中的应用

介绍了二维离散小波变换的一般形式，在图像分解的基础上．分析了二维小波变换在医学图像消噪和图像增强中的应用，同时给出应用实例。结果表明，应用小波分析进行医学图像处理，能够有效地改善图像质量，有利于医生对病情的诊断和治疗。     

用Radon变换迭代法重建含有遮挡物的三维折射率场

本文通过计算机模拟研究，结合气体折射率场的光验知识．考查了将Ｒａｄｏｎ变换迭代法用于光学干涉层析计量含有遮挡物的三维折射率场的重建精度。作为一个应用实例，测量和计算了某一截面的气体温度分布，并与热电偶测量的值进行了比较。     

Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.     

Multi-state autonomous drilling for lunar exploration

Due to the lack of information of subsurface lunar regolith stratification which varies along depth, the drilling device may encounter lunar soil and lunar rock randomly in the drilling process. To meet the load safety requirements of unmanned sampling mission under limited orbital resources, the control strategy of autonomous drilling should adapt to the indeterminable lunar environments. Based on the analysis of two types of typical drilling media (i.e., lunar soil and lunar rock), this paper proposes a multi-state control strategy for autonomous lunar drilling. To represent the working circumstances in the lunar subsurface and reduce the complexity of the control algo-rithm, lunar drilling process was categorized into three drilling states:the interface detection, initi-ation of drilling parameters for recognition and drilling medium recognition. Support vector machine (SVM) and continuous wavelet transform were employed for the online recognition of dril-ling media and interface, respectively. Finite state machine was utilized to control the transition among different drilling states. To verify the effectiveness of the multi-state control strategy, drilling experiments were implemented with multi-layered drilling media constructed by lunar soil simulant and lunar rock simulant. The results reveal that the multi-state control method is capable of detect-ing drilling state variation and adjusting drilling parameters timely under vibration interferences. The multi-state control method provides a feasible reference for the control of extraterrestrial autonomous drilling.     

Real-time object tracking via least squares transformation in spatial and Fourier domains for unmanned aerial vehicles

This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of samples, which is always time-consuming and not suitable for realtime applications. In this paper, we transform the large-scale least-squares problem in the spatial domain to a series of small-scale least-squares problems with constraints in the Fourier domain using the correlation filter technique. Then, this problem is efficiently solved by two stages. In the first stage, a fast method based on recursive least squares is used to solve the correlation filter problem without constraints in the Fourier domain. In the second stage, a weight matrix is constructed to prune the solution attained in the first stage to approach the constraints in the spatial domain. Then, the pruned classifier is used for tracking. To evaluate proposed tracker's performance, comprehensive experiments are conducted on challenging aerial sequences in the UAV123 dataset. Experimental results demonstrate that proposed approach achieves a state-ofthe-art tracking performance in aerial sequences and operates at a mean speed of beyond 40 frames/s. For further analysis of proposed tracker's robustness, extensive experiments are also performed on recent benchmarks OTB50, OTB100, and VOT2016.     

某测试台谐波摇摆运动的控制

某测试台主要运动方式为三自由度正弦摇摆Asinωt,三次谐波摇摆。本文详细阐述了该设备三次谐波谱的精密运动控制、在线实时轨迹显示和静态FFT分析。