强激光热斑形成及其稳定性研究

在论述热点跟踪及其传热机理时将靶材的比热溶、热传导系、在激光的热藕合系数均考虑成温度和时间的函数，又考虑靶材在运动时将激光辐照物体时热点的建立及其稳定情况作一动态数值模拟，为热点跟踪作准备。     

A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

修正型罗德里格斯参数在旋转弹上的应用

光纤陀螺可应用于旋转弹导航上,以提高其精确打击能力。基于光纤陀螺的捷联惯性导航系统是实现这一目的的重要手段之一。其中,姿态更新算法是捷联惯性导航系统的核心。针对旋转弹短时、近程的特点,提出一种基于修正型罗德里格斯参数的全姿态更新算法,并给出适合旋转弹姿态确定的数学模型。仿真结果表明与传统姿态更新算法相比较,新算法可以更有效的抑制不可交换误差,降低跟随时间的累积误差,减少高动态环境下的计算量,提高算法结算的速度和精度。     

MEMS惯性传感器ADIS16355在姿态测量中的应用

设计了基于微电子机械系统(Microelectro mechanical system,MEMS)惯性传感器集成模块ADIS16355的姿态测量系统。该姿态测量系统采用ADIS16355作为惯性测量单元,利用加速度计对重力向量的观测来修正陀螺给出的姿态信息,卡尔曼滤波实现传感器信息融合以计算运动载体的姿态角。介绍了ADIS16355的基本功能模块,阐述了两种传感器融合测量实时姿态角的方法并给出了卡尔曼滤波算法迭代过程。基于ARMv7架构的Cotex-M3微处理器设计了姿态测量系统硬件。采用AHRS500GA对该姿态测量系统性能进行了测量姿态角的验证实验。测试结果表明,该姿态测量系统能在动态条件下准确地测定运动物体实时姿态角,其误差一般在±1°左右。     

运动目标检测与跟踪的DSP实现

研究了运动目标检测与跟踪的DSP（DigitalSignalProcessor）实现算法,以形心跟踪算法为整个处理系统的核心。采用目标形心跟踪算法,通过目标分割阶段的目标标记,如目标面积、周长、形心位置等信息的提取建立目标跟踪波门,实现目标的连续跟踪,并将此算法移植到sEED—VPM642硬件平台,实验结果表明能够达到预定目标。此外,为了克服形心算法的准确性和实时性缺陷,采用粒子滤波对算法进行必要的扩展,从MATLAB的仿真结果看,除个别采样点存在误差较大的情况,真实值曲线与粒子滤波跟踪曲线拟合较好。     

医用药剂中可见异物自动化检测研究

针对国内人工药品检测的低效率、低准确率的不足,文章介绍了一套根据国家行业标准设计的基于智能机器视觉的可见异物自动化检测系统。该系统主要包括机械传动模块、电气控制模块、图像识别与处理模块。根据异物运动连续性和噪声运动无序性等特点,提出了改进的三次差分和能量累积算法来提取运动杂质,然后采用基于基于SIFT特征匹配和最邻近匹配的目标跟踪算法对杂质进行跟踪,获取杂质的大小和数目并分析出杂质总类,最后检测出杂质情况。最后把检测结果与国家标准进行比对,判断出该药剂是否合格。测试表明,系统的检测准确率达90%以上,满足企业要求,基本能替代人工检测。     

Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking

With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

基于强跟踪滤波器的开关变换器故障诊断

Buck变换器又称降压变换器、串联开关稳压电源、三端开关型降压稳压器。广泛用于计算机、家用电器、控制系统和通信系统中的电源几乎都是开关电源,具有超快速负载动态响应、高功率变换效率、高功率密度、低电压、大电流的特性.开关变换器作为它的主要电路,故障率较高,对它故障诊断研究是有意义的。基于强跟踪滤波理论,给出了一种开关变换器电路故障实时诊断的一种方法。该方法以建立一种开关变换器电路状态空间模型为基础,利用强跟踪滤波器对电路状态及元件参数进行估计,当元件参数发生软、硬型故障时,根据强跟踪滤波器对元件参数的跟踪结果及修正的Bayes分类算法,可实时诊断开关变换器电路中的元件故障。仿真结果证明了该方法的有效性。