Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.     

An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

We present an iterative linear quadratic regulator(ILQR) method for trajectory tracking control of a wheeled mobile robot system.The proposed scheme involves a kinematic model linearization technique,a global trajectory generation algorithm,and trajectory tracking controller design.A lattice planner,which searches over a 3D(x,y,θ) configuration space,is adopted to generate the global trajectory.The ILQR method is used to design a local trajectory tracking controller.The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot.The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator(LQR) method.According to the experiments,the new controller improves the control sequences(v,ω) iteratively and produces slightly better results.Specifically,two trajectories,’S’ and ’8’ courses,are followed with sufficient accuracy using the proposed controller.     

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。     

智能家居系统中的短信收发程序设计

针对智能家居系统中安防报警信息实时收发及家电远程控制的需求,提出使用3G模块收发短信的方法,并设计了实现短信同时收发功能的软件架构。实验结果表明,该方法简单实用,能够满足智能家居系统的要求。     

基于自由摆的平板精确控制与激光追踪系统

以TI公司的MSP430F5438单片机为控制核心,结合步进电机和精密电位器WDD35D-4,实现了基于自由摆的平板控制与激光追踪系统。当摆杆摆角为45°～60°时,平板可以承载8枚一元硬币,并在5个摆动周期内维持稳定;当摆杆摆角为30°～60°时,固定在摆杆平板上的激光笔可以在15s内找到并跟踪1.5m远处的靶心。     

基于微电压调节的太阳能充放电系统设计

基于微电压调节的太阳能充放电系统,是以微控制器LPC1114为核心的太阳能电池板充放电电路,是实现高效率的智能充放电设计方案。微电压调节理论整合了经典电压回授法和多种太阳能最大功率点追踪思想,利用简单合理的DC／DC平衡技术,充分考虑系统功耗等问题,以简单的电路设计和创新的软件思想实现更高效率的太阳能系统。随着能源危机的加剧,高效率、低成本的太阳能系统备受社会关注,其在民用、交通等领域的运用也更加重要。