Frequency-modulated continuous-wave LIDAR has broad application prospects. Compared with the traditional pulse LIDAR, the FMCW LIDAR has the advantages of high resolution and long measurement distance. But it still can be affected by several factors, including environmental noise, spectrum aliasing, spectrum leakage and other issues. Some traditional filtering algorithms or signal transformation algorithms can improve the above problems, but the effect is not ideal. This paper proposes a signal correction algorithm called the VMD-based refined cross-power spectral density algorithm (VRCPSD). This algorithm is based on signal decomposition denoising and improved spectrum refinement methods. The algorithm applies variational mode decomposition, spectrum refinement and cross-power spectral density to signal processing. The VRCPSD algorithm is compared with the traditional spectrum correction algorithm on the high-speed linear array APD FMCW LIDAR experimental platform. The results show that the VRCPSD algorithm has a better spectrum correction effect on the LIDAR experimental platform. This algorithm can reduce the margin of error to the centimeter level. Therefore, the algorithm is promising in that it can improve the signal waveform of the FMCW laser radar ranging system, make the spectrum get better correction and make the distance more accurate.

The accuracy of target distance obtained by a frequency modulated continuous wave (FMCW) laser ranging system is often affected by factors such as white Gaussian noise (WGN), spectrum leakage, and the picket fence effect. There are some traditional spectrum correction algorithms to solve the problem above, but the results are unsatisfactory. In this article, a decomposition filtering-based dual-window correction (DFBDWC) algorithm is proposed to alleviate the problem caused by these factors. This algorithm reduces the influence of these factors by utilizing a decomposition filtering, dual-window in time domain and two phase values of spectral peak in the frequency domain, respectively. With the comparison of DFBDWC and these traditional algorithms in simulation and experiment on a built platform, the results show a superior performance of DFBDWC based on this platform. The maximum absolute error of target distance calculated by this algorithm is reduced from 0.7937 m of discrete Fourier transform (DFT) algorithm to 0.0407 m, which is the best among all mentioned spectrum correction algorithms. A high performance FMCW laser ranging system can be realized with the proposed algorithm, which has attractive potential in a wide scope of applications.

传感器课程经过多年改革探索与实践,基于"重视基础、体现现代、趋向前沿、交叉综合"的课程建设原则,提出"三闭环三收放"的教学思维模式——教学内容闭环,理论实践闭环,教学过程闭环;教学内容收放,教学方法收放,实验设计收放.实践表明:基于MOOC的三闭环三收放混合式教学设计,使学生体验了"踮脚才够得着"的学习挑战,增强了学生经过刻苦学习收获能力和素质提高的成就感,进行科研探究的意愿明显增强.该课程教学方法可直接应用于大学生电子设计竞赛、机器人比赛、国际互联网+创新创业大赛等,教书育人效果显著.

在工程教育认证背景下,将OBE教学模式引入传感技术实验教学中,以培养解决复杂工程问题和创新能力为目标,在以学生为中心优化设计实验内容、以学生学习效果为导向改革实验教学方法和实验考核方式等方面做了一些尝试,从而提高了学生自我学习的能力、创新意识和工程实践能力,并获得了进一步持续教学改进的经验.

As for target and background changes,this paper proposes a target tracking algorithm with scale and orienta-tion adaptive mean shift tracking with corrected background-weighted histogram algorithms and fast compression tracking algorithms in collaboration based on image classification.According to the difference of the image changes,the algorithm classifies the images into two categories,global changes and target local interest area changes.Global changes caused by lighting,background similar and background blur,it uses BW-SOAMS algorithm for target tracking.Local area of interest changes caused by size,rotate and occlusion,it uses CT algorithm for target tracking.Firstly,images are done preprocess-ing and classification,and then the appropriate track drift problems are caused by changes in the environment.By experi-ments,the algorithm has improved significantly in precision and efficiency.

利用ANSYS Workbench软件对关节式坐标测量机的测量臂和关节分别进行了静力结构仿真.根据仿真结果分析和计算测量臂和关节受力产生形变时对关节式坐标测量机的最终测量结果的影响.研究结果表明,测量臂和关节受力产生的形变对关节式坐标测量机的最终测量结果产生的影响很小.

提出一种基于内点法的关节式坐标测量机参数自标定方法.首先,基于D-H方法,建立关节式坐标测量机的运动学模型,在此基础上建立参数误差模型.利用自制标定块,采用多位姿测量多点的方法获得标定用数据,从而使该标定方法能够在现场应用;以测量机的长度测量精度为基础建立目标函数,利用内点法进行参数辨识,该算法对迭代初值无要求,可有效提高参数标定成功率.通过标定实验得到坐标测量机的运动学参数,验证了该方法的可行性.经过标定后,关节式坐标测量机的单点重复性精度和长度测量精度分别提高了93.65倍和100.13倍.

针对仿人视觉系统目标跟踪过程中眼、颈的转动速度对跟踪精度的影响,提出了一种基于雅可比矩阵的角度分解最优化方法.首先,建立眼、颈2级四自由度空间坐标系,搭建系统模型;其次,构建与眼、颈转动角度相关的雅可比矩阵,综合考虑眼、颈转动角速度,得到关注不同转动轴角速度变量的目标跟踪角度分解数学模型;最后,通过仿真和物理实验分析了各自由度转动角速度在最优化条件下对角度分解的影响,得到了基于所述系统的目标跟踪角度分解最优化方案.实验结果表明:在给定范围内,眼、颈的转动角度分配比与眼、颈转动角速度的比值相同,且与均分法相比较,文中所述方法在时间效率上具有明显优势.

The process of multi-parametric flow cytometry data analysis is complicate and time-consuming,which requires well-trained professionals to operate on. To overcome this limitation, a method for multi-parameter flow cytometry data processing based on kernel principal component analysis(KPCA) was proposed in this paper. The dimensionality of the data was reduced by nonlinear transform. After the new characteristic variables were obtained,automatical clustering can be achieved using improved K-means algorithm. Experimental data of peripheral blood lymphocyte were processed using the principal component analysis(PCA)-based method and KPCA-based method and then the influence of different feature parameter selections was explored. The results indicate that the KPCA can be successfully applied in the multi-parameter flow cytometry data analysis for efficient and accurate cell clustering, which can improve the efficiency of flow cytometry in clinical diagnosis analysis.

Aimed at improving performance of automatic focusing algorithm in dynamic environment,this paper describes recent studies of focusing evaluation function and search strategy.On the basis of analysis of characteristics of human visual system,new focusing evaluation function based on eight direction Sobel operator edge weighting is proposed.At the same time,adaptive variable step search strategy is used in order to overcome the disadvantage of slow speed of traditional climbing method.Simulation experiment results show eight direction Sobel edge detection operator has good edge detection effect,and focusing evaluation function has better anti-interference ability than traditional two direction Sobel operator focusing evaluation function,which is calculated by this operator combined with characteristics of human visual system giving the edges with different weight coefficients.Finally,an experimental platform based on liquid lens is set up,which verifies the performance of improved auto focus algorithm in dynamic environment.Experimental results show focusing accuracy with proposed algorithm can achieve 97.5 ％ in dynamic environment.

Given the limitations on image quality,weight,volume and power by joint arm laser scanning measuring head,a CMOS image acquisition system of joint arm laser scanning measuring head based on FPGA is designed.The detail of the hardware structure and the module design inside the FPGA is described.With FPGA as the main control processor,it combines the working mode and the driving sequence of CMOS image sensor and uses the Verilog language to write the program.The high-quality image can be captured,cached and displayed.Experiments show that the system solution of image acquisition is reasonable and has high stability and reliability to get the high-quality laser light stripe images.

The traditional clustering process of multi-parametric flow cytometry data analysis is complicated,non-automated and timeconsuming.To overcome this limitation,an automatic clustering method based on kernel entropy analysis (KECA) is proposed.The feature vector with the greatest contribution to the Renyi entropy is selected as the projection direction to carry out the feature extraction.A classifier based on cosine similarity and the K-means algorithm is designed to get the label of each cell,and a method for determining the optimal number of clusters based on the angle of vectors is adopted.Experimental data of peripheral blood lymphocyte is processed,and the results indicate that the proposed method can realize automatically clustering with simple operation and the overall accuracy rate of clustering can reach 97％,which can improve the efficiency of cell analysis.

Aiming at the single character of target image acquisition in the traditional bionic vision system,multispectral visual image processing method based on adaptive regulation of humanoid eye was proposed.Firstly,the improved automatic focusing algorithm was used to collect the high resolution image of visible light and the low resolution image of near-infrared light.In the multispectral imaging system,there were different problems of visible and near-infrared image resolution under fixed focal length due to different refractive index of spectral prism.The improved two-generation wavelet transform was adopted to enhance the image contrast and improve the visual effect.Finally,the performance of automatic focus algorithm and image enhancement algorithm was verified by using a multi-spectral experimental device based on liquid zoom lens.The experimental results indicate that the average time of effective auto focusing system is 756 ms,and the gray variance function value increases by 79.4％ after near infrared image enhancement,which solves the problem of low contrast and fuzzy details and realizes adaptive regulation.

In order to improve the accuracy of articulated arm coordinate measuring machine,the measuring posture optimization based on clustering analysis was proposed.On the basis of the mathematical model,by using systematic cluster analysis,cluster of posture information was constructed,clusters were extracted by scoring method.Measuring posture optimization was finished by result of extraction.The experimental results show that the accuracy of the machine was greatly influenced by the measuring posture,the measuring accuracy was improved by 1 time after the measuring posture optimization.It provides a basis for further research.

Aimed at the problems of classical mean-shift algorithm, which are caused by background similarities and scale change and target occlusion in the process of target tracking, a scale and orientation adaptive mean shift tracking with corrected background-weighted histogram is proposed to solve them. Combined with background-weighted histogram to extract the color histogram enhances the features of target area and reduces the track drift problems caused by background similarities and clutters. For ensuring tracking accuracy, the scale and orientation adaptive covariance matrix estimation is used to satisfy the real-time scale and orientation changes of the target. Compared with other classic mean shift algorithm, by experiments, the algorithm in this paper has improved significantly in precision and efficiency.

圆光栅安装偏心误差对圆光栅编码器的角度测量精度有较大影响,对偏心误差进行补偿可以有效提高测量结果的精度.为了对圆光栅的安装偏心参数进行辨识,建立了双读数头的偏心误差模型,推导出了基于双读数头的圆光栅偏心参数的自标定公式.通过实验利用对径安装的两个读数头对圆光栅的偏心参数进行自标定,求解出了相关的偏心参数,并使用正十二面棱体搭建的实验装置,对自标定参数的补偿效果进行了验证.实验结果表明,用双读数头自标定公式标定出的偏心参数对单读数头的测量结果进行偏心误差补偿后,圆光栅的平均误差从补偿前的0.046 4°减小到了0.003 7°.更多还原

在关节式坐标测量机动态误差对测量机的精度优化的研究中,由于动态误差因素较多,很难建立统一的数学模型。为了有效的对关节式坐标测量机动态误差进行预测与补偿,采用了误差分离的方法,重点研究了圆光栅角度编码器、热形变对测量机测量精度的影响。同时为了解决小样本下获得最优解的问题,采用了nu-SVM回归算法建立动态误差补偿模型。通过仿真对空间测试点进行动态误差补偿后,单点重复性精度提高了50%左右,长度测量精度提高了40%左右,进而证明改进算法的可行性与有效性。

The installation eccentric error of circular grating has great impact on the angle measurement accuracy of circular grating encoder.And the accuracy of the measurement results of the circular grating encoder can be effectively improved by compensating the eccentric error.To identify the installation eccentric error parameters of circular grating,an eccentric error model with two photoelectric detectors is established and the self-calibration formula of the eccentric parameters based on two photoelectric detectors is deduced.The eccentric parameters were self-calibrated in experiments using the two photoelectric detectors installed diametrically,the related eccentric parameters were solved,and an experiment device with dodecahedron was used to verify the compensation effect of the self-calibration parameters.The experiment result shows that after compensating the eccentric error measured with single photoelectric detector using the eccentric parameters calibrated with the self-calibration formula of two photoelectric detectors the average error of the circular grating is decreased from 0.064 6°to 0.003 7°.