SELM: Semi-supervised ELM with application in sparse calibrated location estimation

Indoor location estimation based on Wi-Fi has attracted more and more attention from both research and industry fields. It brings two significant challenges. One is requiring a vast amount of labeled calibration data. The other is real-time training and testing for location estimation task. Traditional machine learning methods cannot get high performance in both aspects. This paper proposed a novel semi-supervised learning method SELM (semi-supervised extreme learning machine) and applied it to sparse calibrated location estimation. There are two advantages of the proposed SELM. First, it employs graph Laplacian regularization to import large number of unlabeled samples which can dramatically reduce labeled calibration samples. Second, it inherits the good property of ELM on extreme training and testing speed. Comparative experiments show that with same number of labeled samples, our method outperforms original ELM and back propagation (BP) network, especially in the case that the calibration data is very sparse.     

Multi-sensor calibration through iterative registration and fusion

In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods.     

Shape Reconstruction and Camera Self-Calibration Using Cast Shadows and Scene Geometries

Recently, various techniques of shape reconstruction using cast shadows have been proposed. These techniques have the advantage that they can be applied to various scenes, including outdoor scenes, without using special devices. Previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes limit the range of application of these techniques. In this paper, we propose a method to reconstruct 3D scenes even when the camera parameters or light source positions are unknown. The technique first recovers the shape with 4-DOF indeterminacy using coplanarities obtained by cast shadows of straight edges or visible planes in a scene, and then upgrades the shape using metric constraints obtained from the geometrical constraints in the scene. In order to circumvent the need for calibrations and special devices, we propose both linear and nonlinear methods in this paper. Experiments using simulated and real images verified the effectiveness of this technique.     

基于校准靶的C形臂相机模型自动校准方法

根据校准靶标志点影像的特点,提出一种自动的离线C形臂相机模型校准方法。应用聚类算法实现标志点信息的自动识别,通过行列索引表简化标志点网格姿态的识别,自动建立标志点在影像坐标系和空间坐标系下坐标的对应关系,经几何变形校正后,实现C形臂相机的校准。实验数据证明,该方法具有良好的鲁棒性,误差小于1 mm,可应用于基于术中C形臂X线影像的手术导航系统。     

基于旋转谐振结构的单芯片二维电场传感器

提出并研制了一种二维电场检测传感芯片,将四个电场测量微型单元和旋转式驱动微结构集成在3. 5 mm ×3. 5 mm的敏感结构上,实现了单芯片的电场二维测量.介绍了传感器的工作原理、敏感结构的设计,以及基于绝缘体上硅( SOI)工艺的单芯片微型二维电场传感器制备工艺技术.成功研制出传感器原理样机,研究了微型二维电场传感器的标定方法,开发了用于电场二维标定的测试装置,并在室温常压下对传感器进行了二维标定.实验结果表明:该传感器能够有效减小电场的轴间耦合干扰,测量误差优于7. 04%,线性度可达到1. 25%.     

基于STM32的手持式测氧仪设计

针对国内传统手持式测氧仪精度低、功耗高的问题,提出了采用嵌入式微控制器STM32F103RBT6作为控制芯片的手持式测氧仪.该仪器通过控制元器件的开断来降低待机功耗,根据所测浓度人工切换系统放大倍数,通过键盘设置时间和报警值等相关参数.算法上采用中位值平均滤波算法进行软件滤波,结合传统两点标定法和多点标定法的优缺点,采用多点标定法进行氧浓度标定,采用分段斜率拟合算法计算氧浓度值.经实验测试证明设计的手持测氧仪具有高精度和高稳定性.     

基于双离心机的加速度计动态校准装置

加速度计动态校准装置基于双离心机原理,通过主离心机和从离心机的同时旋转产生动态线加速度,从而实现对加速度计的动态校准.介绍了加速度计动态校准装置的工作原理,建立了其复现加速度的幅值数学模型,针对在大过载条件下从离心机精密旋转问题,设计了从离心机的结构形式和控制方案,并对校准设备所复现加速度的幅值不确定度进行了分析和试验验证,试验结果表明该装置复现动态加速度幅值测量不确定度优于0.2％.     

Set-membership estimation from poor quality data sets: Modelling ammonia volatilisation in flooded rice systems

A set-membership (bounded-error) estimation approach can handle small and poor quality data sets as it does not require testing of statistical assumptions which is possible only with large informative data sets. Thus, set-membership estimation can be a good tool in the modelling of agri-environmental systems, which typically suffers from limited and poor quality observational data sets. The objectives of the paper are (i) to demonstrate how six parameters in an agri-environmental model, developed to estimate NH₃ volatilisation in flooded rice systems, were estimated based on two data sets using a set-membership approach, and (ii) to compare the set-membership approach with conventional non-linear least-squares methods. Results showed that the set-membership approach is efficient in retrieving feasible parameter-vectors compared with non-linear least-squares methods. The set of feasible parameter-vectors allows the formation of a dispersion matrix of which the eigenvalue decomposition reflects the parameter sensitivity in a region.     

多投影显示系统结构光几何校正算法

针对带有非规则曲面显示墙的多投影显示系统的几何校正问题,提出一种基于结构光投影技术的自动非线性几何校正算法.该算法首先用结构光投影技术计算出每个投影机与显示墙的映射关系;其次使用相机拍摄显示墙上的经纬度点阵;最后通过提取拍摄的图像特征计算得到投影机帧缓存中每个像素点在全局坐标系下的经纬度值,并将其转换为纹理坐标,从而实现了非线性几何校正.模拟仿真实验和虚拟机场系统中的实际应用结果表明,文中算法能够有效地提高系统的几何校正精度,且无需进行相机的标定和具有显示墙解析式的先验知识,适用于任意光滑的曲面显示墙.     

双通道可见光光强计标定实验平台设计

利用双通道检测方法设计了可见光光强计标定实验平台。光源经单片机控制调节产生系列光强值,分束后进入标准信号通道与自制仪器通道,在同步信号下取得不同光强信号的测量值并据此定标。该方法扩大了标定实验平台对光源的选取范围,提高了自制仪器的性能。