基于轨迹多特性的隐私保护算法

现有基于聚类的轨迹隐私保护算法在衡量轨迹间的相似性时大多以空间特征为标准,忽略了轨迹蕴含的其他方面的特性对轨迹相似性的影响。针对这一情况可能导致的匿名后数据可用性较低的问题,提出了一种基于轨迹多特性的隐私保护算法。该算法考虑了轨迹数据的不确定性,综合方向、速度、时间和空间4个特性的差异作为轨迹相似性度量的依据,以提高轨迹聚类过程中同一聚类集合中轨迹之间的相似度;在此基础上,通过空间平移的方式实现同一聚类集合中轨迹的k-匿名。实验结果表明,与经典隐私保护算法相比,在满足一定隐私保护需求的前提下,采用所提算法实施隐私保护之后的轨迹数据整体具有较高的数据可用性。     

基于时空联合性的无线传感网覆盖采样技术

覆盖采样作为无线传感器网络领域的一个基本问题,已经得到了广泛研究。但是目前研究大多只考虑空间覆盖性和网络生存周期等因素,并没有从实用的角度考虑覆盖区域的时空域覆盖特性。本文以综合考虑空间域和时间域为研究视角,提出了一种新型的以应用为导向的覆盖率指数,研究了一种基于时空联合性的无线传感网覆盖采样机制。最后通过时空覆盖率与事件侦测率指标两个指标进行了验证。     

一种有效的基于时空信息的视频运动对象分割算法

为实现视频编码标准MPEG-4中语义对象的自动提取,提出一种基于时空信息的运动对象分割算法。在时域上通过双边加权累积帧差和分块高阶统计算法得到目标的运动区域检测模板,以在充分利用时域信息的同时提高算法的速度;在提取空域信息时,先对视频序列的灰度图进行对比度增强处理,然后利用自适应Canny算子获取准确的空间边缘信息;最后进行时空融合,用空域边缘信息修正过的时域运动模板来提取运动对象。实验结果表明,本算法可以快速准确地分割视频运动对象。     

基于极坐标和蒙特卡罗估计的超声图像去噪方法

针对超声图像采集特性和斑点噪声分布特点, 提出极坐标系下融合蒙特卡罗估计的斑点噪声抑制方法。首先对极坐标系下含噪图像进行对数变换, 然后再与估计点相关的任意径向方向进行全局域采样, 根据样本点与估计点空间相关性和斑点噪声分布模型构造权重因子, 最后利用蒙特卡罗方法实现斑点噪声似然加权估计。实验结果表明, 该算法在滤除斑点噪声的同时, 更好地保持了图像细节信息。     

结合坐标转换和时空信息注入的点云人体行为识别

目的 行为识别中广泛使用的深度图序列存在着行为数据时空结构信息体现不足、易受深色物体等因素影响的缺点,点云数据可以提供丰富的空间信息与几何特征,弥补了深度图像的不足,但多数点云数据集规模较小且没有时序信息。为了提高时空结构信息的利用率,本文提出了结合坐标转换和时空信息注入的点云人体行为识别网络。方法 通过将深度图序列转换为三维点云序列,弥补了点云数据集规模较小的缺点,并加入帧的时序概念。本文网络由两个模块组成,即特征提取模块和时空信息注入模块。特征提取模块提取点云深层次的外观轮廓特征。时空信息注入模块为轮廓特征注入时序信息,并通过一组随机张量投影继续注入空间结构信息。最后,将不同层次的多个特征进行聚合,输入到分类器中进行分类。结果 在3个公共数据集上对本文方法进行了验证,提出的网络结构展现出了良好的性能。其中,在NTU RGB+d60数据集上的精度分别比PSTNet（point spatio-temporal network）和SequentialPointNet提升了1.3%和0.2%,在NTU RGB+d120数据集上的精度比PSTNet提升了1.9%。为了确保网络模型的鲁棒性,在MSR Action3D小数据集上进行实验对比,识别精度比SequentialPointNet提升了1.07%。结论 提出的网络在获取静态的点云外观轮廓特征的同时,融入了动态的时空信息,弥补了特征提取时下采样导致的时空损失。     

变步长解相关Volterra LMS算法

Volterra滤波器的非线性使输入向量自相关矩阵包含了输入信号的高阶统计量,导致矩阵特征值扩展很大,因此LMS算法收敛速度一般很慢。从降低输入信号的相关性出发,提出了一种变步长解相关Volterra NLMS算法。解相关能显著加快LMS算法的收敛速度,变步长能够改善算法的稳态性能,两者的有机结合,能明显改善算法性能。仿真结果表明,在不同输入信号相关性情况下,该算法有更好的收敛速度和稳态性能。     

Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series

Spatiotemporal fusion (STF) technologies are commonly used to acquire high spatiotemporal resolution remote sensing observations. However, most STF technologies fail to consider the nonlinear variation in vegetation in the time domain. Based on the Best Linear Unbiased Estimator (BLUE), this paper proposed a novel STF algorithm (referred to BLUE) which accounts for the phenological characteristics of vegetation. First, annual time series of normalized difference vegetation index (NDVI) data with high spatial resolution but low temporal resolution is fitted using a double logistic function and used as the background field. Then, NDVI data with low spatial resolution but high temporal resolution is used as the observation field. The information in the background and observation fields is fused using the BLUE to obtain high spatiotemporal resolution NDVI data. The proposed algorithm was used to produce dense time series of 30 m resolution NDVI data for a 10 km × 10 km experimental area in 2014. The experimental results demonstrate that the accuracy of fusion results from the proposed BLUE method are higher than those from the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Linear Mixing Growth Model (LMGM), especially when the temporal component of surface heterogeneity is dominant. The proposed algorithm has broad prospects in vegetation monitoring at high spatiotemporal resolution.     

Terminal open-loop control for a hypervelocity unmanned flying vehicle in the atmosphere. Part 1

An algorithm for the terminal open-loop control of a hypervelocity flying vehicle with a high lift-drag (L/D) ratio is proposed; this algorithm ensures the implementation of spatial programmed hitting trajectories steering the vehicle to the given spatial domain with required high accuracy and required conditions of approaching this domain. The algorithm is based on the a priori construction of a rational hitting trajectory and the corresponding control vector; the control vector components are the attack and bank angles. At the beginning of the terminal phase, only constraints on the individual components of the vector of initial conditions are imposed: the ranges of altitudes, velocities, and flight path angle. In addition, the requirement for the minimum admissible resolution of the onboard radar that provides informational support at the terminal phase is taken into account in the trajectory construction. Examples of programmed hitting trajectories are given that confirm the fact that the proposed algorithm for the generation of such trajectories and the corresponding attainability sets is computationally efficient.     

Spatiotemporal sampling of dynamic environment sequences

Environment sampling is a popular technique for rendering scenes with distant environment illumination. However, the temporal consistency of animations synthesized under dynamic environment sequences has not been fully studied. This paper addresses this problem and proposes a novel method, namely spatiotemporal sampling, to fully exploit both the temporal and spatial coherence of environment sequences. Our method treats an environment sequence as a spatiotemporal volume and samples the sequence by stratifying the volume adaptively. For this purpose, we first present a new metric to measure the importance of each stratified volume. A stratification algorithm is then proposed to adaptively suppress the abrupt temporal and spatial changes in the generated sampling patterns. The proposed method is able to automatically adjust the number of samples for each environment frame and produce temporally coherent sampling patterns. Comparative experiments demonstrate the capability of our method to produce smooth and consistent animations under dynamic environment sequences.     

Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things

The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.

     

遥感图像时空融合综述

高时空分辨率的遥感图像大数据在遥感领域发挥着重要作用.然而,由于技术上和预算上的限制等原因,目前单一的卫星传感器无法获取同时具有高空间分辨率和高时间分辨率的遥感影像.因此遥感图像时空融合技术被认为是解决时间分辨率和空间分辨率折衷问题的有效途径之一.随着深度学习在各领域的广泛应用,深度学习技术已经被证实是解决图像问题非常...     

一种压缩域中的快速运动目标提取算法

论文提出了一种工作于MPEG压缩域的快速运动目标提取算法。算法以通过部分解码得到的运动向量和亮度分量的直流DCT系数作为输入,提取P帧的运动目标。首先采用鲁棒性回归分析估计全局运动,标记出与全局运动不一致的宏块,得到运动块的分布;然后将运动向量场插值作为时间域的特征,将重构的直流图像转换到LUV颜色空间作为空间域的特征,采用快速平均移聚类找到时间和空间特征具有相似性的区域,得到细化的区域边界;最后结合运动块分布和聚类分析的结果,通过基于马尔可夫随机场的统计标号方法进行背景分离,得到运动目标的掩模。实验结果表明该算法可以有效地消除运动向量噪声的影响,并有很高的处理速度,对于CIF格式的视频码流,每秒可以处理约50帧。     

Toward a Comprehensive Framework for the Spatiotemporal Statistical Analysis of Longitudinal Shape Data

This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates.     

基于运动轨迹捕捉与正交覆盖的WSN节点定位算法

传统无线传感器网络(WSN)节点定位算法难以适应节点快速移动的高拓扑变化环境,导致识别误差较大。针对该问题,提出一种基于运动轨迹捕捉与正交覆盖机制的WSN节点定位算法。利用捕捉锚节点射频强度的方法对节点运动轨迹进行覆盖定位,获取性能最佳的锚节点及其坐标,改善因锚节点失效或信号强度弱导致的弱定位现象。在此基础上,采用拉格朗日插值函数设计运动轨迹捕捉方法,联合纵向及横向坐标维度进行节点运动矢量的精确捕捉,在精度可控的条件下实现对下一时刻节点坐标的初步预测,优化锚节点对运动节点的区域覆盖。同时利用正交覆盖方式设计基于过滤机制的区域优化方法,提高覆盖区域坐标抽样和网络信号定位精度。仿真结果表明,与2S-HGR机制和TDLM机制相比,该算法具有较好的动态路径捕捉效果与坐标定位准确性。     

基于Transformer模型的四旋翼无人机时空协同航迹预测方法设计

无人机在执行任务时面临的飞行环境复杂多变,为了减少事故的风险,并在飞行时对异常情况进行预测和响应,研究一种基于Transformer模型的四旋翼无人机时空协同航迹预测方法。采集四旋翼无人机原始航迹,实施异常点剔除和缺失点插值处理,以优化和清理原始航迹数据,便于后续的航迹预测。结合深度学习和表示学习方法完成数据降维,基于Transformer模型实现无人机时空协同航迹的精准预测。实验测试结果表明,设计方法的预测结果虽然相对于真实的坐标点稍有偏差,然而整体结果在可接受范围内,验证集所有数据的均方误差在数据条数为300时仅为0.32m,R方测试结果最接近1,具有良好的航迹预测能力。     

基于Frenet坐标系的自动驾驶轨迹规划与优化算法

针对公路场景下作速度保持的自动驾驶汽车实时轨迹规划问题,提出一种基于Frenet坐标系的优化轨迹规划算法.首先,利用Frenet坐标系将车辆运动解耦, 构建无约束横向/纵向独立积分系统;然后,根据初始配置和可内嵌到行为层的目标配置,通过采样生成有限的4次、5次多项式候选轨迹集合;最后,利用以高斯卷积、加速度变化率为核心...     

Watermarking 3D mesh by spherical parameterization

In this paper, a robust 3D trianglular mesh watermarking algorithm is presented by applying spherical parameterization. First, we transform the coordinate signals of the 3D triangular mesh into spherical signals using a global spherical parameterization and an even sampling scheme. Then, spherical harmonic transformation is used to generate some data for embedding watermarks. As a result, the watermarks can be embedded in the Fourier-frequency domain of the original mesh. Experimental results show that our watermarking algorithm is robust since watermarks can be extracted without mesh alignment or re-meshing under a variety of attacks, including noise addition, crop, filtering, enhancement, rotation, translation, scale and re-sampling.     

A Self-Organizing Context-Based Approach to the Tracking of Multiple Robot Trajectories

We have combined competitive and Hebbian learning in a neural network designed to learn and recall complex spatiotemporal sequences. In such sequences, a particular item may occur more than once or the sequence may share states with another sequence. Processing of repeated/shared states is a hard problem that occurs very often in the domain of robotics. The proposed model consists of two groups of synaptic weights: competitive interlayer and Hebbian intralayer connections, which are responsible for encoding respectively the spatial and temporal features of the input sequence. Three additional mechanisms allow the network to deal with shared states: context units, neurons disabled from learning, and redundancy used to encode sequence states. The network operates by determining the current and the next state of the learned sequences. The model is simulated over various sets of robot trajectories in order to evaluate its storage and retrieval abilities; its sequence sampling effects; its robustness to noise and its tolerance to fault.     

Tracking of vehicle trajectory by combining a camera and a laser rangefinder

This article presents a probabilistic method for vehicle tracking using a sensor composed of both a camera and a laser rangefinder. Two main contributions will be set forth in this paper. The first involves the definition of an original likelihood function based on the projection of simplified 3D vehicle models. We will also propose an efficient approach to compute this function using a line-based integral image. The second contribution focuses on a sampling algorithm designed to handle several sources. The resulting modified particle filter is capable of naturally merging several observation functions in a straightforward manner. Many trajectories of a vehicle equipped with a kinematic GPS¹ have been measured on actual field sites, with a video system specially developed for the project. This field input has made it possible to experimentally validate the result obtained from the algorithm. The ultimate goal of this research is to derive a better understanding of driver behavior in order to assist road managers in their effort to ensure network safety.     

基于时空感知增强的深度Q网络无人水面艇局部路径规划

无人水面艇局部路径规划在海事救援、海洋运输等领域中发挥着重要的作用。现有局部路径规划算法在简单场景中取得了不错的效果,但面对环境中存在的复杂障碍物和海流干扰时,性能表现较差。为此,提出了一种基于时空感知增强的深度Q网络强化学习算法,首先,引入多尺度空间注意力模块捕捉距离传感器的多尺度空间信息,提升了复杂障碍物环境的感知能力;其次,利用基于长短时记忆网络的海流感知模块提取海流干扰环境的时间序列特征,增强了对海流干扰的感知能力;此外,对无人水面艇传感器和运动模型进行了模拟,并设计了强化学习状态空间、动作空间和基于方向导引的奖励函数,提升了算法的导航性能和收敛速度。在复杂仿真场景中进行了实验,结果表明,所提算法相比于原始算法在导航成功率和平均到达时间两个指标上均得到了提升,算法表现出较强的复杂环境适应性。