基于Bloom Filter本地差分隐私的基数估计

计算机技术和通信技术的共同发展,使得数据呈现指数大爆炸式的增长。数据中蕴含的巨大价值是有目共睹的。但是对数据集的肆意收集与分析,使用户的隐私数据处在被泄露的风险中。为保护用户的敏感数据的同时实现对基数查询的有效响应,提出一种基于差分隐私的隐私保护算法BFRRCE(Bloom Filter Random Response for Cardinality Estimation)。首先对用户的数据利用Bloom Filter数据结构进行数据预处理,然后利用本地差分隐私的扰动算法对数据进行扰动,达到保护用户敏感数据的目的。

Tight upper bound on the quantum value of Svetlichny operators under local filtering and hidden genuine nonlocality

Nonlocal quantum correlations among the quantum subsystems play essential roles in quantum science.The violation of the Svetlichny inequality provides sufficient conditions of genuine tripartite nonlocality.We provide tight upper bounds on the maximal quantum value of the Svetlichny operators under local filtering operations,and present a qualitative analytical analysis on the hidden genuine nonlocality for three-qubit systems.We investigate in detail two classes of three-qubit states whose hidden genuine nonlocalities can be revealed by local filtering.

Local wavelet-based filtering of electromyographic signals to eliminate the electrocardiographic-induced artifacts in patients with spinal cord injury

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related subwavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Rootmean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has great prospects for applications in geophysical models.In terms of operational applications,along-track sea surface height(AT-SSH),swath sea surface temperature(S-SST)and in-situ temperature and salinity(T/S)profiles are assimilated using the LWEnKF in the northern South China Sea(SCS).To adapt to the vertical S-coordinates of the Regional Ocean Modelling System(ROMS),a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF.The results show that the LWEnKF outperforms the local particle filter(LPF)due to the introduction of the Ensemble Kalman Filter(EnKF)as a proposal density;the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF,but the RMSEs of T/S profiles reduce significantly by approximately 55%for the T profile and 35%for the S profile(relative to the EnKF).As a result,the LWEnKF makes more reasonable predictions of the internal ocean temperature field.In addition,the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.

Reservoir history matching and inversion using an iterative ensemble Kalman filter with covariance localization

Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.

A Novel Tracking-by-Detection Method with Local Binary Pattern and Kalman Filter

Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experiments show that under some scenarios,such as non-uniform illumination changing,serious occlusion,or motion-blurred,it may fails to track the object. In this paper,to surmount some of these shortages,especially for the non-uniform illumination changing,and give full play to the performance of the tracking-learning-detection framework, we integrate the local binary pattern( LBP) with the cascade classifiers,and define a new classifier named ULBP( Uniform Local Binary Pattern) classifiers. When the object appearance has rich texture features,the ULBP classifier will work instead of the nearest neighbor classifier in TLD algorithm,and a recognition module is designed to choose the suitable classifier between the original nearest neighbor( NN) classifier and the ULBP classifier. To further decrease the computing load of the proposed tracking approach,Kalman filter is applied to predict the searching range of the tracking object.A comprehensive study has been conducted to confirm the effectiveness of the proposed algorithm (TLD _ULBP),and different multi-property datasets were used. The quantitative evaluations show a significant improvement over the original TLD,especially in various lighting case.

Comparison of Nonlinear Local Lyapunov Vectors with Bred Vectors, Random Perturbations and Ensemble Transform Kalman Filter Strategies in a Barotropic Model

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors （BVs） are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector （NLLV） scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more com- ponents in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random pertur- bation （RP） technique, and the BV method, as well as its improved version--the ensemble transform Kalman filter （ETKF） method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

Real-time localization estimator of mobile node in wireless sensor networks based on extended Kalman filter

Localization of the sensor nodes is a key supporting technology in wireless sensor networks （WSNs）. In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter （KF） is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter （EKF） is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation （MLE） and traditional KF algorithm.

基于LIO-SAM建图和激光视觉融合定位的温室自主行走系统

为解决传统导航方案在温室内无法应对光照变化大、作物行间距窄、接收GPS信号差等问题,该研究提出了基于即时定位与地图构建技术的激光视觉融合式自主导航算法。该系统利用三维激光雷达VLP-16(Velodyne LiDAR,VLP-16)和惯性测量单元获取温室环境信息,采用基于紧耦合的雷达惯导定位建图(tightly-coupled lidar inertial odometry via smoothing and mapping,LIO-SAM)算法构建导航地图,基于轮式里程计和视觉里程计采用扩展卡尔曼滤波器算法实现局部定位,融合激光点云配准算法和自适应蒙特卡洛定位算法实现全局定位。同时,在自主行走系统应用A*算法规划全局路径和动态窗口算法规划局部路径,从而实现自主导航。试验结果表明,LIO-SAM算法构建的温室导航地图最大相对误差、最大绝对误差和均方根误差分别为9.9%、0.081和0.063 m,在温室内改进后的定位算法横向偏差小于0.020 m,纵向偏差小于0.090 m;当自主行走系统以0.15、0.30和0.50 m/s的速度运行时,横向偏差、纵向偏差和航向偏角的平均值分别小于0.120 m、0.10 m和8.5°,标准差分别小于0.070 m、0.140 m和6.6°。该导航方案满足自主行走系统在温室内高精度建图、定位和导航的需求,可为自主移动平台提供理论与技术支撑。

Indonesia’s Local Material Effect in Clay-Based Ceramic Filter Fabrication as an Alternative for Liquid Radioactive Waste Processing Material

One of the procedures to handle liquid radioactive waste is by filtration process. To do this process, suitable filter should be used because of radioactive nature of the waste. Ceramic filter is one of the suitable filters that could be used for this purpose. This paper will discuss about producing ceramic filter from local clay and test its performance. Performance of the filter is given by its flux, compressive strength, Decontamination Factor (DF) and adsorption efficiency. The results show that there are almost no effects of casting pressure on both flux and compressive strength of ceramic filter, but zeolite addition produces different effect. The higher concentration of zeolite will decrease the filter flux and increase filter compressive strength. The optimal composition from this research is 70% w/o clay-25% w/o zeolite-5% w/o charcoal. It has adsorption efficiency (60.36) and Decontamination Factor (2.52). Besides, Sr concentration after filtration is still higher than environmental standard for Sr-90 and more studies are still needed.

考虑光储协调的配电网多阶段就地-分布式电压控制策略

针对高渗透率光伏并网点附近线路的电压越限问题,基于对含分布式电源配电网的功率调压原理的分析,提出了考虑光储协调的多阶段电压控制方法。采用切比雪夫多项式滤波方法改进基于一致性原理迭代的分布式控制方式,有效加快了分布式控制的收敛速度;提出了多阶段就地-分布式电压控制策略,包括光伏逆变器就地无功补偿的阶段Ⅰ调压、全网光伏逆变器分布式无功调节的阶段Ⅱ调压、全网储能一致性分布式有功调节的阶段Ⅲ调压。以改进的某实际55节点中压配电系统为算例进行仿真,验证了所提多阶段就地-分布式电压控制策略的有效性和优越性。

超长滤袋局部加密对滤袋表面过滤风速分布的影响

针对超长滤袋表面过滤风速沿其轴向分布极为不均的问题,采用数值模拟方法探究了超长滤袋局部加密对滤袋表面过滤风速分布的影响,为提升超长滤袋袋式除尘器的过滤性能寻求解决方法。结果表明:当超长滤袋未进行局部加密时,滤袋表面过滤风速沿其轴向自上而下呈递减趋势,最大过滤风速高达3.18 m/min(设计过滤风速为1.0 m/min),对应过滤风速分布均匀性指数为0.122,过滤风速分布极不均匀,此时滤袋顶部受到高速气流的冲刷作用明显;当对超长滤袋顶部30%长度滤袋进行局部加密后,滤袋表面过滤风速分布均匀性得到显著提升;当局部加密孔隙率由85%降至60%时,滤袋表面过滤风速分布均匀性指数由0.037先增至0.518后降至0.434,对应除尘效率并不受影响;当局部加密孔隙率取值为65%时,滤袋表面最大过滤风速降至2.1 m/min,此时过滤风速分布均匀性指数达到最大值0.518。

基于球面模型的单运动平台无源定位方法

受地球曲率影响,传统基于平面直角坐标系的定位模型随着侦测距离的增大,定位误差显著增大,严重影响远距离目标定位跟踪精确度。对此,提出一种基于球面模型的单运动平台定位方法,将平面直角函数方程转换为球面三角函数方程,降低地球曲率误差影响;利用无轨迹卡尔曼滤波(UKF)算法实现复杂非线性观测方程迭代求解及远距离目标高精确度位置估计。仿真试验结果表明,基于球面模型的定位方法具有较高的定位精确度,定位精确度提升0.3%R~0.6%R。

基于FACET滤波加权局部对比度的红外小目标检测

针对低空复杂背景下红外图像中目标与背景对比度低、边缘高亮的问题,提出一种基于FACET滤波加权局部对比度的红外小目标检测算法。首先,通过FACET滤波运算获取目标候选像素;然后,利用目标区域和背景区域灰度差异的比率计算局部对比度,同时基于目标和背景的异质性设计了一种加权函数,用来增强目标显著性和抑制背景;最后,通过自适应阈值分割提取真实的目标。在5组真实红外小目标图像数据集上与不同的算法进行性能比较分析,实验结果表明,提出的算法在不同低空复杂场景中具有较好的检测性能。

结合局部纹理特征滤波的海天线检测方法

针对视频监测下海天线检测图像易出现的海天对比度低、海上物体、海雾、海浪及云层等干扰条件引起的图像模糊和海天线遮挡问题,提出一种基于偏心邻域的灰度共生矩阵对比度滤波的梯度域下海天线检测方法。方法设计使用上下两方向偏心盒状滤波器获取图像的对比度梯度值图,利用滑动窗对分块图像搜索梯度极大值候选位置,根据设计策略通过构建海天线参数的概率统计模型提取拟合直线作为检测结果。仿真实验表明,本文方法能提升视觉图像下含多景物等干扰的复杂场景海天线检测的准确率,与同类方法相比提升7.69%。

低计算量的窄带ANC算法优化及实车DSP系统试验分析

车内主动噪声控制中常使用的传统滤波-x最小均方(Filtered-x Least Mean Square,FxLMS)算法由于计算复杂度高,往往导致系统硬件算力不足,降噪效果不理想。文章提出一种基于改进局部次级通路建模方法的自适应陷波(Local-secondary-path Filtered-x Least Mean Square,LFxLMS)算法及其相应的窄带主动噪声控制(LFxLMS-based Narrowband Active Noise Control,LFx-NANC)系统。所提出的改进局部次级通路建模方法具有更高的建模精度,且该系统相较于传统系统大大降低了计算复杂度。通过基于Matlab软件的仿真分析,验证了该系统对稳态及非稳态多谐波噪声的降噪性能。基于ADSP-21489控制器搭建车内双通道LFx-NANC系统,实现了在稳态工况下主驾位置处二、四、六阶降噪量分别达到34.67、21.41、10.29 dB(A);在加速工况下主驾位置处总声压级和二阶降噪量分别达到6.01 dB(A)和20.40 dB(A),同时在其他位置均有较好的降噪效果。文中提出的方法为主动噪声控制的工程应用提供了参考。

一种改进的暗通道先验低光照图像增强算法

针对低光照图像增强算法常见的亮度不均匀、色彩失真、图像噪点较多、细节不清晰等问题,提出了一种改进的暗通道先验低光照图像增强算法。该方法对像素值取反后的低光照图像,首先采用引导滤波,解决图像在运用最小值滤波计算暗通道时引起的块效应,其次在剔除像素为255的纯白色点干扰后进行大气光值的计算,然后引入细化系数进行透射率自适应修正使透射率更加平滑,最后采用非局部平均滤波进行噪声去除。实验表明,所提出的算法使图像的亮度增强合适,细节清晰,在Low-Light弱光图像数据集上测试图片,所得到的SSIM值比对比算法提升20.5%,PSNR值提升19.9%,无论从主观感受,还是客观评价指标等各方面,都有优化。

多传感器融合的室内机器人SLAM

为了解决基于二维激光雷达的SLAM在室内环境中存在定位误差较大和建图不完善的问题,提出一种多传感器融合的室内机器人SLAM算法。该算法针对传统ICP算法在激光SLAM前端存在误匹配问题,采用更适合室内环境的PL-ICP算法,并利用扩展卡尔曼滤波融合轮式里程计和IMU为其提供初始的运动估计值。在建图阶段利用深度相机获取的三维点云数据转化的伪二维激光数据和二维激光雷达获取的数据进行融合,弥补二维激光雷达建图没有垂直方向视野的缺陷。实验结果表明:融合里程计数据相比于单一轮式里程计定位精度至少提升了33%,为PL-ICP算法提供了更高精度的初始迭代值。同时融合建图弥补了单一二维激光雷达建图的缺陷,构建了环境信息更加完善的环境地图。

改进的局部最小像素先验遥感图像盲复原算法

为了解决遥感图像盲复原时模糊核估计不准确、复原图像存在振铃效应的问题,提出改进的局部最小像素先验遥感图像盲复原算法。该算法首先引入极端通道先验与局部最小像素先验结合,对图像的强度进行更好的约束,有利于得到更好的潜在清晰图像;然后采用基于梯度的方法估计模糊核,模糊核估计与中间潜在清晰图像估计交替迭代进行,获得较为理想的模糊核;最后引入联合双边滤波器,采用改进的拉普拉斯与正则化图像复原算法抑制图像复原的振铃效应。实验结果表明,本文方法对遥感图像复原效果较好,恢复的图像边缘清晰,振铃伪影得到抑制且模糊核较为理想;客观评价指标峰值信噪比(PSNR)较前沿复原算法平均提高约1.40 dB,结构相似度(SSIM)平均提高约0.02。

面向复杂环境的UWB/LiDAR/IMU组合定位方法

针对室外全球卫星导航系统(GNSS)拒止并且超宽带(UWB)和LiDAR在非视距和点云特征稀疏环境下定位效果较差的问题,提出了一种面向复杂环境的UWB/LiDAR/惯性测量单元(IMU)组合定位方法。首先,利用LiDAR和UWB的互补特性设计了时变因子,用于对车辆进行重定位;然后,引入三种运动模型描述车辆的运动状态,各模型采用无迹卡尔曼滤波方法设计滤波器;最后,将重定位后的车辆定位和IMU的测量数据作为交互式多模型-无迹卡尔曼滤波算法的状态输入,解算出最终的车辆位置。实验结果表明,所提组合定位方法具有较高的定位精度,相对于UWB和LiDAR单一传感器,视距环境下的定位精度分别提升了35.1%和22.8%,非视距环境下分别提升了53.1%和27.2%;所提重定位方法相对于UWB和LiDAR单一传感器,视距环境下的定位精度分别提升了23.2%和8.7%,非视距环境下分别提升了42.9%和11.4%,体现出了较高的定位精度和对复杂环境的适应能力。