A lightweight and efficient raw data collection scheme for IoT systems

With the prevalence of Internet of Things(loT)devices,data collection has the potential to improve people's lives and create a significant value.However,it also exposes sensitive information,which leads to privacy risks.An approach called N-source anonymity has been used for privacy preservation in raw data collection,but most of the existing schemes do not have a balanced efficiency and robustness.In this work,a lightweight and efficient raw data collection scheme is proposed.The proposed scheme can not only collect data from the original users but also protect their privacy.Besides,the proposed scheme can resist user poisoning attacks,and the use of the reward method can motivate users to actively provide data.Analysis and simulation indicate that the proposed scheme is safe against poison attacks.Additionally,the proposed scheme has better performance in terms of computation and communication overhead compared to existing methods.High efficiency and appropriate incentive mechanisms indicate that the scheme is practical for IoT systems.

Piecewise linear mapping algorithm for SAR raw data compression

When the saturation degree （SD） of space-borne SAR raw data is high, the performance of conventional block adaptive quantization （BAQ） deteriorates obviously. In order to overcome the drawback, this paper studies the mapping between the average signal magnitude （ASM） and the standard deviation of the input signal （SDIS） to the A/D from the original reference. Then, it points out the mistake of the mapping and introduces the concept of the standard deviation of the output signal （SDOS） from the A/D. After that, this paper educes the mapping between the ASM and SDOS from the A/D. Monte-Carlo experiment shows that none of the above two mappings is the optimal in the whole set of SD. Thus, this paper proposes the concept of piecewise linear mapping and the searching algorithm in the whole set of SD. According to the linear part, this paper gives the certification and analytical value of k and for nonlinear part, and utilizes the searching algorithm mentioned above to search the corresponding value of k. Experimental results based on simulated data and real data show that the performance of new algorithm is better than conventional BAQ when raw data is in heavy SD.

Efficient Strip-Mode SAR Raw-Data Simulator of Extended Scenes Included Moving Targets Based on Reversion of Series

The Synthetic Aperture Radar(SAR)raw data generator is required to the evaluation of focusing algorithms,moving target analysis,and hardware design.The time-domain SAR simulator can generate the accurate raw data but it needs much time.The frequency-domain simulator not only increases the efficiency but also considers the trajectory deviations of the radar.In addition,the raw signal of the extended scene included static and moving targets can be generated by some frequency-domain simulators.However,the existing simulators concentrate on the raw signal simulation of the static extended scene and moving targets at uniform speed mostly.As for the issue,the two-dimensional signal spectrum of moving targets with constant acceleration can be derived accurately based on the geometric model of a side-looking SAR and reversion of series.And a frequency-domain algorithm for SAR echo signal simulation is presented based on the two-dimensional signal spectrum.The raw data generated with proposed method is verified by several simulation experiments.In addition to reveal the efficiency of the presented frequency-domain SAR scene simulator,the computational complexity of the proposed method is compared with the time-domain approach using the complex multiplication.Numerical results demonstrate that the present method can reduce the computational time significantly without accuracy loss while simulating SAR raw data.

Motion estimation in wide band synthetic aperture sonar based on the raw echo data using the method of displaced phase center antenna

Phase errors in synthetic aperture sonar (SAS) imaging must be reduced to less than one eighth of a wavelength so as to avoid image destruction. Most of the phase errors occur as a result of platform motion errors, for example, sway yaw and surge that are the most important error sources. The phase error of a wide band synthetic aperture sonar is modeled and solutions to sway yaw and surge motion estimation based on the raw sonar echo data with a Displaced Phase Center Antenna (DPCA) method are proposed and their implementations are detailed in this paper. It is shown that the sway estimates can be obtained from the correlation lag and phase difference between the returns at coincident phase centers. An estimate of yaw is also possible if such a technique is applied to more than one overlapping phase center positions. Surge estimates can be obtained by identifying pairs of phase centers with a maximum correlation coefficient. The method works only if the platform velocity is low enough such that a number of phase centers from adjacent pings overlap.

安卓GNSS原始数据转换为RINEX的方法与实现

安卓设备收集的全球导航卫星系统(GNSS)数据在从地理空间定位到环境监测的各种应用中越来越重要。但缺乏将安卓GNSS原始数据转换为接收机独立交换格式(RINEX)的标准化工具,这给初学者和研究人员带来了重大挑战。为了满足这一需求,本文基于Python开发安卓GNSS原始数据转换RINEX工具箱(AGRDRT),旨在简化转换过程并提高安卓GNSS数据的可用性。所提出的工具箱利用Python的versatility提供一个友好的用户界面,用于将安卓GNSS原始数据转换为广泛采用的RINEX 3.05标准格式。AGRDRT主要功能包括强大的数据解析算法、对不同GNSS星座的支持以及与各种安卓设备配置的兼容性。

EFFICIENT STRIP-MODE SAR RAW SIGNAL SIMULATION OF MIXED TARGETS BASED ON ACCURATE 2-D SPECTRUM

An accurate and efficient Synthetic Aperture Radar(SAR)raw data generator is of considerable value for testing system parameters and verifying imaging algorithms.Nevertheless,the existing simulator cannot exactly handle the case of the fast moving targets in high squint geometry.As for the issue,the analytical expression for the two Dimensional(2-D)signal spectrum of moving targets is derived and a fast raw echo simulation method is proposed in this study.The proposed simulator can accommodate the moving targets in the high squint geometry,whose processing steps of the simulation are given in detail and its computational complexity is analyzed.The simulation data for static and moving targets are processed and analyzed,and the results are given to validate the effectiveness of the proposed approach.

Novel modelling strategies for high‑frequency stock trading data

Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

平板扫描仪RAW和默认参数捕获数据差异

本文测量了三种平板扫描仪RAW和默认参数扫描数据捕获的差异,估计两种扫描方式对中性灰再现的影响。实验结果表明,默认参数扫描和参数关闭扫描对中性灰影响不大,因为默认调整参数主要作用于高光和暗调区域。

RAWS异常强降水数据现场核验方法及应用

区域自动气象站记录到历史罕见、超历史或显著超过邻近站的异常强降水时,常规数据质量控制(QC)方法一般判为疑误数据。本文对这种情况提出了现场核验的方法,包括核验的启动,传感器误差核查,仪器性能和探测环境评估,其他资料辅助判别,以及作出核验结论、形成核验报告等步骤。并利用一次实例对这种方法的有效性和适用性进行了检验。

从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows.In this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing images.Moreover,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not possess.Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

Rapid prediction of floating and sinking components of raw coal

A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.

临床研究透明化在针灸临床试验注册中的实践与思考

临床研究透明化是临床研究结果真实可靠的重要保障之一,临床研究透明化通过在临床研究的试验设计、实施、得出结果和数据分析过程进行公开,加强社会监督,保障临床研究结果真实性。通过针灸临床研究注册,针灸临床研究注册数量不断上升,临床研究覆盖范围广泛,许多临床研究过程更加系统化、规范化,数据公开透明,极大程度的保障了临床研究结果的准确性,提高研究质量,促进针灸临床研究高质量发展。

基于历史统计数据分析的全国原煤产量预测研究

煤炭是我国经济建设中的重要能源,其市场供需稳定有着不可忽视的重要性,准确预测我国煤炭产量有助于国家能源政策的制定。故选取2003—2023年国有原煤产量历史数据以构建预测数据库,并介绍了预测煤炭产量的6种传统预测方法。分别采用6种方法进行对应2024年的国有原煤产量预测,对比分析各方法的预测准确程度。综合各研究结果给出了2024年国有原煤产量预测区间,同时分析得出加权移动平均法和一元线性回归预测法具有更好的拟合效果。

基于原始数据报文分析的铁路通信数据网监测技术研究

铁路通信数据网承载业务日益复杂多样,目前存在运维工具滞后、运维和业务部门职责划分不清、运维方式被动等问题。通过分析通信数据网运维现状,提出端到端业务运维方案:择优选取数据网监测可用数据源,对业务原始数据报文进行大数据挖掘,同时优化业务质量分析算法;实时监测数据网,实现数据网从用户端、网络端到服务端的实时监控及业务质量分析。对业务应用状态劣化问题预警,快速定位故障并回溯取证,可利用通信运维视角为业务应用服务,提高数据网故障处理精准度及效率,同时通过各种运维手段的有机结合,对业务应用进行模型层次管理,为铁路通信运维开拓新思路。

《化妆品监督管理条例》实施背景下化妆品新原料备案现状分析及展望

化妆品新原料是产业创新发展的基础,也是我国实现制妆强国的重要抓手。本文基于国家药监局数据查询数据库对化妆品新原料备案信息进行了统计,并从新原料区域分布、工艺、功效、备案人等多维度分析了《化妆品监督管理条例》实施以来我国化妆品新原料注册备案情况。结果表明,《化妆品监督管理条例》的实施,激发了我国化妆品新原料产业创新的活力,新原料研发及产业呈现良好发展态势,发展前景广阔。

基于DBN的故障特征提取及诊断方法研究

随着装备日趋复杂化,依靠专家经验或信号处理技术人工提取和选择故障特征变得越来越困难。此外,以BP神经网络、SVM为代表的浅层模型难以表征被测信号与装备健康状况之间复杂的映射关系,且面临维数灾难等问题。结合深度置信网络(DBN)在提取特征和处理高维、非线性数据等方面的优势,提出一种基于深度置信网络的故障特征提取及诊断方法。该方法通过深度学习利用原始时域信号训练深度置信网络并完成智能诊断,其优势在于能够摆脱对大量信号处理技术与诊断经验的依赖,完成故障特征的自适应提取与健康状况的智能诊断,该方法对时域信号没有周期性要求,具有较强的通用性和适应性。在仿真数据集和轴承数据集上进行了故障特征提取和诊断实验,实验结果表明:本文提出的方法能够有效地从原始信号中进行多种工况、多种故障位置和多种故障程度的故障特征提取和诊断,并且具有较高的故障识别精度。

SAFIRE与FBP重建法对CT图像噪声及扫描剂量影响的比较研究

目的评价应用原始数据迭代重建法(SAFIRE)与滤波反投影法(FBP)对CT图像噪声及扫描剂量的影响。方法应用128层双源Flash CT,在120kV、100kV、80kV、70kV条件下,分别采用200mAs、170mAs、140mAs、110mAs、80mAs、50mAs、20mAs扫描剂量扫描水模,采用原始数据迭代重建法与滤波反投影法分别重建,不同管电压条件下,以预设扫描管电流200mAs FBP重建图像作为对照,比较不同扫描剂量、不同重建方法图像噪声并分析扫描剂量降低的潜力。结果原始数据迭代重建法随着迭代次数的增多,120kV管电压时降低45%～75%的扫描剂量,100kV管电压时降低45%～75%的扫描剂量,80kV管电压时降低45%～60%的扫描剂量,70kV管电压时降低15%～60%的扫描剂量,可以得到与200mAs FBP重建法噪声无统计学差异的图像。结论 SAFIRE重建法与FBP重建法比较可以显著降低图像噪声,从而降低扫描剂量并同时得到满意图像。

区域海洋地质数据库建设纲要

区域海洋地质数据库是中国地质调查局基础数据库的一个重要组成部分,是实现地质调查基础性与公益性战略部署的依托。根据区域海洋地质数据库建设的目标与任务,在数据结构和实施步骤等方面进行了初步研究。区域海洋地质数据库及其管理信息系统适合采取基于Web系统的三层体系结构,在数据构成上亦分为区域海洋地质调查原始资料与成果资料,其中元数据库的建设对数据库的应用非常重要,用户可以通过元数据了解数据的存在性、适宜性、有效性等,确定数据的使用方法,因此,元数据库的建设应采用国际已经普遍认可的ISO/TC211发布的ISO19115空间元数据标准。

中药数据优化处理探讨

要获得高信息含量的、有用的知识,理想情况就是原始数据是不含噪声的正确数据。中药数据库系统是数据库技术在中药领域的运用,是在中药信息研究过程中,对大量不规范的原始数据进行分析、整理、补充、完善建立起来的。本研究结合在建的中草药信息系统回顾中药数据处理加工中的若干关键问题。

中国探空观测与多套再分析资料气温序列的对比研究

为比较中国探空观测与再分析气温的差异,利用中国118站850—30hPa经质量控制和均一化处理后逐月气温和NCEPv1、NCEPv2、ERA-40、ERA-Interim、JRA55、20CR、MERRA和CFSR等8套再分析月平均气温,通过对比1981—2010年探空观测与多套再分析气温序列的平均偏差、相关系数、标准差和变化趋势,分析两者在数值、年际变率、离散度及长期变化的差异。结果表明,中国探空温度原始序列存在较为显著的非均一性,均一化对原始气温序列总体为负订正,对流层上层至平流层下层(200—100hPa)订正值最为显著。均一化气温去除了原始序列中由仪器换型和系统升级等因素导致的系统误差,与再分析气温相关较原始序列明显提高。再分析气温与均一化气温偏差约1℃,多数再分析气温较均一化气温在对流层偏低、平流层偏高。再分析与均一化气温年际变率较为一致,正相关达到显著。多数再分析与均一化气温均在对流层中低层呈上升趋势、平流层中层呈下降趋势。对流层上层和平流层下层不确定性较大。总体上,ERA-Interim、JRA55和MERRA与其他再分析相比更相近中国均一化探空气温。