Design and Implementation of a High-Speed Multi-Channel Data Acquisition System

A data acquisition system （DAS） to implement high-speed, real-time and multi-channel data acquisition and store is presented. The control of the system is implemented by the combination of complex programable logic device （CPLD） and digital signal processing （DSP）, the bulk buffer of the system is implemented by the combination of CPLD, DSP, and synchronous dynamic random access memory （SDRAM）, and the data transfer is implemented by the combination of DSP, first in first out （FIFO）, universal serial bus （USB） and USB hub. The system could not only work independently in single-channel mode, but also implement high-speed real-time multi-channel data acquisition system （MCDAS） by the combination of multiple single-channels. The sampling rate and data storage capacity of each channel could reach up to 100 million sampiing per second and 256 MB respectively.

Robust and Efficient Data Transmission over Noisy Communication Channels Using Stacked and Denoising Autoencoders

We study the effects of quantization and additive white Gaussian noise(AWGN) in transmitting latent representations of images over a noisy communication channel. The latent representations are obtained using autoencoders(AEs). We analyze image reconstruction and classification performance for different channel noise powers, latent vector sizes, and number of quantization bits used for the latent variables as well as AEs’ parameters. The results show that the digital transmission of latent representations using conventional AEs alone is extremely vulnerable to channel noise and quantization effects. We then propose a combination of basic AE and a denoising autoencoder(DAE) to denoise the corrupted latent vectors at the receiver. This approach demonstrates robustness against channel noise and quantization effects and enables a significant improvement in image reconstruction and classification performance particularly in adverse scenarios with high noise powers and significant quantization effects.

The Interdisciplinary Research of Big Data and Wireless Channel: A Cluster-Nuclei Based Channel Model

Recently,internet stimulates the explosive progress of knowledge discovery in big volume data resource,to dig the valuable and hidden rules by computing.Simultaneously,the wireless channel measurement data reveals big volume feature,considering the massive antennas,huge bandwidth and versatile application scenarios.This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication,especially for 5th Generation(5G) and beyond.Considering the big data research progress,then a cluster-nuclei based model is proposed,which takes advantages of both the stochastical model and deterministic model.The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects.Combining the channel properties variation principles with antenna size,frequency,mobility and scenario dug from the channel data,the proposed model can be expanded in versatile application to support future mobile research.

Multi-channel & high-precision data acquisition devi ce for aerospace

This paper describes the detailed desi gn of data acquisition device with multi-channel and high-precision for aerosp ace.Based on detailed analysis of the advantages and disadvantages of tw o common acquisition circuits,the design factors of acquisition device focus o n accuracy,sampling rate,hardware overhead and design space.The me chanical structure of the system is divided into different card layers according to different functions and the structure has the characteristics of high reliability,conveni ence to install and scalability.To ens ure reliable operation mode,the interface uses the optocoupler isolated from th e e xternal circuit.The transmission of signal is decided by the current in the cur rent loop that consists of optocouplers between acquisition device and t est bench.In multi-channel switching circuit,by establ ishing analog multiplexer model,the selection principles of circuit modes are given.

Sampled-Data Consensus Control of MUSV Systems with Channel Fading and Transmission Delay

This paper studies sampled-data consensus control of a collection of unmanned surface vehicles(USV)operating in network environments with fading channels and time-varying transmission delay.The channel fading is modeled as each independent stochastic process whose probability distribution is known.By considering the effects of channel fading and transmission delay from sampler to the controller,a new MUSV system model is formulated in the framework of network.With the novel established model,stability analysis is given at first,then the sampled-data consensus controller is designed,which also extends to the robust control with wave-induced disturbance.The effectiveness of the presented method is demonstrated by numerical simulation.

Channel Estimation and Data Transmission in Massive MIMO TDD Systems

This paper mainly elaborates the studies of channel estimation and downlink data transmission in Massive MIMO. As there are different types of interference in single-cell and multi-cell systems, this paper establishes different models for them separately. In terms of uplink training, for getting channel state information, we introduce LS and MMSE channel estimation algorithms and make a comparison between them. At the same time, the problem of pilot contamination is solved by cell classification and pilot identification. Next, this paper defines mathematical models for downlink data transmission. We use pre-coding methods (including Zero-forcing and Maximal Ratio Combining schemes) and optimize power distribution to improve channel capacity and transmission rate. Furthermore, this paper provides numerical results to show the simulation performance in both single-cell and multi-cell systems and extends to prospects in the future.

基于多模态数据融合的改进中尺度涡检测模型

提出一种基于多模态数据融合的改进中尺度涡检测模型。该模型以海平面高度数据为基础,首次将融合表层海温数据扩展为融合多深度层海温数据;将海温数据的深度层作为通道,嵌入通道注意力机制,使得模型能够关注于海水温度数据中最具有区分度的深度层;模型在编码及解码过程中采用残差学习单元,在加深网络深度的同时,更好地拟合激活函数,缓解训练问题,以提高模型的检测准确率。以中国南海部分海域为例开展实验验证,结果表明该中尺度涡检测模型准确率达到93.62%,模型具备有效性和可靠性。

一种宽带非线性信道校正方法

针对高速数传解调器接收宽带高阶调制信号时因信道非线性导致的性能恶化问题,提出一种基于两层自适应均衡网络的非线性信道校正方法。该方法先利用递归最小二乘法获得Volterra非线性前置校正系数,再利用自适应基带均衡器消除信道的剩余影响,以提升解调器的捕获跟踪能力,并在高速数传解调器工程应用中实现2.4 Gb/s 16QAM信号的稳定接收。

基于扩展现实的航空噪声数据模拟系统开发

该文是基于扩展现实技术提出的各种机型在静态和起飞状态的噪声数据模拟系统。即在虚拟场景中实现各种机型的静态试车和起飞程序,同时使用实际机型的噪声记录作为噪声源,并使用头戴显示器以双声通道实现可听化,呈现出噪声数据的可听化和在虚拟环境中的可视化。系统将使用经验公式处理数据,合成起飞状态的噪声,协助噪声适航审定程序进程更有效的进行,有利于相关部门根据对环境保护的要求,制定出航空噪声的适航规章和控制标准。

基于LabVIEW的风电主轴承螺栓预紧力监测系统

针对大型风电主轴承容易产生轴向振动故障问题,基于端盖螺栓预紧力的变化特性,本文拟开发一款螺栓预紧力采集监测系统。该系统采用一种嵌入分布式的垫片压力传感器测量方法,以DH5902N数据采集卡和计算机为硬件,实现螺栓预紧力数据采集和分析。构建应用于风电机主轴承的压力传感器检测模型,通过建立基于传感器数据测量的螺栓预紧力校正算法,实现风电机主轴承轴向受力检测。选用LabVIEW作为软件开发平台,实现多通道数据采集显示与灯光报警等功能,最终实现对风电主轴承的受力状态监测。

基于FPGA的多通道高速串行数据采集系统设计

针对卫星导航信号抗干扰领域中进行高精度高速多通道数据采集的需要,系统采用ADI公司的16位高速串行AD转换芯片AD9653,完成模数转换,其转换精度可达16比特,转换速率最高为125MSPS。分析AD9653的高速串行数据接口特点,采用Xilinx公司的Kintex-7系列FPGA提供的片同步技术,进行位时钟自适应相位校准和数据帧调整研究,保证了高速串行数据传输的稳定性与正确性。采用内部FIFO数据缓存的方式实现多通道数据的片内同步以保证后续抗干扰算法的正确处理。研究结果表明,使用该方法进行高速串行数据采集时系统稳定可靠、正确无误。为多通道高速串行数据采集提供了一种切实可行的解决方案,对类似需求的数据采集系统具有一定的参考价值。

结合地理信息与AIS数据的海上航道自适应提取方法

海上航道对于海上态势感知具有重要意义。然而,现有的航道提取方法存在航道航路点易丢失、提取准确性不高等问题,本文依据船舶航路点数量与其途经岛屿、港口等地理区域数量相等的准则,提出了一种结合地理信息与自动识别系统(Automatic Identification System, AIS)数据的海上航道自适应提取方法。首先,构建港口之间往返船只AIS航迹数据集,将每条AIS航迹等间距分段并计算每段航迹的平均航速,设置航速差初始阈值,计算相邻航段平均航速差超过阈值的数量作为初始船只转弯次数。其次,统计AIS航迹途经岛屿、港口等地理区域的数量,若其与利用AIS航迹提取的船舶转弯次数不相等,则根据两者的差异自适应调整航速差阈值,重新提取船舶航路点,直至其与途经地理区域的数量相等,将平均航速差超出最终阈值的航迹段的连接点作为船舶航路点。然后,利用基于密度的聚类算法将所有船舶的航路点聚类为航路点集合,计算航路点集合的质心作为航道的航路点。最后,以航道航路点为顶点、航路点之间的连线为边构建有向图,并删除落在陆地上的连线后得到最终的航道提取结果。利用潍坊港—连云港港之间行驶的20条船舶的AIS数据开展了海上航道提取实验,结果表明,与基于交通路径异常检测的航道提取方法相比,本文方法得到的船舶航路点的平均虚检率降低了9.1%,平均漏检率降低了16.7%,显著提升了航道航路点位置提取的准确性。

一种基于动态门限与LMS算法相结合的多径干扰抑制算法

在船舰行驶过程中,信号的传输在岛屿反射与海面散射影响下易产生多径效应,使船舰的无线地空数据接收系统受到影响。为提高接收机的接收性能,首先完成了多径信道的建模,搭建了三径信道模型,并在此模型下,将动态门限法与最小均方(LMS)算法进行改进结合,并用此算法完成了信号处理的研究与仿真。仿真结果表明,所提算法优化方案可以提高在此种情况下信号接收系统的准确性。

基于多源数据聚合的神经网络侧信道攻击

基于深度学习的侧信道攻击需要针对密码算法的每一个密钥字节建模并训练,数据采集和模型训练开销大.针对该问题,提出一种基于多源数据聚合的神经网络侧信道攻击方法.为筛选具有良好泛化效果的密钥字节泄露数据进行数据聚合,以AES-128算法为例,先基于16个密钥字节的泄露数据训练16个单密钥字节模型,分别实现对16个密钥字节的恢复;其次,设计一种打分机制评估各单密钥字节模型的泛化效果,通过得分排序筛选出对各密钥字节恢复效果最好的单密钥字节模型;最后,以筛选模型所对应的各密钥字节泄露数据集构建多源数据聚合模型进行训练,实现密钥恢复.实验测试结果表明,多源数据聚合模型具有良好的泛化效果,有效提高了密钥恢复的准确率和效率,降低了恢复密钥所需的能量迹数量,其在采集能量迹较少的情况下依然具有较好的攻击效果.

基于通道组合-数据对齐-多尺度全局CNN的MI-EEG分类

由于运动想象脑机接口(MI-BCI)范式不需要视觉刺激,应用MI-BCI范式在提高人机交互系统舒适度方面具有重要意义。为实现辅助设备的异步控制,提高模型的鲁棒性,减少通道使用数量以降低BCI系统输入的复杂性,提出一种基于通道组合(channel combination,CC)-数据对齐(euclidean space data alignment,EA)-多尺度全局卷积神经网络(multiscale global convolutional neural network,MGCNN)的运动想象脑电分类方法。通过引入大脑静息状态下的脑电信号,扩展MI-BCI输出指令集;利用CC将22通道脑电数据重构为左右对称通道加中间通道的3通道形式,重构后的数据经过EA方法规范后作为网络输入;构建多尺度卷积模块与全局卷积模块,并行提取脑电信号的局部特征和ERS/ERD全局特征;利用迁移学习提升模型的解码能力。结果表明:该方法在BCI Competition IV 2a数据集上达到了99.28%的平均准确率和0.99的Kappa值,提高了运动想象脑电分类精度,为在线异步运动想象脑机接口的应用与发展作出了贡献。

基于AD7616的16通道数据采集系统设计

针对传统数据采集系统因通道不足或数据量过大,无法满足多通道同步采集的问题,设计了一种基于AD7616的16通道数据采集系统。系统以Xilinx系列FPGA作为主控芯片,ADI公司的AD7616作为模数转换芯片,DDR3 SDRAM作为缓存单元。介绍了各个模块硬件的设计思路,重点介绍了FPGA控制AD7616模数转换模块、DDR3 SDRAM缓存模块的软件程序设计,并对系统的多通道采样功能、同步性能以及实际水下超声信号的采集能力进行了实验验证,结果表明,系统可以完成水下超声信号的多通道同步采集。

基于信道建模的多层星地融合数据分配策略

为了解决空天地一体化网络中面临的星地链路切换效率低、数据分配不均、数据传输中断率高等问题,针对多层卫星架构的星地融合数据传输场景,提出了一种基于信道建模的多层星地融合数据分配策略。该策略考虑星地距离、自由空间路径损失、信道增益、剩余服务时间等因素,基于香农信道容量理论,构建信道模型感知的低地球轨道卫星服务能力量化模型,从而生成数据分配策略。考虑数据传输中断风险,依托数据交互历史信息,通过可靠性确认机制动态调整数据分配策略,以提升数据传输效率,实现中断避免的星地链路无缝切换。仿真结果表明,所提策略在投递率、平均投递时延与中断率方面均优于其他基准算法。

中国数字地震台网数据管理中心Y2K问题的解决方法及getdata_new软件

叙述了中国数字地震台网 (CDSN)数据管理中心 (DMC)的计算机 2 0 0 0年问题 (Y2 K)的解决情况 ,着重介绍了为解决 Y2 K问题而编制的 getdata new软件的功能 ,特点 ,结构和工作原理 ,并结合几个典型实例 ,对用法和应用情况进行说明。另外 ,本文对 getdata new所涉及的部分 ASL的程序也作了简介。

基于云边协同技术的远程安全运维系统

在数字化时代,软件应用的快速交付和高效部署对于企业的成功至关重要,传统的应用部署方式往往复杂且耗时,容易导致运维困难和资源浪费,企业面对软件功能需求的不断变化,需要快速响应产品的变化,快速部署软件以产生效益。随着容器化技术、微服务等云原生技术的发展,为服务的频繁发布、快速交付及风险反馈等提供了技术保障。本系统基于云边协同场景,利用云原生、云边协同和安全数据通道技术,实现了基于本地云、远端服务为一体的云边协同的远程安全运维系统。