基于信号接口的IVI驱动器设计标准——IVI-Signal Interface及其应用

仪器互换性是通用ATS设计的重要指标。回顾仪器互换技术的发展过程 ,重点论述了IVI基金会在IVI -MSS(measure mentandstimulussubsystem)模型基础上提出的IVI -SignalInter face仪器驱动器设计标准 ,分析了它的技术规范、结构框架、工程应用和商业效应。IVI -SignalInterface标准基于信号接口 ,实现了更高层次的仪器互换 ,指明了未来驱动器设计的发展方向。

Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Multi-Source Data

In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.

Interface Design Of Digital Platform For Bio Signal Processing

Bio-sensor arrays for multi-channel recording have been developed recently and signal processing platforms for those signals have been studied actively.But it’s thereal situation which these technologies are generally developed and studied respectively.So the interface design between recording array and signal processing platform is also an important issue to make bio-sensor signal processing system.In this paper,we proposed interface which has unique protocols to control sensor array and operate platform.There are two types of protocols in the interface.One is between sensor array and MCU in platform and the other is between MCU and board for wireless communication.Basically,each protocol has two kinds of modes(single,frame)and it can be extended if needed.

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components;artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented;physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.

On Similarities and Differences of Invasive and Non-Invasive Electrical Brain Signals in Brain-Computer Interfacing

We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.

DEVELOPMENT OF A PRECISE ELECTRO CHEMICAL INTERFACE

Based on the circuit principle of 1186 Electro Chemical Interface preduced by Solartron Electronic Group Ltd., a precise electro chemical interface (ECI) unit, which can provide the interfacing requirements for the control and measurement of characteristics of electro chemical cell, was developed by means of some essential improvements. Not only can it be used to control and measure the steady and non-steady state characteristics, but also it can be directly connected with Solartron 1170 series or 1250 Frequency Response Analysers (FRA) to measure the AC impedance. Besides,the EC1 can also be connected with two- or three-electrode electro chemical cell systems to test convenlently and correctly their DC and AC characteristics, and used as a four-electrode potentlostat combined with four-electrode electro chernical cell system which contains two reference electrodes (RES) for researches on the electro chemical characteristics of oil-water interface, etc.

基于IVI-Signal标准的自动测试系统驱动研究

建立通用测试软件平台,实现测试程序集和仪器资源的无关性设计是通用ATS的发展方向;驱动程序作为连接测试程序集和仪器资源的桥梁,在“无关性设计”中起着决定性作用;在分析自动测试系统基本理论和基于IVI-Signal标准的ATS软件平台体系基础上,运用组件技术划分信号角色、定义信号接口,根据仪器资源信号能力提出资源配置文件设计方法并给出XML语言实例,提供了基于测试资源信号能力的驱动设计方案,验证了IVI-Signal标准的可行性,为进一步工程实现提供了依据。

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

Classification of Imagined Speech EEG Signals with DWT and SVM

With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.

Development of prosthetic arm using body actioned SEMG signals

People's working capability is badly affected when they sufer an amputated arm.Artifcial replacements with prosthetic devices to get a satisfactory level of performance for essential functions with the currently available prosthetic technology are very dificult.Myoelectric arm prostheses are becoming popular because they are operated by a natural contraction of intact muscles.Hence,SEMG based artifdal arm was fabricated.The system cousists of diferent electronic and mechanical assemblies for operation of hand utilizing microcontroller in order to have minimum signal loss during its processing.With the hep of relay switching connected to low power DC motor,system is capable of opening and closing of grip according to individual wish.

A Method of SSVEP Signal Identification Based on Improved eCAA

Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.

不同粗糙度煤岩界面超低摩擦效应与声发射特征试验研究

为了揭示动载扰动作用下煤岩界面粗糙度对超低摩擦型冲击地压影响机制,采用自行研制的煤岩界面超低摩擦试验装置,以沈阳红阳三矿1082 m采深煤岩体为研究对象,通过改变煤块与砂岩块体表面粗糙度来模拟煤岩界面不同粗糙特性,以粗糙度系数表征煤岩界面粗糙程度,工作块体水平位移表征冲击过程中超低摩擦效应强度,声发射能量为工作块体摩擦滑动过程中的信号参数,进行应力波扰动下不同粗糙度煤岩界面超低摩擦试验.研究结果表明:(1)超低摩擦滑动过程中,工作块体水平位移、声发射能量计数以及累计能量曲线呈现出孕育阶段、激发阶段、稳定阶段变化特征;(2)煤岩界面粗糙度越小,工作块体水平位移和声发射能量峰值越大,煤岩界面越易发生超低摩擦效应;(3)不同煤岩界面粗糙度下,相比于其他扰动频率, 2 Hz时更易发生超低摩擦效应;(4)给出了声发射能量峰值与煤岩界面粗糙度系数对应关系.声发射能量峰值可以有效表征超低摩擦效应强度,可以用声发射能量峰值预测超低摩擦效应强度.

跨被试运动想象脑电信号的卷积神经网络识别方法

本文提出一种跨被试的深度神经网络识别方法,应对运动想象脑电信号的非线性、非平稳特性.该方法首先计算协方差矩阵均值,将不同被试者样本集的协方差对齐至单位矩阵,提升样本的被试间泛化性.然后,将对齐后的样本输入至卷积神经网络中,通过留一被试交叉验证法,构建跨被试的运动想象脑电信号识别方法.在BCI Competition IV dataset 2b公开数据集上进行实验,结果表明,新的方法在该数据集上取得了高的识别性能,且测试场景中的时间复杂度与现有方法相同.

轨道交通信号联锁列控一体化系统软硬件设计

该文主要探究联锁列控一体化系统的软硬件设计内容。该系统的软件部分使用VC++6.0开发,包含4个功能模块,分别是进路选排、进路锁闭、信号开放与进路解锁,可以实现车站与区间范围内信号设备的一体控制,并处理进路命令和控制地面设备;该系统的硬件部分包括STM32主控芯片和PCI版驱动采集板,以及CAN通信接口等,在保证通信速率与通信质量的基础上,实现上位机与下位机的信息共享与可靠交互。该系统以轨道交通信号为基础数据,在数据采集、分析的基础上为监控列车运行和保障行驶安全提供帮助。

界面设计影响蒙汉用户视觉注意的脑电研究

目的比较蒙、汉用户在不同界面布局的条件刺激下视觉注意对脑电刺激诱发响应的调节和影响作用。方法基于Oddball实验范式的事件相关电位技术,采用图文形式和纯文本形式的宫格式、标签式,以及侧面展开式界面布局刺激,结合分析脑电数据和行为数据,探究界面布局设计对蒙、汉用户视觉注意的影响。结果对视觉注意的早期阶段进行研究后发现,图文形式宫格式布局对汉族用户的视觉注意具有显著的刺激和调节作用,而图文形式侧面展开式布局对蒙古族双语用户的视觉注意有显著的刺激和调节作用。通过P300成分波幅发现蒙、汉用户的情绪发生变化,蒙、汉用户在纯文本形式标签式布局界面的影响下产生消极情绪,波幅降低。结论在事件相关电位技术中,通过P100成分与P300成分的波幅强度变化,可以作为界面布局刺激条件下衡量比较不同民族地区用户视觉注意与情感体验的观测量。

基于MATLAB的《信号与系统》教学辅助系统设计

信号与系统作为电子信息类专业十分重要的专业基础课程,一直以来都具有理论性强、概念抽象、难以理解的特点。MATLAB具有强大的数值计算、可视化功能和系统建模仿真能力,在高校和科研机构应用极为广泛。为提高学生对本门学科的学习兴趣,加深对课程内容的理解掌握,文中从信号与系统教学的需求、基于MATLAB的信号与系统教学分析现状以及二者结合运用下的广大前景出发,重点介绍了利用信号与系统的各重要知识点基本原理,运用MATLAB工具箱函数编程实现了信号与系统重要内容的模拟仿真,并设计了基于MATLAB的GUI教学辅助系统。

基于FPGA的LVDS+RS422远距离高速通信设计与实现

针对高速信号远距离传输时存在数据可靠性下降的问题,文中设计了一种基于FPGA的软硬件结合的长线传输方案。该系统在数据传输过程中,采用NI公司的LVDS串化器芯片SN65LV1023A和SN65LV1024B作为发送与接收芯片。驱动器和均衡器的配合使用,减少了信号衰减,同时增加了电路的驱动能力和信号传输距离。在指令传输与状态反馈的电路中采用RS422通信协议,并加入保证传输可靠性的隔离芯片,极大地简化了电路。在软件方面为提高抗干扰能力,添加了8B/10B编码、指令的三判二机制和校验字,降低了误码率,提高了传输稳定性。经验证,此设计可在90 m电缆以320 Mbit/s速率零误码传输。

脑机接口技术发展现状及未来展望

本文回顾了脑机接口技术(BCI)的发展历程,从20世纪20年代的科学幻想期到21世纪的技术爆发期,概述了BCI技术在各个阶段的关键进展和成就。文中阐述了BCI技术的基本组成部分及其分类方式,包括不同类型的BCI系统(输出式、输入式、双向交互式)和信号采集方式(非侵入式、半侵入式、侵入式),并探讨了BCI技术在当前面临的心理生理学、技术、产业和监管等方面的挑战。最后,文章展望了BCI技术的未来发展方向,预计在未来几十年内,BCI技术将在硬件优化、软件集成、算法创新等方面取得显著进展,并可能实现在医疗、教育、娱乐乃至人机交互等多个领域的广泛应用。

嵌入式系统中运动想象脑-机接口编解码算法综述

脑-机接口技术通过在大脑与外部设备之间建立信息传输通路,使用户能够对外部设备进行直接控制。近年来,基于运动想象范式的脑-机接口编解码算法研究在医疗健康、教育娱乐及日常生活设备中的应用范围越来越广,这些算法通常需要嵌入到硬件设备中来满足实际应用的需求。介绍了近年来嵌入式系统中运动想象脑-机接口编解码算法研究现状,从传统机器学习算法和深度学习算法两个角度指出其对应的优缺点。重点介绍四类常用嵌入式平台的代表性设备及其优缺点,并针对不同的应用场景给出相应的硬件选型建议。归纳了更适用于嵌入式脑-机接口系统的评价指标并最终总结了领域内现存的挑战与未来发展方向。

脑机接口中脑电图-近红外光谱联合分析进展研究

脑机接口(BCI)能将受试者意图相关的大脑活动转化为外部设备控制指令,在神经疾病治疗、运动康复等方面具有较高应用潜力。BCI的实现需从人脑获取有意义的信号,而脑电图(EEG)可以反映神经电活动,主要用于对反映实时性要求较高的BCI系统;近红外光谱(NIRS)主要反映血流动力学水平,一般用于神经生理状态等需要精确定位脑活跃区域的研究。EEG和NIRS因其非侵入、方便穿戴、成本较低等优点,成为BCI的重要信号获取方法。相比于单模态BCI系统,基于EEG-NIRS联合分析的混合BCI系统由于具有更丰富的信号特征,在生理状态检测、运动想象等领域得到了越来越多的关注与研究。该文从EEG-NIRS联合分析在脑机接口中应用的研究现状出发,在数据和特征融合程度、层面上归纳最近的相关领域研究现状,并对EEG-NIRS信号处理手段的研究前景进行了展望。