Odorant discrimination using functional near-infrared spectroscopy of the main olfactory bulb in rats

We characterize the hemodynamic response changes in the main olfactory bulb(MOB)of anesthetized rats with near-infrared spectroscopy(NIRS)during the presentation of three different odorants:(i)plain air as a reference(Blank),(ii) 2-heptanone(HEP),and(iii)isopropylbenzene(Ib).Odorants generate different changes in the concentrations of oxyhemoglobin.Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.     

Context-aware BPEL workflow system using aspect-oriented programming

Business process execution language(BPEL)is a most recognized standard workflow language.However,it is difficult to be used in the ubiquitous system computing environment because it is difficult to describe the context information in the selection of the flow through the branch.To solve this problem,we propose a new BPEL workflow system with context-awareness by using aspect-oriented programming(AOP).This system is composed of a BPEL system module and a weaving module using AOP for context-aware.The BPEL system module generates a BPEL workflow program.And the weaving module converts a context-aware mark-up language(CAML)document to the aspect-oriented program that is applied to context-aware code without modification of the existing BPEL document.We also define a new document form that is called CAML,which provides a context-aware that is not available in BPEL.The system can generate a context-aware workflow program.It is developed in a way that inserts context information using AOP to provide context-aware services.     

A 4 MHz Lamé mode microresonator-based oscil lator

A 4.13 MHz reference oscillator incor p orating a capacitive single-crystal-silicon(SCS) micromechanical resonator is prese nted.The microresonator is fabricated using a cavity-silicon-on-insulator(c avity-SOI) process and is excited in the Lamé mode with electrostatic driving and capacitive sensing.The Lamé mode may be described as a squ are plate that is contracting along one axis in the fabrication plane,while sim ultaneously extending along an orthogonal axis in the same plane.The microreson ator exhibits a quality factor as high as 1.4×106 and a resonant frequency of 4.13 MHz at a pressure of \{0.08 mbar.\} The output spectrum of the oscillator show s that the silicon micromechanical resonator is adapted as a timing element for a precision oscillator.     

An oscillator with low phase noise based on active resonator using metamaterial in K-band

An oscillator(OSC)with a metamaterial resonator based on high-Q is designed to improve the phase noise in Kband.The proposed metamaterial resonator is a lattice structure resonator(LSR)that is designed to be high-Q by a strong coupling of E-field at the resonance frequency.Thus the output of OSC is about 12.5dBm at the f0.The phase noise is 109.477dBc/Hz at 100kHz offset frequency.     

基于HHT和SVM的运动想象脑电识别

对运动想象脑电信号(EEG)分类识别是脑-机接口(BCI)研究领域的重要问题。本文通过经验模式分解(EMD)将EEG分解为一系列内蕴模式函数(IMF),并对重要IMF的瞬时幅度提取AR模型参数,同时对所有的IMF进行Hilbert变换(HT)得到Hilbert谱,进而求得瞬时能量(IE)。将得到的AR参数和IE,结合时域均值和中值绝对偏差估计(MAD),组成初始特征,然后利用经遗传算法(GA)优化的支持向量机(SVM)进行分类,得到识别结果。对2008年BCI CompetitionⅣDataset 1中想象左手和脚运动的两组数据进行识别,在仅仅使用少数通道的情况下,识别正确率分别达到84.7%和85.8%,初步验证了该方法的有效性。     

Development of a novel MLS controller based on embedded ARM

The controller plays an important rol e in microwave landing system(MLS) navigation equipment.The embedded advanced reduced-instruction-set-computing(RISC) machine(ARM) is introduced to de velop a new MLS controller based on the functional requirements.A design includ ing hardware and software of an ARM system is given.The hardware design involve s a ARM smallest system and its interface.μC/OS-II,a good real-time operating system(RTOS),is i ntroduced in the software design.The task partitioning and management are also i nvolved.The experimental results demonstrate that the embedded ARM technique is an effective way in the complex multi-task system,and it meets the high inte gration and real-time processing requirements of the navigation equipment.     

Development of a Low-cost Hardware-in-the-loop Simulation System as a Test Bench for Anti-lock Braking System

Nowadays validation of anti-lock braking systems(ABS) relies mainly on a large amount of road tests.An alternative means with higher efficiency is employing the hardware-in-the-loop simulation(HILS) system to substitute part of road tests for designing,testing,and tuning electronic control units(ECUs) of ABS.Most HILS systems for ABS use expensive digital signal processor hardware and special purpose software,and some fail-safe functions with regard to wheel speeds cannot be evaluated since artificial wheel speed signals are usually provided.In this paper,a low-cost ABS HILS test bench is developed and used for validating the anti-lock braking performance and tuning control parameters of ABS controllers.Another important merit of the proposed test bench is that it can comprehensively evaluate the fail-safe functions with regard to wheel speed signals since real tone rings and sensors are integrated in the bench.A 5-DOF vehicle model with consideration of longitudinal load transfer is used to calculate tire forces,wheel speeds and vehicle speed.Each of the four real-time wheel speed signal generators consists of a servo motor plus a ring gear,which has sufficient dynamic response ability to emulate the rapid changes of the wheel speeds under strict braking conditions of very slippery roads.The simulation of braking tests under different road adhesion coefficients using the HILS test bench is run,and results show that it can evaluate the anti-lock braking performance of ABS and partly the fail-safe functions.This HILS system can also be used in such applications as durability test,benchmarking and comparison between different ECUs.The test bench developed not only has a relatively low cost,but also can be used to validate the wheel speed-related ECU design and all its fail-safe functions,and a rapid testing and proving platform with a high efficiency for research and development of the automotive ABS is therefore provided.     

Outdoor position estimation based on a combination system of GPS-INS by using UPF

This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.     

Signal processing circuit of laser gyro based on FPGA and DSP

This is a paper about laser gyro sign a l processing circuit which is designed based on field-programmable gate array(FPGA) and digital signal processor(DSP).Through a pre-amplifier circuit,FPGA and DSP,a weak current signal is converted and transferred,then sent to the computer to display the final results.Through the laser gyro performance te sting,the obtained results coincide with those of the existing methods.Thus th e d esigned circuit realizes the function of laser gyro signal processing.     

GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-ⅡFOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION

Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP),which has been proved as NP-Hard,and it is usually modeled as single objective optimization problem when modeling.For multi-objective optimization model,most researches consider two objectives.A multi-objective mathematical model for VRP is proposed,which considers the number of vehicles used,the length of route and the time arrived at each client.Genetic algorithm is one of the most widely used algorithms to solve VRP.As a type of genetic algorithm (GA), non-dominated sorting in genetic algorithm-Ⅱ(NSGA-Ⅱ) also suffers from premature convergence and enclosure competition.In order to avoid these kinds of shortage,a greedy NSGA-Ⅱ(GNSGA-Ⅱ) is proposed for VRP problem.Greedy algorithm is implemented in generating the initial population, cross-over and mutation.All these procedures ensure that NSGA-Ⅱis prevented from premature convergence and refine the performance of NSGA-Ⅱat each step.In the distribution problem of a distribution center in Michigan,US,the GNSGA-Ⅱis compared with NSGA-Ⅱ.As a result,the GNSGA-Ⅱis the most efficient one and can get the most optimized solution to VRP problem.Also,in GNSGA-Ⅱ,premature convergence is better avoided and search efficiency has been improved sharply.     

基于小波包分解的意识脑电特征提取

针对2种不同意识任务(想象左手运动和想象右手运动)的脑-机接口(brain-computer interface,BCI)设计,提出了基于小波包分解的特征提取方法。首先深入研究了小波包变换,结合事件相关去同步化(event-related desynchronization,ERD)/事件相关同步化(event-related synchronization,ERS)现象,提出以小波包分解系数来考虑特征,然后对C3、C4导联脑电信号进行小波包分解系数方差和相对能量2种特征的提取,最后采用最简线性分类器进行分类。结果表明,2种特征对应的最大分类正确率均达到了85%,对应时间分别为4.34 s和4.39 s。因此,在保证分类正确率的前提下,所提方法更加简单和有效,为大脑意识任务分类提供了新思路。     

Detection of motor imagery EEG signals employing Naïve Bayes based learning process

The objective of this study is to develop a reliable and robust analysis system that can automatically detect motor imagery (MI) based electroencephalogram (EEG) signals for the development of brain–computer interface (BCI) systems. The detection of MI tasks provides an important basis for designing a communication way between brain and computer in creating devices for people with motor disabilities. This paper presents a synthesis approach based on optimum allocation system and Naive Bayes (NB) algorithm for detecting mental states based on EEG signals. In this study, an optimal allocation (OA) is introduced to discover the most effective representatives with minimal variability from a large number of MI based EEG data and the NB classifier is employed on the extracted features for discriminating the MI signals. The feasibility and effectiveness of the proposed method is demonstrated by analyzing the results and its success on two public benchmark datasets. The results indicate that the proposed approach outperforms the most recently reported five methods and achieves 0.64–20.90% improvement on average accuracy. The performances of this proposed approach implies that it can be reliably used to detect EEG based MI activity and can be a promising avenue for EEG based BCI applications.     

基于改进GHSOM的运动想象脑电信号自适应识别方法

为解决运动想象脑电信号(MI-EEG)的识别方法泛化能力受限和自适应性差等问题,对传统的生长、分层自组织映射神经网络(GHSOM)进行改进,并提出一种主成分分析法(PCA)与改进的GHSOM神经网络(IGHSOM)相结合的脑电自适应识别方法。由于IGHSOM能够根据上一层扩展神经元的量化误差进行自动分层判断,使得其不仅对数据映射更加准确和详细,而且增强了网络的稳定性和自适应性。基于脑机接口(BCI)竞赛数据库,利用PCA进行特征提取,以IGHSOM为分类器进行实验研究。结果表明,该方法获得了较高的识别精度,验证了GHSOM改进策略及该识别方法的正确性和有效性。     

EEG controlled neuromuscular electrical stimulation of the upper limb for stroke patients

This paper describes the Brain Computer Interface (BCI) system and the experiments to allow post-acute (<3 months) stroke patients to use electroencephalogram (EEG) to trigger neuromuscular electrical stimulation (NMES)-assisted extension of the wrist/fingers, which are essential pre-requisites for useful hand function. EEG was recorded while subjects performed motor imagery of their paretic limb, and then analyzed to determine the optimal frequency range within the mu-rhythm, with the greatest attenuation. Aided by visual feedback, subjects then trained to regulate their mu-rhythm EEG to operate the BCI to trigger NMES of the wrist/finger. 6 post-acute stroke patients successfully completed the training, with 4 able to learn to control and use the BCI to initiate NMES. This result is consistent with the reported BCI literacy rate of healthy subjects. Thereafter, without the loss of generality, the controller of the NMES is developed and is based on a model of the upper limb muscle (biceps/triceps) groups to determine the intensity of NMES required to flex or extend the forearm by a specific angle. The muscle model is based on a phenomenological approach, with parameters that are easily measured and conveniently implemented.     

Improvement of mental tasks with relevant speech imagery for brain-computer interfaces

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been attracted enough attention by researchers. In order to determine whether silent reading can improve mental tasks for BCI systems, this paper proposed a two-step experiment: mental tasks with speech imagery and mental tasks without speech imagery. Reading Chinese characters in mind is set as speech imagery. Since Chinese characters are monosyllabic, it is very convenient to read them in mind with related mental tasks simultaneously. Ten Chinese subjects are trained by two steps in this experiment. Feature vectors of EEG signals are extracted and classified by common spatial patterns (CSP) and support vector machine (SVM), respectively. Compared with just mental tasks, the accuracies between two tasks have been significantly improved by appending speech imagery, and the average of accuracies of ten subjects is increased from 76.3% to 82.3%. During the imagery period, the temporal stability of EEG signals is evaluated by Cronbach’s alpha coefficients. The steadiness of signals is different between mental tasks, and EEG signals are more stabilization with speech imagery. The stability of brain activity is conducive to the operation of BCIs.     

运动意识任务的模式识别方法研究

针对脑机接口研究中运动想象脑电信号的模式识别问题,提出了一种基于离散小波变换和AR双谱的特征提取方法.该方法首先利用Daubechies类小波函数对二路脑电信号进行3层分解,抽取小波系数的均值、能量均值、均方差三个特征;然后,采用5阶AR模型进行双谱估计,抽取双谱切片特征;最后,将这两类特征进行组合后使用马氏距离线性判别进行分类.利用BCI2003竞赛的标准数据,该方法使得EEG的识别正确率达到92.86%,与竞赛的最好结果(89.29%)相比提高了3.57%,为BCI研究中脑电信号的模式识别提供了有效的手段.     

脑机接口中基于ICA-RLS的EOG伪迹自动去除

眼电(EOG)伪迹是基于脑电(EEG)的脑机接口系统中最重要的干扰。为自动去除这种干扰,提出了基于ICA-RLS的EOG伪迹自动去除算法。首先,ICA将多个导联EEG信号分解成数目相等的若干个独立分量(ICs)。然后,计算并依据每个IC的峰态系数,自动地从ICs中识别出EOG分量。最后,识别出的EOG分量用作RLS自适应滤波器的参考信号,并用该滤波器对原始EEG进行滤波,在无需记录EOG情况下实现EOG伪迹的自动去除。用提出算法对2008年脑机接口竞赛数据进行处理,从去除伪迹后信号的观察、信息保留完整性和最终分类结果的计算进行评价。与标准的ICA算法相比,提出算法能够更好去除EOG伪迹,同时获取更高的分类正确率。     

新型EEG检测干电极设计制备和测试研究

设计制作了一款新型的脑电干电极,用于实现对EEG信号的检测。相较于传统的湿电极,该新型电极在测试过程中不需要导电介质,有效降低了实验准备过程的复杂度,可以大大缩短实验准备时间。而且,其接触电阻可以和湿电极在同一数量级,从而可实现长时间实时稳定测量EEG。利用聚吡咯石墨烯纳米复合物对电极探头进行修饰,对干电极与头皮的接触阻抗进行了阻抗谱测试,可以有效降低接触电阻,提高信号的稳定性和可靠性。采用自制干电极与碗状湿电极,在不同脑区进行了脑电信号检测的对比实验,信号线性相关度均在90%以上,将干电极检测的脑电信号分解,分解波段波形符合脑电信号特征波特征,证明该新型干电极可用于无发区和有发区脑电信号的检测。搭建了基于新型干电极的无线脑电采集分析系统,实现了简单的脑机接口设计,进一步证明了干电极的实用性。     

脑-机接口的研究现状与挑战

脑-机接口（Brain—computer interface，BCI）为人们提供了全新的与外界进行信息交流和控制的手段，具有非常重要的科学意义、学术价值和广阔的应用前景，是当今世界研究的热点。本文介绍了BCI的结构、类型和研究现状，分析了现有BCI系统存在的问题以及今后的发展方向。