基于功能磁共振成像仪测谎的人脑功能定位研究

阐述基于功能磁共振成像仪测谎的人脑功能定位研究,简介原理,详述实验设计,例析典型谎、实图像,初步发现说谎认知心理的脑功能定位在颞区及其深部.     

网络游戏者脑疲劳的耳穴信号小波熵特征提取

利用近红外额耳穴超低频信号的小波熵特征,实验研究大学生打反恐精英(Counter-Strike)游戏过程中的脑额区疲劳,依据1是网络成瘾的脑fMRI额叶激活增加证据,依据2是基于小波熵表征动物脑片兴奋性突触后电位的有效性.3名硕士生(平均年龄24岁)的一致实验结果是小波熵增特征:①平静态数值较低且平稳;②游戏态在90 min(均值)出现疲劳正峰.本工作为研究网络游戏者的脑疲劳提供了基础数据.     

基于IAF的alpha波段静息态脑磁信号特征提取

针对精神分裂患者静息态脑磁特征提取的问题,提出一种基于EMD和样本熵的非线性动力学方法对脑磁信号特征进行提取。该方法首先用ICA对静息态MEG数据进行预处理;继而基于IAF进行波段提取,得到快α、慢α1和慢α2波段;然后分别由EMD和样本熵进行处理。结果表明,精神分裂患者的各波段样本熵普遍高于正常人,尤其是慢α1波段大脑左半球额叶、枕叶、颞叶区。     

多频段脑功能网络融合的阿尔茨海默病分类

将图论与机器学习方法相结合的阿尔茨海默病计算机辅助中,脑网络的构建大多是基于滤波去噪后的全频段BOLD信号匹配,忽略了不同脑活动信息的差异.因此,本文提出了一种多频段脑功能网络融合模型.首先将离散小波变换应用于BOLD信号中,得到不同频域下的体素信号,而后计算同频信号的相关性,获取不同频段下相关矩阵.而后计算所有矩阵的网络特征,在特征选择后基于SVM对患者进行分类.从实验结果可以看出,分频下的脑功能网络特征与未分频网络相比能在一定程度上提高分类的准确性;体素级网络由于可以更加详细的表达脑网络的变化,其分类效果要优于脑区级.     

基于sparse group Lasso方法的脑功能超网络构建与特征融合分析

功能超网络广泛地应用于脑疾病诊断和分类研究中,而现有的关于超网络创建的研究缺乏解释分组效应的能力或者仅考虑到脑区间组级的信息,这样构建的脑功能超网络会丢失一些有用的连接或包含一些虚假的信息,因此,考虑到脑区间的组结构问题,引入sparse group Lasso（sgLasso）方法进一步改善超网络的创建。首先,利用sgLasso方法进行超网络创建;然后,引入两组超网络特有的属性指标进行特征提取以及特征选择,这些指标分别是基于单一节点的聚类系数和基于一对节点的聚类系数;最后,将特征选择后得到的两组有显著差异的特征通过多核学习进行特征融合和分类。实验结果表明,所提方法经过多特征融合取得了87.88%的分类准确率。该结果表明为了改善脑功能超网络的创建,需要考虑到组信息,但不能逼迫使用整组信息,可以适当地对组结构进行扩展。     

基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究

脑功能超网络已成功应用于脑疾病的诊断。在之前的研究中,集中通过改变超边的方法来改善超网络的构建,忽略了不同尺度的节点定义对脑功能超网络拓扑的影响。考虑到该问题,提出了基于不同尺度的脑区划分来进行脑功能超网络的创建,从而分析其对脑功能超网络拓扑和分类性能的影响。具体来说,首先,基于自动解剖标记模板,利用聚类算法和随机动态种子点的方法对大脑进行细分割;其次,基于每种节点规模下所得的平均时间序列,利用LASSO方法分别进行脑功能超网络的构建;接着分别提取功能超网络的多组局部特征(节点度、最短路径长度、聚类系数),并利用非参数检验和基于相关的方法选取每种节点规模下的差异特征;最后,分别利用支持向量机构建分类模型。分类结果显示,随着节点规模的增大,所构建的脑功能超网络分类准确率增高,在节点尺度1 501下,准确率高达95.45%。同时,多尺度融合的分类准确率优于任一尺度下的分类准确率,这表明不同尺度的节点定义会影响脑功能超网络的拓扑,在未来的脑功能超网络研究中,除了关注超边的构建方法外,应更加关注大脑划分方案的选择,而且多种基于大脑划分的尺度融合特征可以补充更多的分类信息,提高抑郁症与正常人的分...     

基于时域反射法的电力电缆网络故障诊断方法研究

针对维护大规模老化电力电缆网络这一关键挑战,通过详细描述电力电缆中的信号传输物理模型,提取反射信号和透射信号中的故障特征,研究基于时域反射法的电力电缆网络故障诊断方法,实现电缆网络故障的快速辨识与定位;以最小单元Y型电缆网络为研究对象并建立其物理模型,利用分布式检测时测点之间的反射信号及透射信号所反映出的信号传播路径和阻抗不匹配信息,开展多源信息融合的电缆网络故障诊断方法研究;在Matlab/Simulink软件环境中搭建Y型电缆网络的故障仿真模型,通过设计不同主干、分支线路故障的仿真实验,验证了方法的有效性和适用范围,测量得到的故障距离相对误差限定在1%内。     

矿井外因火灾感知方法

为解决可见光图像在井下受矿灯、车灯、巷道灯、粉尘、红色物体等干扰大的问题,提出了基于近红外和可见光图像的矿井外因火灾感知方法,将近红外图像和可见光图像融合,发挥近红外图像中早期火焰特征明显、可见光图像中中期火焰特征明显的优点,以提高矿井外因火灾识别率。提出了基于近红外图像、可见光图像、温度传感器、气体传感器和烟雾传感器等多信息融合的矿井外因火灾感知方法,该方法融合了近红外图像、可见光图像和传感器信息,传感器不需要无盲区覆盖,仅需要设置一定数量的气体传感器、温度传感器和烟雾传感器,具有及时、可靠、成本低等优点。将报警信息分为图像、温度、烟雾和气体4类。图像类火灾报警信息包括近红外图像和可见光图像信息,当近红外图像或可见光图像监测到火灾时,发出图像类火灾报警信号;当温度超限时,发出温度类报警信号;当烟雾超限时,发出烟雾类报警信号;气体类火灾报警信息包括CO、CO_2、O_2等,当CO、CO_2或O_2超限时,发出气体类报警信号。将火灾报警分为蓝色、黄色、橙色和红色4级,红色为最高级。当图像类、温度类、烟雾类、气体类4类信息中有1类报警,发出火灾蓝色报警信号;当图像类、温度类、烟雾类、气体类4类信息中同时有2类报警,发出火灾黄色报警信号;当图像类、温度类、烟雾类、气体类4类信息中同时有3类报警,发出火灾橙色报警信号;当图像类、温度类、烟雾类、气体类4类全部报警,发出火灾红色报警信号。     

基于接收信号的多用户密钥生成方案

基于信道特征生成密钥的方案为通信安全带来了新思路。但在多用户慢衰落条件下,基于信道特征的密钥生成方案由于参数变化慢,导致密钥更新慢。针对此问题,提出了基于接收信号的多用户密钥生成方案,即通过块对角化预编码实现多个随机信号流的分发,使基站与用户能够从用户接收信号中提取密钥。仿真结果表明,提出的基于接收信号的多用户密钥生成方案能够有效提高多用户在准静态及慢衰落条件下的密钥速率。     

基于小波包分解和遗传神经网络的癫痫识别

基于小波包分解和遗传神经网络对正常脑电和癫痫脑电进行识别。通过分析脑电数据找出信号特征;利用一维离散小波包分解提取含有识别特征的脑电信号频率段,并以脑电各频段的相对能量作为信号特征;然后建立基于遗传算法优化的BP网络,用于对癫痫脑电识别。实验结果表明,该方法可以有效提取信号特征,并且对信号进行准确的识别。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.