Adaptive integration of local region information to detect fine-scale brain activity patterns

With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This provides us the possibility to detect the fine-scale patterns of brain activities. The established univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale patterns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are combined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local homogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity patterns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions. Supported by Chair Professors of Changjiang Scholars Program and CAS Hundred Talents Program, National Program on Key Basic Research Projects (Grant No. 2006CB705700), National High-Tech R&D Program of China (Grant No.2006AA04Z216), National Key Technology R&D Program (Grant No. 2006BAH02A25), Joint Research Fund for Overseas Chinese Young Scholars (Grant No.30528027), National Natural Science Foundation of China (Grant Nos.30600151, 30500131 and 60532050), and Natural Science Foundation of Beijing (Grant Nos. 4051002 and 4071003)     

基于fMRI的图像底层特征关注研究

利用fMRI技术对人脑认知机制进行研究,分析人脑在进行图像认知时所关注的图像特征。首先通过1个功能磁共振成像实验为每个被试定位了脑内的形状、纹理和颜色的感知区域,将这些区域作为研究中的感兴趣区域。然后通过另1个功能磁共振成像实验计算了被试在观看人脸、公共汽车、恐龙及山脉冰川图像刺激时感兴趣区域内的平均信号变化的比例。通过进行数据分析,确定了被试在观看不同类别的图像时,对图像底层特征的关注是不相同的。     

基于自适应线调频高斯基展开的参数识别方法

提出了用自适应线调频高斯基展开及其自适应谱进行参数识别的时频分析方法.用线调频高斯基函数自适应地对响应信号进行匹配分解,得到信号的线调频高斯基展开及其自适应谱.根据自适应谱从响应信号中重构出具有单频特性的振动信号,再由单自由度系统的参数识别方法识别系统的模态参数.并针对一多自由度系统具体论述了固有频率、阻尼比和振型的识别方法,识别结果与理论结果吻合较好.该方法不需要输入信号,并对平稳、非平稳信号均适用,且具有较强的抗噪声能力.算例分析说明提出的方法是参数识别的有效方法之一.     

Method Based on Time Randomization to Resist Fault Sensitivity Analysis

A fault sensitivity analysis (FSA)-resistance model based on time randomization is proposed. The randomization unit is composed of two parts, namely the configurable register array (R-A) and the decoder (chiefly random number generator, RNG). In this way, registers chosen can be either valid or invalid depending on the configuration information generated by the decoder. Thus, the fault sensitivity information can be confusing. Meanwhile, based on this model, a defensive scheme is designed to resist both fault sensitivity analysis (FSA) and differential power analysis (DPA). This scheme is verified with our experiments.     

Intermittent Clutter Removal Approac for wind Profiler Data Based on Fractional Fourier Transform

Through the analysis of the target characteristics and according to the intermittent clutter bursting and short duration characteristics, a new method for the clutter recognition based on the fractional Fourier transform (FRFT) is proposed. This method is predicated on the fact that the FRFT perfectly localizes a chirp signal as an impulse when the angle parameter of the transform matches the chirp rate of the chirp signal. The method involves detecting the presence of the intermittent clutter and correctly estimating its orientation in the time-frequency plane, removing the intermittent clutter in the fractional domain, and completing wind estimation by the power spectrum. By testing the artificial WPR-like signal and data measured from the field, we verify that the FRFT-based method is very effective.     

Intermittent Clutter Removal Approach for Wind Profiler Data Based on Fractional Fourier Transform

Through the analysis of the target characteristics and according to the intermittent clutter bursting and short duration characteristics, a new method for the clutter recognition based on the fractional Fourier transform (FRFT) is proposed. This method is predicated on the fact that the FRFT perfectly localizes a chirp signal as an impulse when the angle parameter of the transform matches the chirp rate of the chirp signal. The method involves detecting the presence of the intermittent clutter and correctly estimating its orientation in the time-frequency plane, removing the intermittent clutter in the fractional domain, and completing wind estimation by the power spectrum. By testing the artificial WPR-like signal and data measured from the field, we verify that the FRFT-based method is very effective.     

Technology of shipboard two-way microwave time synchronization

For the high precision time synchronization demand of ships, advantages and disadvantages of the present time transfer methods are analyzed, the two-way microwave time transfer(TWMTT) method is adopted to resolve the time synchronization problem in the Naval Ship Formation. After expounding the principle and system composition of TWMTT method, the various factors influencing the synchronous precision are analyzed, such as time-interval measurement error, TWMTT equipment delay error, signal propagation error in air, and signal delay error caused by shipping. To improve the time synchronization precision, all the error sources above are deduced with mathematical measures to definite the critical one, and the signal processing measures such as Pseudo code spread spectrum time comparison signal generation technology, FFT fast acquisition technology and precise tracking technology are used into the modem which is the core equipment of the TWMTT. And, calibration method of TWMTT equipment delay are developed. Through theoretical analysis and simulation verification, the precision of shipboard two-way microwave time synchronization can reach 1 ns.     

LSL自适应权向量法检测弱脉冲信号方法研究

自适应权向量ＬＭＳ算法用于检测弱信号具有收敛响应较慢的缺点，本文应用自适应权向量格型ＬＳＬ算法检测噪声中的射频弱脉冲信号取得满意结果。仿真检验表明，本文方法用于检测快变弱信号是一种行之有效的方法。     

索膜结构风振响应的神经网络辅助参数分析方法

针对非线性动力时程分析法求解大规模索膜结构风振响应时动力时程分析的次数受到限制而导致一些参数组合下的响应统计值难以预测的问题,引入神经网络,通过少量样本的训练,建立了参数与结构响应间的映射关系。结果表明：提出的神经网络辅助参数分析方法计算效率高、预测精度令人满意,是一种获取足够数据的有效途径;通过该方法可以得到响应统计量及风振系数随平均风速和索、膜预应力变化的规律,为设计风荷载和结构构件极端响应的计算提供了科学依据。关键词：索膜结构;风振响应;参数分析;神经网络;动力时程分析     

Fast Rail Defect Inspection Based on Half-Cycle Power Demodulation Method and FPGA Implementation

In this paper, a fast-speed and real-time online rail inspection method based on half-cycle orthogonal power demodulation algorithm is proposed. For this method, the power characters of detection signal which represent the degree of rail track can be calculated using only half-cycle detection signal because of the symmetry characteristic of detected sine signal and reference signal. The theoretical analysis, simulation results and experiment results show that the demodulation precision of proposed method is almost equal to fast Fourier transform (FFT) demodulation method and orthogonal demodulation method, but has high demodulation efficiency and less FPGA resources cost. A high-speed experiment system based on three coils structured sensor is built for rail inspection experiment at a moving speed of 200 km/h. The experiment results show that proposed method is more effective for rail inspection and the time resolution of proposed method is double of classic method that based on FFT and orthogonal.     

层状介质中大地电磁场信号时域阻抗分析

在大地电磁测深中观测得到的电磁场信号十分微弱,具有非稳定特性,观测信号易受到噪声的干扰,使用基于稳定电磁信号稳定的频率域阻抗张量的估计方法,导致计算的电阻率准确度差。为了能保证非稳定信号阻抗的准确性和可靠性,从频率域传递函数出发并对其求解,利用傅里叶变换得到时域传递函数,经过推导得到时间域中求解阻抗的表达式。最后模拟得到了层状介质中时域大地电磁测深曲线,并与频率域大地电磁理论曲线对比分析。结果表明:时域比频域估算阻抗或电阻率更准确。     

内燃机噪声源识别的小波相关系数方法研究

为了分析内燃机(ICE)噪声信号的时频特性和识别主要噪声源,研究了小波变换中尺度与频率之间的关系,重新定义了连续小波变换,并基于不同小波对同一信号分解时小波系数之间存在极大相关性,提出用规范化相关系数时频图分析噪声信号和识别内燃机噪声源的新方法.新方法能够准确地对信号进行时间和频率定位,且频域结果与信号功率谱相当吻合.对发动机声学信号进行了时频分析,同传统连续小波变换相比较,该方法能够更好地反映信号能量的时频域分布状况.结合声强结果,声学信号时频图能够直接地显示不同噪声源的时频特征.     

语音识别中DTW特征匹配的改进算法研究

采用基于动态规划方法的动态时间归正技术DTW(Dynamic Time Warping),可成功解决语音信号特征参数序列比较时时长不等的问题.在基于DTW的特征匹配用改进的动态时间归正方法将模板特征序列和语音特征序列进行匹配的基础上,比较两者之间的失真,得出识别判决的依据.实验表明,改进后的算法在孤立词语音识别中获得了良好性能.     

基于驱动响应同步的混沌背景下谐波信号频率估计方法

提出一种基于混沌驱动响应同步的强混沌背景下谐波频率估计的方法.该方法利用采样的混合信号(混沌加谐波)驱动一新构建的同类响应混沌系统,若响应混沌系统同步于驱动混沌信号,则驱动响应信号的误差序列中应含有谐波成分,对误差序列互谱的分析,估计谐波频率.该方法同时也适于其它噪声加混沌干扰的复合背景下的谐波频率估计.理论分析给出了该方法的适用条件,仿真实验证明该方法简单有效.     

研究者出版记录中的会议序列提取研究

对研究者发文序列的研究不仅可帮助了解领域研究现状,而且对于会议评级、热点预测分析等都有重要的意义。通过对研究者出版序列中的会议进行分析,提取会议序列并进行聚类,有助于发现领域相关性,理解领域发展演化。该文针对会议序列提取中直接考虑前后顺序所存在的不准确,提出了根据时间片相关的会议序列提取方法,以提高会议聚类的准确性。通过真实数据集上的定量和定性实验证明了该方法的有效性,并对结果进行了实例分析。     

基于时频消噪TFPF和时频分布MBD的轴承早期故障诊断

噪声是影响轴承、齿轮等机械设备早期微弱故障特征正确提取的主要因素,利用新颖的时频峰值滤波技术TFPT有力的噪声消减特性,将PTFT与改进的时频分布MBD相结合,提出了时频峰值滤波TFPT-时频分布MBD的故障识别新方法,即应用TFPF消减振动信号的随机噪声作为时频分析的前置处理,对消噪的故障信号作MBD时频分析来识别故障特征,给出了时频峰值滤波时频分布的故障诊断模型。诊断实例的分析结果表明了与传统的MBD的故障特征提取相比,提出的改进方法更易提取出强噪声背景下的轴承早期的微弱故障,具有明显的可诊断性和实用性。     

基于时延自相关预处理的Hilbert-Huang变换解调

针对具有噪声干扰的旋转机械故障振动信号解调问题，提出基于时延自相关运算和经验模态分解（
EMD）方法相结合的新方法.讨论了时延相关算法的降噪原理、离散信号时延相关算法和Hilbert Huang变
换理论.采用矩形窗截断故障振动信号自相关函数的无偏估计, 获取较长时间差的部分，得到时延相关函
数.利用EMD方法对时延相关函数进行自适应滤波, 得到固有模态函数（IMF），对IMF进行Hilbert变换，
求得解调结果.不同噪声强度仿真数据和滚动轴承故障振动信号实验数据分析表明，该方法比直接解调或
仅采用时延相关解调更能有效抑制噪声，凸现信号调制信息.     

基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取

为了解决回转支承振动信号微弱,特征信息不易提取的问题,提出基于Wavelet leader方法和经混合灰狼算法优化的等距映射算法（HGWO-ISOMAP）的多分形自适应特征提取方法. 利用Wavelet leader计算多分形特征,挖掘振动数据的几何结构信息,构造高维特征矩阵;通过HGWO优化后的ISOMAP算法对高维特征矩阵进行自适应特征筛选;将筛选后的特征矩阵输入到经遗传算法（GA）优化的最小二乘支持向量机（LSSVM）中进行故障状态识别. 为了验证所提方法的优越性,采用课题组自主研发的回转支承综合性能试验台对某型号回转支承进行全寿命实验. 结果表明,相比一般时域、时频域、频域特征提取方法,所提方法能提高识别精度,缩短计算时间,为回转支承特征提取提供新的有效途径.     

长偏移距瞬变电磁信号小波阈值去噪效果分析

长偏移距瞬变电磁法用于地下深部勘探时,观测到的垂直磁场脉冲响应非常微弱,常伴有严重噪声,从原始信号中消除噪声获得有用信息非常重要。为此笔者研究小波阈值方案对长瞬变电磁信号的去噪能力。实验中对西部某勘探区垂直磁场信号采用Sqtwolog,SURE,Heuristic和Minimax等阈值方案进行小波去噪比较,结果表明：几种小波阈值去噪方案对瞬变电磁法垂直磁场的染噪信号均能提高一定量的信噪比,达到较好的去噪效果;不同的阈值方案去噪效果各有差异,采用启发式阈值方案对实验数据处理效果要优于其它几种方案,因此根据信号统计分布特征选择合适方案去噪效果;实测信号去噪结果包含信息需要结合勘探区情况分析,是地下电性特征的反映还是未去除的噪声要慎重对待。在长偏移距电性源瞬变电磁法用于地下深部勘探时,利用适当小波阈值去噪处理可以对资料处理工作带来有益的帮助。     

改进小波包分频算法及在故障检测中的应用

小波包分析方法是一种能有效地进行时一频定位和微弱信号提取的工具．但是小波滤波器组的频域特性和隔点采样会造成频谱混叠,导致分频结果不正确．改进的小波包分频算法根据小波包混频的原因,结合FFT分析进行处理,较好地消除了混频现象．仿真研究表明,该算法在提取微弱故障信息并进行早期故障诊断方面是有效的．