基于改进小波变换的EEG分析

利用小波变换来进行EEG自动识别的研究,试图将EEG自动识别由当前的癫痫领域扩大到更多的病症中来.提出了一种改进小波基的方法.该方法是利用连续小波变换来对EEG信号进行分析,选定一个最合适的小波基.在自动识别的过程中,用自适应小波基的方法来进行多分辨分析,然后用6个参数进行特征提取.实验证明此方法有良好的可行性和有效性.     

基于改进小波变换的EEG分析

利用小波变换来进行EEG自动识别的研究,试图将EEG自动识别由当前的癫痫领域扩大到更多的病症中来。提出了一种改进小波基的方法。该方法是利用连续小波变换来对EEG信号进行分析,选定一个最合适的小波基。在自动识别的过程中,用自适应小波基的方法来进行多分辨分析,然后用6个参数进行特征提取。实验证明此方法有良好的可行性和有效性。     

基于协方差信息和1D CNN的脑电癫痫检测研究

癫痫是大脑神经元突发性异常放电，导致短暂的大脑功能障碍的一种慢性疾病。临床上主要由专业的神经科医生通过对患者的脑电图(Electroencephalogram,EEG)信号的人工分析来检测癫痫疾病，这种方法耗时长、效率低，且对神经科医生专业技术水平要求极高。及时并准确地检测癫痫对于使用抗癫痫药物治疗起到至关重要的作用，因此设计癫痫自动检测方法的意义重大。提出了一种结合协方差信息和深度学习的方法：计算EEG的协方差矩阵，并将结果展开成向量，保留EEG信号的可分信息，再使用1维卷积神经网络(1D Convolutional Neural Networks,1D CNN）检测癫痫发作。使用CHB-MIT数据集进行两种实验，即癫痫发作间期vs癫痫发作前期和癫痫发作间期vs癫痫发作前期vs癫痫发作期，以验证该方法的可行性。其中，癫痫发作间期vs癫痫发作前期实验的准确率、灵敏度、精密度、特异度和F1-分数的平均值分别为99.36%、98.81%、99.59%、99.16%和99.19%；癫痫发作间期vs癫痫发作前期vs癫痫发作期实验的平均准确率可以达到98.98%。因此证明该方法可以应用于癫痫检测...     

癫痫脑电的互信息和同步性分析

脑电（EEG）分析是研究癫痫的一个重要手段。以临床采集的健康对象和癫痫患者的头皮EEG为研究对象,计算不同导联EEG数据之间的排序互信息,结果表明癫痫患者不同导联之间的互信息明显高于健康对象,因此,排序互信息可以作为癫痫疾病诊断的重要特征。以排序互信息为依据,对癫痫脑电进行了同步性的分析,结果表明癫痫患者左脑区域内、右脑区域内及左右脑区之间的信息交流明显增强,即其同步性强于健康对象。互信息和同步性的分析方法还可对癫痫发作前期和发作阶段的EEG进行分析,对癫痫发作作出预测。     

癫痫脑电的递归确定性分析

利用排序递归图的分析方法对癫痫脑电进行了确定性(DET)的分析,得出癫痫头皮脑电(EEG)的DET高于健康EEG。DET特征的差异性在局部导联上更明显,局部导联的DET特征可以作为癫痫疾病的自动诊断特征。通过分析发作阶段和发作间隙皮层脑电(ECoG)的DET,得出整个频带的DET差别不大,而在beta频带,发作阶段的确定性明显高于发作间隙的DET。Beta频带的DET特征可以作为癫痫发作的预测特征。研究结果为癫痫疾病的自动诊断和癫痫发作预测提供了理论依据。     

脑磁图脑功能连接网络癫痫棘波识别方法研究

脑磁图（MEG）现在被广泛用于临床检查及很多领域的医学研究中,基于静息态的脑磁图脑网络分析能用于研究大脑生理或病理机制。脑磁图分析对癫痫疾病的诊断具有重要的参考价值。对癫痫脑磁信号的自动分类可以及时对患者的情况作出判断,在临床上有很重要的意义。现有文献中对癫痫脑电信号的自动分类方法的研究已比较充分,但对癫痫脑磁信号的研究比较薄弱。提出了一种基于脑功能连接网络的全频段机器学习癫痫脑磁棘波信号自动判别方法,对四种分类器进行了综合判别对比,选择了效果最优的分类器,判别准确率可达到93.8%。因此,该方法在脑磁图癫痫棘波的自动识别与标记方面有较好的应用前景。     

EEG信号动态演化过程的研究

脑电图(EEG)信号的研究是诊断脑疾患的重要手段。以癫痫脑电为例,针对癫痫发作过程的复杂性,对其演化过程进行研究。利用本征正交分解(POD)对EEG信号实行特征压缩,选取能够反映EEG脑电病理特征的多个变量,通过改进的Fisher判别方法判别分解后的信号数据,以最终确定EEG信号动态演化过程的关键点。实验结果表明,将POD分解与Fisher判别方法相结合,不仅能减少数据分析的工作量,而且能够有效判别分析EEG信号动态演化过程。     

基于混合编程的脑电定位的仿真计算

基于头皮电位的脑电(EEG)的研究是一种无创的脑科学研究方法之一,引起了众多学者的关注.本文提出了基于MATLAB与VC 混合编程的脑电定位仿真计算.利用了MATLAB强大的遗传算法工具箱和VC 界面编程容易和代码执行效率高的优点,进行了癫痫棘波脑电定位的实验,实验结果表明,采用混合编程的仿真计算极大的提高了编程效率,减少了运行时间.     

基于相干性的多频段脑肌电信号双向耦合分析

针对皮层肌肉相干性分析时不能确定耦合方向的局限性,根据神经肌肉信息的双向传递性,提出利用不同大脑功能区的脑电信号和动作相关的肌电信号,实现了相干函数对脑肌电信号的双向耦合分析.本文对不同握力模式下同步采集的脑肌电信号进行了多频段耦合分析.通过下行(EEG—>EMG)和上行(EMG—>EEG)分析发现,随着握力的增大,EEG能量、相干幅值和耦合强度均向高频段转移.与基于新型格兰杰因果关系的耦合方法进行比较,验证了相干性方法进行皮层肌肉双向耦合分析的可行性和优势.研究结果为探索基于皮层肌肉相干性的双向手部运动信息解码和上肢运动功能障碍分析提供了依据.     

隧道裂缝自动识别性能影响因素研究

在室内环境下,利用混泥土试验模块,设计一种基于互补金属氧化物半导体（ CMOS）传感器件的装置来进行隧道裂缝自动识别试验。将裂缝图像灰度值分布作为衡量自动识别的重要指标,分析有效像素、检测距离、光照强度等因素对自动检测性能的影响。模拟试验结果表明：有效像素和检测距离对图像分布的特征影响不大;有效像素增大,检测距离减小,相应的检测性能增加;光照强度不仅对裂缝图像灰度分布特征影响大,对自动检测性能也有显著的影响,光照强度过高或过低都会影响检测性能。     

A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing

Efficient forest management demands detailed, timely information. As high spatial resolution remotely sensed imagery becomes more available, there is a great potential for conducting high accuracy forest inventory and analysis automatically and cost-efficiently. Recent research aimed at providing tree-based forest inventory measurements has generated numerous algorithms for automatic individual tree-crown detection and delineation. This article reviews this research with a focus on algorithms applied to passive remote-sensing imagery. The article categorizes and evaluates methods for automatic tree-crown detection and delineation. It considers the types of imagery and the characteristics of the study areas these algorithms are applied to and evaluates the influence of these factors on the methods. The article also reviews and evaluates quantitative accuracy assessment methods for tree-crown delineation and detection. Finally, the article summarizes the commonalities of current algorithms, and the new development that can be expected in the future.     

基于恶意代码的检测技术研究

恶意代码检测是保证信息系统安全的一个重要手段,从传统的特征码匹配到启发式检测,甚至基于神经网络的代码检测,整个检测手段在向着更加智能化更加具有自动适应能力的方向发展,检测系统也越来越具有自动分析与自动学习的能力。     

An approach for EEG of post traumatic sleep spindles and epilepsy seizures detection and classification in rats

The electroencephalographic (EEG) features of post traumatic epilepsy (PTE) are analyzed in the paper. The proposed method allows detection and classification of sleep spindles and epilepsy seizures. The experiments were conducted on a laboratory rats before and after traumatic brain inquiry (TBI). In the introduction, the details of the experiment along with the information about manual markup are provided. In the first part, the new method of sleep spindles and epilepsy seizures detection is described. The method is based on the analysis of the wavelet spectrogram extrema. Moreover, the described procedure of background extraction and ridge segmentation helps to classify signals as epilepsy seizures and sleep spindles. In the second part, the information about the clustering is given. k-Means clustering of seizures and spindles was performed based on signals power and frequency. The results of the clustering, along with the research of TBI effect on the EEG, are provided in the third part. It was shown that PTE may be considered as the cause of the frequency variance among clusters of sleep spindles and epilepsy seizures.     

指针式仪表自动读数识别技术的研究现状与发展

指针式仪表自动判读技术是当前机器视觉研究的热点,也是模式识别领域一项重要的研究内容和前沿技术。在对指针式仪表识别技术进行了一般性概述之后,详细介绍了基于机器视觉的指针式仪表自动读数识别技术的基本概念、基本原理和主要研究内容,介绍了该技术在国内外的研究现状,同时重点介绍了图像校正、圆形表盘轮廓检测、指针线检测和角度计算等主要研究内容的最新进展,最后给出了指针式仪表自动读数识别涉及的关键技术和发展方向。     

Non-linear classifiers applied to EEG analysis for epilepsy seizure detection

This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions.     

智能控制下机械式自动变速器故障检测方法

考虑到当前机械式自动变速器故障检测方法由于故障种类划分能力较差,导致复合故障检测结果正确率较低的情况,设计智能控制下机械式自动变速器故障检测方法。设定信号采样频率,对采集后的信号进行离散处理,提取自动变速器振动信号。使用LSSVM模型构建支持向量机,完成振动信号训练处理。根据机械控制理论结合证据分类检测方法,完成自动变速器故障诊断。至此,智能控制下机械式自动变速器故障检测方法设计完成。构建实验环节,经实验结果证实,新型检测方法的复合故障检测结果正确率得到明显提升,在日后的研究中可应用此方法完成故障检测过程。     

违禁品X光图像检测技术应用研究进展综述

X光图像在安检中应用十分广泛,目前大部分安检工作还要依靠人工完成,但X光安检巨大的工作量和工作强度使自动安检成为必然趋势。如何根据X光图像自动检测其中物体成为研究热点。随着基于深度学习技术的目标检测取得巨大进展,在X光图像违禁品检测中也大量应用深度学习模型进行研究并获得大量成果。为全面、详细总结现有研究,首先介绍X光成像特点、X光图像检测的传统方法以及基于深度学习的方法,然后对比传统方法与深度学习方法的检测效果并分析现有自动安检研究进展,最后指出未来值得关注的研究方向,以期给X光图像违禁品检测的研究提供参考。     

Automatic composition of broadcast sports video

This study examines an automatic broadcast soccer video composition system. The research is important as the ability to automatically compose broadcast sports video will not only improve broadcast video generation efficiency, but also provides the possibility to customize sports video broadcasting. We present a novel approach to the two major issues required in the system’s implementation, specifically the camera view selection/switching module and the automatic replay generation module. In our implementation, we use multi-modal framework to perform video content analysis, event and event boundary detection from the raw unedited main/sub-camera captures. This framework explores the possible cues using mid-level representations to bridge the gap between low-level features and high-level semantics. The video content analysis results are utilized for camera view selection/switching in the generated video composition, and the event detection results and mid-level representations are used to generate replays which are automatically inserted into the broadcast soccer video. Our experimental results are promising and found to be comparable to those generated by broadcast professionals.     

Assessing machine learning classifiers for the detection of animals’ behavior using depth-based tracking

There is growing interest in the automatic detection of animals’ behaviors and body postures within the field of Animal Computer Interaction, and the benefits this could bring to animal welfare, enabling remote communication, welfare assessment, detection of behavioral patterns, interactive and adaptive systems, etc. Most of the works on animals’ behavior recognition rely on wearable sensors to gather information about the animals’ postures and movements, which are then processed using machine learning techniques. However, non-wearable mechanisms such as depth-based tracking could also make use of machine learning techniques and classifiers for the automatic detection of animals’ behavior. These systems also offer the advantage of working in set-ups in which wearable devices would be difficult to use. This paper presents a depth-based tracking system for the automatic detection of animals’ postures and body parts, as well as an exhaustive evaluation on the performance of several classification algorithms based on both a supervised and a knowledge-based approach. The evaluation of the depth-based tracking system and the different classifiers shows that the system proposed is promising for advancing the research on animals’ behavior recognition within and outside the field of Animal Computer Interaction.