Waveform detection with RBF network – Application to automated EEG analysis

Automated detection of different waveforms in physiological signals has been one of the most intensively studied applications of signal processing in the clinical medicine. During recent years an increasing amount of neural network based methods have been proposed. In this paper we present a radial basis function (RBF) network based method for automated detection of different interference waveforms in epileptic EEG. This kind of artefact detector is especially useful as a preprocessing system in combination with different kinds of automated EEG analyzers to improve their general applicability. The results show that our neural network based classifier successfully detects artefacts at the rate of over 75% while the correct classification rate for normal segments is as high as about 95%.     

Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features

Epilepsy is one of the most common neurological disorders with 0.8% of the world population. The epilepsy is unpredictable and recurrent, so it is very difficult to treat. In this paper, we propose a new Electroencephalography (EEG) seizure detection method by using the dual-tree complex wavelet (DTCWT) – Fourier features. These features achieve perfect classification rates (100%) for the EEG database from the University of Bonn. These classification rates outperform a number of existing EEG seizure detection methods published in the literature. However, it should be mentioned that several recent works also achieved this perfect classification rate (100%). Our proposed method should be as good as these works since our method only performs the DTCWT transform for up to 5 scales and our method only conducts the FFT to the 4th and 5th scales of the DTCWT decomposition. In addition, we could replace the conventional FFT in our method by sparse FFT so that our method could be even faster.     

Interactive collision detection in three-dimensional visualizations of simulated construction operations

This paper presents research that led to the design and implementation of fast and interactive collision detection methods that can be used to identify and report undesirable conflicts that occur among static (e.g., structure in-place, idle equipment), dynamic (e.g., active machines and workers), and abstract (e.g., hazard spaces) construction resources in 3D animations of construction operations modeled using discrete-event simulation. Computational efficiency and interactive speed in the designed interference detection methods were the primary challenges that the research addressed. In addition, the efficiency and speed were achieved with minimal trade-off against accuracy. In order to achieve this, the authors capitalized on advanced documented algorithms for proximity queries between arbitrarily moving 3D geometric objects to design mechanisms for interference detection, control, and response in construction process visualizations. The designed methods are implemented in a software tool called C-Collide that integrates as an add-on with the VITASCOPE visualization system.     

基于ICA和图论方法的脑电β波静息态功能连接

为了探究正常人脑电β波(13 ～25 Hz)静息态功能连接,提出了一种结合独立成分分析(ICA)、图论、层次聚类、t检验、标准低分辨率电磁断层成像(sLORETA)技术的分析算法.对利用BP Analyzer 64导脑电仪采集的25个健康被试者在闲眼和睁眼静息状态下的高分辨率脑电信号β波(13 ～25 Hz)进行了功能连接研究,结果表明:(a)β波在闭眼状态下的功能连接明显多于睁眼状态;(b)从闭眼状态到睁眼状态,在右侧大脑顶叶、枕叶、颞叶区域β波功能连接明显减弱,而在双侧额叶连接增强;(c)静息态网络中的默认节点网络、视觉网络、运动感觉网络在闭眼状态下显著.因此,证明该算法适用于研究脑电β波静息态功能连接.     

ICA在思维脑电特征提取中的应用

简要介绍了独立分量分析（ICA）的基本思想及算法，并将其应用在基于多导思维脑电（mental EEG)的特征提取方面。实验结果表明：ICA可以将脑电信号中包含的心电（ECG）、眼电（EOG）等多种干扰信号成功地分离出来，较好地完成了脑电消噪预处理工作。同时，通过使用ICA方法对不同心理作业的脑电信号进行分析处理，发现了与心理作业相对应的脑电独立分量特征，这些稳定的独立分量特征为心理作业分类和脑一机接口技术提供了新的实现方法。     

Automatic recognition of sleep spindles in EEG via radial basis support vector machine based on a modified feature selection algorithm

This paper presents an application of a radial basis support vector machine (RB-SVM) to the recognition of the sleep spindles (SSs) in electroencephalographic (EEG) signal. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients, a set of discrete wavelet transform (DWT) approximation coefficients and a set of adaptive autoregressive (AAR) parameters are calculated and extracted from signals separately as four different sets of feature vectors. Thus, four different feature vectors for the same data are comparatively examined. In the second stage, these features are then selected by a modified adaptive feature selection method based on sensitivity analysis, which mainly supports input dimension reduction via selecting the most significant feature elements. Then, the feature vectors are classified by a support vector machine (SVM) classifier, which is relatively new and powerful technique for solving supervised binary classification problems due to its generalization ability. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of six subjects showed that the best performance is obtained with an RB-SVM providing an average sensitivity of 97.7%, an average specificity of 97.4% and an average accuracy of 97.5%.     

Application of edge detection techniques to detection of the bright band in radar data

An approach to the problem of automatically locating the melting layer is outlined. The approach uses image analysis techniques on dual and single polarization radar data.     

离子色谱-电化学安培检测技术在食品分析中的应用

离子色谱作为高效液相色谱的一种常用技术,自出现以来发展十分迅速,广泛应用于检测各种无机和有机阴、阳离子.通过与电化学安培检测技术联用,极大拓宽了检测范围.该文综述了用离子色谱-电化学安培检测技术在食品分析中的应用,比较了离子色谱与三种不同电化学安培检测技术联用中出现的问题及改进方案,并展望了此法将来的发展方向.     

子波分析在脑电图癫痫波提取中的应用研究

利用子波和子波变换的性质，并根据其对一小段医学信号的异常信号，可以灵敏地通过子波系数反映出来的特点，将子波分析应用于脑电图（ＥＥＧ）信号处理，把癫痫波从常规ＥＥＧ信号中提取出来，文中给出子波变换分析ＥＥＧ信号的实例，理论和实验表明，利用子波变换检测常规ＥＥＧ中的病理波有独到之处。     

Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.     

Edge detection insensitive to changes of illumination in the image

In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Aràndiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225–250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.     

Modelling and solving the intrusion detection problem in computer networks

We introduce a novel anomaly intrusion detection method based on a Within-Class Dissimilarity, WCD. This approach functions by using an appropriate metric WCD to measure the distance between an unknown user and a known user defined respectively by their profile vectors. First of all, each user performs a set of commands (events) on a given system (Unix for example). The events vector of a given user profile is a binary vector, such that an element of this vector is equal to “1” if an event happens, and to “0” otherwise. In addition to this, each user's class k has a typical profile defined by the vector P_k, in order to test if a new user i defined by its profile vector P_i belongs to the same class k or not. The P_k vector is a weighted events vector E_k, such that each weight represents the number of occurrences of an event e_k. If the “distance” d_ki (measured by a dissimilarity parameter) between an unknown profile P_i and a known profile P_k is reasonable according to a given threshold and to some constraints, then there is no intrusion. Else, the user i is suspicious. A simple example illustrates the WCD procedure. A survey of intrusion detection methods is presented.Our proposed method based on clustering users and using simple statistical formulas is very easy for implementation.     

指纹图像的一种奇异性检测算法

分析了现有的指纹图像奇异性检测方法中存在的问题,提出了新方法。将指纹的方向图转换为类似灰度纹理,用所提出的改进的新纹理特征检测奇异性。实验表明新方法检测奇异性的正确率为90．2％。     

Analysis of performance criteria in sampled-data fault detection

Sampled-data control systems are widely used in industry. In this paper the problem of fault detection and isolation (FDI) in sampled-data systems is studied. Many existing methods to design a robust sampled-data FDI are based on optimization of a norm based performance index. Our focus in this study is on the selection of the performance index. It is shown that the existing performance indices are not appropriate choices in the sense that they do not satisfy some expected intuitive properties. To resolve this, an alternative performance index is defined after converting the FDI problem to a standard control problem. This performance index is shown to satisfy the expected properties.     

SEDMI: Saliency based edge detection in multispectral images

Detecting edges in multispectral images is difficult because different spectral bands may contain different edges. Existing approaches calculate the edge strength of a pixel locally, based on the variation in intensity between this pixel and its neighbors. Thus, they often fail to detect the edges of objects embedded in background clutter or objects which appear in only some of the bands.We propose SEDMI, a method that aims to overcome this problem by considering the salient properties of edges in an image. Based on the observation that edges are rare events in the image, we recast the problem of edge detection into the problem of detecting events that have a small probability in a newly defined feature space. The feature space is constructed by the spatial gradient magnitude in all spectral channels. As edges are often confined to small, isolated clusters in this feature space, the edge strength of a pixel, or the confidence value that this pixel is an event with a small probability, can be calculated based on the size of the cluster to which it belongs.Experimental results on a number of multispectral data sets and a comparison with other methods demonstrate the robustness of the proposed method in detecting objects embedded in background clutter or appearing only in a few bands.     

The three-state test for chaos detection in discrete maps

The three-state test (3ST) – a new approach for chaos detection in discrete chaotic maps is presented. The scheme is based on statistical analyses of patterns obtained from ascending sorting of the system states. In addition to its ability for clear discernment between chaotic, quasi-periodic and periodic dynamical systems, the detection of periods of stable cycles is also automated with 3ST. The method is directly applied on data series generated by chaotic maps and does not require a priori knowledge of the equations of the underlying system. It also presents the advantage of not having to depend on the nature of the vector field as well as its dimensionality and is computationally low cost. The effectiveness of the 3ST is confirmed using two well known and widely studied chaotic maps: the logistic map and the Henon 2D map.     

一种新的区域复制图像篡改盲检测技术

提出了一种新的图像盲检测技术,该技术先对图像进行两次分块得到两个子块集,分别对这两个子块集中的子块进行小波变换,将最大变换尺度的小波近似系数以向量形式表示各子块,一个子块集组成一个矩阵,利用主成分分析方法(PCA)对这两个特征矩阵进行二次特征提取,利用Pearson相关系数法对二次提取后的子块特征进行篡改检测,标记出篡改块。实验结果表明,该技术在降低运算复杂度的基础上,不仅能较好地检测进行了多处复制粘贴篡改的图像,且在抗高斯模糊、JPEG有损压缩和噪声方面都有较强的鲁棒性,尤其在篡改图像经过滤波和加性噪声混合严重干扰后,仍能检测出大部分篡改区域。     

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent's trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.     

Feature analysis for discriminative confidence estimation in spoken term detection

Discriminative confidence based on multi-layer perceptrons (MLPs) and multiple features has shown significant advantage compared to the widely used lattice-based confidence in spoken term detection (STD). Although the MLP-based framework can handle any features derived from a multitude of sources, choosing all possible features may lead to over complex models and hence less generality. In this paper, we design an extensive set of features and analyze their contribution to STD individually and as a group. The main goal is to choose a small set of features that are sufficiently informative while keeping the model simple and generalizable. We employ two established models to conduct the analysis: one is linear regression which targets for the most relevant features and the other is logistic linear regression which targets for the most discriminative features. We find the most informative features are comprised of those derived from diverse sources (ASR decoding, duration and lexical properties) and the two models deliver highly consistent feature ranks. STD experiments on both English and Spanish data demonstrate significant performance gains with the proposed feature sets.     

Edge detection algorithm based on ICA-domain shrinkage in noisy images

We propose a robust edge detection method based on ICA-domain shrinkage （in- dependent component analysis）. It is known that most basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or recon- structed with sparse components. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in ICA-domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detec- tion even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.