Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection

The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT‐CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 277–283, 2016     

冗余测量回路信号有效性诊断判据

给出了一种对模拟信号进行有效性诊断的方法,它可用在冗余配置的测量系统中,对生产过程中的关键工艺参量进行连续的高可靠准确监测．     

An eigenstructure-based 2D DOA estimation method using dual-size spatial invariance array

A sparse planar array vertically placed in y-z plane is proposed to improve the angle estimation accuracy of sources in low angle region with reflection multipath.With two sizes of spatial invariances along both dimensions,the array estimates the azimuth and elevation angles using unitary ESPRIT(estimation of signal parameters via rotational invariance techniques).The first spatial invariance with a displacement of half-wavelength yields unambiguous coarse estimates of high-variance,while the second spatial...     

带有自适应补偿的不确定时滞系统鲁棒跟踪控制

为了克服高增益反馈项的缺陷, 设计了一个带有自适应补偿的控制器用于不确定时滞系统鲁棒跟踪控制. 本文证明了自适应补偿项的引入不会影响原来闭环系统的稳定性. 因此, 可以根据跟踪性能的需求和控制增益的约束来决定是否采用自适应补偿项. 最后, 数值仿真表明了控制器的有效性.     

基于TMS320C6722的高速铁路轨道信号发送与接收模拟系统

介绍一套高速铁路轨道信号模拟系统,讨论了系统的硬件和软件设计方法。系统以TMS320C6722浮点DSP为数据处理核心,ARM协处理器为控制核心,能够模拟自主开发的、适合中国高速铁路的改进型数字编码和正交化FSK轨道信号的发送和解调过程。     

Tracking periodic signals for a class of uncertain nonlinear systems

In this paper, a conditional learning control (CLC) is proposed to track periodic signals for a class of nonlinear systems with unknown dynamics. The main advantage of the CLC over the conventional iterative learning control is that monotonic convergence of the control sequence in the iteration domain is achieved, as the CLC ensures the learning is based on the steady‐state output, and hence the effect of the vanishing and unknown transient output is minimized. Following this result, the convergence of the tracking errors is obtained. Further, the optimal setting of the learning gains can be obtained in a min–max sense. A simulation example is presented to illustrate the performance and implementation of the CLC. Copyright © 2009 John Wiley & Sons, Ltd.     

Social signal processing: Survey of an emerging domain

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.     

Suppressing intersample behavior in iterative learning control

Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.