网络演算的矩阵解释

网络演算是离散事件动态系统理论在计算机网络中的应用,网络演算通过到达曲线和服务曲线计算网络的性能参数,这两个概念封装了复杂的理论背景,从而易于在实际中应用,但对到达曲线和服务曲线概念的理论研究比较缺乏.文中采用幂等矩阵的角度描述到达曲线和服务曲线,演算的过程成为矩阵运算,通过结合矩阵双子理论和余理论的研究结果,得出了由矩阵表演算的基本定理.研究表明,幂等矩阵理论为网络演算提供了很好的理论解释.文中还提出一种基于变换矩阵的方法求某些网络元素的服务曲线.     

分布式视频编码虚拟依赖信道模型研究

视频信息固有的非平稳特性,如冲突区域等,使时域预测技术变得非常复杂.在分布式视频编码(DVC)中,由于解码端不能获取当前编码帧的信息,精确地对时域相关噪声进行建模变得更为困难.文中以虚拟依赖信道模型为切入点对如何降低时域相关噪声进行了研究.首先对DVC虚拟依赖信道进行了建模,并对影响边信息的主要因素进行了分析,分析结果表明在变换域中不同的频率子带对时域相关噪声的敏感度不同.在此基础上提出了一种新的基于小波变换域的虚拟依赖信道模型VCMDWT,基于分类编码的思想对较为平稳的LL子带进行Wyner-Ziv编码,对非平稳的高频子带进行SPIHT帧内编码.实验结果表明,与基于像素域的方法相比,所提出的VCMDWT模型能够得到更稳定的虚拟信道,提高DVC系统的率失真性能达到2.6dBs以上.     

三相交流异步电机磁场定向控制系统

讨论了利用可编程外围芯片、双口静态数据存储器与16位微处理器等组成简洁、高可靠性的双单片机系统。依据异步电动机的特点,按矢量变换控制原理,设置各环节的PID等工作参数,采用电流模型法进行计算,系统程序存储器中,保存1个弱磁表,以节省CPU的开销,根据准最优PWM法则设计、编制软件,实现交流异步电动机磁场的定向智能控制。     

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

Abstract: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.     

Research on the application of 4-weighted fractional Fourier transform in communication system

The paper reveals the relationship between the weighting coefficients and weighted functions via the research of coefficients matrix and based on the original definition of 4-weighted fractional Fourier transform(4-WFRFT).The multi-parameters expression of weighting coefficients are given.Moreover, the 4-WFRFT of discrete sequences is defined by introducing DFT into it, which makes it suitable for digital communication systems.After analyzing the properties of WFRFT, a typical scheme for modulation/demodula...     

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.     

3D noisy discrete objects: Segmentation and application to smoothing

We propose in this paper a segmentation process that can deal with noisy discrete objects. A flexible approach considering arithmetic discrete planes with a variable width is used to avoid the over-segmentation that might happen when classical segmentation algorithms based on regular discrete planes are used to decompose the surface of the object. A method to choose a seed and different segmentation strategies according to the shape of the surface is also proposed, as well as an application to smooth the border of convex noisy discrete objects.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.