Information of deterministic functions and quantum information of non probabilistic matrices A unified approach and new results

This paper provides new results on the entropy of functions on the one hand, and exhibits a unified approach to entropy of functions and quantum entropy of matrices with or without probability. The entropy of continuously differentiable functions is extended to stair-wise functions, a measure of relative information between two functions is obtained, which is fully consistent with Kullback cross-entropy, Renyi cross-entropy and Fisher information. The theory is then applied to stochastic processes to yield some concepts of random geometrical entropies defined on path space, which are related to fractal dimension in the special case when the process is a fractional Brownian. Then it shows how one can obtain Shannon entropy of random variables by combining the maximum entropy principle with Hartley entropy. Lastly quantum entropy of non probabilistic matrices (extension of Von Neumann quantum mechanical entropy) is derived as a consequence of Shannon entropy of random variables.     

Classifier fusion based on evidence theory and its application in face recognition

A multiple classifier fusion approach based on evidence combination is proposed in this paper. The individual classifier is designed based on a refined Nearest Feature Line (NFL), which is called Cen-ter-based Nearest Neighbor (CNN). CNN retains the advantages of NFL while it has relatively low compu-tational cost. Different member classifiers are trained based on different feature spaces respectively. Corre-sponding mass functions can be generated based on proposed mass function determination approach. The classification decision can be made based on the combined evidence and better classification performance can be expected. Experimental results on face recognition provided verify that the new approach is rational and effective.     

On the role of pattern matching in information theory

In this paper, the role of pattern matching in information theory is motivated and discussed. We describe the relationship between a pattern's recurrence time and its probability under the data-generating stochastic source. We show how this relationship has led to great advances in universal data compression. We then describe nonasymptotic uniform bounds on the performance of data-compression algorithms in cases where the size of the training data that is available to the encoder is not large enough so as to yield the asymptotic compression: the Shannon entropy. We then discuss applications of pattern matching and universal compression to universal prediction, classification, and entropy estimation     

改进的证据组合规则及其在融合目标识别中的应用

针对D-S理论在解决证据冲突时的局限性,提出了一种改进的证据组合规则.该方法充分考虑了证据源信息和冲突证据本身的信息,利用证据间的支持度矩阵,得到各证据的可信度.并以此对证据进行加权平均;在此基础上,将支持证据冲突的概率按各个命题在所有命题中所占的比重进行分配.仿真实验结果表明,该方法在处理冲突证据问题及证据比较一致的问题时,都能得到合理的融合结果.在实际的多传感器融合目标识别中,可以充分利用多个传感器的互补信息,最大程度地降低矛盾冲突因子对识别结果的影响,从而提高了融合识别的可靠性和有效性.     

Hilbert space methods for detection theory and pattern recognition

The problem of classifying an observation into one of several different categories, or patterns, is considered. The observation consists of a sample function of a continuous-time parameter stochastic process observed over a finite-time interval. When only two categories are involved the general pattern recognition problem reduces to the signal detection problem. The methods used are based upon results from the theory of reproducing kernel Hilbert spaces. This theory has been developed within the last few years and the application of these results to problems of statistical inference for stochastic processes has taken place only recently. Therefore, a reasonably serf-contained exposition of the results required from the theory of reproducing kernel Hilbert spaces is presented. It is pointed out that the decision rule employed by the optimum pattern recognition system is based on the likelihood ratio. This quantity exists fi, and only if, the probability measures are equivalent, i.e., mutually absolutely continuous with respect to each other. In the present work only Gaussian processes are considered, in which case it is well known that the probability measures can only be either equivalent or perpendicular, i.e., mutually singular. It is shown that the reproducing kernel Hilbert space provides a natural tool for investigating the equivalence of Gaussian measures. In addition, this approach provides a convenient means for actually evaluating the likelihood ratio. The results are applied to two pattern recognition problems. The first problem involves processes which have the same covariance function but different mean-value functions and the second problem concerns processes with different covariance functions and zero mean-value functions.     

粗糙集理论及其应用研究

粗糙集理论是一种处理不确定和不完全数据的强有力数学工具,其主要思想是在保持分类能力不变的前提下,通过数据约简得出问题的决策或分类规则.目前,粗糙集理论已成功地应用于机器学习、决策分析、过程控制、数据挖掘等领域,是当前人工智能领域研究热点之一.现介绍了粗糙集理论及其应用的国内外研究现状,总结了主要研究方法,最后指出了粗糙集理论及其应用所面临的主要问题.     

Parabolic function expansion and its application to EM theory

A parabolic function expansion for an arbitrary function in L ₂ space is derived with the aid of the classical eigenfunction expansion theory of Titchmarsh. The Green's function in free space in terms of the parabolic function is expanded as an example. The theory is then applied to the integral transform of the Helmholtz equation in paraboloidal coordinates.     

DS2720功能及其应用技术研究

介绍了一种智能、高效、可程序控制监测的单节锂离子电池保护器件DS2720,给出了DS2720的特点、内部结构和工作原理,分析了以其设计的锂电池保护电路的工作过程及保护功能,讨论了DS2720与主微处理器进行数据通信的方法和控制流程.     

New closed-form Green's functions for microstrip structures theory and results

This paper presents an efficient technique to evaluate the Green's functions of single-layer and multilayer structures. Using the generalized pencil of function method, a Green's function in the spectral domain is accurately approximated by a short series of exponentials, which represent images in spatial domain. New compact closed-form spatial-domain Green's functions are found from these images using several semi-infinite integrals of Bessel functions. With the numerical integration of the Sommerfeld integrals avoided, this method has the advantages of speed and simplicity over numerical techniques, and it leads to closed-form expressions for the method-of-moments matrix coefficients. Numerical examples are given and compared with those from numerical integration     

Further results on the Beaulieu series

A frequent problem in digital communications is the computation of the probability density function (PDF) and cumulative distribution function (CDF), given the characteristic function (CHF) of a random variable (RV). This problem arises in signal detection, equalizer performance, equal-gain diversity combining, intersymbol interference, and elsewhere. Often, it is impossible to analytically invert the CHF to get the PDF and CDF in closed form. Beaulieu (1990, 1991) has derived an infinite series for the CDF of a sum of RVs that has been widely used. We rederive his series using the Gil-Pelaez (1951) inversion formula and the Poisson sum formula. This derivation has several advantages including both the bridging of the well-known sampling theorem with Beaulieu's series and yielding a simple expression for calculating the truncation error term. It is also shown that the PDF and CDF can be computed directly using a discrete Fourier transform.     

RS理论在自组织调制识别技术中的应用研究

通信信号处理中最主要的问题是调制样式自动识别,其实质就是通过对输入信号的判别和归类进而理解该信号的结构和属性.本文研究了将粗糙集理论应用于自组织调制识别中的技术.它利用粗糙集理论进行信号的属性约简,求出约简和核;神经网络利用最简决策表构建分类器和提取主要特征参数.二者结合实现了信号自动调制识别,这不仅简化了网络结构和缩...     

MET1证据理论在不变矩目标识别中的应用研究

分析了简化的证据理论识别方法,并将其用于目标识别中傅里叶-梅林矩和小波矩的特征融合。在证据理论的具体应用中,运用相关系数法和相对距离度量法构造出了两种新的基本支持函数并证明了其合理性。实验结果表明,证据理论的识别方法既可以提取图像全局信息,也可以很好地提取局部信息,比单独使用的傅里叶-梅林矩或小波矩识别效果更好。     

Manifold-manifold distance and its application to face recognition with image sets

In this paper, we address the problem of classifying image sets for face recognition, where each set contains images belonging to the same subject and typically covering large variations. By modeling each image set as a manifold, we formulate the problem as the computation of the distance between two manifolds, called manifold-manifold distance (MMD). Since an image set can come in three pattern levels, point, subspace, and manifold, we systematically study the distance among the three levels and formulate them in a general multilevel MMD framework. Specifically, we express a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrate the distances between pairs of subspaces from one of the involved manifolds. We theoretically and experimentally study several configurations of the ingredients of MMD. The proposed method is applied to the task of face recognition with image sets, where identification is achieved by seeking the minimum MMD from the probe to the gallery of image sets. Our experiments demonstrate that, as a general set similarity measure, MMD consistently outperforms other competing nondiscriminative methods and is also promisingly comparable to the state-of-the-art discriminative methods.     

一种快速张量分解算法及其在人脸识别中的应用

基于张量代数的人脸识别技术对姿态、光照和表情的变化具有很好的鲁棒性.本文在高阶奇异值分解的基础上,提出了一种基于特征空间的快速张量分解算法.首先使用传统的子空间学习方法对观测图像进行降维,然后在低维的特征空间对训练数据进行张量分解.通过在Weizmann人脸数据库上进行人脸识别实验,验证了本文方法的有效性.     

语音识别技术及应用综述

语音识别作为信息技术中一种人机接口的关键技术,具有重要的研究意义和广泛的应用价值。介绍了语音识别技术发展的历程,具体阐述了语音识别概念、基本原理、声学建模方法等基本知识,并对语音识别技术在各领域的应用作了简要介绍。     

The Hidden Markov Model of co-articulation and its application to the continuous speech recognition

The co-articulation is one of the main reasons that makes the speech recognition difficult. However, the traditional Hidden Markov Models(HMM) can not model the co-articulation, because they depend on the first-order assumption. In this paper, for modeling the co-articulation, a more perfect HMM than traditional first order HMM is proposed on the basis of the authors' previous works(1997, 1998) and they give a method in that this HMM is used in continuous speech recognition by means of multilayer perceptrons(MLP), i.e. the hybrid HMM/MLP method with triple MLP structure. The experimental result shows that this new hybrid HMM/MLP method decreases error rate in comparison with authors' previous works.     

Algorithms for sliding block codes---An application of symbolic dynamics to information theory

Ideas which have origins in Shannon's work in information theory have arisen independently in a mathematical discipline called symbolic dynamics. These ideas have been refined and developed in recent years to a point where they yield general algorithms for constructing practical coding schemes with engineering applications. In this work we prove an extension of a coding theorem of Marcus and trace a line of mathematics from abstract topological dynamics to concrete logic network diagrams.     

多源信息融合技术及其应用研究

首先,文章阐述了多源信息融合的概念与模型;其次,探讨了多源信息融合的层次和方法;最后,介绍了多源信息融合技术的应用领域和发展趋势,特别讨论了几个新兴的应用领域。