Writer identification of Chinese handwriting documents using hidden Markov tree model

Handwriting-based writer identification, a branch of biometrics, is an active research topic in pattern recognition. Since most existing methods and models aim to on-line and/or text-dependent writer identification, it is necessary to propose new methods for off-line, text-independent writer identification. At present, two-dimensional Gabor model is widely acknowledged as an effective and classic method for off-line, text-independent handwriting identification, while it still suffers from some inherent shortcomings, such as the excessive calculational cost. In this paper, we present a novel method based on hidden Markov tree (HMT) model in wavelet domain for off-line, text-independent writer identification of Chinese handwriting documents. Our experiments show this HMT method, compared with two-dimensional Gabor model, not only achieves better identification results but also greatly reduces the elapsed time on computation.     

Bayesian identification of a cracked plate using a population-based Markov Chain Monte Carlo method

Estimating damage in structural systems is a challenging problem due to the complexity of the likelihood function describing the observed data. From a Bayesian perspective a complicated likelihood means efficient sampling of the posterior distribution is difficult and standard Markov Chain Monte Carlo samplers may no longer be sufficient. This work describes a population-based Markov Chain Monte Carlo approach for efficient sampling of the damage parameter posterior distributions. The approach is shown to accurately estimate the state of damage in a cracked plate structure using simulated, free-decay response data. The use of this approach in identifying structural damage has not previously been explored.     

Subspace identification – a Markov parameter approach

Estimating observability matrices or state sequences is the central component of existing subspace identification methods. In this paper a different approach, in which Markov parameters are first estimated under general input excitation, is proposed. The prominent difference of this approach is that a three-block arrangement of data matrices is used. It is shown that one advantage of this approach over other subspace algorithms is that several unbiased estimating procedures can be carried out. One immediate application is to obtain balanced or nearly balanced models directly from the estimated Markov parameters. Another application is that with the estimated Markov parameters, consistently initialized Kalman filter state sequences can be obtained, from which the system matrices can be easily determined without bias. Performance of the proposed algorithms is investigated in two case studies which are based on real data taken from two industrial systems. The algorithms developed in this paper have been implemented and are publicly available.     

串行通信中的字节与字符

本文讨论了VB串行通信中MSComm控件InpuMode属性的使用方法。在Binary模式下传输数据，可以解决ASCⅡ码中传送超过128的字符时所存在的问题，消除了在Text模式下，String变量只能处理ASCⅡ码0－127的文本字符的缺陷，真正实现Windows环境下计算机与单片同的通信。同时给出了串行通信中字节的发送与接收的应用实例，具有一定的实用价值。     

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AIC_c). The use of three data sets was statistically significant at R² = 0.75 (RMSE = 52.17 m³ ha^?1), R² = 0.84 (RMSE = 45.24 m³ ha^?1), and R² = 0.91 (RMSE = 31.48 m³ ha^?1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R² = 0.78, R² = 0.53, and R² = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.     

Bayesian system identification via Markov chain Monte Carlo techniques

The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing accurate quantification of estimation error that is valid for arbitrary, and hence even very short length data records. The main innovation is the employment of the Metropolis-Hastings algorithm to construct an ergodic Markov chain with invariant density equal to the required posterior density. Monte Carlo analysis of samples from this chain then provides a means for efficiently and accurately computing posteriors for model parameters and arbitrary functions of them.     

Adaptive estimation and identification for discrete systems with Markov jump parameters

Linear discrete-time stochastic dynamical systems with parameters which may switch among a finite set of values are considered. The switchings are modeled by a finite state ergodic Markov chain whose transition probability matrix is unknown and is assumed to belong to a compact set. A novel scheme, called truncated maximum likelihood estimation, is proposed for consistent estimation of the transition probabilities given noisy observations of the system output variables. Conditions for strong consistency are investigated assuming that the measurements are taken after the system has achieved a statistical steady state. The case when the true transition matrix does not belong to the unknown transition matrix set is also considered. The truncated maximum likelihood procedure is computationally feasible, whereas the standard maximum likelihood procedure is not, given large observation records. Finally, using the truncated ML algorithm, a suboptimal adaptive state estimator is proposed and its asymptotic behavior is analyzed.     

Hybrid pattern recognition using Markov networks

Markov networks are inferred automatically for different classes of learning strings. In subsequent string-to-network alignments for test samples, the networks are used to deduce structural characteristics and to provide similarity measures. By processing the similarity measures as numerical-value features, standard nonparametric decision-theoretic pattern classifiers may be applied to determine class membership. The nearest-neighbor rule and linear discriminant-function classifiers are discussed, and their performances are compared with that of a maximum-likelihood classifier. The hybrid system's ability to determine string orientation correctly is investigated. Experiments with several thousand human banded chromosomes are reported     

Classic cryptanalysis using hidden Markov models

In this article, the authors present a detailed introduction to hidden Markov models (HMM). They then apply HMMs to the problem of solving simple substitution ciphers, and they empirically determine the accuracy as a function of the ciphertext length and the number of random restarts. Application to homophonic substitutions and other classic ciphers is briefly considered.     

Infrared-image classification using hidden Markov trees

An image of a three-dimensional target is generally characterized by the visible target subcomponents, with these dictated by the target-sensor orientation (target pose). An image often changes quickly with variable pose. We define a class as a set of contiguous target-sensor orientations over which the associated target image is relatively stationary with aspect. Each target is in general characterized by multiple classes. A distinct set of Wiener filters are employed for each class of images, to identify the presence of target subcomponents. A Karhunen-Loeve representation is used to minimize the number of filters (templates) associated with a given subcomponent. The statistical relationships between the different target subcomponents are modeled via a hidden Markov tree (HMT). The HMT classifier is discussed and example results are presented for forward-looking-infrared (FLIR) imagery of several vehicles.     

Gaussian activation functions using Markov chains

We extend, in two major ways, earlier work in which sigmoidal neural nonlinearities were implemented using stochastic counters. 1) We define the signal to noise limitations of unipolar and bipolar stochastic arithmetic and signal processing. 2) We generalize the use of stochastic counters to include neural transfer functions employed in Gaussian mixture models. The hardware advantages of (nonlinear) stochastic signal processing (SSP) may be offset by increased processing time; we quantify these issues. The ability to realize accurate Gaussian activation functions for neurons in pulsed digital networks using simple hardware with stochastic signals is also analyzed quantitatively.     

一种Java字节码保护技术的研究和实现

分析了Java字节码保护技术的现状,在此基础上提出了一种基于JVMTI的Java字节码保护技术,使得Java字节码的安全级别相当于传统的二进制代码。最后,给出了该技术在Win-dows平台和Linux平台下的实现方案。     

字节流式数据传输协议的设计与实现

本文介绍了一种基于ＰＣ－ＭＯＤＥＭ－Ｔｅｌｅｐｈｏｎｅ系统的面向字节流的远程异步数据传输协议的研究开发和实现。该协议借鉴了目前国际市场上流行的ＸＭＯＤＥＭ协议,改进了原协议的数据包的格式,创建了专门的通用调用接口,并以两个可灵活调用的函数形式进行封装,用户可透明地调用,所研制的协议已在某银行在线电子支付系统中成功应用。     

Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees

Hidden Markov models have been found very useful for a wide range of applications in machine learning and pattern recognition. The wavelet transform has emerged as a new tool for signal and image analysis. Learning models for wavelet coefficients have been mainly based on fixed-length sequences, but real applications often require to model variable-length, very long or real-time sequences. In this paper, we propose a new learning architecture for sequences analyzed on short-term basis, but not assuming stationarity within each frame. Long-term dependencies will be modeled with a hidden Markov model which, in each internal state, will deal with the local dynamics in the wavelet domain, using a hidden Markov tree. The training algorithms for all the parameters in the composite model are developed using the expectation-maximization framework. This novel learning architecture could be useful for a wide range of applications. We detail two experiments with artificial and real data: model-based denoising and speech recognition. Denoising results indicate that the proposed model and learning algorithm are more effective than previous approaches based on isolated hidden Markov trees. In the case of the ‘Doppler’ benchmark sequence, with 1024 samples and additive white noise, the new method reduced the mean squared error from 1.0 to 0.0842. The proposed methods for feature extraction, modeling and learning, increased the phoneme recognition rates in 28.13%, with better convergence than models based on Gaussian mixtures.     

基于置换移位的单字节分组保密方法

为减少无线传感器网络编码的冗余字节,提高基于Feistel结构的无线传感器网络分组加密的安全性,提出了一种新的单字节分组密码保密方法.采用生成单位矩阵的幂次和密钥变换矩阵生成密钥,明文通过单字节的置换和循环移位得到加密密文.首先介绍无线传感器网络Feistel结构分组加密算法,然后给出了置换和移位操作编码原理,并给出了设计的加密解密算法,最后进行了分析和实验.提出的方法既可以实现加密,也可以实现解密.分析结果表明,密钥具有较高的安全性能,可以增强基于Feistel结构的无线传感器网络分组加密安全性.     

UDT与字节数组转换在通信编程中的应用

本文介绍UDT与字节数组的相互转换技术,为网络通信／串口通信编程提供一种处理多种复杂用户包的统一方法。     

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and complete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to generate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach.     

Handwritten word-spotting using hidden Markov models and universal vocabularies

Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians.     

Dance performance evaluation using hidden Markov models

We present in this paper a hidden Markov model‐based system for real‐time gesture recognition and performance evaluation. The system decodes performed gestures and outputs at the end of a recognized gesture, a likelihood value that is transformed into a score. This score is used to evaluate a performance comparing to a reference one. For the learning procedure, a set of relational features has been extracted from high‐precision motion capture system and used to train hidden Markov models. At runtime, a low‐cost sensor (Microsoft Kinect) is used to capture a learner's movements. An intermediate step of model adaptation was hence requested to allow recognizing gestures captured by this low‐cost sensor. We present one application of this gesture evaluation system in the context of traditional dance basics learning. The estimation of the log‐likelihood allows giving a feedback to the learner as a score related to his performance. Copyright © 2016 John Wiley & Sons, Ltd.