Using Hidden Markov Models in Vehicular Crash Detection

This paper presents a system for automotive crash detection based on hidden Markov models (HMMs). The crash pulse library used for training comprises a number of head-on and oblique angular crash events involving rigid and offset deformable barriers. Stochastic distribution characteristics of crash signals are validated to ensure conformity with the modeling assumptions. This step is achieved by analyzing the quantile–quantile (Q–Q) plot of actual pulses against the assumed bivariate Gaussian distribution. HMM parameters are next induced by utilizing the expectation–maximization (EM) procedure. The search for an optimal crash pulse model proceeds using the “leave-one-out” technique with the exploration encompassing both fully connected and left–right HMM topologies. The optimal crash pulse architecture is identified as a seven-state left–right HMM with its parameters computed using real and computer-aided engineering (CAE)-generated data. The system described in the paper has the following advantages. First, it is fast and can accurately detect crashes within 6 ms. Second, its implementation is simple and uses only two sensors, which makes it less vulnerable to failures, considering the overall simplicity of interconnects. Finally, it represents a general and modularized algorithm that can be adapted to any vehicle line and readily extended to use additional sensors.      

Hidden Markov Models With Stick-Breaking Priors

The number of states in a hidden Markov model (HMM) is an important parameter that has a critical impact on the inferred model. Bayesian approaches to addressing this issue include the nonparametric hierarchical Dirichlet process, which does not extend to a variational Bayesian (VB) solution. We present a fully conjugate, Bayesian approach to determining the number of states in a HMM, which does have a variational solution. The infinite-state HMM presented here utilizes a stick-breaking construction for each row of the state transition matrix, which allows for a sparse utilization of the same subset of observation parameters by all states. In addition to our variational solution, we discuss retrospective and collapsed Gibbs sampling methods for MCMC inference. We demonstrate our model on a music recommendation problem containing 2250 pieces of music from the classical, jazz, and rock genres.     

A Speech Recognition IC Using Hidden Markov Models with Continuous Observation Densities

This paper presents the design of a speech recognition IC using hidden Markov models (HMMs) with continuous observation densities. Results of offline and live recognition tests are also given. Our design employs a table look-up method to simplify the computation and hence the architecture of the circuit. Currently each state of the HMMs is represented by a double-mixture Gaussian distribution. With minor modifications, the proposed architecture can be extended to implement a recognizer in which models with higher order multi-mixture Gaussian distribution are used for more precise acoustic modeling. The test chip is fabricated with a 0.35 μm CMOS technology. The maximum operating frequency is 62.5 MHz at 3.3 V. For a 50-word vocabulary, the estimated recognition time is about 0.16 s. Using noise-corrupted utterances, the recognition accuracy is 93.8% for isolated English digits. Such a performance is comparable to the software implementation with the same algorithm. Live recognition test was also run for a vocabulary of 11 Chinese words. The accuracy is 91.8% for five male and five female speakers.

隐马尔可夫模型在生物信息学中的应用

结合DNA序列分析例子,介绍了HMMs与其的解码、估计、学习3个计算问题。综述了HMMs在生物信息学中的应用情况,同时对HMMs在生物信息学中可能的发展方向进行了展望。     

Multiuser Detection Using Hidden Markov Model

Many existing multiuser detection algorithms assume that the user sequences are independent and identically distributed (i.i.d.). These algorithms, however, may not be efficient when the user sequences sent to a multiuser system are time correlated due to signal processing procedures such as channel coding. In this paper, we assume that the user sequences are time correlated and can be modeled as first-order, finite-state Markov chains. The proposed algorithm applies the decision feedback framework in which a linear filter based on the maximum target likelihood (MTL) criterion is derived to remove the interferences. A hidden Markov model (HMM) estimator is applied to the output of the MTL filter to estimate the user data, noise variance, and state transition probabilities. The estimated user data in turn are applied to update the parameters of the MTL filter. By exploiting the transmission of training symbols, the proposed algorithm requires neither knowledge of the user codes nor the timing information. Simulation results show the performance improvement of the proposed algorithm by exploiting the time-correlated redundancy of the Markov sources.     

2维隐马尔可夫模型的基本问题求解

研究了2维隐马尔可夫模型的三个基本问题,包括概率评估问题、最优状态问题和参数估计问题.通过把2维隐马尔可夫模型行或者列上的状态序列看作一个马尔可夫模型,从理论上分别给出了解决这三个基本问题的新算法;计算机仿真对新算法的实现和运行作了进一步的说明.     

Hidden Markov Models with Factored Gaussian Mixtures Densities

1　Introduction　Manyrealobserveddataarecharacterizedbymultiplecoupledcausesorfactors.Forinstance ,faceimagesmaybegeneratedbycombiningeyebrows,eyes ,noseandmouth .Similarly ,speechsignalsmayresultfromanin teractionofmotionsoffactorssuchasthejaw ,tongue ,velum ,lipandmouth .RecentlyZemelandHintonpro posedafactoriallearningarchitecture[1～ 2 ] todealwithfactorialdata .Thegoaloffactoriallearningistodiscov erthemultipleunderlyingcausesorfactorsfromtheob serveddataandfindarepresentationthatwillbo…     

Structural Bayesian Linear Regression for Hidden Markov Models

Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. The regression parameters are usually shared among sets of Gaussians in HMMs where the Gaussian clusters are represented by a tree. This paper realizes a fully Bayesian treatment of linear regression for HMMs considering this regression tree structure by using variational techniques. This paper analytically derives the variational lower bound of the marginalized log-likelihood of the linear regression. By using the variational lower bound as an objective function, we can algorithmically optimize the tree structure and hyper-parameters of the linear regression rather than heuristically tweaking them as tuning parameters. Experiments on large vocabulary continuous speech recognition confirm the generalizability of the proposed approach, especially when the amount of adaptation data is limited.     

A Configurable Logic Based Architecture for Real-Time Continuous Speech Recognition Using Hidden Markov Models

An architecture is presented for real-time continuous speech recognition based on a modified hidden Markov model. The algorithm is adapted to the needs of continuous speech recognition by efficient encoding of the state space, and logarithmic encoding of the weights so that products can be computed as sums. The paper presents the algorithm and its application related modifications, the mapping of the algorithm to a special purpose architecture, and the detailed design of this architecture using configurable logic. Emphasis is given on how the attributes of the algorithm are exploited in a configurable logic based design. A concrete design example is presented with a coprocessor engine having one large FPGA, 64 Mbytes of synchronous DRAM (SDRAM), a small FPGA as a SDRAM controller, and 2 Mbytes SRAM. This engine operating at 66 MHz performs roughly nine times as fast as a high end personal computer running a fully optimized version of the same algorithm.     

Maximum a Posteriori Estimation of Coupled Hidden Markov Models

Coupled Hidden Markov Models (CHMM) are a tool which model interactions between variables in state space rather than observation space. Thus they may reveal coupling in cases where classical tools such as correlation fail. In this paper we derive the maximum a posteriori equations for the Expectation Maximisation algorithm. The use of the models is demonstrated on simulated data, as well as in a variety of biomedical signal analysis problems.     

隐马氏过程在语音合成技术中的应用

不同于传统基于拼接(Concatenative)的语音合成技术,基于隐马氏模型(Hidden Markov Models)的参数统计语音合成技术(HMM-Based Speech Synthesis System,又名HTS)在过去十年中得到了长足的发展。相比传统的基于拼接的合成技术,HTS技术具有所需训练录音少、语音合成模型小、合成可调节灵活性强等特点,因而被广泛应用在语音合成领域特别是移动设备上。介绍了HTS的语音合成原理,HTS技术的一些典型应用场景,以及在此领域的最新研究发展方向。     

基于小波变换及隐式马尔科夫树模型的图像信号去噪

研究基于小波变换的隐式马尔科夫模型树(HMT)方法,用于去除图像信号中的白噪声。该方法利用了小波变换域系数间的相关性和自相似信息,并在Lenna图像中验证了方法的有效性。对不同程度污染的高斯白噪声图像的去噪效果与传统方法进行比较;结果表明,基于小波变换的HMT方法有更好的去噪效果,所建立的HMT模型更能体现图像的特征。     

基于隐含马尔可夫模型网络的视频识别方法

提出用隐含马尔可夫模型网络（Hidden Markov Model Networks）描述和识别短视频序列（如广告视频）。实验结果表明,该方法不仅对转码产生的图像质量鲁棒性好,而且可以很好识别截短的视频序列。     

Hidden Markov Model Based Localization Using Array Antenna

We present a hidden Markov model (HMM) based localization using array antenna. In this method, we use the eigenvector spanning signal subspace as a location dependent feature. The eigenvector does not depend on received signal strength but on direction of arrival of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the radio wave. The conventional localization method based on fingerprinting does not take previous information into account. In our proposal algorithm with HMM, we take previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference point according to setting in advance. In an indoor environment represented in a quantized grid, we design the transition probability due to previous estimated position. Because of this, target’s movable range is obtained. In addition, we use maximum likelihood estimation method based on statics of correlation values. The correlation value is an indicator of pattern matching in a fingerprinting method. The most likely trajectory is calculated by Viterbi algorithm with above mentioned probabilities. The experimental results show that the localization accuracy is improved owing to the use of HMM.     

基于改进小波域隐马尔可夫模型的遥感图像分割

该文提出了一种基于改进小波域隐马尔可夫树(HMT)模型进行图像分割的方法。该方法利用基于希尔伯特变换对的二维方向小波,这种小波变换具有平移不变性、方向检测性好的特点。同时该方法还利用拓展HMT对该改进小波域中尺度间的小波系数相关性进行建模,并结合多背景融合技术进行遥感图像的分割,得到了优于已有文献的分割结果,而且与同类算法相比,降低了算法所需的计算量。     

A Robust Hidden Markov Gauss Mixture Vector Quantizer for a Noisy Source

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.     

一种基于隐马尔可夫模型的协议识别技术

目前的协议识别技术主要是基于端口映射或静态报文特征匹配的。随着网络协议的发展,一些新的协议采用动态端口进行通信或不具有明显的静态报文特征,且部分协议采用了加密技术。这使得传统的识别技术准确率大幅下降。针对传统协议识别技术的局限性,这里提出一种基于隐马尔可夫模型(Hidden Markov Model,HMM)的协议识别技术。它是一种基于统计特性的识别方法,选用对于加密不敏感的特征如包的大小、达到时间等来实现协议的识别。实验结果证明,与传统识别技术相比,它能有效地提高协议识别的准确率,并能用于加密条件下的协议识别。     

Facial animation parameters extraction and expression recognition using Hidden Markov Models

The video analysis system described in this paper aims at facial expression recognition consistent with the MPEG4 standardized parameters for facial animation, FAP. For this reason, two levels of analysis are necessary: low-level analysis to extract the MPEG4 compliant parameters and high-level analysis to estimate the expression of the sequence using these low-level parameters.The low-level analysis is based on an improved active contour algorithm that uses high level information based on principal component analysis to locate the most significant contours of the face (eyebrows and mouth), and on motion estimation to track them. The high-level analysis takes as input the FAP produced by the low-level analysis tool and, by means of a Hidden Markov Model classifier, detects the expression of the sequence.     

Novel Text Recognition Based on Modified K-Clustering and Hidden Markov Models

Wireless Personal Communications - Currently, many researchers have paid more attention to identifying scene texts from the image with background interferences. This study aims to develop an App...