检索结果-内蒙古大学图书馆

2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop(KAM 2008)

作者： Xuchao Li College of Information Science and Media Jinggangshan University Ji’an,ChinaSuxuan Bian College of Nursing Jinggangshan University Ji’an,China

A statistical object detection and tracking mutual feedback scheme,combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectation maximization (EM) Kalman filter algorithm,is proposed in this *** space object detection stage,PCA provides compact and decorrelated feature space,the tracked object feature is statistically represented as GMM in RGB color space,objects are detected by maximum a posteriori (MAP) *** temporal tracking stage,the tracked object is determined by the Bhattacharyya similarity measurement,the object position of consecutive frame is predicted by EM Kalman filter *** integration of object detection and tracking spatio-temporal mutual feedback scheme can decrease the accumulation *** have applied the proposed method to object detection and tracking under the partial occlusion and the changes of moving speed with encouraging results.

关键词： object detection and tracking Kalman filter expectation maximization algorithm feedback

来源：评论

学校读者我要写书评

暂无评论

Missing data: A comparison of neural network and expectation maximization techniques

引用

CURRENT SCIENCE 2007年第11期93卷 1514-1521页

作者： Nelwamondo, Fulufhelo V. Mohamed, Shakir Marwala, Tshilidzi Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa

Two techniques have emerged from the recent literature as candidate solutions to the problem of missing data imputation. These are the expectation maximization (EM) algorithm and the auto-associative neural network and genetic algorithm (GA) combination. Both these techniques have been discussed individually and their merits discussed at length in the available literature. However, they have not been compared with each other. This article provides a comparison of the two techniques using datasets of an industrial power plant, an industrial winding process and HIV seroprevalence survey data. Results show that the EM algorithm is more suitable and performs better in cases where there is little or no interdependency between the input variables, whereas the auto-associative neural network and GA combination is suitable when there are inherent nonlinear relationships between some of the given variables.

关键词： expectation maximization algorithm genetic algorithm missing data neural network

来源：评论

学校读者我要写书评

暂无评论

Bayesian methods for predicting interacting protein pairs using domain information

引用

BIOMETRICS 2007年第3期63卷 824-833页

作者： Kim, Inyoung Liu, Yin Zhao, Hongyu Yale Univ Dept Epidemiol & Publ Hlth Sch Med New Haven CT 06520 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Sch Med Dept Genet New Haven CT 06520 USA

Protein-protein interactions (PPIs) play important roles in most fundamental cellular processes including cell cycle, metabolism, and cell proliferation. Therefore, the development of effective statistical approaches to predicting protein interactions based on recently available large-scale experimental data is very important. Because protein domains are the functional units of proteins and PPIs are mostly achieved through domain-domain interactions (DDIs), the modeling and analysis of protein interactions at the domain level may be more informative and insightful. However, due to the large number of domains, the number of parameters to be estimated is very large, yet the amount of information for statistical inference is quite limited. In this article we propose a full Bayesian method and a semi-Bayesian method for simultaneously estimating DDI probabilities, the false positive rate, and the false negative rate of high-throughput data through integrating data from several organisms. We also propose a model to associate protein interaction probabilities with domain interaction probabilities that reflects the number of domains in each protein, Our Bayesian methods are compared with the likelihood-based approach (Deng et al., 2002, Genome Research 12, 1504-1508;Liu, Liu, and Zhao, 2005, Bioinformatics 21, 3279-3285) developed using the expectation maximization algorithm. We show that the full Bayesian method has the smallest mean square error through both simulations and theoretical justification under a special scenario. The large-scale PPI data obtained from high-throughput yeast two-hybrid experiments are used to demonstrate the advantages of the Bayesian approaches.

关键词： Bayesian method domain-domain interaction expectation maximization algorithm protein-protein interaction

来源：评论

学校读者我要写书评

暂无评论

Exact reduced-complexity maximum likelihood reconstruction of multiple 3-d objects from unlabeled unoriented 2-d projections and electron microscopy of viruses

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2007年第12期16卷 2865-2878页

作者： Lee, Junghoon Doerschuk, Peter C. Johnson, John E. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Scripps Res Inst Dept Mol Biol La Jolla CA 92037 USA

In cryo-electron microscopy, the data is comprised of noisy 2-D projection images of the 3-D electron scattering intensity of the object where the orientation of the projections is unknown. Often, the images show randomly selected objects from a mixture of different types of objects. Objects of different type may be unrelated, e.g., different species of virus, or related, e.g., different conformations of the same species of virus. Due to the low SNR and the 2-D nature of the data, it is challenging to determine the type of the object shown in an individual image. A statistical model and maximum likelihood estimator that computes simultaneous 3-D reconstruction and labels using an expectation maximization algorithm exists but requires extensive computation due to the numerical evaluation of 3-D or 5-D integrations of a square matrix of dimension equal to the number of degrees of freedom in the 3-D reconstruction. By exploiting the geometry of rotations in 3-D, the estimation problem can be transformed so that the inner-most numerical integral has a scalar rather than a matrix integrand. This leads to a dramatic reduction in computation, especially as the number of degrees of freedom in the 3-D reconstruction increases. Numerical examples of the 3-D reconstructions are provided based on synthetic and experimental images where the objects are small spherical viruses.

关键词： cryo-electron microscopy (cryo EM) expectation maximization algorithm maximum likelihood estimation structural biology 3-D signal reconstruction tomography virology virus

来源：评论

学校读者我要写书评

暂无评论

Analysis of E. coli promoter recognition problem in dinucleotide feature space

引用

BIOINFORMATICS 2007年第5期23卷 582-588页

作者： Rani, T. Sobha Bhavani, S. Durga Bapi, Raju S. Univ Hyderabad Dept Comp & Informat Sci Computat Intelligence Lab Hyderabad 500046 Andhra Pradesh India

Motivation: Patterns in the promoter sequences within a species are known to be conserved but there exist many exceptions to this rule which makes the promoter recognition a complex problem. Although many complex feature extraction schemes coupled with several classifiers have been proposed for promoter recognition in the current literature, the problem is still open. Results: A dinucleotide global feature extraction method is proposed for the recognition of sigma-70 promoters in Escherichia coli in this article. The positive data set consists of sigma-70 promoters with known transcription starting points which are part of regulonDB and promec databases. Four different kinds of negative data sets are considered, two of them biological sets (Gordon et al., 2003) and the other two synthetic data sets. Our results reveal that a single-layer perceptron using dinucleotide features is able to achieve an accuracy of 80% against a background of biological non-promoters and 96% for random data sets. A scheme for locating the promoter regions in a given genome sequence is proposed. A deeper analysis of the data set shows that there is a bifurcation of the data set into two distinct classes, a majority class and a minority class. Our results point out that majority class constituting the majority promoter and the majority non-promoter signal is linearly separable. Also the minority class is linearly separable. We further show that the feature extraction and classification methods proposed in the paper are generic enough to be applied to the more complex problem of eucaryotic promoter recognition. We present Drosophila promoter recognition as a case study. Availability: http://202.41.85.117/htmfiles/faculty/tsr/*** Contact: tsrcs@***.

关键词： expectation maximization algorithm NEURAL NETWORKS BINDING SITES SEQUENCES IDENTIFICATION TRANSCRIPTION STRENGTHS REGIONS

来源：评论

学校读者我要写书评

暂无评论

A generalized BCJR algorithm and its use in iterative blind channel identification

引用

IEEE SIGNAL PROCESSING LETTERS 2007年第10期14卷 661-664页

作者： Gunther, Jake Keller, David Moon, Todd Utah State Univ Logan UT 84322 USA

Recently, the well-known Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm was generalized to compute joint posterior probabilities of arbitrary sets of symbols given noisy observations of those symbols at the output of an intersymbol interference (ISI) channel. This letter explores using pair-wise joint posterior probabilities produced by generalized BCJR together with expectation maximization for blind identification of the ISI channel impulse response and noise variance. Simulations indicate that the blind algorithm accurately estimates the channel response and noise variance and yields bit error rates comparable to a channel-informed BCJR equalizer.

关键词： Bahl-Cocke-Jelinek-Raviv (BUR) algorithm blind channel identification expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

EM based semi-blind beamforming algorithm in MIMO-OFDM systems

EM based semi-blind beamforming algorithm in MIMO-OFDM syste...

引用

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Zamiri-Jafarian, H. Bokharaiee-Najafee, S. Pasupathy, S. Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781424411894

A new EM based semi-blind beamforming algorithm is proposed in this paper for multiple input multiple output (MIMO) systems in which orthogonal frequency division multiplexing (OFDM) technique is used. Employing singular value decomposition (SVD) technique, the semi-blind beamforming algorithm eliminates space interference in an iterative manner by shaping antenna patterns in transmitter and receiver sides in frequency domain. A 16-QAM MIMO-OFDM system is considered for computer simulations in order to evaluate the performance of the new algorithm under different scenarios. The results show that the proposed algorithm outperforms subspace based semi-blind beamforming method.

关键词： beamforming expectation maximization algorithm MIMO OFDM semi-blind SVD

来源：评论

学校读者我要写书评

暂无评论

Maximum-likelihood carrier-frequency synchronization and channel estimation for MIMO-OFDM systems

Maximum-likelihood carrier-frequency synchronization and cha...

引用

Wireless Telecommunications Symposium

作者： Salari, S. Ahmadian, M. Ardebilipour, M. Meghdadi, V. Cances, J. P. KN Toosi Univ Technol Tehran Iran Univ Limoges Limoges France

ISBN: (纸本)9781424406968

In this paper, we propose a new scheme for maximum-likelihood (ML) estimation of both carrier-frequency offset (CFO) and channel coefficients in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. To reduce the prohibitive complexity of ML estimate, the expectation-maximization (EM) algorithm is employed. From Simulations, it is shown that the accuracy of proposed scheme is close to the Cramer-Rao bound (CRB) for both CFO and channel estimations.

关键词： MIMO communication OFDM modulation channel estimation maximum likelihood estimation Cramer-Rao bound MIMO OFDM carrier frequency offset carrier frequency synchronization channel coefficients expectation maximization algorithm maximum l channel estimation Channel Coefficient carrier frequency offset expectation-maximisation algorithm MIMO communication Cramer-Rao bounds frequency synchronization maximum likelihood estimation maximum likelihood CARRIER FREQUENCY

来源：评论

学校读者我要写书评

暂无评论

Speech emotion recognition using Gaussian mixture vector autoregressive models

Speech emotion recognition using Gaussian mixture vector aut...

引用

32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： El Ayadi, Moataz M. H. Kamel, Mohamed S. Karray, Fakhri Univ Waterloo Pattern Anal & Machine Intelligence Lab Waterloo ON N2L 3G1 Canada

ISBN: (纸本)1424407281

It is believed that modeling temporal structure of the speech data may be useful for the problem of speech emotion recognition [1]. In this paper, Gaussian mixture vector autoregressive model is proposed as a statistical classifier for this task. The main motivation behind using such a model is its ability to model the dependency among extracted speech feature vectors as well as the multi-modality in their distribution. When applied to the Berlin emotional speech database, the proposed technique provides a classification accuracy of 76% versus 71% for the hidden Markov model, 67% for the k-nearest neighbors, 55% for feed-forward neural networks. The model gives also better discrimination between high-arousal, low arousal, and neutral emotions than the HMM.

关键词： expectation maximization algorithm Gaussian mixture models maximum likelihood estimation speech emotion recognition vector autoregressive models

来源：评论

学校读者我要写书评

暂无评论

Unsupervised algorithm for radiographic image segmentation based on the Gaussian mixture model

Unsupervised algorithm for radiographic image segmentation b...

引用

International Conference on Computer as a Tool (EUROCON 2007)

作者： Mekhalfa, Faiza Nacereddine, Nafaa Goumeidane, Aicha Baya Welding & NDT Res Ctr Image & Signal Proc Lab Cheraga Algeria

ISBN: (纸本)9781424408122

In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.

关键词： weld defect radiographic images image segmentation Gaussian mixture model expectation maximization algorithm fuzzy c-means algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：