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IEEE International Symposium on Circuits and Systems

作者： Hao Zhu Henry Leung Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada

ISBN: (纸本)9781467357609

In this paper, the problem of state estimation of complex dynamical network with unknown noisy transmission channel is considered. A likelihood function of the complex network is formulated. An expectation maximum (EM) algorithm is proposed to estimate the complex network's state and the noise parameter simultaneously. The proposed method can obtain suboptimal estimate of the state and noise parameter. At each iteration of the EM algorithm, the complex network's state is estimated by extend Kalman filter in the E-step, while the noise parameter is updated in the M-step. Computer simulation results verify the effectiveness of the proposed method.

关键词： state estimation noisy channel expectation maximum extend Kalman filter State estimation Noise parameters noisy channel maximum likelihood Extended Kalman filters dynamic network complex networks Transmission channels Likelihood Functions expectation-maximisation algorithm

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AUTOMATIC DETECTION OF BURNED AREAS IN WETLANDS BY REMOTE SENSING MULTITEMPORAL IMAGES

AUTOMATIC DETECTION OF BURNED AREAS IN WETLANDS BY REMOTE SE...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Daniel Capella Zanotta Hiran Zani Yosio Edemir Shimabukuro National Institute for Space Research - S?o José dos Campos - SP - Brazil

ISBN: (纸本)9781479911127

In this paper, a methodology for automatic detection of burned areas is suggested. The classification criterion is performed using Bayesian statistical parameter (mean and covariance matrix) extracted automatically using the expectation Maximization algorithm and taking into account the spectral similarity between burned and flooded areas. In this work the final process involves the application of morphological operators of erosion and dilation of images in order to insert information from the spatial context, refining the final map. Experiments were conducted to a TM-Landsat scene with areas affected by fires and seasonal flooding. The results show that the accuracy is increased with the consideration of flooding mask and the subsequent application of spatial context, reaching values up to 97% of accuracy when compared with a reference map.

关键词： Optical imagery Land surface change Pattern recognition Optical Imaging Pattern recognition Wetlands automatic detection expectation-maximisation algorithm AUTOMATIC REFERENCE MAPS images morphological operator Flooded areas remote sensing Burns spatial context variance covariance matrix

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Visual Tracking with Sensing Dynamics Compensation Using the expectation-Maximization algorithm

Visual Tracking with Sensing Dynamics Compensation Using the...

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American Control Conference

作者： Chung-Yen Lin Cong Wang Masayoshi Tomizuka Department of Mechanical Engineering University of California at Berkeley Berkeley CA 94720 USA.

ISBN: (纸本)9781479901777

Advances in vision-based technologies allow robots to perform sophisticated and intelligent tasks. Even with these advances, there still remain inherent problems with using vision-based technologies. Slow sampling rate and large latency is a problem associated with most vision hardware used in industry. We refer to these characteristics as the sensing dynamics associated with the vision sensor. This paper presents a compensation method that alleviates sensing dynamics issues in visual feedback tracking problems. We view the sensing dynamics compensation problem as two separate mathematical problems. Namely, we first deal with identifying the target model and then we deal with estimating the target position using the identified model and delayed measurements. The expectation-Maximization algorithm and Kalman filtering are utilized to solve each problem respectively. The visual servo scheme associated with the proposed approach is also studied. Simulations and experiments are designed to test the performance capability of the proposed method.

关键词： expectation-Maximization algorithm vision-based technologies perform sophisticated expectation-maximisation algorithm visual tracking compensation method vision sensors dynamics compensated Kalman filtering

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An expectation-Maximization Method for Calibrating Synchronous Machine Models

An Expectation-Maximization Method for Calibrating Synchrono...

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IEEE Power and Energy Society General Meeting

作者： Da Meng Ning Zhou Shuai Lu Guang Lin PNNL Richland WA

ISBN: (纸本)9781479913022

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using the maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using the EM method also are presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

关键词： Extended Kalman Filter EM algorithms State Estimation Parameter Calibration State estimation Extended Kalman filters synchronous machines expectation-maximisation algorithm single machine infinite bus system Phasor measurement units dynamic state Noise measurement maximum likelihood estimation parameter uncertainty machine model

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PERMUTATION-FREE CONVOLUTIVE BLIND SOURCE SEPARATION VIA FULL-BAND CLUSTERING BASED ON FREQUENCY-INDEPENDENT SOURCE PRESENCE PRIORS

PERMUTATION-FREE CONVOLUTIVE BLIND SOURCE SEPARATION VIA FUL...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Nobutaka Ito Shoko Araki Tomohiro Nakatani NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation

ISBN: (纸本)9781479903573

We propose permutation-free frequency-domain blind source separation (BSS) via full-band clustering of the time-frequency (T-F) components based on time-varying signal presence priors. Frequency-domain methods of BSS usually process each frequency bin separately, and therefore necessitate the subsequent alignment of the permutation ambiguity that arises between frequency bins. In contrast, the proposed method simultaneously processes all frequency bins by using a mixture model with time-varying, frequency-independent mixture weights. We propose to assume non-sparse priors on the mixture weights to prevent the degradation of source separation performance by the time-varying mixture weights. We propose a customized expectation-maximization (EM) algorithm for the maximum a posteriori (MAP) estimation of the model parameters, to which we introduce a novel technique to avoid convergence to local maxima. For audio source separation, we use the normalized observation vector as the feature vector, and the Watson mixture model (WMM) as the mixture model. Evaluations confirm that the proposed permutation-free BSS results in source separation performance comparable to the state-of-the-art clustering-based BSS composed of bin-wise clustering and permutation alignment.

关键词： Blind source separation Clustering EM algorithm Mixture model Permutation problem Blind source separation expectation-maximisation algorithm Source separation Mixture models Cluster Analysis Eigenvector time varying

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UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Elsa DUPRAZ Aline ROUMY Michel KIEFFER L2S - CNRS - SUPELEC - Univ Paris-Sud 91192 Gif-sur-Yvette France INRIA Campus de Beaulieu 35042 Rennes France

ISBN: (纸本)9781479903573

This paper considers the problem of lossy source coding with side information at the decoder only, for Gaussian sources, when the joint statistics of the sources are partly unknown. We propose a practical universal coding scheme based on scalar quantization and non-binary LDPC codes, which avoids the binarization of the quantized coefficients. We first explain how to choose the rate and to construct the LDPC coding matrix. Then, a decoding algorithm that jointly estimates the source sequence and the joint statistics of the sources is proposed. The proposed coding scheme suffers no loss compared to the practical coding scheme with same rate but known variance.

关键词： Source coding with side information Rate-distortion Low Density Parity Check codes expectation Maximization algorithm low density parity check codes expectation-maximisation algorithm Rate distortion labeling scheme Gauss scalar quantization lossy source coding binarization

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Composite Statistical Inference for Semantic Segmentation

Composite Statistical Inference for Semantic Segmentation

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Fuxin Li Joao Carreira Guy Lebanon Cristian Sminchisescu Georgia Institute of Technology ISR - University of Coimbra Lund University

ISBN: (纸本)9781467364102

In this paper we present an inference procedure for the semantic segmentation of images. Different from many CRF approaches that rely on dependencies modeled with unary and pairwise pixel or superpixel potentials, our method is entirely based on estimates of the overlap between each of a set of mid-level object segmentation proposals and the objects present in the image. We define continuous latent variables on superpixels obtained by multiple intersections of segments, then output the optimal segments from the inferred superpixel statistics. The algorithm is capable of recombine and refine initial mid-level proposals, as well as handle multiple interacting objects, even from the same class, all in a consistent joint inference framework by maximizing the composite likelihood of the underlying statistical model using an EM algorithm. In the PASCAL VOC segmentation challenge, the proposed approach obtains high accuracy and successfully handles images of complex object interactions.

关键词： multiple intersections expectation-maximisation algorithm images Unary segmentation Segmentation Statistical inference Semantics Handles image segmentation Composites

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Probabilistic Principal Geodesic Analysis 13

Probabilistic Principal Geodesic Analysis

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Annual Conference on Neural Information Processing Systems

作者： Miaomiao Zhang P. Thomas Fletcher School of Computing University of Utah Salt Lake City UT

ISBN: (纸本)9781632660244

Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA) for dimensionality reduction of data on a Riemannian manifold. Currently PGA is defined as a geometric fit to the data, rather than as a probabilistic model. Inspired by probabilistic PCA, we present a latent variable model for PGA that provides a probabilistic framework for factor analysis on manifolds. To compute maximum likelihood estimates of the parameters in our model, we develop a Monte Carlo expectation Maximization algorithm, where the expectation is approximated by Hamiltonian Monte Carlo sampling of the latent variables. We demonstrate the ability of our method to recover the ground truth parameters in simulated sphere data, as well as its effectiveness in analyzing shape variability of a corpus callosum data set from human brain images.

关键词： principal components analysis Prostaglandins A Probability Probabilistic Model dimensionality reduction Manifolds Factor analysis Passive Cutaneous Anaphylaxis Brain Imaging Hypoderma Generalization expectation-maximisation algorithm

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Precoding in downlink multi-carrier code division multiple access systems using expectation maximisation algorithm

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IET COMMUNICATIONS 2008年第10期2卷 1279-1288页

作者： Haghbin, A. Azmi, P. Tarbiat Modares Univ Dept Elect Engn Tehran Iran

The presence of multiple access interference and hardware complexity of the mobile terminal are two major burdens for multi-carrier code division multiple access schemes. Both burdens can be overcome in downlink by precoding the transmitted signal using the knowledge of the channel state information. In multi-user precoding techniques, the transmitter is optimised to combat channel impairments through the use of new spreading sequences that are obtained by solving an optimisation problem based on some criterion. Among these optimisation problems, the problem based on maximum likelihood is hard and complex to solve. In the proposed precoding scheme, the well-suited expectation maximisation algorithm is used to solve this problem. The proposed precoding technique is simulated and its performance is analysed and compared with some other precoding and detecting techniques. The results show that the proposed scheme considerably outperforms the previous precoding schemes.

关键词： multiuser precoding techniques maximum likelihood decoding multiple access interference mobile terminal code division multiple access hardware complexity radiofrequency interference expectation maximisation algorithm optimisation problem precoding mobile radio expectation-maximisation algorithm channel state information downlink multicarrier code division multiple access systems multiuser channels

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Distributed maximum likelihood estimation for flow and speed density prediction in distributed traffic detectors with Gaussian mixture model assumption

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IET INTELLIGENT TRANSPORT SYSTEMS 2012年第2期6卷 215-222页

作者： Ramezani, A. Moshiri, B. Abdulhai, B. Kian, A. R. Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran Univ Toronto Dept Civil Engn Toronto ITS Ctr Toronto ON Canada

In this study a distributed maximum likelihood estimator (MLE) has been presented to estimate ML function of traffic flow and mean traffic speed in a freeway. This algorithm uses traffic measurements including volume, occupancy and mean speed which gathered by some inductive loop detectors. These traffic detectors (traffic sensors) located in certain distances in the freeway network such that they establish a distributed sensor network (DSN). The presented distributed estimator has employed a distributed expectation maximisation algorithm to calculate MLE. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbours and estimate global sufficient statistics in each node. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximise the log-likelihood in the same way as in the standard EM algorithm. As the consensus filter only requires each node to communicate with its neighbours, the distributed algorithm is scalable and robust. A set of field traffic data from Minnesota freeway network has been used to simulate and verify the proposed distributed estimator performance.

关键词： Traffic engineering computing distributed expectation maximisation algorithm Gaussian mixture model assumption distributed traffic detector M-step Filtering methods in signal processing speed density prediction ML function estimation mean traffic speed E-step distributed maximum likelihood estimation Gaussian processes traffic measurements Digital signal processing MLE inductive loop detectors filtering theory Minnesota freeway network local sufficient statistics distributed sensors log likelihood consensus filter Sensing devices and transducers global sufficient statistics distributed sensor network road traffic traffic flow prediction Other topics in statistics automated highways traffic sensors expectation-maximisation algorithm

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