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检索条件"主题词=Expectation-Maximisation algorithm"

共 493 条记录，以下是261-270 订阅

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A MAP-Based Channel Estimation algorithm for SIMO Systems over Extended SV Channel Model

A MAP-Based Channel Estimation Algorithm for SIMO Systems ov...

引用

IEEE International Conference on Communication Systems (ICCS)

作者： Zhang, Li Chew, Yong Huat Wong, Wai-Choong Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Agcy Sci Res & Technol Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781467320542;9781467320528

In this paper, a maximum a posterior (MAP) based channel estimation algorithm is proposed to estimate both the temporal and spatial domain channel parameters for single-input multiple-output (SIMO) systems transmitting over an extended Saleh-Valenzuela (SV) channel. The proposed algorithm leverages on prior knowledge of the statistical distributions of signal clusters and rays within a cluster, and uses the expectation-maximization (EM) algorithm to resolve the high dimensional optimization problem into iteratively solving the multiple 3-dimensional optimizations. The successive interference cancellation (SIC) method is applied in the initialization stage to obtain the initial guess to the EM algorithm. Simulations are carried out in two typical indoor scenarios following the extended SV model. The proposed algorithm is shown to outperform the maximum likelihood (ML) based algorithm for SIMO systems.

关键词： channel estimation channel model expectation-maximisation algorithm statistical distribution algorithms initialization Prior Learning rays rays single input-multiple output

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Iterative Joint Channel and Noise Variance Estimation and Primary User Signal Detection for Cognitive Radios

Iterative Joint Channel and Noise Variance Estimation and Pr...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Assra, Ayman Vakili, Arash Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2T5 Canada

ISBN: (纸本)9781467307536

In this paper, we introduce a joint channel and noise variance estimation, and primary user (PU) signal detection scheme using the expectation-maximization (EM) algorithm for cognitive radios. In our investigation, we consider two scenarios: In the first scenario, the channel and noise variance are assumed to be perfectly known by the secondary user (SU). Here, we propose a maximum-likelihood (ML) solution of the PU signal detection as an upper bound on the performance of the proposed joint estimation and detection (JED) scheme. We also provide an iterative implementation of the ML-based detector using the EM algorithm. In the second case, we extend our work to the problem of channel and noise variance estimation in cognitive radios, where we propose an iterative JED scheme based on the EM algorithm. The simulation results show that the proposed JED scheme can iteratively attain a reliable performance with few iterations and modest computational complexity.

关键词： Cognitive radio expectation-maximisation algorithm signal acquisition Noise Variance Iterative complexity classes

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A Novel Framework of Software Reliability Evaluation with Software Reliability Growth Models and Software Metrics

A Novel Framework of Software Reliability Evaluation with So...

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15th IEEE International Symposium on High-Assurance Systems Engineering (HASE)

作者： Okamura, Hiroyuki Dohi, Tadashi Hiroshima Univ Dept Informat Engn Grad Sch Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9781479934652

This paper proposes a novel framework of software reliability growth models with software metrics. Our approach is to integrate a classical Poisson-regression-based fault prediction with non-homogeneous Poisson process based software reliability growth models. The remarkable feature of this approach is to handle time series data of fault detections and software metrics for a number of modules at the same time. In the paper, we present the modeling framework that combines Poissonregression-based fault prediction and software reliability growth models, and also develop an efficient algorithm to estimate model parameters based on EM (expectation-maximization) algorithm. In numerical experiments, by comparing the proposed model with both Poisson-regression-based fault prediction and nonhomogeneous Poisson process based software reliability growth models, we discuss the effectiveness of using time series data of fault detections and software metrics from both viewpoints of reliability estimation and fault prediction.

关键词： expectation-maximisation algorithm fault diagnosis parameter estimation regression analysis software metrics software reliability stochastic processes time series EM algorithm Poisson-regression-based fault prediction expectation-maximization algorithm fault detections model parameter estimation nonhomogeneous Poisson process reliability estimation software metrics software reliability evaluation software reliability growth models time series data Computational modeling Data models Fault detection Predictive models Software Software metrics Software reliability EM algorithm Poisson regression software metrics software reliability growth model

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Hybrid Identification of Time-varying Parameter with Particle Filtering and expectation Maximization

Hybrid Identification of Time-varying Parameter with Particl...

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21st Mediterranean Conference on Control and Automation (MED)

作者： Hartmann, Andras Vinga, Susana Lemos, Joao M. INESC ID R Alves Redol 9 P-1000029 Lisbon Portugal IST UTL P-1049001 Lisbon Portugal FCM UNL P-1169056 Lisbon Portugal

ISBN: (纸本)9781479909957;9781479909971

The problem of time-varying parameter identification is considered on a class of nonlinear hybrid systems. It is assumed that inputs and outputs are directly measured, and a subset of system parameters can take different values from a finite set at each time instance. An offline (batch) algorithm that combines particle filtering and the expectation maximization is introduced for the identification of such systems. The efficiency of the proposed method is illustrated through simulated examples.

关键词： expectation-maximisation algorithm nonlinear systems parameter estimation particle filtering (numerical methods) time-varying systems

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Estimating the process noise variance for vehicle motion models 18

Estimating the process noise variance for vehicle motion mod...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Stellet, Jan Erik Straub, Fabian Schumacher, Jan Branz, Wolfgang Zoellner, J. Marius Robert Bosch GmbH Corp Res Vehicle Safety & Assistance Syst D-71272 Renningen Germany Robert Bosch GmbH Gasoline Syst Engn SW Basesyst D-70469 Stuttgart Germany Res Ctr Informat Technol FZI D-76131 Karlsruhe Germany

ISBN: (纸本)9781467365963

Vehicle motion models are employed in driver assistance systems for tracking and prediction tasks. For probabilistic decision making and uncertainty propagation, the prediction's inaccuracy is taken into account in the form of process noise. This work estimates Gaussian process noise models from measured vehicle trajectories using the expectation maximisation (EM) algorithm. The method is exemplified and the results evaluated for three commonly used motion models based on a large-scale dataset. A novel closed-form adaptation of the algorithm to a covariance matrix with Kronecker product structure, as in models for translational motion, is presented. The findings suggest that the longitudinal prediction errors feature a non-Gaussian distribution but a reasonable approximation is given by the estimated model.

关键词： Gaussian processes approximation theory covariance matrices decision making driver information systems expectation-maximisation algorithm probability EM algorithm Gaussian process noise model estimation Kronecker product structure approximation closed-form adaptation covariance matrix driver assistance systems expectation maximisation algorithm longitudinal prediction errors nonGaussian distribution prediction task probabilistic decision making process noise variance estimation tracking task translational motion uncertainty propagation vehicle motion models Estimation Mathematical model Noise Predictive models Trajectory Uncertainty Vehicles

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A Maximum-Entropy Based Fast Estimation of Power Quality for Smart Microgrid

A Maximum-Entropy Based Fast Estimation of Power Quality for...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Ali, Sardar Wu, Kui Marinakis, Dimitri Univ Victoria Dept Comp Sci Victoria BC Canada

ISBN: (纸本)9781479915262

Power quality plays a critical role in the reliability of power networks. The monitoring of power quality, however, is a non-trivial task due to the high cost of measurement devices and the requirement on real-time responses. As a result, we need fast algorithms to estimate the power quality in various segments of a power network, using only a small number of measurement points. In this paper, we propose a maximum-entropy (MaxEnt) based approach to estimating power quality in smart microgrid. Compared to other existing methods such as Monte Carlo expectation Maximization (MCEM), the MaxEnt based approach is much faster.

关键词： Monte Carlo methods distributed power generation expectation-maximisation algorithm maximum entropy methods power distribution reliability power supply quality power system measurement smart power grids

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A Statistical Framework on Software Aging Modeling with Continuous-Time Hidden Markov Model 36

A Statistical Framework on Software Aging Modeling with Cont...

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36th IEEE Symposium on Reliable Distributed Systems (SRDS)

作者： Okamura, Hiroyuki Zheng, Junjun Dohi, Tadashi Hiroshima Univ 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan

ISBN: (纸本)9781538616796

This paper considers the statistical approach to model software degradation process from time series data of system attributes. We first develop the continuous-time Markov chain (CTMC) model to represent the degradation level of system. By combining the CTMC with system attributes distributions, a continuous-time hidden Markov model (CT-HMM) is proposed as the basic model to represent the degradation level of system. To estimate model parameters, we develop the EM algorithm for CT-HMM. The advantage of this modeling is that the estimated model is directly applied to existing CTMC-based software aging and rejuvenation models. In numerical experiments, we exhibit the performance of our method by simulated data and also demonstrate estimating the software degradation process with experimental data in MySQL database system.

关键词： Hidden Markov models expectation-maximisation algorithm computer software computer programs basic model time series data Rejuvenation Modeling simulated data Aging model parameters Database systems

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CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECTORIES

CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECT...

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IEEE International Conference on Image Processing

作者： Nascimento, Jacinto C. Marques, Jorge S. Figueiredo, Mario A. T. Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal Inst Super Tecn Inst Telecommun P-1049001 Lisbon Portugal

ISBN: (纸本)9781424479948

We propose a method to classify human trajectories, modeled by a set of motion vector fields, each tailored to describe a specific motion regime. Trajectories are modeled as being composed of segments corresponding to different motion regimes, each generated by one of the underlying motion fields. Switching among the motion fields follows a probabilistic mechanism, described by a field of stochastic matrices. This yields a space-dependent motion model which can be estimated using an expectation-maximization (EM) algorithm. To address the model selection question (how many fields to use?), we adopt a discriminative criterion based on classification accuracy on a held out set. Experiments with real data (human trajectories in a shopping mall) illustrate the ability of the proposed approach to classify complex trajectories into high level classes (client versus non-client).

关键词： complex pedestrian activity expectation maximization algorithm expectation-maximisation algorithm human trajectory classification image classification motion estimation motion vector field probabilistic mechanism space dependent motion model stochastic matrices video surveillance

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Embedded One-Class Classification on RF Generator using Mixture of Gaussians

Embedded One-Class Classification on RF Generator using Mixt...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Bowen, Ryan M. Sahin, Ferat Radomski, Aaron Sarosky, Dan Rochester Inst Technol Microsyst Engn Dept Rochester NY 14623 USA MKS ENI Prod Elect & Microelect Engn Dept Rochester NY USA MKS ENI Prod Adv Dev Grp Rochester NY USA

ISBN: (纸本)9781479938407

In this paper we apply a specific machine learning technique for classification of normal and not-normal operation of RF (Radio Frequency) power generators. Pre-processing techniques using FFT and bandpower convert time-series system signatures into single feature vectors. These feature vectors are modeled using k-component Mixture of Gaussians (MoG) where components and corresponding parameters are learned using the expectation Maximization (EM) algorithm. Data is obtained from three different generator models operating under normal and multiple different not-normal conditions. Exploration into algorithmic parameter effects is conducted and empirical evidence used to select sub-optimum parameters. Robust testing is reported to achieve a 3 sigma classification accuracy of 95.91% for the targeted RF generator. Additionally, a custom C++ library is implemented to utilize the learned model for accurate classification of time-series data within an embedded environment such as a RF generator. The embedded implementation is reported to have a small storage footprint, reasonable memory consumption and overall fast execution time.

关键词： Gaussian processes electronic engineering computing embedded systems learning (artificial intelligence) mixture models pattern classification radiofrequency integrated circuits EM FFT MoG RF power generators bandpower convert time-series system signatures custom C++ library embedded environment embedded one-class classification expectation maximization algorithm machine learning technique mixture of Gaussians normal operation not-normal operation radio frequency single feature vectors time-series data Accuracy Data models Generators Radio frequency Robustness Training Vectors radiofrequency rf generator expectation-maximisation algorithm time series data electronic engineering computing Radio frequency integrated circuits Fast Fourier transform data models normal operation dynamos Gaussian processes Pattern recognition Gauss Embedded systems Functional training

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Iterative Signal Detection of EM-based Receivers with Multiple Antennas for OFDM Communications

Iterative Signal Detection of EM-based Receivers with Multip...

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IEEE 78th Vehicular Technology Conference (VTC Fall)

作者： Muraoka, Kazushi Fukawa, Kazuhiko Suzuki, Hiroshi Suyama, Satoshi Tokyo Inst Technol Meguro Ku Tokyo 1528550 Japan

ISBN: (纸本)9781467361873

Joint signal detection and channel estimation based on the expectation-maximization (EM) algorithm has been investigated for orthogonal frequency-division multiplexing (OFDM) mobile communications over fast-fading channels. In order to make the signal detection suitable for the iterative EM-based receiver with multiple antennas, this paper proposes spatial removal from the perspective of a message-passing algorithm on factor graphs. The spatial removal performs the channel estimation of a targeted antenna by using detected signals that are obtained from the received signals of all antennas other than the targeted antenna. It can avoid the repetitive use of unreliable received signals for consecutive signal detection and channel estimation. Computer simulations under fast-fading conditions demonstrate that appropriate application of the spatial removal can exploit such a removal effect and the spatial diversity, resulting in improvement of the packet error rate (PER).

关键词： OFDM modulation antennas expectation-maximisation algorithm fading channels graph theory iterative methods message passing mobile radio radio receivers signal detection telecommunication computing

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