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

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Interference Suppression Using EM algorithm in OFDM Transmissions

Interference Suppression Using EM Algorithm in OFDM Transmis...

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IEEE Global Communications Conference (GLOBECOM)

作者： Yoda, Naotoshi Ohtsuki, Tomoaki Mashino, Jun Sugiyama, Takatoshi Keio Univ Dept Informat & Comp Sci Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

ISBN: (纸本)9781479935123

Interference detection and suppression schemes are proposed for coded OFDM systems in the presence of narrow-band interference. Previously proposed interference detection schemes perform with a recursive forward error correcting (FEC) decoding, thus its calculation is heavy. The proposed scheme is based on an expectation maximization (EM) algorithm to detect the interference before the FEC decoding process. The complexity is reduced because the FEC decoding is performed only once. Simulation results indicate that the proposed scheme achieves almost the same bit error rate performance as the conventional scheme, while decreasing the times of the FEC decoding.

关键词： OFDM modulation error statistics expectation-maximisation algorithm forward error correction interference suppression iterative decoding modulation coding radiocommunication recursive estimation signal detection EM algorithm bit error rate performance coded OFDM transmission system expectation maximization algorithm interference suppression scheme narrowband interference detection scheme recursive FEC iterative decoding wireless communication Bit error rate Decoding Forward error correction Interference Iterative decoding OFDM Receivers

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expectation Maximization Based Parameter Estimation by Sigma-Point and Particle Smoothing 17

Expectation Maximization Based Parameter Estimation by Sigma...

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17th International Conference on Information Fusion (FUSION)

作者： Kokkala, Juho Solin, Arno Sarkka, Simo Aalto Univ Dept Biomed Engn & Computat Sci Espoo Finland

ISBN: (纸本)9781479916344

We consider parameter estimation in non-linear state space models by using expectation-maximization based numerical approximations to likelihood maximization. We present a unified view of approximative EM algorithms that use either sigma-point or particle smoothers to evaluate the integrals involved in the expectation step of the EM method, and compare these methods to direct likelihood maximization. For models that are linear in parameters and have additive noise, we show how the maximization step of the EM algorithm is available in closed form. We compare the methods using simulated data, and discuss the differences between the approximations.

关键词： Approximation algorithms Approximation methods Equations Mathematical model Numerical models Parameter estimation Smoothing methods Parameter estimation smoothing methods Approximation method Approximation algorithms numerical models Mathematical Model expectation-maximisation algorithm smoothing

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DOA Estimation for Low-Cost Array Antenna System Based on GROUSE and EM algorithms

DOA Estimation for Low-Cost Array Antenna System Based on GR...

引用

IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

作者： Yamada, Keita Ichige, Koichi Yokohama Natl Univ Dept Elect & Comp Engn Hodogaya Ku Yokohama Kanagawa 2408501 Japan

ISBN: (纸本)9781479952304

This paper proposes a high-resolution DOA (Direction Of Arrival) estimation method using GROUSE and EM algorithms for low-cost array antenna system. The low-cost array antenna system, which uses less receiver modules than array elements, often leads worse DOA estimation accuracy when comparing with the full-cost array antenna system. The proposed method first employs the modified GROUSE algorithm to pre-estimate DOAs, and then the EM algorithm is applied to further improve the DOA estimation accuracy. Performance of the proposed approach is evaluated through computer simulation.

关键词： antenna arrays direction-of-arrival estimation expectation-maximisation algorithm DOA estimation method EM algorithms array antenna system direction of arrival modified GROUSE algorithm Accuracy Antenna arrays Arrays Direction-of-arrival estimation Estimation Receivers Signal to noise ratio Direction of arrival estimation expectation-maximisation algorithm Antenna arrays signal to noise ratios Receivers Grouse Angle of arrival ANTENNA SYSTEM

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A Short-term Rain-induced Attenuation Model for Satellite Link Quality Prediction

A Short-term Rain-induced Attenuation Model for Satellite Li...

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11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Poolsawut, Jirutchaya Limpiti, Tulaya Puttarak, Nattakan King Mongkuts Inst Technol Ladkrabang Fac Engn Bangkok 10520 Thailand

ISBN: (纸本)9781479929931

This paper proposes a novel but simple satellite link quality prediction based on the relationship between rainfall rate and satellite signal attenuation. After quantifying their relationship statistically, we describe their contemporaneous variations using a linear dynamical system response model. The expectation-maximization (EM) algorithm is used to obtain the maximum likelihood estimates of the model parameters. Subsequently, the communication link quality is predicted by thresholding the estimated short-term signal attenuation level obtained from the expectation step of the EM algorithm. The prediction performance of the proposed method is compared against the measured beacon signals from Thaicom-4 (IPSTAR) satellite during rainfalls. Using solely the observed rainfall rates from a tipping bucket rain gauge, the algorithm is able to decently predict the satellite link quality. We believe the proposed model can be modified as a predictive tool for real-time link unavailability monitoring of satellite communication system.

关键词： Attenuation Attenuation measurement Prediction algorithms Predictive models Rain Satellites Time series analysis attenuation measurement Prediction algorithms rain satellites time series analysis Prediction models Satellite links Attenuation Attenuation expectation-maximisation algorithm Satellite communication systems Rainfall rate

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On-line Handwritten Mathematical Expression Recognition Method based on Statistical and Semantic Analysis

On-line Handwritten Mathematical Expression Recognition Meth...

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11th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Hu, Yang Peng, Liangrui Tang, Yejun Tsinghua Univ Dept Elect Engn Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781479932436

Recognition of handwritten mathematical expressions (HMEs) has become a cutting edge research topic recently, as there are increasingly needs for pen-inputting applications. In this paper, we presented a novel framework to analyse HME layout and semantic information. This framework includes three steps, namely symbol segmentation, symbol recognition and semantic relationship analysis. For symbol segmentation, a decomposition on strokes is operated, then dynamic programming is adopted to find the paths corresponding to the best segmentation manner and reduce the stroke searching complexity. For symbol recognition, spatial geometry and directional element features are classified by a Gaussian Mixture Model learnt through expectation-Maximization algorithm. At last, in the semantic relationship analysis module, a ternary tree is utilized to to store the ranked symbols through calculating the operator priorities. The motivation for our work comes from the apparent difference in writing styles across western and Chinese populations. Our results are reasonable and show promise on the private dataset.

关键词： Handwritten Mathematical Expression Online Recognition Statistical and Semantic Analysis on-line identification Semantics expectation-maximisation algorithm expression recognition Stroke Recognition (Psychology) dynamic programming segmentation Segmentation

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Supervised Topic Regression via Experts

Supervised Topic Regression via Experts

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International Joint Conference on Neural Networks (IJCNN)

作者： Lin, Song Guo, Ping Xin, Xin Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781479914845

This paper focuses on the research issue of supervised topic models. Traditionally, an unsupervised topic model is typically supervised by incorporating a supervised linear operator. Although this kind of methods has successfully achieved supervised topic representations, as well as tractable computational complexity, the main limitation lies in that it assumes the topic data is linearly distributed. Therefore, when the practical data does not follow the linear property, the model cannot perform well. To solve this problem, a non-linear supervised topic model is proposed in this paper. Specifically, the mixture of experts (ME), as a kind of "divide and conquer", is utilized to deal with the regression of non-linear of topic data. We integrate the mixture of experts and unsupervised latent Dirichlet allocation (LDA) in a Bayesian manner. The proposed model can also reduce overfitting problem of ME with inputting high-dimensional data. An elegant learning algorithm is derived based on variational expectation maximization algorithm. Experimental results show that the proposed model has better predictive performances compared with usupervised topic models, and some state-of-the-art supervised topic regression models (including sLDA model and MedLDA model), on two textual datasets and one image dataset.

关键词： Professional personnel Regression (Psychology) expectation-maximisation algorithm Dataset

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Non-binary LDPC decoding using truncated messages in the Walsh-Hadamard domain

Non-binary LDPC decoding using truncated messages in the Wal...

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International Symposium on Information Theory and its Applications (ISITA)

作者： Sayir, Jossy Univ Cambridge Cambridge CB2 1TN England

ISBN: (纸本)9784885522925

The Extended Min-Sum (EMS) algorithm for non-binary low-density parity-check (LDPC) defined over an alphabet of size q operates on truncated messages of length q' to achieve a complexity of the order q'(2). In contrast, Walsh-Hadamard (WH) transform based iterative decoders achieve a complexity of the order q log q, which is much larger for q' << q. In this paper, we demonstrate that considerable savings can be achieved by letting WH based decoders operate on truncated messages as well. We concentrate on the direct WH transform and compute the number of operations required if only q' of the q inputs are non-zero. Our paper does not cover the inverse WH transform and hence further research is needed to construct WH based decoders that can compete with the EMS algorithm on complexity terms.

关键词： Approximation methods Complexity theory Decoding Energy management Parity check codes Transforms Vectors Transform parity check codes DECODING Complexity theory Approximation method Energy management decoders Messages expectation-maximisation algorithm low density parity check codes

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Learning Energy Consumption Profiles from Data 5

Learning Energy Consumption Profiles from Data

引用

IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Andreoli, Jean-Marc Xerox Res Ctr Europe Grenoble France

ISBN: (纸本)9781479945184

A first step in the optimisation of the power consumption of a device infrastructure is to detect the power consumption signature of the involved devices. In this paper, we are especially interested in devices which spend most of their time waiting for a job to execute, as is often the case of shared devices in a networked infrastructure, like multi-function printing devices in an office or transaction processing terminals in a public service. We formulate the problem as an instance of power disaggregation in non intrusive load monitoring (NILM), with strong prior assumptions on the sources but with specific constraints: in particular, the aggregation is occlusive rather than additive. We use a specific variant of Hidden Semi Markov Models (HSMM) to build a generative model of the data, and adapt the expectation-maximisation (EM) algorithm to that model, in order to learn, from daily operation data, the physical characteristics of the device, separated from those linked to the job load or the device configurations. Finally, we show some experimental results on a multifunction printing device.

关键词： energy conservation expectation-maximisation algorithm hidden Markov models power consumption printers EM algorithm HSMM NILM device infrastructure hidden semi Markov models learning energy consumption profiles multifunction printing device networked infrastructure nonintrusive load monitoring physical characteristics power consumption signature power disaggregation public service transaction processing terminal Data models Energy consumption Hidden Markov models Markov processes Optimization Power demand Printing expectation-maximisation algorithm Hidden Markov models Power consumption printers Power demand data models Printing energy consumption Physical characteristics Markov chain Energy conservation

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Blind Modulation Classification for MIMO systems using expectation Maximization 33

Blind Modulation Classification for MIMO systems using Expec...

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33rd Annual IEEE Military Communications Conference on Affordable Mission Success - Meeting the Challenge (MILCOM)

作者： Zhu, Zhechen Nandi, Asoke K. Brunel Univ Sch Engn & Design Dept Elect & Comp Engn London UB8 2PL England

ISBN: (纸本)9781479967704

In this paper, we propose a blind modulation classifier for multiple-input multiple-output (MIMO) systems. The assumption of unknown channel matrix and noise variance has not been considered prior to this work. For each modulation candidate, the channel parameters are jointly estimated via expectation maximization (EM). The resulting estimation is used for the likelihood evaluation of the corresponding modulation candidate. Classification decision is reached using the maximum likelihood (ML) criterion. Classification performance is validated in simulated fading channel with white Gaussian noise. The proposed classifiers achieves robust classification accuracy in most scenarios for BPSK, QPSK, and 16-QAM modulations.

关键词： MIMO communication expectation-maximisation algorithm fading channels quadrature amplitude modulation quadrature phase shift keying 16-QAM modulations BPSK MIMO systems QPSK blind modulation classification expectation maximization maximum likelihood criterion noise variance simulated fading channel unknown channel matrix white Gaussian noise Accuracy Binary phase shift keying Channel estimation Estimation MIMO

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Composite Likelihood Data Augmentation for Within-Network Statistical Relational Learning 14

Composite Likelihood Data Augmentation for Within-Network St...

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Pfeiffer, Joseph J., III Neville, Jennifer Bennett, Paul N. Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Dept Stat W Lafayette IN 47907 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781479943036

The prevalence of datasets that can be represented as networks has recently fueled a great deal of work in the area of Relational Machine Learning (RML). Due to the statistical correlations between linked nodes in the network, many RML methods focus on predicting node features (i.e., labels) using the network relationships. However, many domains are comprised of a single, partially-labeled network. Thus, relational versions of expectation Maximization (i.e., R-EM), which jointly learn parameters and infer the missing labels, can outperform methods that learn parameters from the labeled data and apply them for inference on the unlabeled nodes. Although R-EM methods can significantly improve predictive performance in networks that are densely labeled, they do not achieve the same gains in sparsely labeled networks and can perform worse than RML methods. In this work, we show the fixed-point methods that R-EM uses for approximate learning and inference result in errors that prevent convergence in sparsely labeled networks. We then propose two methods that do not experience this problem. First, we develop a Relational Stochastic EM (R-SEM) method, which uses stochastic parameters that are not as susceptible to approximation errors. Then we develop a Relational Data Augmentation (R-DA) method, which integrates over a range of stochastic parameter values for inference. R-SEM and R-DA can use any collective RML algorithm for learning and inference in partially labeled networks. We analyze their performance with two RML learners over four real world datasets, and show that they outperform independent learning, RML and R-EM-particularly in sparsely labeled networks.

关键词： approximation theory data handling expectation-maximisation algorithm learning (artificial intelligence) network theory (graphs) stochastic processes expectation-maximisation algorithm data processing Approximation Theory Markov chain Network Stochastic Processes Dataset

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