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Joint channel estimation and symbol detection for space-time block code

Journal of Systems Engineering and Electronics 2004年第3期15卷 266-269页

作者：单淑伟罗汉文宋文涛 DepartmentofElectronicEngineering ShanghaiJiaotongUniversityShanghai200030P.R.China

The simplified joint channel estimation and symbol detection based on the EM (expectation-maximization) algorithm for space-time block code (STBC) are proposed. By assuming channel to be invariant within only one STBC word and utilizing the orthogonal structure of STBC, the computational complexity and cost of this algorithm are both very low, so it is very suitable to implementation in real systems.

关键词： space-time block code expectation-maximization algorithm channel estimation symbol detection.

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Multiple model PMHT and its application to the second benchmark radar tracking problem

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2004年第4期40卷 1337-1350页

作者： Ruan, YH Willett, P Univ Connecticut Storrs CT 06269 USA

The probabilistic multiple hypothesis tracker (PMHT) uses the expectation-maximization (EM) algorithm to solve the measurement-origin uncertainty problem. Here we explore some of its variants for maneuvering targets and in particular discuss the multiple model PMHT. We apply this PMHT to the six "typical" tracking scenarios given in the second benchmark problem from Blair et al. [6]. The manner in which the PMHT is used to track the targets and to manage radar allocation is discussed, and the results compared with those of the interacting multiple model probabilistic data association filter (IMM/PDAF) and IMM/MHT (multiple hypothesis tracker). The PMHT works well: its performance lies between those of the IMM/PDAF and IMM/MHT both in terms of tracking performance and computational load.

关键词： probability radar tracking target tracking tracking filters IMM/MHT IMM/PDAF benchmark radar tracking expectation-maximization algorithm interacting multiple model probabilistic data association filter measurement-origin uncertainty problem multiple mode Benchmark testing expectation-maximisation algorithm radar target tracking Target tracking tracking filters

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Detection of land-cover transitions by combining multidate classifiers

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PATTERN RECOGNITION LETTERS 2004年第13期25卷 1491-1500页

作者： Bruzzone, L Cossu, R Vernazza, G Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framework of the compound classification (CC) decision rule. To develop as uncorrelated as possible classification procedures, the estimates of statistical parameters of classifiers are carried out according to different approaches (i.e., multilayer perceptron neural networks, radial basis functions neural networks, and k-nearest neighbour technique). The outputs provided by different classifiers are combined according to three standard stratcaies extended to the compound classification case (i.e., Majority voting, Bayesian average, and Bayesian,weighted average). Experiments, carried out on a multitemporal. remote-sensing data set, confirm the effectiveness of the proposed system. (C) 2004 Elsevier B.V. All rights reserved.

关键词： detection of land-cover transitions change detection multitemporal classification multiple classifier systems multilayer perceptron neural networks radial basis function neural networks k-nn technique expectation-maximization algorithm remote sensing images

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Scale-space processing of point-sampled geometry for efficient 3D object segmentation

Scale-space processing of point-sampled geometry for efficie...

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International Conference on Cyberworlds (CW 2004)

作者： Laga, H Takahashi, H Nakajima, M Tokyo Inst Technol Grad Sch Informat Sci & Engn Meguro Ku Tokyo 1528552 Japan

ISBN: (纸本)0769521401

In this paper we present a new framework for analyzing and segmenting point-sampled 3D objects. Our method first computes for each surface point the surface curvature distribution by applying the Principal Component Analysis on local neighborhoods with different sizes. Then we model in the four dimensional space the joint distribution of surface curvature and position features as a mixture of Gaussians using the expectation maximization algorithm. Central to our method is the extension of the scale-space theory from the 2D domain into the three-dimensional space to allow feature analysis and classification at different scales. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss the performance of our framework on a collection of point sampled 3D objects.

关键词： scale-space 3D object segmentation expectation-maximization algorithm

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Iterative PIC detection and channel estimation for DS-CDMA 3G communications

Iterative PIC detection and channel estimation for DS-CDMA 3...

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IEEE Wireless Communications and Networking Conference

作者： Morosi, S Rufolo, P Fantacci, R Univ Florence Elect & Telecommun Dept I-50139 Florence Italy

ISBN: (纸本)0780383443

This paper presents it Turbo Multiuser Detector for Turbo-Coded DS-CDMA systems, based on the utilization of a PIC and a bank of turbo decoders, in which the PIC performs interference cancellation after each constituent decoder of the turbo decoding scheme. Moreover, the soft output of turbo decoders are used iteratively to improve the updating step of the channel parameter estimation which is formally equivalent to one step of the expectation-maximization algorithm. By means of computer simulations, we will show that the proposed receiver achieves performance comparable with systems which suppose perfect channel parameters knowledge for medium to high system loads. in AWGN channel. The proposed receiver is also tested in a satellite channel.

关键词： iterative decoding channel estimation code division multiple access spread spectrum communication 3G mobile communication turbo codes interference suppression AWGN channels multiuser detection fading channels optimisation radiofrequency interference DS-CDMA 3G communications iterative PIC detection turbo multiuser detector interference cancellation turbo decoding scheme channel parameter estimation expectation-maximization algorithm AWGN channel satellite channel parallel interference cancellation

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Detection of land-cover transitions by combining multidate classifiers

Detection of land-cover transitions by combining multidate c...

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2nd International Workshop on Pattern Recognition for Remote Sensing

作者： Bruzzone, L Cossu, R Vernazza, G Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

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A Bayesian molecular interaction library

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2003年第7期17卷 435-461页

作者： Rantanen, VV Gyllenberg, M Koski, T Johnson, MS Univ Turku Dept Math FIN-20014 Turku Finland Abo Akad Univ Dept Biochem & Pharm FIN-20521 Turku Finland Linkoping Univ Dept Math S-58183 Linkoping Sweden

We describe a library of molecular fragments designed to model and predict non-bonded interactions between atoms. We apply the Bayesian approach, whereby prior knowledge and uncertainty of the mathematical model are incorporated into the estimated model and its parameters. The molecular interaction data are strengthened by narrowing the atom classification to 14 atom types, focusing on independent molecular contacts that lie within a short cutoff distance, and symmetrizing the interaction data for the molecular fragments. Furthermore, the location of atoms in contact with a molecular fragment are modeled by Gaussian mixture densities whose maximum a posteriori estimates are obtained by applying a version of the expectation-maximization algorithm that incorporates hyperparameters for the components of the Gaussian mixtures. A routine is introduced providing the hyperparameters and the initial values of the parameters of the Gaussian mixture densities. A model selection criterion, based on the concept of a 'minimum message length' is used to automatically select the optimal complexity of a mixture model and the most suitable orientation of a reference frame for a fragment in a coordinate system. The type of atom interacting with a molecular fragment is predicted by values of the posterior probability function and the accuracy of these predictions is evaluated by comparing the predicted atom type with the actual atom type seen in crystal structures. The fact that an atom will simultaneously interact with several molecular fragments forming a cohesive network of interactions is exploited by introducing two strategies that combine the predictions of atom types given by multiple fragments. The accuracy of these combined predictions is compared with those based on an individual fragment. Exhaustive validation analyses and qualitative examples ( e. g., the ligand-binding domain of glutamate receptors) demonstrate that these improvements lead to effective modeling and predic

关键词： combining posterior probabilities expectation-maximization algorithm maximum a posteriori estimates mixture model protein-ligand recognition

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Clustering ensembles of neural network models

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NEURAL NETWORKS 2003年第2期16卷 261-269页

作者： Bakker, B Heskes, T Univ Nijmegen SNN NL-6525 EZ Nijmegen Netherlands

We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. In some cases this summary may even yield better function estimates. We present a method to find representative models through clustering based on the models' outputs on a data set. We apply the method on an ensemble of neural network models obtained from bootstrapping on the Boston housing data, and use the results to discuss bootstrapping in terms of bias and variance. A parallel application is the prediction of newspaper sales, where we learn a series of parallel tasks. The msults indicate that it is not necessary to store all samples in the ensembles: a small number of representative models generally matches, or even surpasses, the performance of the full ensemble. The clustered representation of the ensemble obtained thus is much better suitable for qualitative analysis, and will be shown to yield new insights into the data. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： clustering bootstrapping bias/variance analysis multitask learning deterministic annealing expectation-maximization algorithm multilayered perceptron nonlinear model

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Adaptive sparseness for supervised learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2003年第9期25卷 1150-1159页

作者： Figueiredo, MAT Univ Tecn Lisboa Inst Telecommun Inst Super Tecn P-1049001 Lisbon Portugal Univ Tecn Lisboa Dept Elect & Comp Engn Inst Super Tecn P-1049001 Lisbon Portugal

The goal of supervised teaming is to infer a functional mapping based on a set of training examples. To achieve good generalization, it is necessary to control the "complexity" of the learned function. In Bayesian approaches, this is done by adapting a prior for the parameters of the function being teamed. We propose a Bayesian approach to supervised teaming, which leads to sparse solutions;that is, in which irrelevant parameters are automatically set exactly to zero. Other ways to obtain sparse classifiers (such as Laplacian priors, support vector machines) involve (hyper)parameters which control the degree of sparseness of the resulting classifiers;these parameters have to be somehow adjusted/estimated from the training data. In contrast, our approach does not involve any (hyper)parameters to be adjusted or estimated. This is achieved by a hierarchical-Bayes interpretation of the Laplacian prior, which is then modified by the adoption of a Jeffreys' noninformative hyperprior. Implementation is carried out by an expectation-maximization (EM) algorithm. Experiments with several benchmark data sets show that the proposed approach yields state-of-the-art performance. In particular, our method outperforms SVMs and performs competitively with the best alternative techniques, although it involves no tuning or adjustment of sparseness-controlling hyperparameters.

关键词： supervised learning classification regression sparseness feature selection kernel methods expectation-maximization algorithm

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Image restoration: The wavelet-based approach

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2003年第1期17卷 151-162页

作者： Ndjountche, T Unbehauen, R Univ Erlangen Nurnberg Dept Elect Elect & Informat Engn D-91058 Erlangen Germany

Wavelet-based techniques are suitable for recovering a signal corrupted by noise. The time- and frequency-localization capabilities of wavelets provide better noise reduction and less signal distortion than conventional filtering methods. The noise reduction technique used in this paper is based on the hidden Markov model (HMM) structure, which can efficiently shape the statistical characteristics of practical data. As confirmed by numerical results, the HMM based approach provides a significant performance improvement over competing methods.

关键词： wavelet transform thresholding hidden Markov model expectation-maximization algorithm noise reduction

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