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检索条件"主题词=expectation-maximization algorithms"

共 270 条记录，以下是211-220 订阅

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Demodulation of binary PSK signals without explicit carrier synchronization

Demodulation of binary PSK signals without explicit carrier ...

引用

IEEE International Conference on Communications (ICC)

作者： J. Qin Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

Demodulation of binary phase-shift-keying (BPSK) signals is modeled as a problem of maximum likelihood parameter estimation from incomplete data. The received signals are corrupted by additive white Gaussian noise with the carrier phase unknown and/or random. The expectation-maximization algorithm is applied in such a way that the demodulation is conducted directly on the received signal without any explicit carrier synchronization being established. Simulation shows that the bit error probability performance of the proposed algorithm is very close to that of the ideal coherent BPSK demodulation. The implementation of the proposed algorithm is simple and open-loop, meaning that no feedback tracking or voltage-controlled oscillator is needed, and hence can be applied to burst mode transmission such as time division multiple access (TDMA) in satellite communications.< >

关键词： Demodulation Phase shift keying Binary phase shift keying Time division multiple access Maximum likelihood estimation Parameter estimation Additive white noise expectation-maximization algorithms Error probability Feedback

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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 Conference on Wireless Communications and Networking

作者： S. Morosi P. Rufolo R. Fantacci Electronics and Telecommunication Department University of Florence Florence Italy

This paper presents a 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 the 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 have shown 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： Channel estimation Multiaccess communication Iterative decoding Detectors Turbo codes Interference cancellation Parameter estimation expectation-maximization algorithms Computer simulation AWGN channels

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Neural networks for novelty detection in airframe strain data

Neural networks for novelty detection in airframe strain dat...

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International Joint Conference on Neural Networks (IJCNN)

作者： S.J. Hickinbotham J. Austin Advanced Computer Architecture Group University of York UK

The structural health of airframes is often monitored by analysis of the frequency of occurrence matrix (FOOM) produced after each flight. Each cell in the matrix records a stress event of a particular severity. These matrices are used to determine how much of the aircraft's life has been used up in each flight. Unfortunately, the sensors that produce this data are subject to degradation themselves, resulting in corruption of FOOMs. The paper reports a method of automating detection of sensor faults. It is the only known method that is capable of detecting such faults. The method is in essence a dimensionality reduction algorithm coupled to a novelty detection algorithm that produce measures of unusual counts of stress events at the level of the individual cell and unusual distributions of counts over the entire FOOM. Cell-level error is detected using a probability threshold and a sum of standard deviations. FOOM-level error is detected using a novel application of the eigenface algorithm. Novelty is measured using a Gaussian basis function neural network fitted using the expectation-maximisation algorithm.

关键词： Neural networks Capacitive sensors Fault detection Monitoring Frequency Aircraft Degradation Detection algorithms Stress measurement expectation-maximization algorithms

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An Efficient expectation-Maximisation Algorithm for Spike Classification

An Efficient Expectation-Maximisation Algorithm for Spike Cl...

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International Conference on Digital Signal Processing (DSP)

作者： Pedro Tomas Leonel Sousa INESC-ID/IST Technical University of Lisbon Lisboa Portugal

This paper presents a new expectation-Maximisation algorithm for classifying a data set originated from an unknown number of sources. The proposed algorithm is based on the Kullback-Leibler divergence and uses a minimum message length criteria to penalise adding extra data sources. It is able to estimate the parameters of the model for each data source and to determine the total number of sources producing the data. We apply our algorithm to the classification of spikes originated from multiple neurons but recorded by a single microelectrode. The obtained experimental results show the effectiveness of the proposed algorithm.

关键词： expectation-maximization algorithms Iterative algorithms Neurons Retina Microelectrodes Unsupervised learning Blind source separation Parameter estimation Classification algorithms Extracellular

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Analysis of system with pure time-delay using computer algebra systems

Analysis of system with pure time-delay using computer algeb...

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Telecommunications Forum (TELFOR)

作者： Miroslav D. Lutovac Vladimir M. Mladenović Dragutin Lj. Debeljković Ovaj rad delimicno je finansiran sa projekta Nauke i tehnoloskog razvoja Republike Srbije

ISBN: (纸本)9781509000562

In this paper, a new approach of solving the continuous-time systems, with pure time-delay elements, using symbolic tools is presented. Although symbolic computer tools cannot find the closed form solution in frequency domain (using Laplace transform method), the final solution can be derived in time domain. Described symbolic solution is implemented by using software package Mathematica (the Wolfram language), and application package SchematicSolver.

关键词： Tungsten Computers Telecommunications Algebra MIMO expectation-maximization algorithms Electronic mail

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Affine object tracking with kernel-based spatial-color representation

Affine object tracking with kernel-based spatial-color repre...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haihong Zhang Weimin Huang Zhiyong Huang Liyuan Li Institute for Infocomm Research Singapore School of Computing National University of Singapore Singapore

ISBN: (纸本)0769523722

This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise representation; the precise representation enables tracking objects in highly-cluttered environment. The basis of the method is a kernel-based similarity measure called affine matching that describes the relationship between image regions with respect to affine transformation parameters. Based on the similarity measure, a mathematical solution is derived for estimating the transformation parameters for moving objects in videos. Various experiments have yielded positive results.

关键词： Kernel Computer vision Target tracking Histograms Deformable models Motion estimation Parameter estimation Videos Gaussian distribution expectation-maximization algorithms

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Partitioning the variables for alternating optimization of real-valued scalar fields

Partitioning the variables for alternating optimization of r...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： J.C. Bezdek Comput. Sci. Dept. Univ. of West Florida Pensacola FL USA

Summary form only given, as follows. Let x be a real-valued scalar field, partitioned into t subsets of non-overlapping variables X/sub i/ (i=1, ..., t). Alternating optimization (AO) is an iterative procedure for minimizing (or maximizing) the function f(x)= f(X/sub 1/, X/sub 2/, ..., X/sub t/) jointly over all variables by alternating restricted minimizations (or maximizations) over the individual subsets of variables X/sub 1/, ..., X/sub t/. AO is the basis for the c-means clustering algorithm (t=2), many forms of vector quantization (t = 2, 3 and 4) and the expectation maximization algorithm (t=4) for normal mixture decomposition. First we review where and how AO fits into the overall optimization landscape. Then we state (without proofs) two new theorems that give very general local and global convergence and rate-of-convergence results which hold for all partitionings of x. Finally, we discuss the important problem of choosing a "best" partitioning of the input variables for the AO approach. We show that the number of possible AO iteration schemes is larger than the number of standard partitions of the input variables. Two numerical examples are given to illustrate that the selection of the t subsets of x has an important effect on the rate of convergence of the corresponding AO algorithm to a solution.

关键词： Computer science Input variables Fuzzy logic Minimization methods Clustering algorithms Iterative algorithms Vector quantization expectation-maximization algorithms Convergence of numerical methods Partitioning algorithms

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Accurate estimation of the signal baseline in DNA chromatograms

Accurate estimation of the signal baseline in DNA chromatogr...

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IEEE Workshop on Neural Networks for Signal Processing

作者： L. Andrade E.S. Manolakos Communications and Digital Signal Processing (CDSP) Center for Research and Graduate Studies Electrical and Computer Engineering Department Northeastern University Boston MA USA

Estimating accurately the varying baseline level in different parts of a DNA chromatogram is a challenging and important problem for accurate base-calling. We are formulating the problem in a statistical learning framework and propose an expectation-maximization algorithm for its solution. In addition we also present a faster, iterative histogram based method for estimating the background of the signal in small size windows. The two methods can be combined with regression techniques to correct the baseline in all regions of the chromatogram and are shown to work well even in areas of low SNR. By improving the separation of clusters, baseline correction actions reduce the classification errors when using the BEM base-caller developed in our group.

关键词： Statistical learning Bayesian methods Signal resolution Signal to noise ratio Data mining Digital signal processing DNA computing expectation-maximization algorithms Iterative methods Histograms

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Quantitative Stochastic Analysis of Magnetic Resonance Images of the Brain

Quantitative Stochastic Analysis of Magnetic Resonance Image...

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International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA

作者： M. Rouainia N. Doghmane LAS laboratory Skikda university Algeria LAMA Laboratory Badji Mokhtar University Annaba Algeria

The aim of our work is to perform an automated tool for brain MRI tissues quantification. The method we develop is based on MRI intensity stochastic analysis. By the use of the Gaussian mixture model for these intensities, we estimate MRI tissues parameters with a combination of the expectation-maximization algorithm and the Markov random field model witch provide contextual constraints that improve the classification of image pixels into three classes of tissue: white matter, grey matter and cerebro-spinal fluid. The automated model based algorithm is also extended to take in account an important MRI artefact: the bias field caused by electromagnetic field inhomogeneities. The resulting automated MRI analysis method simultaneously corrects from MR field inhomogeneities, estimates tissue classes distribution parameters, classifies the image and detects multiple sclerosis lesions when treated images present this pathology. We validate our method on simulated data then on real MRI scans

关键词： Stochastic resonance Image analysis Magnetic analysis Magnetic resonance Magnetic resonance imaging Context modeling Stochastic processes Parameter estimation expectation-maximization algorithms Markov random fields

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Robot exploration using the expectation-maximisation algorithm

Robot exploration using the expectation-maximisation algorit...

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： N. Monekosso P. Remagnino School of Computing and Information Systems Kingston University UK

Adaptability is an important attribute for any robotic system operating in an unstructured environment. The paper describes the first steps towards an adaptable robotic platform, capable of learning behaviours. This involves learning a new low-level behaviour 'on the fly' and integrating it into the existing set of behaviours. The first task selected for the robot to learn is obstacle avoidance. The paper will introduce an innovative and structured method of building knowledge acquired during robotic explorations. The aim is to make direct use of sensory information to construct abstractions of 'perceptions' and build strategies based on constructed knowledge to solve simple navigation tasks.

关键词： expectation-maximization algorithms Robot kinematics Hidden Markov models Robot sensing systems Home computing Information systems Buildings Navigation Working environment noise Testing

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