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Fluorescence lifetime discrimination using expectation-maximization algorithm with joint deconvolution

JOURNAL OF BIOMEDICAL OPTICS 2009年第6期14卷 064009-064009页

作者： Fu, Chit Yaw Ng, Beng Koon Razul, Sirajudeen Gulam Nanyang Technol Univ Sch Elect & Elect Engn Photon Res Ctr Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Div Informat Engn Singapore 639798 Singapore

The fluorescence lifetime technique offers an effective way to resolve fluorescent components with overlapping emission spectra. The presence of multiple fluorescent components in biological compounds can hamper their discrimination. The conventional method based on the nonlinear least-squares technique is unable to consistently determine the correct number of fluorescent components in a fluorescence decay profile. This can limit the applications of the fluorescence lifetime technique in biological assays and diagnoses where more than one fluorescent component is typically encountered. We describe the use of an expectation-maximization (EM) method with joint deconvolution to estimate the fluorescence decay parameters, and the Bayesian information criterion (BIC) to accurately determine the number of fluorescent components. A comprehensive simulation and experimental study is carried out to compare the performance and accuracy of the proposed method. The results show that the EMBIC method is able to accurately identify the correct number of fluorescent components in samples with weakly fluorescing components. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3258835]

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Data Assimilation in Air Contaminant Dispersion Using a Particle Filter and expectation-maximization algorithm

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ATMOSPHERE 2017年第9期8卷 170-170页

作者： Wang, Rongxiao Chen, Bin Qiu, Sihang Zhu, Zhengqiu Qiu, Xiaogang Natl Univ Def Technol Coll Informat Syst & Management 109 Deya Rd Changsha 410073 Hunan Peoples R China

The accurate prediction of air contaminant dispersion is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in chemical industry parks. Conventional atmospheric dispersion models can seldom give accurate predictions due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion models, two data assimilation methods (i.e., the typical particle filter & the combination of a particle filter and expectation-maximization algorithm) are proposed to assimilate the virtual Unmanned Aerial Vehicle (UAV) observations with measurement error into the atmospheric dispersion model. Two emission cases with different dimensions of state parameters are considered. To test the performances of the proposed methods, two numerical experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model predictions when the dimension of state parameters is relatively low. In contrast, when the dimension of state parameters becomes higher, the method of particle filter combining the expectation-maximization algorithm performs better in terms of the parameter estimation accuracy. Therefore, the proposed data assimilation methods are able to effectively support air quality monitoring and emergency management in chemical industry parks.

关键词： air contaminant dispersion data assimilation particle filter expectation-maximization algorithm

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Offered load estimation in random access multipacket perception systems using the expectation-maximization algorithm

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SIGNAL PROCESSING 2021年 179卷 107827-107827页

作者： Orozco-Lugo, Aldo G. Lara, Mauricio Sandoval-Curmina, Victor Galvan-Tejada, Giselle M. IPN Ctr Res & Adv Studies Dept Elect Engn Commun Sect Av IPN 2508 Mexico City 07630 DF Mexico Tecnol Nacl Mexico Inst Tecnol Merida Dept Elect & Elect Engn Av Tecnol Km 4-5 S-N Merida 97118 Mexico

This paper considers offered load estimation in probabilistic dynamic frame slotted ALOHA systems where the receiver can perceive multiple concurrent packets in a slot. This type of random access protocol finds current application in the areas of machine-to-machine communications and tag processing in RFID communications. The perception of multiple concurrent packets happens when a receiver is able to detect the number of received packets or to decode them. In the first case, we talk about multiple packet detection (MPD) whereas in the second we refer to multiple packet reception (MPR). The finite detection capability of MPD or separation capacity of MPR implies that, ideally, whenever the number of simultaneously received packets exceeds a given value M , a collision results and no information about the packets or their number can be extracted. Hence, we are confronted with an incomplete data estimation problem. We reformulate the problem so as to apply the expectation-maximization (EM) algorithm and derive the required iterative equations to obtain a maximum likelihood estimator for the offered load. A numerical example of the algorithm's execution is included and application scenarios are indicated. Simulation studies are presented to show that the proposed estimation method compares favorably with alternative techniques. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Offered load estimation Multipacket perception expectation-maximization algorithm Dynamic frame ALOHA RFID systems Tag cardinality estimation

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MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm

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MICROBIOME 2014年第1期2卷 26-26页

作者： Wu, Yu-Wei Tang, Yung-Hsu Tringe, Susannah G. Simmons, Blake A. Singer, Steven W. Joint BioEnergy Inst Emeryville CA 94608 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab Phys Biosci Div Berkeley CA 94720 USA City Coll San Francisco San Francisco CA 94112 USA Joint Genome Inst Walnut Creek CA 94598 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab Genom Div Berkeley CA 94720 USA Sandia Natl Labs Biol & Mat Sci Ctr Livermore CA 94551 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab Earth Sci Div Berkeley CA 94720 USA

Background: Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. Results: We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. Conclusions: The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacter

关键词： Binning Metagenomics expectation-maximization algorithm

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An innovative multimodal/multispectral image registration method for medical images based on the expectation-maximization algorithm

An innovative multimodal/multispectral image registration me...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Edgar Arce-Santana Daniel U. Campos-Delgado Aldo Mejia-Rodriguez Isnardo Reducindo Facultad de Ciencias Universidad Autonoma de San Luis Potosi SLP Mexico

ISBN: (纸本)9781424492695

In this paper, we present a methodology for multimodal/multispectral image registration of medical images. This approach is formulated by using the expectation-maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

关键词： EM medical images based expectation-maximization algorithm expectation-maximisation algorithm image registration Geometric transformation images Medical imaging Medical intensity distribution

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An expectation-maximization algorithm based Kalman Smoother Approach for Single-Trial Estimation of Event-related Potentials

An Expectation-Maximization Algorithm based Kalman Smoother ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Chee-Ming Ting S. Balqis Samdin Sh-Hussain Salleh M. Hafizi Omar I. Kamarulafizam Center for Biomedical Engineering UTM 81310 Skudai Johor Malaysia

ISBN: (纸本)9781457717871

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.

关键词： Event-related potentials Kalman smoother expectation-maximization algorithm

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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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Estimating the parameters of the generalized KA distribution by applying the expectation maximization algorithm

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JOURNAL OF APPLIED REMOTE SENSING 2019年第1期13卷

作者： Melesanaki, Theonymphi M. Vardiambasis, Ioannis O. Ioannidou, Melina P. Kokkinos, Evangelos A. Maras, Andreas M. Technol Educ Inst Crete Dept Elect Engn Khania Greece Alexander Technol Educ Inst Thessaloniki Dept Elect Engn Thessaloniki Greece Univ Peloponnese Dept Informat & Telecommun Tripoli Greece

Generalization of the KA distribution is formulated by combining the class A and K distributions;the resulting distribution is termed as generalized KA distribution. It is obtained as a mixture of a generalized Rayleigh and a class A distribution with gamma-distributed mean intensity, and it may be used to describe clutter statistics. Its parameters are estimated by implementing the expectation maximization algorithm. The latter provides estimates in the framework of the maximum likelihood principle, and it is widely used when the data set is incomplete and/or of limited size. The numerical results show that the absolute relative error of the estimated parameters may be <8% even in the case of 100 data samples, whereas it is reduced significantly as the size of the data set increases. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： radar clutter expectation-maximization algorithm generalized KA distribution KA distribution maximum likelihood reverberation

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Reliability evaluation of components with multiple failure modes based on mixture Weibull distribution using expectation maximization algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第2期38卷 649-660页

作者： Wu, Yuting Lu, Zhong Wu, Jiayu Liang, Xihui Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada

The mixture Weibull distribution is widely used in the reliability analysis of lifetime data of components, especially for those caused by multiple failure modes. In order to accurately estimate the parameters of mixture Weibull distributions, an optimized expectation maximization (EM) algorithm is proposed based on the particle swarm optimization (PSO) algorithm. The Weibull probability plot (WPP) method is introduced to determine the approximate values and rough limits of unknown parameters, and the approximate values will be used as the initial points in the optimization of the EM procedure. By taking the maximum of the Q function as the optimization goal and the rough limits of each parameter as the constraints, a parameter estimation model of the mixture Weibull distribution is built. The PSO algorithm has been introduced into the maximization step of the EM algorithm to deal with the transcendental equations. Numerical cases are used to illustrate the accuracy of our method in the parameter estimation for the mixture Weibull distribution, and an engineering case is used to show the application of our method in the reliability evaluation of components with multiple failure modes. Compared with the least squares estimation and the maximum likelihood estimation, our method shows advantages in both accuracy and efficiency.

关键词： expectation-maximization algorithm Mixture Weibull distribution Multiple failure modes Parameter estimation Particle swarm optimization

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A numerically efficient implementation of the expectation maximization algorithm for state space models

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APPLIED MATHEMATICS AND COMPUTATION 2014年 241卷 222-232页

作者： Mader, Wolfgang Linke, Yannick Mader, Malenka Sommerlade, Linda Timmer, Jens Schelter, Bjoern Univ Freiburg Inst Phys Freiburg Germany Univ Freiburg Freiburg Ctr Data Anal & Modeling FDM Freiburg Germany Univ Med Ctr Freiburg Dept Neuropediat & Muscular Dis Freiburg Germany Univ Aberdeen Inst Complex Syst & Math Biol Aberdeen AB9 1FX Scotland

Empirical time series are subject to observational noise. Naive approaches that estimate parameters in stochastic models for such time series are likely to fail due to the error-in-variables challenge. State space models (SSM) explicitly include observational noise. Applying the expectation maximization (EM) algorithm together with the Kalman filter constitute a robust iterative procedure to estimate model parameters in the SSM as well as an approach to denoise the signal. The EM algorithm provides maximum likelihood parameter estimates at convergence. The drawback of this approach is its high computational demand. Here, we present an optimized implementation and demonstrate its superior performance to naive algorithms or implementations. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Kalman filter expectation-maximization algorithm Parameter estimation State-space model

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