Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Custo-efetividade do tratamento da infecção pelo vírus da hepatite C em candidatos a transplante renal submetidos à diálise

O estudo tem como objetivo geral avaliar a razão de custo-utilidade do tratamento da infecção pelo vírus da hepatite C (VHC) em pacientes dialisados, candidatos a transplante renal, tendo como esquemas terapêuticos alternativos o interferon-_ em monoterapia; o interferon peguilado em monoterapia; o interferon-_ em terapia combinada com ribavirina e o interferon peguilado em terapia combinada com ribavirina, comparando-os com o nãotratamento. A perspectiva do estudo foi a do Sistema Único de Saúde(SUS), que também serviu de base para estimar o impacto orçamentário da estratégia de tratamento mais custo efetiva. Para o alcance dos objetivos, foi construído um modelo de Makov para simulação de custos e resultados de cada estratégia avaliada. Para subsidiar o modelo, foi realizada uma revisão de literatura, a fim de definir os estados de saúde relacionados à infecção pelo vírus da hepatite C em transplantados e a probabilidade de transição entre os estados. Medidas de utilidade foram derivadas de consultas a especialistas. Os custos foram derivados da tabela de procedimentos do SUS. Os resultados do estudo demonstraram que o tratamento da infecção pelo VHC antes do transplante renal é mais custo-efetivo que o não tratamento, apontando o interferon-a como a melhor opção. O impacto orçamentário para adoção dessa estratégia pelo SUS corresponde a 0,3% do valor despendido pelo SUS com terapia renal substitutiva ao longo do ano de 2007.

Custo-efetividade da Terapia Tripla para pacientes adultos portadores do genótipo 1 da hepatite C crônica.

As ações de prevenção, diagnóstico e tratamento da hepatite C crônica integram as agendas das políticas de saúde do Brasil e do mundo, pois se trata de uma doença com grande número de acometidos, com alto custo tratamento e que ocasiona graves desfechos e incapacidade, o que acaba por onerar seu custo social. Os protocolos clínicos e diretrizes terapêuticas demonstram os esforços de inúmeras entidades no combate da hepatite C, pois informam aos profissionais de saúde, pacientes e familiares e cidadãos em geral, qual seria a melhor forma, comprovada cientificamente, de se proceder frente a uma infecção desta natureza. Realizouse uma análise de custoefetividade, sob a perspectiva do SUS, das estratégias: tratamento e retratamento com a terapia dupla, tratamento com a terapia dupla e retratamento com a terapia tripla e tratamento com a terapia tripla. Através de modelo de simulação baseado em cadeias Markov foi criada uma coorte hipotética de 1000 indivíduos adultos, acima de 40 anos, de ambos os sexos, sem distinção declasse socioeconômica, com diagnóstico confirmado para hepatite C crônica, monoinfectados pelo genótipo 1 do VHC e com ausência de comorbidades. A simulação foi iniciada com todos os indivíduos portando a forma mais branda da doença, tida como a classificação histológica F0 ou F1 segundo a escala Metavir. Os resultados demonstram que as duas opções, ou seja, a terapia dupla/tripla e a terapia tripla estão abaixo do limiar de aceitabilidade para incorporação de tecnologia proposto pela OMS (2012) que é de 72.195 (R$/QALY) (IBGE, 2013; WHO, 2012). Ambas são custoefetivas, visto que o ICER da terapia dupla/tripla em relação alinha de base foi de 7.186,3 (R$/QALY) e o da terapia tripla foi de 59.053,8 (R$/QALY). Entretanto o custo incremental de terapia tripla em relação à dupla/tripla foi de 31.029 e a efetividade incremental foi de 0,52. Em geral, quando as intervenções analisadas encontramse abaixo do limiar, sugerese a adoção do esquema de maior efetividade. A terapia tripla, apesar de ter apresentado uma efetividade um pouco acima da terapia dupla/tripla, apresentou custo muito superior. Assim, como seria coerente a adoção de uma ou da outra para utilização no SUS, visto que este sistema apresenta recursos limitados, indicase a realização de um estudo de impacto orçamentário para obterse mais um dado de embasamento da decisão e assim poder apoiar o protocolo brasileiro existente ou sugerir a confecção de novo documento.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

An online expectation-maximisation algorithm for nonnegative matrix factorisation models

In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters. We also propose a sequential Monte Carlo approximation of our online EM algorithm. We show the performance of the proposed method with two numerical examples. © 2012 IFAC.

Approximate Bayesian Computation for Smoothing

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. © Taylor & Francis Group, LLC.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

Keyword spotting system based on biomimetic pattern recognition

The mandarin keyword spotting system was investigated, and a new approach was proposed based on the principle of homology continuity and point location analysis in high-dimensional space geometry theory which are both parts of biomimetic pattern recognition theory. This approach constructed a hyper-polyhedron with sample points in the training set and calculated the distance between each test point and the hyper-polyhedron. The classification resulted from the value of those distances. The approach was tested by a speech database which was created by ourselves. The performance was compared with the classic HMM approach and the results show that the new approach is much better than HMM approach when the training data is not sufficient.

基于随机网络编码的无线广播重传方案及性能分析

针对自动最复重传(ARQ)机制在无线广播系统中吞吐量性能不佳的缺陷,提出一种基于随机网络编码的广播重传方案RNC-ARQ。对于广播节点,采用随机线性码对所有丢失包进行编码组合重传。对于接收节点,当接收的编码包累积到一定数量后可通过解码操作恢复出原始数据。该方案可有效减少重传次数,改善无线广播的吞吐量性能。基于Gilbert-Elliott模型描述的突发错误信道,建立了信道状态和节点接收处理流程合并的多状态马尔可夫模型,并以此为基础推导了RNC-ARQ方案的TQ吐量闭合解。最后,使用NS-2模拟器评估RNC-ARQ方案的性能,结果表明在突发差错信道下,基于随机网络编码重传方案的吞吐量优于传统的选择重传ARQ方案和基于异或编码的重传方案。

The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints

Danny S. Tuckwell, Matthew J. Nicholson, Christopher S. McSweeney, Michael K. Theodorou and Jayne L. Brookman (2005). The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints. Microbiology, 151 (5) pp.1557-1567 Sponsorship: BBSRC / Stapledon Memorial Trust RAE2008

Skin Color-Based Video Segmentation under Time-Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24% is observed in 17 out of 21 test sequences. In all but one case the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.

Discovering Clusters in Motion Time-Series Data

A new approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation-maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence is assigned to only a single HMM. In contrast, the formulation presented in this paper allows each sequence to belong to more than a single HMM with some probability, and the hard decision about the sequence class membership can be deferred until a later time when such a decision is required. Experiments with simulated data demonstrate the benefit of using this EM-based approach when there is more "overlap" in the processes generating the data. Experiments with real data show the promising potential of HMM-based motion clustering in a number of applications.

Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.

Modelling the flow of congestive heart failure patients through a hospital system

The number of hospital admissions in England due to heart failure is projected to increase by over 50% during the next 25 years. This will incur greater pressures on hospital managers to allocate resources in an effective manner. A reliable indicator for measuring the quantity of resources consumed by hospital patients is their length of stay (LOS) in care. This paper proposes modelling the length of time heart failure patients spend in hospital using a special type of Markov model, where the flow of patients through hospital can be thought of as consisting of three stages of care—short-, medium- and longer-term care. If it is assumed that new admissions into the ward are replacements for discharges, such a model may be used to investigate the case-mix of patients in hospital and the expected patient turnover during some specified period of time. An example is illustrated by considering hospital admissions to a Belfast hospital in Northern Ireland, between 2000 and 2004.