With the huge amount of subjective contents in on-line documents, there is a clear need for an information retrieval system that supports retrieval of documents containing opinions about the topic expressed in a user’s query. In recent years, blogs, a new publishing medium, have attracted a large number of people to express personal opinions covering all kinds of topics in response to the real-world events. The opinionated nature of blogs makes them a new interesting research area for opinion retrieval. Identification and extraction of subjective contents from blogs has become the subject of several research projects. In this thesis, four novel methods are proposed to retrieve blog posts that express opinions about the given topics. The first method utilizes the Kullback-Leibler divergence (KLD) to weight the lexicon of subjective adjectives around query terms. Considering the distances between the query terms and subjective adjectives, the second method uses KLD scores of subjective adjectives based on distances from the query terms for document re-ranking. The third method calculates KLD scores of subjective adjectives for predefined query categories. In the fourth method, collocates, words co-occurring with query terms in the corpus, are used to construct the subjective lexicon automatically. The KLD scores of collocates are then calculated and used for document ranking. Four groups of experiments are conducted to evaluate the proposed methods on the TREC test collections. The results of the experiments are compared with the baseline systems to determine the effectiveness of using KLD in opinion retrieval. Further studies are recommended to explore more sophisticated approaches to identify subjectivity and promising techniques to extract opinions.

With the huge amount of subjective contents in on-line documents, there is a clear need for an information retrieval system that supports retrieval of documents containing opinions about the topic expressed in a user’s query. In recent years, blogs, a new publishing medium, have attracted a large number of people to express personal opinions covering all kinds of topics in response to the real-world events. The opinionated nature of blogs makes them a new interesting research area for opinion retrieval. Identification and extraction of subjective contents from blogs has become the subject of several research projects. In this thesis, four novel methods are proposed to retrieve blog posts that express opinions about the given topics. The first method utilizes the Kullback-Leibler divergence (KLD) to weight the lexicon of subjective adjectives around query terms. Considering the distances between the query terms and subjective adjectives, the second method uses KLD scores of subjective adjectives based on distances from the query terms for document re-ranking. The third method calculates KLD scores of subjective adjectives for predefined query categories. In the fourth method, collocates, words co-occurring with query terms in the corpus, are used to construct the subjective lexicon automatically. The KLD scores of collocates are then calculated and used for document ranking. Four groups of experiments are conducted to evaluate the proposed methods on the TREC test collections. The results of the experiments are compared with the baseline systems to determine the effectiveness of using KLD in opinion retrieval. Further studies are recommended to explore more sophisticated approaches to identify subjectivity and promising techniques to extract opinions.

觀察性研究資料中，透過傾向分數的使用，可以使基準變數在實驗與對照兩組間達到某種程度的平衡，並可視同為一隨機試驗，進而進行有效的統計推論。文獻中有關平衡與否的診斷，大多聚焦於平均數與變異數的比較。本文中我們提出使用KLIC(Kullback-Leibler Information Criterion)及KS(Kolmogorov and Simonov)兩種比較分配函數差異的統計量，作為另一種平衡診斷工具的構想，並針對其可行性進行探討與評比。此外，數據顯示KLIC及KS與透過傾向分數配對的成功比例呈現負相關。由於配對成功比例過低將導致後續統計推論結果的侷限性，因此本文也就KLIC及KS作為是否進行配對的一個先行指標之可行性作探討。模擬結果顯示，二者的答案均是肯定的。 / In observational studies, propensity scores are frequently used as tools to balance the distribution of baseline covariates between treated and untreated groups to some extent so that the data could be treated as if they were from a randomized controlled trial (RCT) and causal inferences could thus be made. In the past, balance or not was usually diagnosed in terms of the means and/or the variances. In this study, we proposed using either Kullback-Leibler Information Criterion (KLIC) or Kolmogorov and Simonov (KS) statistic as a diagnostic measure, and evaluated its feasibility. In addition, since low propensity score matching rate decreases the power of the statistical inference and a pilot study showed that the matching rate was negatively correlated with KLIC and KS; thus, we also discussed the possibilities of using KLIC and KS to be pre-indices before implementing propensity score matching. Both considerations appear to be positive through our simulation study.

L'intérêt des recherches dans le domaine de la fusion de données multi-capteurs est en plein essor en raison de la diversité de ses secteurs d'applications. Plus particulièrement, dans le domaine de la robotique et de la localisation, l'exploitation des différentes informations fournies par les capteurs constitue une étape primordiale afin d'assurer une estimation fiable de la position. Dans ce contexte de fusion de données multi-capteurs, nous nous attachons à traiter le diagnostic, menant à l'identification de la cause d'une défaillance, et la tolérance de l'approche proposée aux défauts de capteurs, peu abordés dans la littérature.Nous avons fait le choix de développer une approche basée sur un formalisme purement informationnel : filtre informationnel d'une part, et outils de la théorie de l'information d'autre part. Des résidus basés sur la divergence de Kullback-Leibler sont développés. Via des méthodes optimisées de seuillage, ces résidus conduisent à la détection et à l'exclusion de ces défauts capteurs. La théorie proposée est éprouvée sur deux applications de localisation. La première application concerne la localisation collaborative, tolérante aux défauts d'un système multi-robots. La seconde application traite de la localisation en milieu ouvert utilisant un couplage serré GNSS/odométrie tolérant aux défauts. / The interest of research in the multi-sensor data fusion field is growing because of its various applications sectors. Particularly, in the field of robotics and localization, the use of different sensors informations is a vital step to ensure a reliable position estimation. In this context of multi-sensor data fusion, we consider the diagnosis, leading to the identification of the cause of a failure, and the sensors faults tolerance aspect, discussed in limited work in the literature. We chose to develop an approach based on a purely informational formalism: information filter on the one hand and tools of the information theory on the other. Residuals based on the Kullback-Leibler divergence are developed. These residuals allow to detect and to exclude the faulty sensors through optimized thresholding methods. This theory is tested in two applications. The first application is the fault tolerant collaborative localization of a multi-robot system. The second application is the localization in outdoor environments using a tightly coupled GNSS/odometer with a fault tolerant aspect.

Environ un tiers des personnes âgées vivant à domicile souffrent d'une chute chaque année. Les chutes les plus graves se produisent lorsque la personne est seule et incapable de se lever, ce qui entraîne un grand nombre de personnes âgées admis au service de gériatrique et un taux de mortalité malheureusement élevé. Le système PAL (Personally Assisted Living) apparaît comme une des solutions de ce problème. Ce système d’intelligence ambiante permet aux personnes âgées de vivre dans un environnement intelligent et pro-actif. Le travail de cette thèse s’inscrit dans le cadre de suivi des personnes âgées avec un maintien à domicile, la reconnaissance quotidienne des activités et le système automatique de détection des chutes à l'aide d'un ensemble de capteurs non intrusifs qui accorde l'intimité et le confort aux personnes âgées. En outre, une méthode de fusion tolérante aux fautes est proposée en utilisant un formalisme purement informationnel: filtre informationnel d’une part, et outils de la théorie de l’information d’autre part. Des résidus basés sur la divergence de Kullback-Leibler sont utilisés. Via un seuillage adéquat, ces résidus conduisent à la détection et à l’exclusion des défauts capteurs. Les algorithmes proposés ont été validés avec plusieurs scénarii différents contenant les différentes activités: marcher, s’asseoir, debout, se coucher et tomber. Les performances des méthodes développées ont montré une sensibilité supérieure à 94% pour la détection de chutes de personnes et plus de 92% pour la discrimination entre les différentes ADL (Activités de la vie quotidienne). / About one third of home-dwelling older people suffer a fall each year. The most painful falls occur when the person is alone and unable to get up, resulting in huge number of elders which are associated with institutionalization and high morbidity-mortality rate. The PAL (Personally Assisted Living) system appears to be one of the solutions of this problem. This ambient intelligence system allows elderly people to live in an intelligent and pro-active environment. This thesis describes the ongoing work of in-home elder tracking, activities daily living recognition, and automatic fall detection system using a set of non-intrusive sensors that grants privacy and comfort to the elders. In addition, a fault-tolerant fusion method is proposed using a purely informational formalism: information filter on the one hand, and information theory tools on the other hand. Residues based on the Kullback-Leibler divergence are used. Using an appropriate thresholding, these residues lead to the detection and the exclusion of sensors faults. The proposed algorithms were validated with many different scenarios containing the different activities: walking, sitting, standing, lying down, and falling. The performances of the developed methods showed a sensitivity of more than 94% for the fall detection of persons and more than 92% for the discrimination between the different ADLs (Activities of the daily life).

In this thesis, we consider the clustering of time series data; specifically, time series that can be modeled in the state space framework. Of primary focus is the pairwise discrepancy between two state space time series. The state space model can be formulated in terms of two equations: the state equation, based on a latent process, and the observation equation. Because the unobserved state process is often of interest, we develop discrepancy measures based on the estimated version of the state process. We compare these measures to discrepancies based on the observed data. In all, seven novel discrepancies are formulated. First, discrepancies derived from Kullback-Leibler (KL) information and Mahalanobis distance (MD) measures are proposed based on the observed data. Next, KL information and MD discrepancies are formulated based on the composite marginal contributions of the smoothed estimates of the unobserved state process. Furthermore, an MD is created based on the joint contributions of the collection of smoothed estimates of the unobserved state process. The cross trajectory distance, a discrepancy heavily influenced by both observed and smoothed data, is proposed as well as a Euclidean distance based on the smoothed state estimates. The performance of these seven novel discrepancies is compared to the often used Euclidean distance based on the observed data, as well as a KL information discrepancy based on the joint contributions of the collection of smoothed state estimates (Bengtsson and Cavanaugh, 2008). We find that those discrepancy measures based on the smoothed estimates of the unobserved state process outperform those discrepancy measures based on the observed data. The best performance was achieved by the discrepancies founded upon the joint contributions of the collection of unobserved states, followed by the discrepancies derived from the marginal contributions. We observed a non-trivial degradation in clustering performance when estimating the parameters of the state space model. To improve estimation, we propose an iterative estimation and clustering routine based on the notion of finding a series' most similar counterparts, pooling them, and estimating a new set of parameters. Under ideal circumstances, we show that the iterative estimation and clustering algorithm can potentially achieve results that approach those obtained in settings where parameters are known. In practice, the algorithm often improves the performance of the model-based clustering measures. We apply our methods to two examples. The first application pertains to the clustering of time course genetic data. We use data from Cho et al. (1998) where a time course experiment of yeast gene expression was performed in order to study the yeast mitotic cell cycle. We attempt to discover the phase to which 219 genes belong. The second application seeks to answer whether or not influenza and pneumonia mortality can be explained geographically. Data from a collection of cities across the U.S. are acquired from the Morbidity and Mortality Weekly Report (MMWR). We cluster the MMWR data without geographic constraints, and compare the results to clusters defined by MMWR geographic regions. We find that influenza and pneumonia mortality cannot be explained by geography.

En esta tesis hemos querido básicamente considerar el diseño de funciones de coste para adaptación ciega en un amplio margen de aplicaciones en el ámbito de las comunicaciones digitales, tomando en consideración los siguientes problemas de recuperación de señales:Ecualización, recorrelación y conformación de haz.Introducimos un criterio de diseño para algoritmos ciegos, así como dos funciones de coste que implementan dicho criterio.El diseño de funciones de coste para los problemas anteriores se consigue mediante aplicación de los criterios de referencia espacial y referencia temporal, normalmente, de tipo error cuadrático medio. La idea motriz de esta tesis consiste en añadir un tercer criterio a los dos anteriormente citados: el criterio de referencia estadística, aplicable a algoritmos de adaptación ciega. Es decir, en la recuperación de señales tomamos como referencia la distribución de probabilidad de la señal que deseamos recuperar, que suponemos, naturalmente, conocida. Este criterio es especialmente robusto en el caso de conformación adaptativa de haz donde deseamos recuperar una señal de una determinada estadística de entre todas las incidencias en el array.Incluso, la recuperación es posible en presencia de interferencias más potentes que la misma señal deseada.Otro ejemplo como el de recorrelación de señales CDMA, presenta un cariz algo distinto. En este caso las señales de todos los usuarios tienen la misma estadística. Por lo tanto debemos utilizar un criterio de referencia estadística que utilice información sobre la función de densidad de probabilidad conjunta de todos los usuarios.Análogamente a los criterios de referencia temporal, se introduce el concepto de función de regeneración de datos. La ausencia de una referencia en algoritmos ciegos se resuelve entonces mediante una función no lineal, que al aplicarla a la señal de salida del sistema adaptativo regenera una referencia. / This thesis considers a design criterion for cost functions in adaptive signal processing, in order to enforce a target probability density function at the output of an adaptive system. This philosophy is named Statistical Reference, in contrast to temporal and spatial reference methods used in array processing. The Kullback-Leibler distance between probability density functions contitutes the theoretical basis to achieve the so-called "distribution matching". Applications are considered in the field of digital communications: blind cannel equalization and array processing

No description available.

The idea of `probabilistic distances' (also called divergences), which in some sense assess how `close' two probability distributions are from one another, has been widely employed in probability, statistics, information theory, and related fields. Of particular importance due to their generality and applicability are the Renyi divergence measures. While the closely related concept of Renyi entropy of a probability distribution has been studied extensively, and closed-form expressions for the most common univariate and multivariate continuous distributions have been obtained and compiled, the literature currently lacks the corresponding compilation for continuous Renyi divergences. The present thesis addresses this issue for analytically tractable cases. Closed-form expressions for Kullback-Leibler divergences are also derived and compiled, as they can be seen as an extension by continuity of the Renyi divergences. Additionally, we establish a connection between Renyi divergence and the variance of the log-likelihood ratio of two distributions, which extends the work of Song (2001) on the relation between Renyi entropy and the log-likelihood function, and which becomes practically useful in light of the Renyi divergence expressions we have derived. Lastly, we consider the Renyi divergence rate between two zero-mean stationary Gaussian processes. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2011-08-30 13:37:41.792

This report present the work to develop a misfire detection algorithm for onboard diagnostics on a spark ignited combustion engine. The work is based on a previous developed model-based detection algorithm, created to meet more stringent future legislation and reduce the cost of calibration. In the existing approach a simplified engine model is used to estimate the torque from the flywheel angular velocity, and the algorithm can detect misfires in various conditions. The main contribution in this work, is further development of the misfire detection algorithm with focus on improving the handling of disturbances and variations between different vehicles. The resulting detection algorithm can be automatically calibrated with training data and manage disturbances such as manufacturing errors on the flywheel and torsional vibrations in the crankshaft occurring after a misfire. Furthermore a robustness analysis with different engine configurations is carried out, and the algorithm is evaluated with the Kullback- Leibler divergence correlated to the diagnosis requirements. In the validation, data from vehicles with four cylinder engines are used and the algorithm show good performance with few false alarms and missed detections.