[Truncated abstract] The development of an automated classification system of wear particles or surfaces is of great interest to the machine condition monitoring industry. The system, once developed, may also find applications in medical diagnostics. Such a tool will be able to replace human experts in the detection of the onset of early machine failure, or in the diagnosis and prognosis of, for example, joint diseases. This will improve efficiency, reliability and also reduce costs of monitoring or diagnostic systems. Current literature available on this topic has included various studies on different classification methods. However, there has been no work conducted on the development of a totally integrated automated classification system. The first part of this thesis presents a study investigating the efficiency and robustness of various pattern recognition methods currently described in literature. A special computer program was developed to test each of the classification methods against both standard image databases and tribological surface images. There are three core components of a pattern recognition system that need to be analysed: (1) feature extraction, (2) feature reduction and (3) classifier. Each of these components provides a vital link that can affect the reliability of the complete classification system. ... The optimal classifier was the Linear Support Vector Classifier. This part of research is described in Paper 2. The second part of this thesis contains work verifying the performance of the automated classification system developed using both tribological and bio-tribological surface images. Experiments were carried out to generate wear particles created under different wear mechanisms (adhesive, abrasive and fatigue wear) and various operating conditions representing different degree of wear severity. The automated classification system developed was able to successfully classify wear particles with respect to both the type of wear mechanism operating and the wear severity. The results of this classification are described in Papers 3 and 5. The success of the automated classification system was also confirmed by its ability to classify different groups of worn (osteoarthritic) cartilage surfaces (Paper 4). This could lead to potential applications of the system for early detection of the onset of osteoarthritis. In conclusion, the automated classification system developed can accurately classify both tribological and bio-tribological surface images. This system could become a vitally important tool in both machine condition monitoring and medical diagnostics.

Thesis (Ph.D.) -- Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.Sc.)--York University, 2007. Graduate Programme in Computer Science and Engineering. / Typescript. Includes bibliographical references (leaves 187-189). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR38770

abstract: Query Expansion is a functionality of search engines that suggest a set of related queries for a user issued keyword query. In case of exploratory or ambiguous keyword queries, the main goal of the user would be to identify and select a specific category of query results among different categorical options, in order to narrow down the search and reach the desired result. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. These empirical methods fail to cover all semantics of categories present in the query results. More importantly these methods do not consider the semantic relationship between the keywords featured in an expanded query. Contrary to a normal keyword search setting, these factors are non-trivial in an exploratory and ambiguous query setting where the user's precise discernment of different categories present in the query results is more important for making subsequent search decisions. In this thesis, I propose a new framework for keyword query expansion: generating a set of queries that correspond to the categorization of original query results, which is referred as Categorizing query expansion. Two approaches of algorithms are proposed, one that performs clustering as pre-processing step and then generates categorizing expanded queries based on the clusters. The other category of algorithms handle the case of generating quality expanded queries in the presence of imperfect clusters. / Dissertation/Thesis / M.S. Computer Science 2011

abstract: As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data. / Dissertation/Thesis / Ph.D. Computer Science 2011

abstract: Keyword search provides a simple and user-friendly mechanism for information search, and has become increasingly popular for accessing structured or semi-structured data. However, there are two open issues of keyword search on semi/structured data which are not well addressed by existing work yet. First, while an increasing amount of investigation has been done in this important area, most existing work concentrates on efficiency instead of search quality and may fail to deliver high quality results from semantic perspectives. Majority of the existing work generates minimal sub-graph results that are oblivious to the entity and relationship semantics embedded in the data and in the user query. There are also studies that define results to be subtrees or subgraphs that contain all query keywords but are not necessarily ``minimal''. However, such result construction method suffers from the same problem of semantic mis-alignment between data and user query. In this work the semantics of how to {\em define} results that can capture users' search intention and then the generation of search intention aware results is studied. Second, most existing research is incapable of handling large-scale structured data. However, as data volume has seen rapid growth in recent years, the problem of how to efficiently process keyword queries on large-scale structured data becomes important. MapReduce is widely acknowledged as an effective programming model to process big data. For keyword query processing on data graph, first graph algorithms which can efficiently return query results that are consistent with users' search intention are proposed. Then these algorithms are migrated to MapReduce to support big data. For keyword query processing on schema graph, it first transforms a keyword query into multiple SQL queries, then all generated SQL queries are run on the structured data. Therefore it is crucial to find the optimal way to execute a SQL query using MapReduce, which can minimize the processing time. In this work, a system called SOSQL is developed which generates the optimal query execution plan using MapReduce for a SQL query $Q$ with time complexity $O(n^2)$, where $n$ is the number of input tables of $Q$. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

De nos jours, la plupart des dispositifs numériques d’acquisition et d’affichage d’images utilisent un petit nombre de couleurs dites primaires afin de représenter n’importe quelle couleur visible. Par exemple, la majorité des appareils photos "grand public" quantifient la couleur comme une certaine combinaison de Rouge, Vert et Bleu(RVB). Ce genre de technologie est qualifiée de tri-chromatique et, au même titre que les modèles tetra-chromatiques communs en imprimerie, elle présente un certain nombre d’inconvénients, tels que le métamérisme ou encore la limitation aux longueurs d’onde visibles. Afin de palier à ces limitations, les technologies multi-, hyper,voire ultra-spectrale ont connu un gain notable d’attention depuis plusieurs décennies. Un image spectrale est constituée d’un nombre de bandes (ou canaux) supérieur à 3, représentant des régions spectrales spécifiques et permettant de recouvrer la radiance ou reflectance d’objets avec plus de précision et indépendamment du capteur utilisé. De nombreux travaux de recherche ont fait considérablement progresser les méthodes d’acquisition et d’analyse, mais beaucoup de challenges demeurent, particulièrement en ce qui concernel a visualisation de ce type de données. En effet, si une image contient plusieurs dizaines de canaux comment la représenter sur un écran qui n’en accepte que trois ? Dans cette thèse, nous présentons un certain nombre de méthodes d’extraction d’attributs pour l’analyse d’images spectrales, avec une attention particulière sur la problématique de la visualisation. / Nowadays, digital imaging is mostly based on the paradigm that a combinations of a small number of so-called primary colors is sufficient to represent any visible color. For instance, most cameras usepixels with three dimensions: Red, Green and Blue (RGB). Such low dimensional technology suffers from several limitations such as a sensitivity to metamerism and a bounded range of wavelengths. Spectral imaging technologies offer the possibility to overcome these downsides by dealing more finely withe the electromagnetic spectrum. Mutli-, hyper- or ultra-spectral images contain a large number of channels, depicting specific ranges of wavelength, thus allowing to better recover either the radiance of reflectance of the scene. Nevertheless,these large amounts of data require dedicated methods to be properly handled in a variety of applications. This work contributes to defining what is the useful information that must be retained for visualization on a low-dimensional display device. In this context, subjective notions such as appeal and naturalness are to be taken intoaccount, together with objective measures of informative content and dependency. Especially, a novel band selection strategy based on measures derived from Shannon’s entropy is presented and the concept of spectral saliency is introduced.

Pas de résumé / No abstract

Le point de départ de cette recherche est le constat d’une rupture dans le fonctionnement sémantico-référentiel du genre grammatical, qui oppose d’un côté noms d’inanimés et d’animés non-anthropomorphisés, et de l’autre noms d’animés humains ou anthropomorphisés. Ce constat amène inévitablement la question, souvent traitée, du type de répartition des substantifs (arbitraire ou motivé) que permet le genre grammatical. Le fait de centrer le propos sur les noms d’humains, et plus précisément, sur la classe des « dénominations de la personne », permet de sortir de cette opposition pour analyser plus précisément les difficultés posées par la catégorie grammaticale du genre dans son lien avec la bipartition sexuée des êtres humains, généralement et traditionnellement pensée comme première. Ce travail de thèse, appuyé sur les théories de la dénomination et affiné par les outils du matérialisme et des réflexions queer sur le langage, est centré sur une proposition d’analyse stéréotypique du sens du genre grammatical. Celle-ci permet à la fois de ne pas penser l’idée d’une hiérarchie entre les genres grammaticaux (« le masculin l’emporte... ») comme structurelle et interne au système linguistique français, et de comprendre certains phénomènes en apparence contradictoires et généralement rejetés comme idéologiques et/ou politiques, donc non-linguistiques. Cette hypothèse émerge d’une réflexion sur le concept de catégorisation et sur les difficultés phénoménologiques et linguistiques qui lui sont liées. La proposition avancée est de plus orientée vers le développement d’un modèle lexicographique : le travail engagé dans cette thèse de doctorat vise donc une applicabilité potentielle. / This research was initiated with the idea of a semantic and referential splitting of grammatical gender within the French language between nouns denoting inanimates or non-anthropomorphic animates, and nouns denoting human or anthropomorphic animates. This splitting inevitably leads to the traditional question of the arbitrary or motivated nature of grammatical gender. The fact that this study focuses only on nouns denoting human animates, and more specifically on person denominations, enables to surpass this question and analyze more carefully the difficulties that arise from the idea of a link between grammatical gender and sexual bipartition. My work, nourished both by denomination theories and material and queer theories on language, is thus centered on proposing a stereotype-based semantic analysis of grammatical gender. This analysis opposes the idea of a structural hierarchy between masculine and feminine grammatical genders, and enables to understand some of the phenomena that are usually not considered as linguistic, but rejected as ideological or political. This hypothesis is thus born of a discussion of categorization theories, and of the phenomenological and linguistic difficulties that they present. Finally, one of the goals of this work is to be appliable : I will thus propose a lexicographic model of the stereotype-based hypothesis.

This research addresses the problem of automatic keyphrase extraction from large documents and back of the book indexing. The potential benefits of automating this process are far reaching, from improving information retrieval in digital libraries, to saving countless man-hours by helping professional indexers creating back of the book indexes. The dissertation introduces a new methodology to evaluate automated systems, which allows for a detailed, comparative analysis of several techniques for keyphrase extraction. We introduce and evaluate both supervised and unsupervised techniques, designed to balance the resource requirements of an automated system and the best achievable performance. Additionally, a number of novel features are proposed, including a statistical informativeness measure based on chi statistics; an encyclopedic feature that taps into the vast knowledge base of Wikipedia to establish the likelihood of a phrase referring to an informative concept; and a linguistic feature based on sophisticated semantic analysis of the text using current theories of discourse comprehension. The resulting keyphrase extraction system is shown to outperform the current state of the art in supervised keyphrase extraction by a large margin. Moreover, a fully automated back of the book indexing system based on the keyphrase extraction system was shown to lead to back of the book indexes closely resembling those created by human experts.