In this dissertation we propose several DFT techniques specific to 3D stacked IC systems. The goal has explicitly been to create techniques that integrate easily with existing IC test systems. Specifically, this means utilizing scan- and wrapper-based techniques, two foundations of the digital IC test industry. First, we describe a general test architecture for 3D ICs. In this architecture, each tier of a 3D design is wrapped in test control logic that both manages tier test pre-bond and integrates the tier into the large test architecture post-bond. We describe a new kind of boundary scan to provide the necessary test control and observation of the partial circuits, and we propose a new design methodology for test hardcore that ensures both pre-bond functionality and post-bond optimality. We present the application of these techniques to the 3D-MAPS test vehicle, which has proven their effectiveness. Second, we extend these DFT techniques to circuit-partitioned designs. We find that boundary scan design is generally sufficient, but that some 3D designs require special DFT treatment. Most importantly, we demonstrate that the functional partitioning inherent in 3D design can potentially decrease the total test cost of verifying a circuit. Third, we present a new CAD algorithm for designing 3D test wrappers. This algorithm co-designs the pre-bond and post-bond wrappers to simultaneously minimize test time and routing cost. On average, our algorithm utilizes over 90% of the wires in both the pre-bond and post-bond wrappers. Finally, we look at the 3D vias themselves to develop a low-cost, high-volume pre-bond test methodology appropriate for production-level test. We describe the shorting probes methodology, wherein large test probes are used to contact multiple small 3D vias. This technique is an all-digital test method that integrates seamlessly into existing test flows. Our experimental results demonstrate two key facts: neither the large capacitance of the probe tips nor the process variation in the 3D vias and the probe tips significantly hinders the testability of the circuits. Taken together, this body of work defines a complete test methodology for testing 3D ICs pre-bond, eliminating one of the key hurdles to the commercialization of 3D technology.

With advancement in the development of the new laboratory instruments and experimental techniques, the protein data has an explosive increasing rate. Therefore how to efficiently store, retrieve and modify protein data is becoming a challenging issue that most biological scientists have to face and solve. Traditional data models such as relational database lack of support for complex data types, which is a big issue for protein data application. Hence many scientists switch to the object-oriented databases since object-oriented nature of life science data perfectly matches the architecture of object-oriented databases, but there are still a lot of problems that need to be solved in order to apply OODB methodologies to manage protein data. One major problem is that the general-purpose OODBs do not have any built-in data types for biological research and built-in biological domain-specific functional operations. In this dissertation, we present an application system with built-in data types and built-in biological domain-specific functional operations that extends the Object-Oriented Database (OODB) system by adding domain-specific additional layers Protein-QL, Protein Algebra Architecture and Protein-OODB above OODB to manage protein structure data. This system is composed of three parts: 1) Client API to provide easy usage for different users. 2) Middleware including Protein-QL, Protein Algebra Architecture and Protein-OODB is designed to implement protein domain specific query language and optimize the complex queries, also it capsulates the details of the implementation such that users can easily understand and master Protein-QL. 3) Data Storage is used to store our protein data. This system is for protein domain, but it can be easily extended into other biological domains to build a bio-OODBMS. In this system, protein, primary, secondary, and tertiary structures are defined as internal data types to simplify the queries in Protein-QL such that the domain scientists can easily master the query language and formulate data requests, and EyeDB is used as the underlying OODB to communicate with Protein-OODB. In addition, protein data is usually stored as PDB format and PDB format is old, ambiguous, and inadequate, therefore, PDB data curation will be discussed in detail in the dissertation.

Reproduction de : Thèse de doctorat : Informatique : Lille 1 : 2006. / N° d'ordre (Lille 1) : 3904. Titre provenant de la page de titre du document numérisé. Bibliogr. p. 113-119.

Software quality ensures that applications that are developed are failure free. Some modern systems are intricate, due to the complexity of their information processes. Software fault prediction is an important quality assurance activity, since it is a mechanism that correctly predicts the defect proneness of modules and classifies modules that saves resources, time and developers’ efforts. In this study, a model that selects relevant features that can be used in defect prediction was proposed. The literature was reviewed and it revealed that process metrics are better predictors of defects in version systems and are based on historic source code over time. These metrics are extracted from the source-code module and include, for example, the number of additions and deletions from the source code, the number of distinct committers and the number of modified lines. In this research, defect prediction was conducted using open source software (OSS) of software product line(s) (SPL), hence process metrics were chosen. Data sets that are used in defect prediction may contain non-significant and redundant attributes that may affect the accuracy of machine-learning algorithms. In order to improve the prediction accuracy of classification models, features that are significant in the defect prediction process are utilised. In machine learning, feature selection techniques are applied in the identification of the relevant data. Feature selection is a pre-processing step that helps to reduce the dimensionality of data in machine learning. Feature selection techniques include information theoretic methods that are based on the entropy concept. This study experimented the efficiency of the feature selection techniques. It was realised that software defect prediction using significant attributes improves the prediction accuracy. A novel MICFastCR model, which is based on the Maximal Information Coefficient (MIC) was developed to select significant attributes and Fast Correlation Based Filter (FCBF) to eliminate redundant attributes. Machine learning algorithms were then run to predict software defects. The MICFastCR achieved the highest prediction accuracy as reported by various performance measures. / School of Computing / Ph. D. (Computer Science)

The technology today makes it possible to integrate a complete system on a single chip, called "System-on-Chip'' (SOC). Nowadays SOC designers use previously designed hardware modules, called cores, together with their user defined logic (UDL), to form a complete system on a single chip. The manufacturing process may result in defect chips, for instance due to the base material, and therefore testing chips after production is important in order to ensure fault-free chips. The testing time for a chip will affect its final cost. Thus it is important to minimize the testing time for each chip. For core-based SOCs this can be done by testing several cores at the same time, instead of testing the cores sequentially. However, this will result in a higher activity in the chip, hence higher power consumption. Due to several factors in the manufacturing process there are limitations of the power consumption for a chip. Therefore, the power limitations should be carefully considered when planning the testing of a chip. Otherwise it can be damaged during test, due to overheating. This leads to the problem of minimizing testing time under such power constraints. In this thesis we discuss test power modeling and its application to SOC testing. We present previous work in this area and conclude that current power modeling techniques in SOC testing are rather pessimistic. We therefore propose a more accurate power model that is based on the analysis of the test data. Furthermore, we present techniques for test pattern reordering, with the objective of partitioning the test power consumption into low parts and high parts. The power model is included in a tool for SOC test architecture design and test scheduling, where the scheduling heuristic is designed for SOCs with fixed- width test bus architectures. Several experiments have been conducted in order to evaluate the proposed approaches. The results show that, by using the presented power modeling techniques in test scheduling algorithms, we will get lower testing times and thus lower test cost.

This work focus at data and especially text mining from Web pages, an overview of programs for downloading the text and ways of their extraction. It also contains an overview of the most frequently used programs for extracting data from internet. The output of this thesis is a Java program that can download text from a selection of servers and save them into xml le.

Made available in DSpace on 2015-04-11T14:02:41Z (GMT). No. of bitstreams: 1 andre.pdf: 821975 bytes, checksum: 8b72d2493d068d6a827082e5eb108bf6 (MD5) Previous issue date: 2011-07-28 / There is a huge amount of information in the World Wide Web in pages composed by similar objects. E-commerce Web sites and on-line catalogs, in general, are examples of such data repositories. Although this information usually occurs in semi-structured texts, it is designed to be interpreted and used by humans and not processed by machines. The identification of these objects inWeb pages is performed by external applications called extractors or wrappers. In this work we propose and evaluate an automatic approach to the problem of generating wrappers capable of extracting and structuring data records and the values of their attributes. It uses the Tree Alignment Algorithm to find in the Web page examples of objects of interest. Then, our method generates regular expressions for extracting objects similar to the examples given using the Multiple Sequence Alignment Algorithm. In a final step, the method decomposes the objects in sequences of text using the regular expression and common formats and delimiters, in order to identify the value of the attributes of the data records. Experiments using a collection composed by 128 Web pages from different domains have demonstrated the feasibility of our extraction method. It is evaluated regarding the identification of blocks of HTML source code that contain data records and regarding record extraction and the value of its attributes. It reached a precision of 83% and a recall of 80% when extracting the value of attributes. These values mean a gain in precision of 43.37% and in recall of 68.75% when compared to similar proposals. / Existe uma grande quantidade de informação na World Wide Web em páginas compostas por objetos similares. Web sites de comércio eletrônico e catálogos online, em geral, são exemplos destes repositórios de dados. Apesar destes dados serem apresentados em porções de texto semi-estruturados, são projetados para serem interpretados e utilizados por humanos e não processados por máquinas. A identificação destes objetos em páginas Web é feita por aplicações externas chamadas extratores ou wrappers. Neste trabalho propomos e avaliamos um método automático para o problema de extrair e estruturar registros e valores de seus atributos presentes em páginas Web ricas em dados. O método utiliza um Algoritmo de Alinhamento de Árvores para encontrar nestas páginas exemplos de registros que correspondem a objetos de interesse. Em seguida, o método gera expressões regulares para extrair objetos similares aos exemplos dados usando o Algoritmo de Alinhamento de Múltiplas Sequências. Em um passo final, o método decompõe os registros em sequências de texto aplicando a expressão regular criada e formatações e delimitadores comuns, com o intuito de identificar os valores dos atributos dos registros. Experimentos utilizando uma coleção composta por 128 páginasWeb de diferentes domínios demonstram a viabilidade do nosso método de extração. O método foi avaliado em relação à identificação de blocos de código HTML que contêm os registros e quanto à extração dos registros e dos valores de seus atributos. Obtivemos precisão de 83% e revocação de 80% na extração de valores de atributos. Estes valores significam um ganho na precisão de 43,37% e na revocação de 68,75%, em relação a propostas similares

Lists of records in machine-printed documents contain much useful information. As one example, the thousands of family history books scanned, OCRed, and placed on-line by FamilySearch.org probably contain hundreds of millions of fact assertions about people, places, family relationships, and life events. Data like this cannot be fully utilized until a person or process locates the data in the document text, extracts it, and structures it with respect to an ontology or database schema. Yet, in the family history industry and other industries, data in lists goes largely unused because no known approach adequately addresses all of the costs, challenges, and requirements of a complete end-to-end solution to this task. The diverse information is costly to extract because many kinds of lists appear even within a single document, differing from each other in both structure and content. The lists' records and component data fields are usually not set apart explicitly from the rest of the text, especially in a corpus of OCRed historical documents. OCR errors and the lack of document structure (e.g. HMTL tags) make list content hard to recognize by a software tool developed without a substantial amount of highly specialized, hand-coded knowledge or machine learning supervision. Making an approach that is not only accurate but also sufficiently scalable in terms of time and space complexity to process a large corpus efficiently is especially challenging. In this dissertation, we introduce a novel family of scalable approaches to list discovery and ontology population. Its contributions include the following. We introduce the first general-purpose methods of which we are aware for both list detection and wrapper induction for lists in OCRed or other plain text. We formally outline a mapping between in-line labeled text and populated ontologies, effectively reducing the ontology population problem to a sequence labeling problem, opening the door to applying sequence labelers and other common text tools to the goal of populating a richly structured ontology from text. We provide a novel admissible heuristic for inducing regular expression wrappers using an A* search. We introduce two ways of modeling list-structured text with a hidden Markov model. We present two query strategies for active learning in a list-wrapper induction setting. Our primary contributions are two complete and scalable wrapper-induction-based solutions to the end-to-end challenge of finding lists, extracting data, and populating an ontology. The first has linear time and space complexity and extracts highly accurate information at a low cost in terms of user involvement. The second has time and space complexity that are linear in the size of the input text and quadratic in the length of an output record and achieves higher F1-measures for extracted information as a function of supervision cost. We measure the performance of each of these approaches and show that they perform better than strong baselines, including variations of our own approaches and a conditional random field-based approach.

This paper presents a piece of software to automatically extract requirements captured in Microsoft Word files while using domain knowledge. In a subsequent step, these requirements are enhanced for implementation purposes and ultimately saved to ReqIF, an XML-based file format for the exchange of specification documents. ReqIF can be processed by a wide range of industry-standard requirements management tools. By way of this enhancement a formalization of both the document structure and selected elements of its natural language contents is achieved. In its current version, the software was specifically developed for processing the Subset-026, a conceptually demanding specification document covering the core functionality of the pan-European train protection system ETCS. Despite this initial focus, the two-part design of this thesis facilitates a generic applicability of its findings: Section 2 presents the fundamental challenges of weakly structured specification documents and devotes a large part to the computation of unique, but human-readable requirement identifiers. Section 3, however, delves into more domain-specific features, the text processing capabilities, and the actual implementation of this novel software. Due to the open-source nature of the application, an adaption to other use-cases can be achieved with comparably little effort. / Diese Arbeit befasst sich mit einer Software zur automatisierten Extraktion von Anforderungen aus Dokumenten im Microsoft Word Format unter Nutzung von Domänenwissen. In einem nachgelagerten Schritt werden diese Anforderungen für Implementierungszwecke aufgewertet und schließlich als ReqIF, einem XML-basierten Dateiformat zum Austausch von Spezifikationsdokumenten, gespeichert. ReqIF wird von zahlreichen branchenüblichen Anforderungsmanagementwerkzeugen unterstützt. Durch die Aufwertung wird eine Formalisierung der Struktur sowie ausgewählter Teile der natürlichsprachlichen Inhalte des Dokuments erreicht. Die jetzige Version der Software wurde speziell für die Verarbeitung des Subset-026 entwickelt, eines konzeptionell anspruchsvollen Anforderungsdokuments zur Beschreibung der Kernfunktionalität des europaweiten Zugsicherungssystems ETCS. Trotz dieser ursprünglichen Intention erlaubt die zweigeteilte Gestaltung der Arbeit eine allgemeine Anwendung der Ergebnisse: Abschnitt 2 zeigt die grundsätzlichen Herausforderungen in Bezug auf schwach strukturierte Anforderungsdokumente auf und widmet sich dabei ausführlich der Ermittlung von eindeutigen, aber dennoch menschenlesbaren Anforderungsidentifikatoren. Abschnitt 3 befasst sich hingegen eingehender mit den domänenspezifischen Eigenschaften, den Textaufbereitungsmöglichkeiten und der konkreten Implementierung der neuen Software. Da die Software unter open-source Prinzipien entwickelt wurde, ist eine Anpassung an andere Anwendungsfälle mit relativ geringem Aufwand möglich.

The thesis deals with OpenCV library and its implementation into scripting language Lua. The first part of the thesis concentrates on description of the course Computer vision MPOV and description of mathematical basics needed for further understandings. The second part describes OpenCV library and its potential usage in the MPOV. Next chapter examines the programming scripting language Lua. The description of the implementation of binding the OpenCV library to Lua language along with its overall functionality is included in the practical part of the thesis. The use of LuaCV is more comfortable thanks to Open Source projects for cross-platform compilation and distribution. Part of the thesis is also generator of Latex documentation for LuaCV binding. The last chapter deals with testing LuaCV in course MPOV and analysis of criticism from students.