Selon les prévisions de Cisco , il y aura plus de 50 milliards d'appareils connectés à Internet d'ici 2020. Les appareils et les données produites sont principalement exploitées pour construire des applications « Internet des Objets (IdO) ». D'un point de vue des données, ces applications ne sont pas interopérables les unes avec les autres. Pour aider les utilisateurs ou même les machines à construire des applications 'Internet des Objets' inter-domaines innovantes, les principaux défis sont l'exploitation, la réutilisation, l'interprétation et la combinaison de ces données produites par les capteurs. Pour surmonter les problèmes d'interopérabilité, nous avons conçu le système Machine-to-Machine Measurement (M3) consistant à: (1) enrichir les données de capteurs avec les technologies du web sémantique pour décrire explicitement leur sens selon le contexte, (2) interpréter les données des capteurs pour en déduire des connaissances supplémentaires en réutilisant autant que possible la connaissance du domaine définie par des experts, et (3) une base de connaissances de sécurité pour assurer la sécurité dès la conception lors de la construction des applications IdO. Concernant la partie raisonnement, inspiré par le « Web de données », nous proposons une idée novatrice appelée le « Web des règles » afin de partager et réutiliser facilement les règles pour interpréter et raisonner sur les données de capteurs. Le système M3 a été suggéré à des normalisations et groupes de travail tels que l'ETSI M2M, oneM2M, W3C SSN et W3C Web of Things. Une preuve de concept de M3 a été implémentée et est disponible sur le web (http://www.sensormeasurement.appspot.com/) mais aussi embarqué / According to Cisco's predictions, there will be more than 50 billions of devices connected to the Internet by 2020.The devices and produced data are mainly exploited to build domain-specific Internet of Things (IoT) applications. From a data-centric perspective, these applications are not interoperable with each other.To assist users or even machines in building promising inter-domain IoT applications, main challenges are to exploit, reuse, interpret and combine sensor data.To overcome interoperability issues, we designed the Machine-to-Machine Measurement (M3) framework consisting in:(1) generating templates to easily build Semantic Web of Things applications, (2) semantically annotating IoT data to infer high-level knowledge by reusing as much as possible the domain knowledge expertise, and (3) a semantic-based security application to assist users in designing secure IoT applications.Regarding the reasoning part, stemming from the 'Linked Open Data', we propose an innovative idea called the 'Linked Open Rules' to easily share and reuse rules to infer high-level abstractions from sensor data.The M3 framework has been suggested to standardizations and working groups such as ETSI M2M, oneM2M, W3C SSN ontology and W3C Web of Things. Proof-of-concepts of the flexible M3 framework have been developed on the cloud (http://www.sensormeasurement.appspot.com/) and embedded on Android-based constrained devices.

Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 164-171).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1975. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliography: leaves 300-305.

Thesis (M.S.)--University of Wisconsin--Madison, 1968. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1976. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliography: leaves 157-161.

Thesis (M.S.)--University of Wisconsin--Madison, 1982. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 105-106).

The design of machine elements such as belts, chain and sprocket, gears, cams, bearings, etc., is a complex procedure. The analytical procedure to design such elements is non-linear, often based upon statistical information and frequently heuristic in nature. The procedure is iterative and involves choosing various weighing factors based upon the application and usage. The procedure is greatly influenced by a designer's intuitive skill and experience. Due to the increased popularity of the Internet as a source of information, a number of machine element manufacturers have published design procedures for the design of machine elements. Here, an attempt has been made to arrange all the formulae, tables, charts, standards, and web resources, which are inherent to the design process, to best aid the designer. A number of computer applications for machine design have been developed in the recent past but are limited in scope and utility as they tend to allow the designer to change only certain parameters while assigning fixed values to most parameters. This stifles the designer's creativity and diverts focus on the computer program rather than the physical problem. An attempt has been made here to allow the designer flexibility in design and increased productivity, by keeping a record of the various steps undertaken by the designer, in a particular design process. / Master of Science

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Wan Zhang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 91-103). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction to Classification --- p.1 / Chapter 1.2 --- Problem Definition --- p.4 / Chapter 1.3 --- Major Contributions --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Fisher's Linear Discriminant --- p.8 / Chapter 2.2 --- Radial Basis Function Networks --- p.9 / Chapter 2.3 --- Decision Tree --- p.10 / Chapter 2.4 --- Nearest Neighbor --- p.12 / Chapter 2.5 --- Support Vector Machine --- p.13 / Chapter 2.5.1 --- Linear Separable Case --- p.14 / Chapter 2.5.2 --- Non Linear-separable Case --- p.15 / Chapter 2.5.3 --- Nonlinear Case --- p.18 / Chapter 2.5.4 --- Multi-class SVM --- p.19 / Chapter 2.5.5 --- RSVM --- p.21 / Chapter 2.6 --- Summary --- p.23 / Chapter 3 --- Computational Geometry --- p.25 / Chapter 3.1 --- Convex hull --- p.26 / Chapter 3.1.1 --- Separable Case --- p.26 / Chapter 3.1.2 --- Inseparable Case --- p.28 / Chapter 3.2 --- Proximity Graph --- p.32 / Chapter 3.2.1 --- Voronoi Diagram and Delaunay Triangulation --- p.32 / Chapter 3.2.2 --- Gabriel Graph and Relative Neighborhood Graph --- p.34 / Chapter 3.2.3 --- β-skeleton --- p.36 / Chapter 4 --- Data Editing --- p.39 / Chapter 4.1 --- Hart's Condensed Rule and Its Relatives --- p.39 / Chapter 4.2 --- Order-independent Subsets --- p.40 / Chapter 4.3 --- Minimal Size Training-set Consistent Subsets --- p.40 / Chapter 4.4 --- Proximity Graph Methods --- p.41 / Chapter 4.5 --- Comparing Results of Different Classifiers with Edited Dataset as the Training Set --- p.42 / Chapter 4.5.1 --- Time Complexity --- p.47 / Chapter 4.5.2 --- Editing Size of Training Data --- p.48 / Chapter 4.5.3 --- Accuracy --- p.50 / Chapter 4.5.4 --- Efficiency --- p.54 / Chapter 4.5.5 --- Summary --- p.58 / Chapter 5 --- Techniques Speeding Up Data Editing --- p.60 / Chapter 5.1 --- Parallel Computing --- p.61 / Chapter 5.1.1 --- Basic Idea of Parallel --- p.61 / Chapter 5.1.2 --- Details of Parallel Technique --- p.63 / Chapter 5.1.3 --- Comparing Effects of the Choice of Number of Threads on Efficiency --- p.64 / Chapter 5.2 --- Tree Indexing Structure --- p.67 / Chapter 5.2.1 --- R-tree and R*-tree --- p.67 / Chapter 5.2.2 --- SS-tree --- p.69 / Chapter 5.2.3 --- SR-tvee --- p.70 / Chapter 5.2.4 --- β-neighbor Algorithm Based on SR-tree Structure --- p.71 / Chapter 5.2.5 --- Pruning Search Space for β-neighbor Algorithm --- p.72 / Chapter 5.2.6 --- Comparing Results of Non-index Methods with Those of Methods with Indexing --- p.80 / Chapter 5.3 --- Combination of Parallelism and SR-tree Indexing Structure --- p.83 / Chapter 5.3.1 --- Comparing Results of Both Techniques Applied --- p.84 / Chapter 5.4 --- Summary --- p.87 / Chapter 6 --- Conclusion --- p.89 / Bibliography --- p.91

Liu, Yang. / Thesis Ph.D. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 163-188). / Abstracts also in Chinese. / Title from PDF title page (viewed on 25, October, 2016).