by Henry Yurianto. / "18 September 1998." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 138-149). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

The integration of information from multiple geophysical and other prospection surveys of archaeological sites and regions leads to a richer and more complete understanding of subsurface content, structure, and physical relationships. Such fusions of information occur within a single geophysical data set or between two or more geophysical and other prospection sources in one, two, or three dimensions. An absolute requirement is the accurate coregistration of all information to the same coordinate space. Data integrations occur at two levels. At the feature level, discrete objects that denote archaeological features are defined, usually subjectively, through the manual digitization of features interpreted in the data, although there is growing interest in automated feature identification and extraction. At the pixel level, distributional issues of skewness and outliers, high levels of noise that obfuscate targets of interest, and a lack of correlation between largely independent dimensions must be confronted. Nevertheless, successful fusions occur using computer graphic methods, simple arithmetic combinations, and advanced multivariate methods, including principal components analysis and supervised and unsupervised classifications. Four case studies are presented that illustrate some of these approaches and offer advancement into new domains.

Hui, Fan Fong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 153-181). / Abstract also in Chinese. / Acknowledgements --- p.ii / Abstract --- p.iii / Abbreviations --- p.vii / Table of Contents --- p.ix / List of Tables --- p.xv / List of Tables --- p.xv / List of Figures --- p.xvi / Major Conference Presentations --- p.xix / Publications in Preparation --- p.xxii / Chapter Chapter 1 --- Study Background --- p.1 / Chapter 1. --- Introduction --- p.2 / Chapter 1.1. --- Spinal Deformities --- p.2 / Chapter 1.1.1. --- Treatment --- p.2 / Chapter 1.2. --- Spinal fusion --- p.4 / Chapter 1.2.1. --- Gold Standard of Spinal Fusion --- p.4 / Chapter 1.2.2. --- Decortication in Spinal Fusion --- p.4 / Chapter 1.2.3. --- Autograft in Spinal Fusion --- p.4 / Chapter 1.2.4. --- Local Factors Influencing Spinal Fusion --- p.5 / Chapter 1.2.5. --- Ultimate Goals of Spinal Fusion --- p.7 / Chapter 1.2.6. --- Limitations of Spinal fusion --- p.7 / Chapter 1.3. --- Alternatives of Different Components for Enhancing Spinal Fusion / Chapter 1.3.1. --- Bone Graft Substitute --- p.9 / Chapter 1.3.2. --- Bioactive Factors --- p.15 / Chapter 1.4. --- Limitations of the Alternative Methods in Spinal Fusion Enhancement --- p.19 / Chapter 1.4.1. --- BMPs --- p.19 / Chapter 1.4.2. --- Gene Therapy --- p.20 / Chapter 1.4.3. --- Biophysical Stimulation --- p.20 / Chapter 1.5. --- Recent Methods in Enhancing Spinal Fusion --- p.21 / Chapter 1.5.1. --- Low Intensity Pulsed Ultrasound --- p.21 / Chapter 1.5.2. --- Mesenchymal Stem Cells in Spinal Fusion --- p.24 / Chapter 1.6. --- Conclusion --- p.26 / Chapter Chapter 2 --- "Hypothesis, Objectives and Plan of Study" --- p.29 / Chapter 2. --- "Hypothesis, Objectives and Plan of Study" --- p.30 / Chapter 2.1 --- Study Hypothesis --- p.31 / Chapter 2.2 --- Study Objectives --- p.31 / Chapter 2.3 --- Plan of Study --- p.32 / Chapter 2.3.1 --- For First Objective --- p.32 / Chapter 2.3.2 --- For Second Objective --- p.32 / Chapter 2.3.3 --- For Third Objective --- p.33 / Chapter Chapter 3 --- In vitro Study of Effect of Low Intensity Pulsed Ultrasound on Mesenchymal Stem Cells --- p.34 / Chapter 3.1. --- Introduction --- p.35 / Chapter 3.2. --- Materials and Methods --- p.36 / Chapter 3.2.1. --- Experimental Animal --- p.36 / Chapter 3.2.2. --- Materials and Reagents --- p.36 / Chapter 3.2.2.1. --- Dulbecco，s Modified Eagle Medium (DMEM) --- p.36 / Chapter 3.2.2.2. --- Phosphate Buffered Saline (PBS) --- p.37 / Chapter 3.2.2.3. --- Osteogenic Medium (OS) --- p.37 / Chapter 3.2.2.4. --- Alkaline Phosphatase (ALP) Buffer --- p.37 / Chapter 3.2.2.5. --- ALP Substrate Buffer --- p.38 / Chapter 3.2.2.6. --- MTT Stock Solution --- p.38 / Chapter 3.2.2.7. --- MTT Working Solution --- p.38 / Chapter 3.2.2.8. --- Lysis buffer --- p.38 / Chapter 3.2.2.9. --- Alkaline Phosphatase (ALP) Working Reagents --- p.39 / Chapter 3.2.3. --- Isolation of Bone Marrow Derived Mesenchymal Stem Cells (BM derived MSCs) --- p.39 / Chapter 3.2.4. --- In vitro Low Intensity Pulsed Ultrasound Treatment --- p.40 / Chapter 3.2.4.1. --- In vitro LIPUS Devices --- p.40 / Chapter 3.2.4.2. --- Treatment Procedure and Experimantal Groupings --- p.40 / Chapter 3.2.5. --- Effect of LIPUS on Cell Viability and Osteogenesis in bone marrow derived-MSCs --- p.41 / Chapter 3.2.5.1. --- Cell Viability Assay --- p.41 / Chapter 3.2.5.2. --- Alkaline Phosphatase (ALP) Enzyme Activity --- p.42 / Chapter 3.2.5.3. --- Cell Morphology and Alkaline Phosphatase Cytochemistry --- p.42 / Chapter 3.2.6. --- Statistical Analysis --- p.43 / Chapter 3.3. --- Results --- p.43 / Chapter 3.3.1. --- Morphology --- p.43 / Chapter 3.3.2. --- Total Number of Viable Cells --- p.44 / Chapter 3.3.3. --- ALP Activity Absorbance --- p.44 / Chapter 3.3.4. --- ALP staining --- p.45 / Chapter 3.3.5. --- Qualitative Analysis --- p.45 / Chapter 3.3.6. --- Quantitative Analysis --- p.46 / Chapter 3.4. --- Discussion --- p.46 / Chapter 3.4.1. --- LIPUS have No Enhancing Effect on Proliferation of MSCs in Basal Medium Nor Osteogenic Medium --- p.47 / Chapter 3.4.2. --- LIPUS Stimulate Proliferation of MSCs in Early Period --- p.49 / Chapter 3.4.3. --- LIPUS Further Enhanced Osteogenesis of MSCs in Osteogenic Medium --- p.49 / Chapter 3.4.4. --- 10 mins LIPUS treatment for 7 days can positively enhance osteogenic differentiation --- p.50 / Chapter 3.4.5. --- Optimum Conditions of LIPUS was Cell Type Dependent --- p.51 / Chapter 3.4.6. --- LIPUS Promoted Osteogenesis in MSCs through Accelerated Mineralization --- p.52 / Chapter Chapter 4 --- Enhancement of Posterior Spinal Fusion The Effect of Tissue-Engineered MSC and Calcium Phosphate Ceramic composite treated with LIPUS in Vivo --- p.68 / Chapter 4.1. --- Introduction --- p.69 / Chapter 4.1.1. --- TCP Biomaterials --- p.70 / Chapter 4.2. --- Materials and Methods --- p.71 / Chapter 4.2.1. --- Materials and Reagents --- p.71 / Chapter 4.2.2. --- Preparation of MSC Derived Osteogenic Cells-tricalcium Phosphate Ceramics Composite --- p.73 / Chapter 4.2.3. --- Posterior Spinal Fusion Surgery --- p.74 / Chapter 4.2.4. --- In vivo LIPUS treatment --- p.75 / Chapter 4.2.5. --- Assessment of Fusion Mass --- p.76 / Chapter 4.2.6. --- Histology --- p.77 / Chapter 4.2.7. --- Statistical Analysis --- p.79 / Chapter 4.3. --- Results --- p.79 / Chapter 4.3.1. --- Fusion by Manual Palpation --- p.79 / Chapter 4.3.2. --- pQCT Analysis --- p.80 / Chapter 4.3.3. --- Histological Analysis --- p.81 / Chapter 4.4. --- Discussion --- p.85 / Chapter 4.4.1. --- Summary of the Findings from Different Assessment Methods --- p.85 / Chapter 4.4.2. --- Addition of MSCs to TCP ceramic in Spinal Fusion --- p.87 / Chapter 4.4.3. --- The Needs of Differentiated MSC in Spinal Fusion --- p.89 / Chapter 4.4.4. --- bFGF Masked the Effect of OS in MSC --- p.91 / Chapter 4.4.5. --- LIPUS Enhanced Bone Formation --- p.95 / Chapter 4.4.6. --- LIPUS Enhanced Bone Formation through Mineralization --- p.96 / Chapter 4.4.7. --- LIPUS Enhanced Spinal Fusion through Bone Remodeling-induced Fusion Mass --- p.97 / Chapter 4.4.8. --- LIPUS Enhanced Bone Formation through Endochondral Ossification --- p.99 / Chapter Chapter 5 --- In Vivo Monitoring of Spinal Fusion in Animal Model with High-resolution Peripheral Quantitative Computed Tomography-A New Pilot Study --- p.122 / Chapter 5.1. --- Introduction --- p.123 / Chapter 5.2. --- Materials and Methods --- p.124 / Chapter 5.2.1. --- Animal Groupings --- p.124 / Chapter 5.2.2. --- Preparation of MSC Derived Osteogenic Cells-tricalcium Phosphate Ceramics Composite --- p.124 / Chapter 5.2.3. --- Posterior Spinal Fusion Operation Procedures --- p.125 / Chapter 5.2.4. --- LIPUS treatment --- p.125 / Chapter 5.2.5. --- High-resolution Peripheral Quantitative Computed Tomography …- --- p.125 / Chapter 5.2.6. --- Analysis with HR-pQCT --- p.126 / Chapter 5.3. --- Result --- p.128 / Chapter 5.3.1. --- Qualitative Observations from HR-pQCT Images --- p.128 / Chapter 5.3.2. --- Quantitative Analysis --- p.129 / Chapter 5.4. --- Discussion --- p.130 / Chapter Chapter 6 --- "Overall Summary, Discussion and Conclusion" --- p.140 / Chapter 6.1. --- Overall Summary and Discussion --- p.141 / Chapter 6.2. --- Limitations and Further Studies --- p.145 / Chapter 6.3. --- Conclusions --- p.147 / Chapter 6.4. --- Summary Flowchart of the whole thesis --- p.148 / References --- p.153

Der technologische Fortschritt im Bereich multimedialer Sensorik und zugehörigen Methoden zur Datenaufzeichnung, Datenhaltung und -verarbeitung führt im Big Data-Umfeld zu immensen Datenbeständen in Mediatheken und Wissensmanagementsystemen. Zugrundliegende State of the Art-Verarbeitungsalgorithmen werden oftmals problemorientiert entwickelt. Aufgrund der enormen Datenmengen lassen sich nur bedingt zuverlässig Rückschlüsse auf Güte und Anwendbarkeit ziehen. So gestaltet sich auch die intellektuelle Erschließung von großen Korpora schwierig, da die Datenmenge für valide Aussagen nahezu vollumfänglich semi-intellektuell zu prüfen wäre, was spezifisches Fachwissen aus der zugrundeliegenden Datendomäne ebenso voraussetzt wie zugehöriges Verständnis für Datenhandling und Klassifikationsprozesse. Ferner gehen damit gesonderte Anforderungen an Hard- und Software einher, welche in der Regel suboptimal skalieren, da diese zumeist auf Multi-Kern-Rechnern entwickelt und ausgeführt werden, ohne dabei eine notwendige Verteilung vorzusehen. Folglich fehlen Mechanismen, um die Übertragbarkeit der Verfahren auf andere Anwendungsdomänen zu gewährleisten. Die vorliegende Arbeit nimmt sich diesen Herausforderungen an und fokussiert auf die Konzeptionierung und Entwicklung einer verteilten holistischen Infrastruktur, die die automatisierte Verarbeitung multimedialer Daten im Sinne der Merkmalsextraktion, Datenfusion und Metadatensuche innerhalb eines homogenen Systems ermöglicht. Der Fokus der vorliegenden Arbeit liegt in der Konzeptionierung und Entwicklung einer verteilten holistischen Infrastruktur, die die automatisierte Verarbeitung multimedialer Daten im Sinne der Merkmalsextraktion, Datenfusion und Metadatensuche innerhalb eines homogenen aber zugleich verteilten Systems ermöglicht. Dabei sind Ansätze aus den Domänen des Maschinellen Lernens, der Verteilten Systeme, des Datenmanagements und der Virtualisierung zielführend miteinander zu verknüpfen, um auf große Datenmengen angewendet, evaluiert und optimiert werden zu können. Diesbezüglich sind insbesondere aktuelle Technologien und Frameworks zur Detektion von Mustern zu analysieren und einer Leistungsbewertung zu unterziehen, so dass ein Kriterienkatalog ableitbar ist. Die so ermittelten Kriterien bilden die Grundlage für eine Anforderungsanalyse und die Konzeptionierung der notwendigen Infrastruktur. Diese Architektur bildet die Grundlage für Experimente im Big Data-Umfeld in kontextspezifischen Anwendungsfällen aus wissenschaftlichen Evaluationskampagnen, wie beispielsweise TRECVid. Hierzu wird die generische Applizierbarkeit in den beiden Aufgabenfeldern Instance Search und Activity in Extended Videos eruiert.:Abbildungsverzeichnis Tabellenverzeichnis 1 Motivation 2 Methoden und Strategien 3 Systemarchitektur 4 Instance Search 5 Activities in Extended Video 6 Zusammenfassung und Ausblick Anhang Literaturverzeichnis / Technological advances in the field of multimedia sensing and related methods for data acquisition, storage, and processing are leading to immense amounts of data in media libraries and knowledge management systems in the Big Data environment. The underlying modern processing algorithms are often developed in a problem-oriented manner. Due to the enormous amounts of data, reliable statements about quality and applicability can only be made to a limited extent. Thus, the intellectual exploitation of large corpora is also difficult, as the data volume would have to be analyzed for valid statements, which requires specific expertise from the underlying data domain as well as a corresponding understanding of data handling and classification processes. In addition, there are separate requirements for hardware and software, which usually scale in a suboptimal manner while being developed and executed on multicore computers without provision for the required distribution. Consequently, there is a lack of mechanisms to ensure the transferability of the methods to other application domains. The focus of this work is the design and development of a distributed holistic infrastructure that enables the automated processing of multimedia data in terms of feature extraction, data fusion, and metadata search within a homogeneous and simultaneously distributed system. In this context, approaches from the areas of machine learning, distributed systems, data management, and virtualization are combined in order to be applicable on to large data sets followed by evaluation and optimization procedures. In particular, current technologies and frameworks for pattern recognition are to be analyzed and subjected to a performance evaluation so that a catalog of criteria can be derived. The criteria identified in this way form the basis for a requirements analysis and the conceptual design of the infrastructure required. This architecture builds the base for experiments in the Big Data environment in context-specific use cases from scientific evaluation campaigns, such as TRECVid. For this purpose, the generic applicability in the two task areas Instance Search and Activity in Extended Videos is elicited.:Abbildungsverzeichnis Tabellenverzeichnis 1 Motivation 2 Methoden und Strategien 3 Systemarchitektur 4 Instance Search 5 Activities in Extended Video 6 Zusammenfassung und Ausblick Anhang Literaturverzeichnis