Cílem disertační práce je navrhnout efektivně pracující algoritmus, který na základě bezfázového měření v blízkém poli antény bude schopen zrekonstruovat komplexní blízké pole antény resp. vyzařovací diagram antény ve vzdáleném poli. Na základě těchto úvah byly zkoumány vlastnosti minimalizačního algoritmu. Zejména byl analyzován a vhodně zvolen minimalizační přistup, optimalizační metoda a v neposlední řadě i optimalizační funkce tzv. funkcionál. Dále pro urychlení celého minimalizačního procesu byly uvažovány prvotní odhady. A na závěr byla do minimalizačního algoritmu zahrnuta myšlenka nahrazující hledané elektrické pole několika koeficienty. Na základě předchozích analýz byla navržená bezfázová metoda pro charakterizaci vyzařovacích vlastností antén. Tato metoda kombinuje globální optimalizaci s obrazovou kompresní metodou a s lokální metodou ve spojení s konvečním amplitudovým měřením na dvou površích. V našem případě je globální optimalizace použita k nalezení globálního minima minimalizovaného funkcionálu, kompresní metoda k redukci neznámých proměnných na apertuře antény a lokální metoda zajišťuje přesnější nalezení minima. Navržená metoda je velmi robustní a mnohem rychlejší než jiné dostupné minimalizační algoritmy. Další výzkum byl zaměřen na možnosti využití měřených amplitud pouze z jednoho měřícího povrchu pro rekonstrukci vyzařovacích charakteristik antén a využití nového algoritmu pro rekonstrukci fáze na válcové geometrii.

This paper deals with various approaches to solving optimization tasks. In prolog some examples from real life that show the application of optimization methods are given. Then term optimization task is defined and introducing of term fitness function which is common to all optimization methods follows. After that approaches by particle swarm optimization, ant colony optimization, simulated annealing, genetic algorithms and reinforcement learning are theoretically discussed. For testing we are using two discrete (multiple knapsack problem and set cover problem) and two continuous tasks (searching for global minimum of Ackley's and Rastrigin's function) which are presented in next chapter. Description of implementation details follows. For example description of solution representation or how current solutions are changed. Finally, results of measurements are presented. They show optimal settings for parameters of given optimization methods considering test tasks. In the end are given test tasks, which will be used for finding optimal settings of given approaches.

Organic transistors are a core component for basically all relevant types of fully organic circuits and consumer electronics. The Organic Permeable Base Transistor (OPBT) is a transistor with a sandwich geometry like in Organic Light Emitting Diodes (OLEDs) and has a vertical current transport. Therefore, it combines simple fabrication with high performance due its short transit paths and has a fairly good chance of being used in new organic electronics applications that have to fall back to silicon transistors up to now. A detailed understanding of the operation mechanism that allows a targeted engineering without trial-and-error is required and there is a need for universal optimization techniques which require as little effort as possible. Several mechanisms that explain certain aspects of the operation are proposed in literature, but a comprehensive study that covers all transistor regimes in detail is not found. High performances have been reported for organic transistors which are, however, usually limited to certain materials. E. g., n-type C60 OPBTs are presented with excellent performance, but an adequate p-type OPBT is missing. In this thesis, the OPBT is investigated under two aspects: Firstly, drift-diffusion simulations of the OPBT are evaluated. By comparing the results from different geometry parameters, conclusions about the detailed operation mechanism can be drawn. It is discussed where charge carriers flow in the device and which parameters affect the performance. In particular, the charge carrier transmission through the permeable base layer relies on small openings. Contrary to an intuitive view, however, the size of these openings does not limit the device performance. Secondly, p-type OPBTs using pentacene as the organic semiconductor are fabricated and characterized with the aim to catch up with the performance of the n-type OPBTs. It is shown how an additional seed-layer can improve the performance by changing the morphology, how leakage currents can be defeated, and how parameters like the layer thickness should be chosen. With the combination of all presented optimization strategies, pentacene OPBTs are built that show a current density above 1000 mA/cm^2 and a current gain of 100. This makes the OPBT useful for a variety of applications, and also complementary logic circuits are possible now. The discussed optimization strategies can be extended and used as a starting point for further enhancements. Together with the deep understanding obtained from the simulations, purposeful modifications can be studied that have a great potential.:1 Introduction and Motivation 2 Theory 2.1 Organic Semiconductors 2.1.1 Organic Molecules and Solids 2.1.2 Charge Carrier Transport 2.1.3 Charge Carrier Injection 2.1.4 Doping 2.2 Organic Permeable Base Transistors 2.2.1 Structure 2.2.2 Basic Operation Principle 3 Overview of Different Transistor Architectures 3.1 Organic Field Effect Transistors 3.2 Organic Permeable Base Transistors 3.2.1 Development of the Permeable Base Transistor 3.2.2 Optimization Strategies 3.3 Comparison to Inorganic Transistors 3.4 Other Emerging Transistor Concepts 3.4.1 OSBT 3.4.2 Step-Edge OFET 3.4.3 VOFET 3.4.4 IGZO Devices 4 Experimental 4.1 Materials and their Properties 4.1.1 Pentacene 4.1.2 F6TCNNQ 4.1.3 Aluminum Oxide 4.2 Fabrication 4.2.1 Thermal Vapor Deposition 4.2.2 Chamber Details and Processing Procedure 4.2.3 Sample Structure 4.3 Characterization Methods and Tools 4.3.1 Electrical Characterization 4.3.2 Morphology 4.3.3 XPS 5 Simulations and Working Mechanism 5.1 Simulation Setup 5.1.1 Overview 5.1.2 OPBT Model 5.1.3 Drift-Diffusion Solver 5.1.4 Post-Processing of Simulation Data 5.2 Basic Concept 5.2.1 Base Sweep Regions 5.2.2 Correlation with charge carrier density and potential 5.3 Charge Carrier Accumulation 5.3.1 Accumulation at Emitter and Collector 5.3.2 Current Flow 5.3.3 Area contributing to the current flow 5.4 Current Limitation Mechanisms 5.4.1 Varying Size of the Opening 5.4.2 Channel Potential 5.4.3 Limitation of Base-Emitter Transport 5.4.4 Intrinsic Layer Variation 5.5 Opening Shapes 5.5.1 Cylindrical Opening and Symmetry 5.5.2 Truncated Cone Setup 5.6 Base Leakage Currents 5.6.1 Description of the Insulator 5.6.2 Top and Bottom Contribution 5.6.3 Validity of Calculation 5.7 Analytical Description of the OPBT base sweep 5.7.1 Description of operation regions 5.7.2 Transition Voltages and Full Characteristics 5.7.3 Comparison to Experiment 5.8 Output Characteristics 5.8.1 Saturation region 5.8.2 Linear region 5.8.3 Intrinsic Gain 5.9 Summary of Operation Mechanism 6 Nin-Devices and Structuring 6.1 Effect of Accumulation and Scalability 6.1.1 Active Area and Electrode Overlap 6.1.2 Indirect Structuring 8 Contents 6.1.3 Four-Wire Measurement 6.1.4 Pulsed Measurements 6.2 Mobility Measurement 6.2.1 Mobility Extraction from a Single IV Curve 6.2.2 Verification of the SCLC using Thickness Variations 6.3 Geometric Diode 7 Optimization of p-type Permeable Base Transistors 7.1 Introduction to p-type Devices 7.2 Characteristics of OPBTs 7.2.1 Diode characteristics 7.2.2 Base sweep 7.2.3 Output characteristics 7.3 Seed-Layer 7.3.1 Process of Opening Formation 7.3.2 Performance using different Seed-Layers 7.4 Built-in field 7.4.1 Effect on Performance 7.4.2 Explanation for the Transmission Improvement 7.5 Base Insulation 7.5.1 Importance of Base Insulation 7.5.2 Additional Insulating Layers and Positioning 7.5.3 Enhancement of Native Aluminum Oxide 7.6 Complete Optimization 7.6.1 Indirect Structuring in OPBTs 7.6.2 Combination of different Optimization Techniques 7.7 Potential of the Technology 7.7.1 Future Improvements 7.7.2 Achievable Performance 7.8 Demonstration of the Organic Permeable Base Transistor 7.8.1 Simple OLED driver 7.8.2 An Astable Oscillator using p-type OPBTs 7.8.3 An OLED Driver using n-type OPBTs controlled by Organic Solar Cells 8 Conclusion / Organische Transistoren stellen eine Kernkomponente für praktisch jede Art von organischen Schaltungen und Elektronikgeräten dar. Der “Organic Permeable Base Transistor” (OPBT, dt.: Organischer Transistor mit durchlässiger Basis) ist ein Transistor mit einem Schichtaufbau wie in organischen Leuchtdioden (OLEDs) und weist einen vertikalen Stromfluss auf. Somit wird eine einfache Herstellung mit gutem Verhalten und Leistungsfähigkeit kombiniert, welche aus den kurzen Weglängen der Ladungsträger resultiert. Damit ist der OPBT bestens für neuartige organische Elektronik geeignet, wofür andernfalls auf Siliziumtransistoren zurückgegriffen werden müsste. Notwendig sind ein tiefgehendes Verständnis der Funktionsweise, welches ein zielgerichtetes Entwickeln der Technologie ohne zahlreiche Fehlversuche ermöglicht, sowie universell einsetzbare und leicht anwendbare Optimierungsstrategien. In der Literatur werden einige Mechanismen vorgeschlagen, die Teile der Funktionsweise betrachten, aber eine umfassende Untersuchung, die alle Arbeitsbereiche des Transistors abdeckt, findet sich derzeit noch nicht. Ebenso gibt es einige Veröffentlichungen, die Transistoren mit hervorragender Leistungsfähigkeit zeigen, aber meist nur mit Materialien für einen Ladungsträgertyp erzielt werden. So gibt es z.B. n-typ OPBTs auf Basis von C60, für die bisher vergleichbare p-typ OPBTs fehlen. In dieser Arbeit werden daher die folgenden beiden Aspekte des OPBT untersucht: Einerseits werden Drift-Diffusions-Simulationen von OPBTs untersucht und ausgewertet. Kennlinien und Ergebnisse von Transistoren aus verschiedenen Parametervariationen können verglichen werden und erlauben damit Rückschlüsse auf verschiedenste Aspekte der Funktionsweise. Der Fluss der Ladungsträger sowie für die Leistungsfähigkeit wichtige Parameter werden besprochen. Insbesondere sind für die Transmission von Ladungsträgern durch die Basisschicht kleine Öffnungen in dieser nötig. Die Größe dieser Öffnungen stellt jedoch entgegen einer intuitiven Vorstellung keine Begrenzung für die erreichbaren Ströme dar. Andererseits werden p-typ OPBTs auf Basis des organischen Halbleiters Pentacen hergestellt und charakterisiert. Das Ziel ist hierbei die Leistungsfähigkeit an die n-typ OPBTs anzugleichen. In dieser Arbeit wird gezeigt, wie durch eine zusätzliche Schicht die Morphologie und die Transmission verbessert werden kann, wie Leckströme reduziert werden können und welche Parameter bei der Optimierung besondere Beachtung finden sollten. Mit all den Optimierungen zusammen können Pentacen OPBTs hergestellt werden, die Stromdichten über 1000 mA/cm^2 und eine Stromverstärkung über 100 aufweisen. Damit kann der OPBT für eine Vielzahl von Anwendungen eingesetzt werden, unter anderem auch in Logik-Schaltungen zusammen mit n-typ OPBTs. Die besprochenen Optimierungen können weiterentwickelt werden und somit als Startpunkt für anschließende Verbesserungen dienen. In Verbindung mit erlangten Verständnis aus den Simulationsergebnissen können somit aussichtsreiche Veränderungen an der Struktur des OPBTs zielgerichtet eingeführt werden.:1 Introduction and Motivation 2 Theory 2.1 Organic Semiconductors 2.1.1 Organic Molecules and Solids 2.1.2 Charge Carrier Transport 2.1.3 Charge Carrier Injection 2.1.4 Doping 2.2 Organic Permeable Base Transistors 2.2.1 Structure 2.2.2 Basic Operation Principle 3 Overview of Different Transistor Architectures 3.1 Organic Field Effect Transistors 3.2 Organic Permeable Base Transistors 3.2.1 Development of the Permeable Base Transistor 3.2.2 Optimization Strategies 3.3 Comparison to Inorganic Transistors 3.4 Other Emerging Transistor Concepts 3.4.1 OSBT 3.4.2 Step-Edge OFET 3.4.3 VOFET 3.4.4 IGZO Devices 4 Experimental 4.1 Materials and their Properties 4.1.1 Pentacene 4.1.2 F6TCNNQ 4.1.3 Aluminum Oxide 4.2 Fabrication 4.2.1 Thermal Vapor Deposition 4.2.2 Chamber Details and Processing Procedure 4.2.3 Sample Structure 4.3 Characterization Methods and Tools 4.3.1 Electrical Characterization 4.3.2 Morphology 4.3.3 XPS 5 Simulations and Working Mechanism 5.1 Simulation Setup 5.1.1 Overview 5.1.2 OPBT Model 5.1.3 Drift-Diffusion Solver 5.1.4 Post-Processing of Simulation Data 5.2 Basic Concept 5.2.1 Base Sweep Regions 5.2.2 Correlation with charge carrier density and potential 5.3 Charge Carrier Accumulation 5.3.1 Accumulation at Emitter and Collector 5.3.2 Current Flow 5.3.3 Area contributing to the current flow 5.4 Current Limitation Mechanisms 5.4.1 Varying Size of the Opening 5.4.2 Channel Potential 5.4.3 Limitation of Base-Emitter Transport 5.4.4 Intrinsic Layer Variation 5.5 Opening Shapes 5.5.1 Cylindrical Opening and Symmetry 5.5.2 Truncated Cone Setup 5.6 Base Leakage Currents 5.6.1 Description of the Insulator 5.6.2 Top and Bottom Contribution 5.6.3 Validity of Calculation 5.7 Analytical Description of the OPBT base sweep 5.7.1 Description of operation regions 5.7.2 Transition Voltages and Full Characteristics 5.7.3 Comparison to Experiment 5.8 Output Characteristics 5.8.1 Saturation region 5.8.2 Linear region 5.8.3 Intrinsic Gain 5.9 Summary of Operation Mechanism 6 Nin-Devices and Structuring 6.1 Effect of Accumulation and Scalability 6.1.1 Active Area and Electrode Overlap 6.1.2 Indirect Structuring 8 Contents 6.1.3 Four-Wire Measurement 6.1.4 Pulsed Measurements 6.2 Mobility Measurement 6.2.1 Mobility Extraction from a Single IV Curve 6.2.2 Verification of the SCLC using Thickness Variations 6.3 Geometric Diode 7 Optimization of p-type Permeable Base Transistors 7.1 Introduction to p-type Devices 7.2 Characteristics of OPBTs 7.2.1 Diode characteristics 7.2.2 Base sweep 7.2.3 Output characteristics 7.3 Seed-Layer 7.3.1 Process of Opening Formation 7.3.2 Performance using different Seed-Layers 7.4 Built-in field 7.4.1 Effect on Performance 7.4.2 Explanation for the Transmission Improvement 7.5 Base Insulation 7.5.1 Importance of Base Insulation 7.5.2 Additional Insulating Layers and Positioning 7.5.3 Enhancement of Native Aluminum Oxide 7.6 Complete Optimization 7.6.1 Indirect Structuring in OPBTs 7.6.2 Combination of different Optimization Techniques 7.7 Potential of the Technology 7.7.1 Future Improvements 7.7.2 Achievable Performance 7.8 Demonstration of the Organic Permeable Base Transistor 7.8.1 Simple OLED driver 7.8.2 An Astable Oscillator using p-type OPBTs 7.8.3 An OLED Driver using n-type OPBTs controlled by Organic Solar Cells 8 Conclusion

<p dir="ltr">The integration of scientific machine learning with computational structural mechanics offers a range of opportunities to address some of the most significant challenges currently experienced by multiphysical simulations, design optimization, and inverse sensing problems. While traditional mesh-based numerical methods, such as the Finite Element Method (FEM), have proven to be very powerful when applied to complex and geometrically inhomogeneous domains, their performance deteriorates very rapidly when faced with simulation scenarios involving high-dimensional systems, high-frequency inputs and outputs, and highly irregular domains. All these elements contribute to increase in the overall computational cost, the mesh dependence, and the number of costly matrix operations that can rapidly render FEM inapplicable. In a similar way, traditional inverse solvers, including global optimization methods, also face important limitations when handling high-dimensional, dynamic design spaces, and multiphysics systems. Recent advances in machine learning (ML) and deep learning have opened new ways to develop alternative techniques for the simulation of complex engineering systems. However, most of the existing deep learning methods are data greedy, a property that strides with the typically limited availability of physical observations and data in scientific applications. This sharp contrast between needed and available data can lead to poor approximations and physically inconsistent solutions. An opportunity to overcome this problem is offered by the class of so-called physics-informed or scientific machine learning methods that leverage the knowledge of problem-specific governing physics to alleviate, or even completely eliminate, the dependence on data. As a result, this class of methods can leverage the advantages of ML algorithms without inheriting their data greediness. This dissertation aims to develop scientific ML methods for application to forward and inverse problems in acoustics and structural mechanics while simultaneously overcoming some of the most significant limitations of traditional computational mechanics methods. </p><p dir="ltr">This work develops fully physics-driven deep learning frameworks specifically conceived to perform forward <i>simulations</i> of mechanical systems that provide approximate, yet physically consistent, solutions without requiring labeled data. The proposed set of approaches is characterized by low discretization dependence and is conceived to support parallel computations in future developments. These characteristics make these methods efficient to handle high degrees of freedom systems, high-frequency simulations, and systems with irregular geometries. The proposed deep learning frameworks enforce the governing equations within the deep learning algorithm, therefore removing the need for costly training data generation while preserving the physical accuracy of the simulation results. Another noteworthy contribution consists in the development of a fully physics-driven deep learning framework capable of improving the computational time for simulating domains with irregular geometries by orders of magnitude in comparison to the traditional mesh-based methods. This novel framework is both geometry-aware and maintains physical consistency throughout the simulation process. The proposed framework displays the remarkable ability to simulate systems with different domain geometries without the need for a new model assembly or a training phase. This capability is in stark contrast with current numerical mesh-based methods, that require new model assembly, and with conventional ML models, that require new training.</p><p dir="ltr">In the second part of this dissertation, the work focuses on the development of ML-based approaches to solve inverse problems. A new deep reinforcement learning framework tailored for dynamic <i>design optimization</i> tasks in coupled-physics problems is presented. The framework effectively addresses key limitations of traditional methods by enabling the exploration of high-dimensional design spaces and supporting sequential decision-making in complex multiphysics systems. Maintaining the focus on the class of inverse problems, ML-based algorithms for <i>remote sensing</i> are also explored with particular reference to structural health monitoring applications. A modular neural network framework is formulated by integrating three essential modules: physics-based regularization, geometry-based regularization, and reduced-order representation. The concurrent use of these modules has shown remarkable performance when addressing the challenges associated with nonlinear, high-dimensional, and often ill-posed remote sensing problems. Finally, this dissertation illustrates the efficacy of deep learning approaches for experimental remote sensing. Results show the significant ability of these techniques when applied to learning inverse mappings based on high-dimensional and noisy experimental data. The proposed framework incorporates data augmentation and denoising techniques to handle limited and noisy experimental datasets, hence establishing a robust approach for training on experimental data.</p>

This diploma thesis describes how to create or convert traditional Java programs to desktop-like rich internet applications with the Google Web Toolkit. The Google Web Toolkit is an open source development environment, which translates Java code to browser and device independent HTML and JavaScript. Most of the GWT framework parts, including the Java to JavaScript compiler as well as important security issues of websites will be introduced. The famous Agricola board game will be implemented in the Model-View-Presenter pattern to show that complex user interfaces can be created with the Google Web Toolkit. The Google Web Toolkit framework will be compared with the JavaServer Faces one to find out which toolkit is the right one for the next web project. / Diese Diplomarbeit beschreibt die Erzeugung desktopähnlicher Anwendungen mit dem Google Web Toolkit und die Umwandlung klassischer Java-Programme in diese. Das Google Web Toolkit ist eine Open-Source-Entwicklungsumgebung, die Java-Code in browserunabhängiges als auch in geräteübergreifendes HTML und JavaScript übersetzt. Vorgestellt wird der Großteil des GWT Frameworks inklusive des Java zu JavaScript-Compilers sowie wichtige Sicherheitsaspekte von Internetseiten. Um zu zeigen, dass auch komplizierte graphische Oberflächen mit dem Google Web Toolkit erzeugt werden können, wird das bekannte Brettspiel Agricola mittels Model-View-Presenter Designmuster implementiert. Zur Ermittlung der richtigen Technologie für das nächste Webprojekt findet ein Vergleich zwischen dem Google Web Toolkit und JavaServer Faces statt.

This diploma thesis describes how to create or convert traditional Java programs to desktop-like rich internet applications with the Google Web Toolkit. The Google Web Toolkit is an open source development environment, which translates Java code to browser and device independent HTML and JavaScript. Most of the GWT framework parts, including the Java to JavaScript compiler as well as important security issues of websites will be introduced. The famous Agricola board game will be implemented in the Model-View-Presenter pattern to show that complex user interfaces can be created with the Google Web Toolkit. The Google Web Toolkit framework will be compared with the JavaServer Faces one to find out which toolkit is the right one for the next web project.:I Abstract II Contents III Acronyms and Glossary III.I Acronyms III.II Glossary IV Credits 1 Introduction 2 Basics 2.1 Development of the World Wide Web 2.2 Hypertext Markup Language 2.3 Cascading Style Sheets 2.4 JavaScript 2.5 Hypertext Markup Language Document Object Model 2.6 Asynchronous JavaScript and XML 3 GWT toolbox and compiler 3.1 GWT in action 3.2 A short overview of the toolkit 3.3 GWT compiler and JSNI 3.3.1 Overview of GWT compiler and JSNI 3.3.2 Deferred binding and bootstrapping process 3.3.3 GWT compiler steps and optimizations 3.4 Java Runtime Environment Emulation 3.5 Widgets and Panels 3.5.1 Overview of GWT Widgets 3.5.2 Event handlers in GWT Widgets 3.5.3 Manipulating browser’s DOM with GWT DOM class 3.5.4 GWT Designer and view optimization using UiBinder 3.6 Remote Procedure Calls 3.6.1 Comparison of Remote Procedure Calls with Remote Method Invocations 3.6.2 GWT’s RPC service and serializable whitelist 3.7 History Management 3.8 Client Bundle 3.8.1 Using ImageResources in the ClientBundle interface 3.8.2 Using CssResources in the ClientBundle interface 4 Model-View-Presenter Architecture 4.1 Comparison of MVP and MVC 4.2 GWT Model-View-Presenter pattern example: Agricola board game 4.3 Extending the Agricola web application with mobile views 4.4 Introducing activities in the Agricola Model-View-Presenter pattern enabling browser history 5 Comparison of the two web frameworks: GWT and JSF 5.1 Definitions of comparison fields 5.2 Comparison in category 1: Nearly completely static sites with a little bit of dynamic content, e.g. news update 5.3 Comparison in category 2: Doing a survey in both technologies 5.4 Comparison in category 3: Creating a forum to show data 5.5 Comparison in category 4: Writing a chat application 5.6 Comparison in category 5: Writing the speed game Snake 5.7 Summary 6 Security 6.1 Download Tomcat 6.2 Dynamic Web Application Project with GWT and Tomcat 6.3 Establish HTTPS connections in Tomcat 6.3.1 Create a pem certificate 6.3.2 Convert pem certificate into a key store object 6.3.3 Configure Tomcat’s XML files to enable HTPPS 6.4 Establish a database connection in Tomcat 6.4.1 Create TomcatGWT user and schema, and add the table countries 6.4.2 Configure Tomcat’s XML files to get access to the database connection 6.4.3 PreparedStatements avoid MySQL injections 6.5 Login mechanism in Tomcat 6.6 SafeHtml 7 Presenting a complex software application written in GWT 8 Conclusions 8.1 Summary 8.2 Future work A Appendix A 1 Configure the Google Web Toolkit framework in Eclipse A 1.1 Install the Java Developer Kit A 1.2 Download Eclipse A 1.3 Install the GWT plugin in Eclipse A 1.4 Create first GWT Java Project A 2 Figures A 3 Listings A 3.1 Source code of the Agricola board game A 3.2 Source code of GWT and JSF comparison A 4 Tables R Lists and References R 1 Lists R 1.1 List of Tables R 1.2 List of Figures R 1.3 List of Listings R 2 References R 2.1 Books R 2.2 Online resources / Diese Diplomarbeit beschreibt die Erzeugung desktopähnlicher Anwendungen mit dem Google Web Toolkit und die Umwandlung klassischer Java-Programme in diese. Das Google Web Toolkit ist eine Open-Source-Entwicklungsumgebung, die Java-Code in browserunabhängiges als auch in geräteübergreifendes HTML und JavaScript übersetzt. Vorgestellt wird der Großteil des GWT Frameworks inklusive des Java zu JavaScript-Compilers sowie wichtige Sicherheitsaspekte von Internetseiten. Um zu zeigen, dass auch komplizierte graphische Oberflächen mit dem Google Web Toolkit erzeugt werden können, wird das bekannte Brettspiel Agricola mittels Model-View-Presenter Designmuster implementiert. Zur Ermittlung der richtigen Technologie für das nächste Webprojekt findet ein Vergleich zwischen dem Google Web Toolkit und JavaServer Faces statt.:I Abstract II Contents III Acronyms and Glossary III.I Acronyms III.II Glossary IV Credits 1 Introduction 2 Basics 2.1 Development of the World Wide Web 2.2 Hypertext Markup Language 2.3 Cascading Style Sheets 2.4 JavaScript 2.5 Hypertext Markup Language Document Object Model 2.6 Asynchronous JavaScript and XML 3 GWT toolbox and compiler 3.1 GWT in action 3.2 A short overview of the toolkit 3.3 GWT compiler and JSNI 3.3.1 Overview of GWT compiler and JSNI 3.3.2 Deferred binding and bootstrapping process 3.3.3 GWT compiler steps and optimizations 3.4 Java Runtime Environment Emulation 3.5 Widgets and Panels 3.5.1 Overview of GWT Widgets 3.5.2 Event handlers in GWT Widgets 3.5.3 Manipulating browser’s DOM with GWT DOM class 3.5.4 GWT Designer and view optimization using UiBinder 3.6 Remote Procedure Calls 3.6.1 Comparison of Remote Procedure Calls with Remote Method Invocations 3.6.2 GWT’s RPC service and serializable whitelist 3.7 History Management 3.8 Client Bundle 3.8.1 Using ImageResources in the ClientBundle interface 3.8.2 Using CssResources in the ClientBundle interface 4 Model-View-Presenter Architecture 4.1 Comparison of MVP and MVC 4.2 GWT Model-View-Presenter pattern example: Agricola board game 4.3 Extending the Agricola web application with mobile views 4.4 Introducing activities in the Agricola Model-View-Presenter pattern enabling browser history 5 Comparison of the two web frameworks: GWT and JSF 5.1 Definitions of comparison fields 5.2 Comparison in category 1: Nearly completely static sites with a little bit of dynamic content, e.g. news update 5.3 Comparison in category 2: Doing a survey in both technologies 5.4 Comparison in category 3: Creating a forum to show data 5.5 Comparison in category 4: Writing a chat application 5.6 Comparison in category 5: Writing the speed game Snake 5.7 Summary 6 Security 6.1 Download Tomcat 6.2 Dynamic Web Application Project with GWT and Tomcat 6.3 Establish HTTPS connections in Tomcat 6.3.1 Create a pem certificate 6.3.2 Convert pem certificate into a key store object 6.3.3 Configure Tomcat’s XML files to enable HTPPS 6.4 Establish a database connection in Tomcat 6.4.1 Create TomcatGWT user and schema, and add the table countries 6.4.2 Configure Tomcat’s XML files to get access to the database connection 6.4.3 PreparedStatements avoid MySQL injections 6.5 Login mechanism in Tomcat 6.6 SafeHtml 7 Presenting a complex software application written in GWT 8 Conclusions 8.1 Summary 8.2 Future work A Appendix A 1 Configure the Google Web Toolkit framework in Eclipse A 1.1 Install the Java Developer Kit A 1.2 Download Eclipse A 1.3 Install the GWT plugin in Eclipse A 1.4 Create first GWT Java Project A 2 Figures A 3 Listings A 3.1 Source code of the Agricola board game A 3.2 Source code of GWT and JSF comparison A 4 Tables R Lists and References R 1 Lists R 1.1 List of Tables R 1.2 List of Figures R 1.3 List of Listings R 2 References R 2.1 Books R 2.2 Online resources