Tabla de Contenidos: “…Three - Experiments: developing the basis for going beyond ITER -- 11 - National Spherical Torus eXperiment -- 11.1 Introduction -- 11.2 Transport and turbulence -- 11.3 Macroscopic stability -- 11.4 Energetic particles -- 11.5 Boundary physics -- 11.6 Solenoid-free operation and wave physics -- 11.7 NSTX-Upgrade -- References -- 12 - The mega amp spherical tokamak -- 12.1 The MAST device -- 12.2 Key scientific achievements of 15years of MAST research -- 12.2.1 Plasma confinement -- 12.2.1.1 Access to high confinement (H-mode) -- 12.2.1.2 Energy confinement -- 12.2.1.3 Particle confinement and fuelling -- 12.2.1.4 Momentum transport -- 12.2.2 Pedestal and ELM physics -- 12.2.2.1 The ELM crash -- 12.2.2.2 Pedestal evolution -- 12.2.2.3 ELM control -- 12.2.3 Fast-ion physics and current drive -- 12.2.3.1 Alfvén instabilities -- 12.2.3.2 Effect of MHD modes on fast ions -- 12.2.3.3 Neutral beam current drive -- 12.2.4 Scrape-off layer and exhaust physics -- 12.2.4.1 Target heat loads -- 12.2.4.2 SOL transport -- 12.2.5 Macroscopic stability -- 12.2.5.1 The internal n=1 kink mode -- 12.2.5.2 Sawtooth physics -- 12.2.5.3 NTM physics -- 12.2.5.4 Disruptions -- 12.2.6 Plasma start-up -- 12.2.6.1 Merging compression start-up -- 12.2.6.2 ECRH/EBW start-up and heating -- 12.3 The upgrade to MAST -- 12.3.1 A staged approach toward a future ST -- 12.3.2 MAST-U as divertor test facility -- 12.3.3 Toward a fully noninductive flat top -- 12.3.3.1 The neutral beam current drive system -- 12.3.3.2 The EBW current drive potential -- References -- 13 - Experimental advanced superconducting tokamak -- 13.1 EAST mission and orientation -- 13.1.1 Overview -- 13.1.2 EAST mission and orientation -- 13.2 Main progress and achievements on the EAST tokamak -- 13.2.1 Development of high-performance long-pulse operation -- 13.2.2 Main progresses in RF physics…”

Tabla de Contenidos: “…10.4.2 Results of Association Rule Mining on Original Sequences -- 10.4.3 Results of the Proposed Approach -- 10.5 Conclusion and Future Work -- Notes -- References -- Chapter 11: Geoinformatics and Social Media: New Big Data Challenge -- 11.1 Introduction: Social Media and Ambient Geographic Information -- 11.2 Characteristics of Big Geosocial Data -- 11.3 Geosocial Complexity -- 11.4 Modeling and Analyzing Geosocial Multimedia: Heterogeneity and Integration -- 11.5 Outlook: Grand Challenges and Opportunities for Big Geosocial Data -- Notes -- References -- Chapter 12: Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern -- 12.1 Introduction -- 12.2 Trajectory Modeling -- 12.2.1 TMC-Pattern -- 12.2.1.1 Determining Meaningful Location -- 12.2.2 Time Correlation -- 12.2.3 Location Context Awareness -- 12.2.4 Relevance Measures of a Region -- 12.2.5 TMC-Pattern -- 12.2.5.1 Determining Residence Mode of a Region -- 12.2.6 Trajectory Extraction -- 12.3 Trajectory Mining -- 12.3.1 Frequent Locations from TMC-Pattern -- 12.3.2 TMC-Pattern and Markov Chain for Prediction -- 12.3.2.1 Markov Chains -- 12.3.2.2 Markov Chain from TMC-Pattern -- 12.3.2.3 Computation of Markov Chain Transition Probability -- 12.3.2.4 Computation of Scores from TMC-Pattern -- 12.4 Empirical Evaluations -- 12.4.1 Experimental Dataset -- 12.4.2 Evaluation of TMC-Pattern Extraction -- 12.4.2.1 Single-User Data -- 12.4.2.2 Multiuser Data -- 12.4.3 Frequent Patterns -- 12.4.4 Location Prediction -- 12.5 Summary -- References -- Chapter 13: Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web -- 13.1 Introduction -- 13.2 Big Data Challenges on the Worldwide Sensor Web -- 13.3 Worldwide Sensor Web Architecture -- 13.4 GeoCENS Architecture -- 13.4.1 OGC-Based Sensor Web servers…”

Tabla de Contenidos: “…5.7.1 try...except‌ -- 5.8 Aufgaben -- 5.9 Lösungen -- Kapitel 6: Funktion‌en -- 6.1 Aufruf von Funktion‌en -- 6.2 Definition von Funktionen‌ -- 6.3 Schrittweise Verfeinerung‌ -- 6.4 Ausführung von Funktio‌nen -- 6.4.1 Global‌‌e und lokale Name‌‌n -- 6.4.2 Seiteneffekt‌e - die global‌-Anweisung‌ -- 6.4.3 Parameterübergabe‌ -- 6.5 Voreingestellte Parameterwerte‌‌ -- 6.5.1 Schlüsselwort-Argument‌e -- 6.6 Funktion‌en mit beliebiger Anzahl von Parametern‌ -- 6.7 Lokale Funktion‌en -- 6.8 Rekursive Funktion‌‌en -- 6.9 Experimente zur Rekursion mit der Turtle-Grafi‌k -- 6.9.1 Turtle-Befehle im interaktiven Modus -- 6.9.2 Eine rekursive Spirale -- 6.9.3 Baumstrukturen -- 6.9.4 Künstlicher Blumenkohl - selbstähnlich‌e Bilder -- 6.10 Rekursive Zahlenfunktionen -- 6.11 Hintergrund: Wie werden rekursive Funktionen ausgeführt? …”

Tabla de Contenidos: “…8.6.2 Background 265 -- 8.6.3 Problem Formulation 266 -- 8.6.4 Routing Based on Priority Queuing 267 -- 8.6.5 Problem Solution 269 -- 8.6.6 Performance Evaluation 270 -- 8.7 Cross-Layer Optimization for Video Summary Transmission 272 -- 8.7.1 Background 272 -- 8.7.2 Problem Formulation 274 -- 8.7.3 System Model 276 -- 8.7.4 Link Adaptation for Good Content Coverage 278 -- 8.7.5 Problem Solution 280 -- 8.7.6 Performance Evaluation 283 -- 8.8 Conclusions 287 -- References 287 -- 9 Content-based Video Communications 291 -- 9.1 Network-Adaptive Video Object Encoding 291 -- 9.2 Joint Source Coding and Unequal Error Protection 294 -- 9.2.1 Problem Formulation 295 -- 9.2.2 Solution and Implementation Details 299 -- 9.2.3 Application on Energy-Efficient Wireless Network 301 -- 9.2.4 Application on Differentiated Services Networks 303 -- 9.3 Joint Source-Channel Coding with Utilization of Data Hiding 305 -- 9.3.1 Hiding Shape in Texture 308 -- 9.3.2 Joint Source-Channel Coding 309 -- 9.3.3 Joint Source-Channel Coding and Data Hiding 311 -- 9.3.4 Experimental Results 315 -- References 322 -- 10 AVC/H.264 Application / Digital TV 325 -- 10.1 Introduction 325 -- 10.1.1 Encoder Flexibility 326 -- 10.2 Random Access 326 -- 10.2.1 GOP Bazaar 327 -- 10.2.2 Buffers, Before and After 332 -- 10.3 Bitstream Splicing 335 -- 10.4 Trick Modes 337 -- 10.4.1 Fast Forward 338 -- 10.4.2 Reverse 338 -- 10.4.3 Pause 338 -- 10.5 Carriage of AVC/H.264 Over MPEG-2 Systems 338 -- 10.5.1 Packetization 339 -- 10.5.2 Audio Video Synchronization 344 -- 10.5.3 Transmitter and Receiver Clock Synchronization 344 -- 10.5.4 System Target Decoder and Timing Model 344 -- References 345 -- 11 Interactive Video Communications 347 -- 11.1 Video Conferencing and Telephony 347 -- 11.1.1 IP and Broadband Video Telephony 347 -- 11.1.2 Wireless Video Telephony 348 -- 11.1.3 3G-324M Protocol 348 -- 11.2 Region-of-Interest Video Communications 351 -- 11.2.1 ROI based Bit Allocation 351 -- 11.2.2 Content Adaptive Background Skipping 356.…”

Tabla de Contenidos: “…2.1.8 Radiation and Hertz Dipole -- 2.1.9 Fundamental Antenna Parameters -- 2.1.9.1 Radiation Power Density -- 2.1.9.2 Radiation Intensity -- 2.1.9.3 Directivity -- 2.1.9.4 Input Impedance, Radiation and Loss Resistance -- 2.1.9.5 Gain and Radiation Ef ciency -- 2.1.10 Dipole Antennas -- 2.1.11 Pocklington Integro-Differential Equation for a Straight Thin Wire -- 2.2 Introduction to Numerical Methods in Electromagnetics -- 2.2.1 Weighted Residual Approach -- 2.2.1.1 Fundamental Lemma of Variational Calculus -- 2.2.2 The Finite Element Method (FEM) -- 2.2.2.1 Basic Concepts of FEM -- 2.2.2.2 One-Dimensional FEM -- 2.2.2.3 Incorporation of Boundary Conditions -- 2.2.2.4 Computational Example: 1D Problem -- 2.2.2.5 Two-Dimensional FEM -- 2.2.2.6 The Weak Formulation for Generalized Helmholtz Equation -- 2.2.2.7 Computation of Fluxes on the Domain Boundary -- 2.2.2.8 Computation of Sources on a Finite Element -- 2.2.2.9 Three-Dimensional Elements -- 2.2.3 The Boundary Element Method (BEM) -- 2.2.3.1 Integral Equation Formulation -- 2.2.3.2 Boundary Element Discretization -- 2.2.3.3 Constant Boundary Elements -- 2.2.3.4 Linear and Quadratic Elements -- 2.2.4 Numerical Solution of Integral Equations Over Unknown Sources -- References -- 3 Incident Electromagnetic Field Dosimetry -- 3.1 Assessment of External Electric and Magnetic Fields at Low Frequencies -- 3.1.1 Fields Generated by Power Lines -- 3.1.1.1 The Electric Field -- 3.1.1.2 The Magnetic Field -- 3.1.2 Fields Generated by Substation Transformers -- 3.1.2.1 The Electric Field -- 3.1.2.2 The Magnetic Field -- 3.1.3 Assessment of Circular Current Density Induced in the Body -- 3.1.4 On the Basic Principles of Measurement of LF Fields -- 3.1.4.1 Measurement of LF Electric Fields -- 3.1.4.2 Measurement of LF Magnetic Fields -- 3.1.4.3 Comparison of Calculated and Experimental Results…”

Tabla de Contenidos: “…4 Learning Convolutional Neural Networks for Object Detection with Very Little Training Data -- 4.1 Introduction -- 4.2 Fundamentals -- 4.2.1 Types of Learning -- 4.2.2 Convolutional Neural Networks -- 4.2.2.1 Arti cial neuron -- 4.2.2.2 Arti cial neural network -- 4.2.2.3 Training -- 4.2.2.4 Convolutional neural networks -- 4.2.3 Random Forests -- 4.2.3.1 Decision tree -- 4.2.3.2 Random forest -- 4.3 Related Work -- 4.4 Traf c Sign Detection -- 4.4.1 Feature Learning -- 4.4.2 Random Forest Classi cation -- 4.4.3 RF to NN Mapping -- 4.4.4 Fully Convolutional Network -- 4.4.5 Bounding Box Prediction -- 4.5 Localization -- 4.6 Clustering -- 4.7 Dataset -- 4.7.1 Data Capturing -- 4.7.2 Filtering -- 4.8 Experiments -- 4.8.1 Training and Test Data -- 4.8.2 Classi cation -- 4.8.3 Object Detection -- 4.8.4 Computation Time -- 4.8.5 Precision of Localizations -- 4.9 Conclusion -- Acknowledgment -- References -- 5 Multimodal Fusion Architectures for Pedestrian Detection -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Visible Pedestrian Detection -- 5.2.2 Infrared Pedestrian Detection -- 5.2.3 Multimodal Pedestrian Detection -- 5.3 Proposed Method -- 5.3.1 Multimodal Feature Learning/Fusion -- 5.3.2 Multimodal Pedestrian Detection -- 5.3.2.1 Baseline DNN model -- 5.3.2.2 Scene-aware DNN model -- 5.3.3 Multimodal Segmentation Supervision -- 5.4 Experimental Results and Discussion -- 5.4.1 Dataset and Evaluation Metric -- 5.4.2 Implementation Details -- 5.4.3 Evaluation of Multimodal Feature Fusion -- 5.4.4 Evaluation of Multimodal Pedestrian Detection Networks -- 5.4.5 Evaluation of Multimodal Segmentation Supervision Networks -- 5.4.6 Comparison with State-of-the-Art Multimodal Pedestrian Detection Methods -- 5.5 Conclusion -- Acknowledgment -- References -- 6 Multispectral Person Re-Identi cation Using GAN for Color-to-Thermal Image Translation…”

Tabla de Contenidos: “…5.6.3 Disentangle identity and attributes -- 5.7 Conclusion and discussions -- References -- 6 Deep face recognition using full and partial face images -- 6.1 Introduction -- 6.1.1 Deep learning models -- 6.1.1.1 The structure of a CNN -- 6.1.1.2 Methods of training CNNs -- 6.1.1.3 Datasets for deep face recognition experimentation -- 6.2 Components of deep face recognition -- 6.2.1 An example of a trained CNN model for face recognition -- 6.2.1.1 Feature extraction -- 6.2.1.2 Feature classification -- 6.3 Face recognition using full face images -- 6.3.1 Similarity matching using the FaceNet model -- 6.4 Deep face recognition using partial face data -- 6.5 Specific model training for full and partial faces -- 6.5.1 Suggested architecture of the model -- 6.5.2 Training phase -- 6.6 Discussion and conclusions -- References -- Biographies -- 7 Unsupervised domain adaptation using shallow and deep representations -- 7.1 Introduction -- 7.2 Unsupervised domain adaptation using manifolds -- 7.2.1 Unsupervised domain adaptation using product manifolds -- 7.3 Unsupervised domain adaptation using dictionaries -- 7.3.1 Generalized domain adaptive dictionary learning -- 7.3.2 Joint hierarchical domain adaptation and feature learning -- 7.3.3 Incremental dictionary learning for unsupervised domain adaptation -- 7.4 Unsupervised domain adaptation using deep networks -- 7.4.1 Discriminative approaches for domain adaptation -- 7.4.2 Generative approaches for domain adaptation -- 7.5 Summary -- References -- Biographies -- 8 Domain adaptation and continual learning in semantic segmentation -- 8.1 Introduction -- 8.1.1 Problem formulation -- 8.2 Unsupervised domain adaptation -- 8.2.1 Domain adaptation problem formulation -- 8.2.2 Adaptation focus -- 8.2.2.1 Input level adaptation -- 8.2.2.2 Feature level adaptation -- 8.2.2.3 Output level adaptation…”

“…Differenzierung in Bezug auf prozedurale Kompetenzen wie Hypothesen aufstellen, Experimente planen, Experimente durchführen und Schlussfolgerungen ziehen wurden bisher kaum entwickelt. …”

“…Generative AI Security hosted by bestselling author and speaker Omar Santos offers the latest insights and strategies for securing AI and LLM applications, essential for anyone involved in cybersecurity, AI development, and experimentation…”

“…Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. …”

“…We will learn and be inspired from similar benchmarking efforts in related fields and also hope to advance the state of experimentation in our field and beyond…”

“…It is shown by analysis and experimentally, that both approaches are suitable for autonomous driving, but cover different driving situations…”

“…It covers the ongoing search to verify the prediction experimentally and discusses the physical properties of this novel form of matter. …”

“…Bonus coding challenges encourage independent experimentation"--…”

“…The project, for schools from the primary to upper secondary level, proposes flexible architecture for an “intelligent school” network, and was developed by CISEM, the Centre for Educational Innovation and Experimentation of Milan…”

“…We will learn and be inspired from similar benchmarking efforts in related fields and also hope to create ideas that advance the state of experimentation in our field and beyond…”

“…This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population…”