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UKRI-SPC |
Modelling Methodology
Continuous, discrete and hybrid simulation methodology, Simulation environments, Multi-paradigm simulation, Simulation uncertainty, Simulation visualisation, Integration of simulation and geographical information systems, Object-oriented programming and Languages, Multi-paradigm Languages, Software comparisons. Numerical Methods for Simulation, Mathematical Analysis in Simulation, Parallel Simulation Methodology, Discrete Event Simulation, Simulation Fidelity and Performance Evaluation, Advanced Training and Simulation Concepts for Education, Multiparameter Sequential Optimization Methods in Simulation, Verification, Validation, and Control in Complex Systems Simulation, Distributed and Parallel Systems Simulation, Combined Continuous and Discrete Event Models, Symbol Analysis and Manipulation of Equation-Based Models, Simultaneous vs Modular Simulation Methods, Standardization Issues.
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Modelling Tools
Simulation languages; Object oriented modeling languages; UML and simulation; Model libraries and modularity; Component-oriented simulation; Special simulation tools and environments; Meta-models and automatic model generation; Graphical simulation environments and simulation software tools; Intelligent simulation environments; Database management of models and results; Java and Web enabled simulations, UML and OO Simulation.
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Analysis Methodology
Advanced Input Modeling, Simulation Optimization, Cross Entropy, Output Analysis, Input Modeling, Simulation Optimization, Input Analysis, Difficult Queueing Problems, New Output Analysis.
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Simulation of Networks and Communication
Modeling and simulation of analogue circuits; Modeling and simulation of digital circuits at switch and/or at logic level; Hardware accelerators for circuit-level simulation; Hardware accelerators for logic simulation; Distributed simulation of circuits, components, and systems; Modeling and simulation of computer systems; Fault simulation; Parallel and distributed systems; High-speed networks; Network simulation software; Computer and telecommunication systems; Telecommunication devices and systems; Intelligent telecommunication networks; ISDN; ATM communications.
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Complex Systems Simulation
Design and Simulation, - Process Control and Optimisation, - Information Technology Systems, - Space and Airborne Systems - Communication Networks, - Cybernetics and Control, - Building Engineering and Urban Infrastructures - Nonlinear Systems, Integration of AI Techniques and Simulation, Knowledge Elicitation and Representation for Complex Models, Drawing Understanding and Pattern Recognition, Machine Learning, Neural Networks and Genetic Algorithms, Simulation in Robotics and Automation, Continuous Simulation of Technical Processes, Fuzzy Models in Simulation, Wireless Communication, Mobile Communication Networks, Satellite Communication, LAN and WAN Protocols, Simulation of Switching Equipment, Design and Coding of Communication Handling Software.
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Simulation of Signal and Image Processing
Modelling and simulation are powerful tools in image and signal processing applications. For example, the modelling and simulation of communication networks enable the analysis and evaluation of data communication techniques in a much more time and cost efficient way than field tests. Modelling and simulations are also extensively used in biomedical signal processing to study and predict physical behaviour. Mathematical models such as Markov random fields and optical flows have been successfully used to solve a variety of image processing problems, including image restoration, reconstruction, segmentation, and classification as well as texture analysis.
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Energy Systems Simulation
Simulators: Real-Time simulation methods, GUI, Advanced modelling tools, Trainees' performance evaluation, Simulator Projects Simulation Studies: Simulation during design, Safety and environmental hazard estimation, Production optimisation. Methodology: Real-time simulation and visualisation tools, Parallel and distributed simulation.
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Simulation in the Chemical and Petrochemical Industry
Simulation of Chemical Plants, Flow simulation, Plant control systems, network simulation, geological simulations, drilling simulations, oil transport simulations.
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Multimedia and Virtual Reality Systems
This track covers simulation programmes used in Multimedia and VR systems enhanced simulation
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Decision Processing in Management
Business process engineering and simulation, Simulation in customer-oriented service processes, Decision support systems, Simulation in Finance and Risk Management, Simulation in workflow management, Process mapping and simulation, Simulation of production processes and equipment, Business Information Modeling Methods and Methodologies, UML and UP in Business Modelling, UML and UP for Enterprise Modeling.
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Modelling and Simulation in Biomedical Systems
Topics are biological systems, medical systems and ecosystems with the domain specific topics such as molecular modelling, genetic algorithms in biosystems, fuzzy sets and neural nets in biosystems, physiology, cardiology, anesthesia, cancer, circulatory system, respiratory system, renal system, biomechanics, agricultural production, simulation of global change, ecotechnology and eco-engineering, GIS, population dynamics, etc Simulation of Patient Care,Quality of Service, Hospital Logistics, Simulation of Disease Proliferation, Virtual Reality in Surgical Procedures, Simulating Biological Phenomena and Organs, Simulation as a Tool for Diagnosis, Simulation of Emergency Procedures (Disaster Gaming), General Medical Simulations, Pharmacometric Modelling, Physiological Simulations.
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Modelling and Simulation for Industrial Applications
The application section covers: Automation, CAD/CAM/CAE, Defense Electronics, Design Automation, Simulation in industrial Design, Industrial Engineering, Industrial and Process Simulation, Manufacturing, Simulations, Logistics and Transport, Power Plants, Multibody Systems, Aerospace, etc..
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Web based Simulation
Web-based Simulation Environments (WSE), Web-based Distributed Interactive Simulation (WDIS) Sharing and reuse of simulation models and tools in WSE , Techniques and Standards for model integration, Communication interoperability in WSE and WDIS, WSE and WDIS applications to education, training and learning. , Simulation visualization/animation in WSE and WDIS, Web-based Distributed Simulation (distributed modelling via the Web, Java based, Federated, and so on)
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Simulation in Information Processing and Software Development
text to be added later
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Simulation in Fuzzy Systems
Classification, Data analysis, Fault tolerance, Forecasting, Knowledge acquisition, Economics and Finance, Planning, Pre-treatment of data, Process control, Robotics, Speech and image recognition, Web intelligence, involving methodologies such as: Hybrid systems (GA, fuzzy, symbolic representation), Methods or tools for evaluating ANN performance, Reinforcement Learning, Simulation tools (research, education, development).
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Neural Networks and Genetic Algorithms
Neural nets for simulation: modelling of parts (components) of the system simulated by neural networks, evaluation of simulation models using neural nets, decision support in simulation models by neural nets; Simulation of neural nets: systems of pre-designed neural networks, techniques and tools for simulation and programming of neural networks. Expert Controllers and Genetic Algorithms in Simulation, Knowledge Based Simulation Tools, AI and Expert Systems in Simulation.
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Simulation in the Built Environment
The built environment is a complicated and many-faceted energy system encompassing many different yet synergistic energy flow paths. Each flow path changes its own (and hence the system's) characteristics. tics dynamically as time progresses. To accurately model such phenomena a simulation model must take into account all relevant energy subsystems and the interrelationships between them.
The areas covered by building simulation models are numerous and varied and include conduction modelling, air/fluid flow modelling, plant side modelling, insolation analysis, lighting studies and building climatic interaction.
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Simulation in Archaeology
Archaeologists commonly use theoretical models, experiments, and observations of the world as it is today to try to explain what happened in the past. They have attempted to explain, for example, why people first began to walk upright and why civilizations that once flourished suddenly collapsed. Good explanations come from well-thought-out theoretical models that propose ways in which the existing archaeological record might have been formed. Explanations can include factors such as environmental changes, demographic shifts (changes in population makeup and size), migrations, and patterns of thought and behavior.
In recent years, many archaeologists have begun to use geographic information systems (GIS) to aid in mapping sites. These computer-based systems allow the collection, storage, and manipulation of environmental, geographic, and geologic data, together with archaeological information, in a single database. Using this technology, archaeologists can create maps that simulate different environments and ways in which people might have used land, living space, and material goods.
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Simulation Projects in the Arab World
Roundtable discussion on present projects in the Arab World. If you have a topic proposal.
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