Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe
Membrane Computing Models: Implementations (eBook, PDF)
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Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe
Membrane Computing Models: Implementations (eBook, PDF)
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The theoretical basis of membrane computing was established in the early 2000s with fundamental research into the computational power, complexity aspects and relationships with other (un)conventional computing paradigms. Although this core theoretical research has continued to grow rapidly and vigorously, another area of investigation has since been added, focusing on the applications of this model in many areas, most prominently in systems and synthetic biology, engineering optimization, power system fault diagnosis and mobile robot controller design. The further development of these…mehr
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The theoretical basis of membrane computing was established in the early 2000s with fundamental research into the computational power, complexity aspects and relationships with other (un)conventional computing paradigms. Although this core theoretical research has continued to grow rapidly and vigorously, another area of investigation has since been added, focusing on the applications of this model in many areas, most prominently in systems and synthetic biology, engineering optimization, power system fault diagnosis and mobile robot controller design. The further development of these applications and their broad adoption by other researchers, as well as the expansion of the membrane computing modelling paradigm to other applications, call for a set of robust, efficient, reliable and easy-to-use tools supporting the most significant membrane computing models. This work provides comprehensive descriptions of such tools, making it a valuable resource for anyone interested in membrane computing models.
Produktdetails
- Produktdetails
- Verlag: Springer Singapore
- Erscheinungstermin: 1. Juli 2021
- Englisch
- ISBN-13: 9789811615665
- Artikelnr.: 62178903
- Verlag: Springer Singapore
- Erscheinungstermin: 1. Juli 2021
- Englisch
- ISBN-13: 9789811615665
- Artikelnr.: 62178903
Gexiang Zhang
Professor at School of Control Engineering at Chengdu University of Information Technology, Chengdu, China. The President of the International Membrane Computing Society (IMCS), IET Fellow, IEEE Senior Member. Managing Editor of Journal of Membrane Computing (Springer) and Editorial Board member of International Journal of Parallel, Emergent and Distributed Systems. He is the main investigator of 5 scientific research projects funded by National Natural Science Foundation of China and of more than 20 scientific research projects at the national and provincial levels. He is the winner of the Grigore Moisil Prize of the Romanian Academy in 2019 and was awarded Sichuan Provincial Natural Science Award or Science and Technology Progress Awards in three consecutive years 2017-2019. Research areas include membrane computing, artificial intelligence, robotics, power systems, and their interactions. Author/co-author of more than 200 publications, two monographs, and (lead) guest editor/co-editor of more than 10 volumes/proceedings. He has more than 3800 citations with an H index of 34, according to Google Scholar.
Mario J. Pérez-Jiménez
Full Professor at the Department of Computer Science and Artificial Intelligence at Universidad de Sevilla, Spain, since 2009, and currently Emeritus Professor. From 2005 to 2007 he was a Guest Professor of the Huazhong University of Science and Technology, Wuhan, China. He is a numerary member of the Academia Europaea (The Academy of Europe) in the Section of Informatics. His main research interests include theory of computation, computational complexity theory, natural computing (DNA computing and membrane computing), bioinformatics and computational modelling for complex systems. He has published 19 books in computer science and mathematics, and over 300 scientific papers in international journals (collaborating with researchers worldwide) and he is a member of the Editorial Board of six ISI journals. He has been the first scientist awarded with “Important Contributions to Membrane Computing” under the auspices of the European Molecular Computing Consortium, Edinburgh, 2008. In 2014, he received the University of Sevilla’s FAMA award for his outstanding research career. He has been the main researcher in various European, Asian, Spanish and Andalusian research grants. From 2003 he is an expert reviewer of the Prospective and Evaluation National Agency of Spain. From May 2006 he is an European Science Foundation peer reviewer, from July 2008 he is an expert reviewer from the Romanian National University Research Council and from October 2015 he is an international expert from the Russian Science Foundation, invited by the Russian International Affairs Council.
Agustín Riscos-Nuñez
Associate professor at the Department of Computer Science and Artificial Intelligence, guarantor researcher at the Smart Computer systems Research and Engineering Lab (SCORE), head of the Research Group on Natural Computing, founding member and Secretary of the Research Institute of Computer Engineering (I3US) at Universidad de Sevilla, Spain. Member of the Steering committee and the Program committee of the “International Conference on Membrane Computing” and the “Asian Conference on Membrane Computing”. Founding member of the “International Membrane Computing Society (IMCS)”, IEEE Member.
His main areas of expertise are bio-inspired computing and artificial intelligence. His research interests mainly focus in computational modelling of complex systems and population dynamics, as well as other practical applications in the fields of bioinformatics, biomedicine, high performance computing and robotics. Co-author of more than 30 papers on relevant international journals and several book chapters, more than 30 contributions to international conferences (several times as invited speaker), and co-editor for more than 10 volumes/proceedings.
Sergey Verlan
Associate Professor at the Department of Computer Science at the University of Paris Est Créteil, France, member of the International Conference on Membrane Computing steering committee, and member of the International Membrane Computing Society (IMCS). His research interests cover several topics, including membrane computing, natural/DNA computing, unconventional computing, formal language theory, study of complex dynamic systems, FPGA digital circuit design and applications in biological modeling and robotics. He has more than 100 publications, including 6 edited volumes of conference proceedings and special issues and more than 1400 citations, according to Google Scholar.
Savas Konur
Reader in Computer Science at University of Bradford. A committee member of IMCS, fellow of UK HEA, IEEE Member, and an Editor of Journal of Membrane Computing. His research interests cover computational modelling, formal verification, data-driven modelling and analysis, stochastic simulations and high-performance computing with applications to membrane computing, systems and synthetic biology, safety-critical systems and real-time systems. He has published in numerous prestigious journals as well as many leading conferences. He has led a number of research projects (funded by EPSRC, Innovate UK and Access Innovation), requiring a wide range of interdisciplinary collaborations. He has a strong track record on software platform development for engineering and life science research.
Thomas Hinze
Associate Professor at Friedrich Schiller University Jena (Germany), Department of Bioinformatics, senior lecturer and head of the Natural Computing group, member of the membrane computing steering committee and member of the International Membrane Computing Society (IMCS). His research interests cover principles of biological information processing including evolutionary, membrane, molecular, neural computing, and artificial life complemented by aspects of systems biology and bioinformatics. His record of scientific publications comprises around 90 contributions as author or co-author including two patents, two text books, and six edited volumes of conference proceedings.
Marian Gheorghe
Professor and 50th Anniversary Chair in Computational Models and Software Engineering at the University of Bradford, UK, Head of the Department of Computer Science (2018-2020) and Director of Research of the School of Electrical Engineering and Computer Science (2016-2018). His research interests cover of broad spectrum of topics, including membrane computing, various computational models, formal verification and testing, simulation based on agent systems, applications to systems and synthetic biology, software engineering, high performance computing. He has more that 100 papers featured by DPLP, with an H index of 30 and more than 3800 citations, according to Google Scholar.
Professor at School of Control Engineering at Chengdu University of Information Technology, Chengdu, China. The President of the International Membrane Computing Society (IMCS), IET Fellow, IEEE Senior Member. Managing Editor of Journal of Membrane Computing (Springer) and Editorial Board member of International Journal of Parallel, Emergent and Distributed Systems. He is the main investigator of 5 scientific research projects funded by National Natural Science Foundation of China and of more than 20 scientific research projects at the national and provincial levels. He is the winner of the Grigore Moisil Prize of the Romanian Academy in 2019 and was awarded Sichuan Provincial Natural Science Award or Science and Technology Progress Awards in three consecutive years 2017-2019. Research areas include membrane computing, artificial intelligence, robotics, power systems, and their interactions. Author/co-author of more than 200 publications, two monographs, and (lead) guest editor/co-editor of more than 10 volumes/proceedings. He has more than 3800 citations with an H index of 34, according to Google Scholar.
Mario J. Pérez-Jiménez
Full Professor at the Department of Computer Science and Artificial Intelligence at Universidad de Sevilla, Spain, since 2009, and currently Emeritus Professor. From 2005 to 2007 he was a Guest Professor of the Huazhong University of Science and Technology, Wuhan, China. He is a numerary member of the Academia Europaea (The Academy of Europe) in the Section of Informatics. His main research interests include theory of computation, computational complexity theory, natural computing (DNA computing and membrane computing), bioinformatics and computational modelling for complex systems. He has published 19 books in computer science and mathematics, and over 300 scientific papers in international journals (collaborating with researchers worldwide) and he is a member of the Editorial Board of six ISI journals. He has been the first scientist awarded with “Important Contributions to Membrane Computing” under the auspices of the European Molecular Computing Consortium, Edinburgh, 2008. In 2014, he received the University of Sevilla’s FAMA award for his outstanding research career. He has been the main researcher in various European, Asian, Spanish and Andalusian research grants. From 2003 he is an expert reviewer of the Prospective and Evaluation National Agency of Spain. From May 2006 he is an European Science Foundation peer reviewer, from July 2008 he is an expert reviewer from the Romanian National University Research Council and from October 2015 he is an international expert from the Russian Science Foundation, invited by the Russian International Affairs Council.
Agustín Riscos-Nuñez
Associate professor at the Department of Computer Science and Artificial Intelligence, guarantor researcher at the Smart Computer systems Research and Engineering Lab (SCORE), head of the Research Group on Natural Computing, founding member and Secretary of the Research Institute of Computer Engineering (I3US) at Universidad de Sevilla, Spain. Member of the Steering committee and the Program committee of the “International Conference on Membrane Computing” and the “Asian Conference on Membrane Computing”. Founding member of the “International Membrane Computing Society (IMCS)”, IEEE Member.
His main areas of expertise are bio-inspired computing and artificial intelligence. His research interests mainly focus in computational modelling of complex systems and population dynamics, as well as other practical applications in the fields of bioinformatics, biomedicine, high performance computing and robotics. Co-author of more than 30 papers on relevant international journals and several book chapters, more than 30 contributions to international conferences (several times as invited speaker), and co-editor for more than 10 volumes/proceedings.
Sergey Verlan
Associate Professor at the Department of Computer Science at the University of Paris Est Créteil, France, member of the International Conference on Membrane Computing steering committee, and member of the International Membrane Computing Society (IMCS). His research interests cover several topics, including membrane computing, natural/DNA computing, unconventional computing, formal language theory, study of complex dynamic systems, FPGA digital circuit design and applications in biological modeling and robotics. He has more than 100 publications, including 6 edited volumes of conference proceedings and special issues and more than 1400 citations, according to Google Scholar.
Savas Konur
Reader in Computer Science at University of Bradford. A committee member of IMCS, fellow of UK HEA, IEEE Member, and an Editor of Journal of Membrane Computing. His research interests cover computational modelling, formal verification, data-driven modelling and analysis, stochastic simulations and high-performance computing with applications to membrane computing, systems and synthetic biology, safety-critical systems and real-time systems. He has published in numerous prestigious journals as well as many leading conferences. He has led a number of research projects (funded by EPSRC, Innovate UK and Access Innovation), requiring a wide range of interdisciplinary collaborations. He has a strong track record on software platform development for engineering and life science research.
Thomas Hinze
Associate Professor at Friedrich Schiller University Jena (Germany), Department of Bioinformatics, senior lecturer and head of the Natural Computing group, member of the membrane computing steering committee and member of the International Membrane Computing Society (IMCS). His research interests cover principles of biological information processing including evolutionary, membrane, molecular, neural computing, and artificial life complemented by aspects of systems biology and bioinformatics. His record of scientific publications comprises around 90 contributions as author or co-author including two patents, two text books, and six edited volumes of conference proceedings.
Marian Gheorghe
Professor and 50th Anniversary Chair in Computational Models and Software Engineering at the University of Bradford, UK, Head of the Department of Computer Science (2018-2020) and Director of Research of the School of Electrical Engineering and Computer Science (2016-2018). His research interests cover of broad spectrum of topics, including membrane computing, various computational models, formal verification and testing, simulation based on agent systems, applications to systems and synthetic biology, software engineering, high performance computing. He has more that 100 papers featured by DPLP, with an H index of 30 and more than 3800 citations, according to Google Scholar.
Chapter 1 Introduction
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
Chapter 1 Introduction
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
Chapter 1 Introduction
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
Chapter 1 Introduction
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation
1.1 General introduction of P systems implementation
1.2 Challenging problems of P systems implementation
1.3 Review of software implementations
1.4 Review of hardware implementations
1.5 Other implementation platforms
Chapter 2 P systems Implementation on P-Lingua framework
2.1 Overview
2.2 P-Lingua language for P systems variants
2.3 Simulation algorithms
2.4 MeCoSim
Chapter 3 Software implementation for P systems
3.1 Automatic design of cell-like P systems with P-Lingua
3.2 Automatic design of spiking neural P systems with P-Lingua
3.3 Modelling real ecosystems with MeCoSim
3.4 Robot motion planning
Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Modelling
4.3.2 Simulation
4.3.3 Verification
4.3.4 Parameter Optimization
4.4 Case Studies
4.5 Next Generation Infobiotics
4.5.1 Prediction-based stochastic simulations
4.5.2 High-performance simulation and verification
4.5.3 Biocompilation
4.6 Conclusions and discussions
Chapter 5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-inspired Approaches (JENA)
5.1 Motivation and Introduction
5.2 JENA at a Glance
5.3 JENA Descriptive Capacity
5.4 JENA Source Code Design
5.5 Selection of JENA Case Studies
Chapter 6 P systems Implementation on CUDA
6.1 Overview
6.2 Specific simulations
6.3 Generic simulations
6.4 Adaptative simulations
Chapter 7 P systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P systems (GNPS)
7.4 Implementing GNPS on FPGA
7.5 FPGA implementations of other models of P systems
7.6 Discussion
Chapter 8 Hardware implementations and applications
8.1 Knapsack problems with CUDA implementation
8.2 Robot membrane controllers with FPGA implementation
8.3 Robot path planning with FPGA implementation
8.4 Image processing with FPGA implementation