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Learning Core Audio: A Hands-On Guide to Audio Programming for Mac and iOS
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77
Introduction to Programming with Fortran - with coverage of Fortran 90, 95, 2003 and 77
MATLAB - Modelling, Programming and Simulations
-MATLAB: Modelling, Programming and Simulations- ed. by Emilson Pereira Leite (Repost)
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77 [Repost]
An Introduction to Programming and Numerical Methods in MATLAB [Repost]
"MATLAB: Modelling, Programming and Simulations" ed. by Emilson Pereira Leite (Repost)
Programming Social Applications: Building Viral Experiences with OpenSocial, OAuth, OpenID, and Distributed Web... (repost)
MATLAB Programming for Engineers (2nd edition) [Repost]
Sriranga Veeraraghavan, "Sams Teach Yourself Shell Programming in 24 Hours" (Repost)
Programming Computer Vision with Python - Tools and algorithms for analyzing images
Matlab: A Practical Introduction to Programming and Problem Solving (2nd edition) [Repost]
ECOOP 2011 - Object-Oriented Programming
Mobile JavaScript Application Development - Bringing Web Programming to Mobile Devices [Paperback]
Concurrent Programming on Windows (repost)
Expert WSS 3.0 and MOSS 2007 Programming (repost)
Embedded Software Design and Programming of Multiprocessor System-on-Chip (repost)
OpenCV 2 Computer Vision Application Programming Cookbook
Programming Computer Vision with Python: Tools and algorithms for analyzing images
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77
Introduction to Programming with Fortran - with coverage of Fortran 90, 95, 2003 and 77
MATLAB - Modelling, Programming and Simulations
-MATLAB: Modelling, Programming and Simulations- ed. by Emilson Pereira Leite (Repost)
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77 [Repost]
An Introduction to Programming and Numerical Methods in MATLAB [Repost]
"MATLAB: Modelling, Programming and Simulations" ed. by Emilson Pereira Leite (Repost)
Programming Social Applications: Building Viral Experiences with OpenSocial, OAuth, OpenID, and Distributed Web... (repost)
MATLAB Programming for Engineers (2nd edition) [Repost]
Sriranga Veeraraghavan, "Sams Teach Yourself Shell Programming in 24 Hours" (Repost)
Programming Computer Vision with Python - Tools and algorithms for analyzing images
Matlab: A Practical Introduction to Programming and Problem Solving (2nd edition) [Repost]
ECOOP 2011 - Object-Oriented Programming
Mobile JavaScript Application Development - Bringing Web Programming to Mobile Devices [Paperback]
Concurrent Programming on Windows (repost)
Expert WSS 3.0 and MOSS 2007 Programming (repost)
Embedded Software Design and Programming of Multiprocessor System-on-Chip (repost)
OpenCV 2 Computer Vision Application Programming Cookbook
Programming Computer Vision with Python: Tools and algorithms for analyzing images
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Programming Real-Time Applications with Stochastic Task Execution Times: Analysis and Optimisation
Posted on 2010-03-16
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More Real-Time Applications with Stochastic Task Execution Times: Analysis and Optimisation Embedded systems have become indispensable in our lives: household appliances, cars, airplanes, power plant control systems, medical equipment, telecommunication systems, space technology, they all contain digital computing systems with dedicated functionality. Most of them, if not all, are real-time systems, i.e. their responses to stimuli have timeliness constraints. The timeliness requirement has to be met despite some unpredictable, stochastic behaviour of the system. In this book, we address one cause of such stochastic behaviour, namely the application and platform-dependent stochastic task execution times. The problem of accurately determining the deadline miss ratio of an application and to optimise the performance of a system taking the aforementioned ratio into consideration is extremely important as it has a significant impact on the cost of the end product. Real-Time Applications with Stochastic Task Execution Times presents three approaches to the analysis of the deadline miss ratio of applications with stochastic task execution times. Each of the three approaches fits best to a different context. The first approach is an exact one and is efficiently applicable to monoprocessor systems. The second approach is an approximate one, which allows for designer-controlled trade-off between analysis accuracy and analysis speed. It is efficiently applicable to multiprocessor systems. The third approach is less accurate but sufficiently fast in order to be placed inside optimisation loops. Based on the last approach, we propose a heuristic for task mapping and priority assignment for deadline miss ratio minimisation.
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