Embedded Commissioning of Building Systems

This book describes state-of-the-art techniques for designing real-time computer systems. The author shows how to estimate precisely the effect of cache architecture on the execution time of a program

For all the innovation in building principles and techniques these days, especially regarding environmental and resource issues, there is no reliable way to measure a building's performance, says Akin

The great strides made over the last decade in the complexity and network functionality of embedded systems have significantly enhanced their attractiveness for use in critical applications such as me

Readers of this book will be shown how, with the adoption of ubiquituous sensing, extensive data-gathering and forecasting, and building-embedded advanced actuation, intelligent building systems with

Readers of this book will be shown how, with the adoption of ubiquituous sensing, extensive data-gathering and forecasting, and building-embedded advanced actuation, intelligent building systems with

The building blocks of today’s embedded systems-on-a-chip (SoC) are complex IP components and programmable processor cores. This means that more and more system functionality is implemented in softwar

Building computers that can be used to design embedded real-time systems is the subject of this title. Real-time embedded software requires increasingly higher performances. The authors therefore cons

This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

The building blocks of today’s embedded systems-on-a-chip (SoC) are complex IP components and programmable processor cores. This means that more and more system functionality is implemented in softwar