Addressing current issues of which any engineer or computer
scientist should be aware, this monograph is a response to the need
to adopt a new computational paradigm as the methodological basis
for designing pervasive embedded systems with sensor capabilities.
The requirements of this paradigm are to control complexity, to
limit cost and energy consumption and to provide adaptation and
cognition abilities allowing the embedded system to interact
proactively with the real world. The quest for such intelligence
requires the formalization of a new generation of intelligent
systems able to exploit advances in digital architectures and in
sensing technologies. The book sheds light on the theory behind
intelligence for embedded systems with specific focus on:
. robustness (the robustness of a computational flow and its
evaluation);
. intelligence (how to mimic the adaptation and cognition
abilities of the human brain),
. the capacity to learn in non-stationary and evolving
environments by detecting changes and reacting accordingly; and
. a new paradigm that, by accepting results that are correct in
probability, allows the complexity of the embedded application the
be kept under control.
Theories, concepts and methods are provided to motivate
researchers in this exciting and timely interdisciplinary area.
Applications such as porting a neural network from a high-precision
platform to a digital embedded system and evaluating its robustness
level are described. Examples show how the methodology introduced
can be adopted in the case of cyber-physical systems to manage the
interaction between embedded devices and physical world.
Researchers and graduate students in computer science and
various engineering-related disciplines will find the methods and
approaches propounded in "Intelligence for Embedded Systems "of
great interest. The book will also be an important resource for
practitioners working on embedded systems and applications."
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