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The work presents new approaches to Machine Learning for Cyber
Physical Systems, experiences and visions. It contains some
selected papers from the international Conference ML4CPS - Machine
Learning for Cyber Physical Systems, which was held in Lemgo,
October 25th-26th, 2017. Cyber Physical Systems are characterized
by their ability to adapt and to learn: They analyze their
environment and, based on observations, they learn patterns,
correlations and predictive models. Typical applications are
condition monitoring, predictive maintenance, image processing and
diagnosis. Machine Learning is the key technology for these
developments.
Considered a major field of photonics, plasmonics offers the
potential to confine and guide light below the diffraction limit
and promises a new generation of highly miniaturized photonic
devices. This book combines a comprehensive introduction with an
extensive overview of the current state of the art. Coverage
includes plasmon waveguides, cavities for field-enhancement,
nonlinear processes and the emerging field of active plasmonics
studying interactions of surface plasmons with active media.
Considered one of the major fields of photonics of the beginning
21st century, plasmonics offers the potential to confine and guide
light below the diffraction limit and promises a new generation of
highly miniaturized photonic devices. Offering both a comprehensive
introduction to the field and an extensive overview of the current
state of the art, Plasmonics - Fundamentals and Applications should
be of great value to the newcomer and to the experienced
researcher. The topics covered include plasmon waveguides, cavities
for field-enhancement, nonlinear processes and the emerging field
of active plasmonics studying interactions of surface plasmons with
active media.
This open access proceedings presents new approaches to Machine
Learning for Cyber Physical Systems, experiences and visions. It
contains selected papers from the fifth international Conference
ML4CPS - Machine Learning for Cyber Physical Systems, which was
held in Berlin, March 12-13, 2020. Cyber Physical Systems are
characterized by their ability to adapt and to learn: They analyze
their environment and, based on observations, they learn patterns,
correlations and predictive models. Typical applications are
condition monitoring, predictive maintenance, image processing and
diagnosis. Machine Learning is the key technology for these
developments.
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