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Books > Computing & IT > Applications of computing > Signal processing
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The Handbook of Multimodal-Multisensor Interfaces, Volume 2 - Signal Processing, Architectures, and Detection of Emotion and Cognition (Hardcover)
Loot Price: R3,214
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The Handbook of Multimodal-Multisensor Interfaces, Volume 2 - Signal Processing, Architectures, and Detection of Emotion and Cognition (Hardcover)
Series: ACM Books
Expected to ship within 10 - 15 working days
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The Handbook of Multimodal-Multisensor Interfaces provides the
first authoritative resource on what has become the dominant
paradigm for new computer interfaces: user input involving new
media (speech, multi-touch, hand and body gestures, facial
expressions, writing) embedded in multimodal-multisensor interfaces
that often include biosignals. This edited collection is written by
international experts and pioneers in the field. It provides a
textbook, reference, and technology roadmap for professionals
working in this and related areas. This second volume of the
handbook begins with multimodal signal processing, architectures,
and machine learning. It includes recent deep learning approaches
for processing multisensorial and multimodal user data and
interaction, as well as context-sensitivity. A further highlight is
processing of information about users' states and traits, an
exciting emerging capability in next-generation user interfaces.
These chapters discuss real-time multimodal analysis of emotion and
social signals from various modalities, and perception of affective
expression by users. Further chapters discuss multimodal processing
of cognitive state using behavioral and physiological signals to
detect cognitive load, domain expertise, deception, and depression.
This collection of chapters provides walk-through examples of
system design and processing, information on tools and practical
resources for developing and evaluating new systems, and
terminology and tutorial support for mastering this rapidly
expanding field. In the final section of this volume, experts
exchange views on the timely and controversial challenge topic of
multimodal deep learning. The discussion focuses on how
multimodal-multisensor interfaces are most likely to advance human
performance during the next decade.
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