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Traditional wireless sensor networks (WSNs) capture scalar data
such as temperature, vibration, pressure, or humidity. Motivated by
the success of WSNs and also with the emergence of new technology
in the form of low-cost image sensors, researchers have proposed
combining image and audio sensors with WSNs to form wireless
multimedia sensor networks (WMSNs). This introduces practical and
research challenges, because multimedia sensors, particularly image
sensors, generate huge amounts of data to be processed and
distributed within the network, while sensor nodes have restricted
battery power and hardware resources. This book describes how
reconfigurable hardware technologies such as field-programmable
gate arrays (FPGAs) offer cost-effective, flexible platforms for
implementing WMSNs, with a main focus on developing efficient
algorithms and architectures for information reduction, including
event detection, event compression, and multicamera processing for
hardware implementations. The authors include a comprehensive
review of wireless multimedia sensor networks, a complete
specification of a very low-complexity, low-memory FPGA WMSN node
processor, and several case studies that illustrate information
reduction algorithms for visual event compression, detection, and
fusion. The book will be of interest to academic researchers,
R&D engineers, and computer science and engineering graduate
students engaged with signal and video processing, computer vision,
embedded systems, and sensor networks.
Traditional wireless sensor networks (WSNs) capture scalar data
such as temperature, vibration, pressure, or humidity. Motivated by
the success of WSNs and also with the emergence of new technology
in the form of low-cost image sensors, researchers have proposed
combining image and audio sensors with WSNs to form wireless
multimedia sensor networks (WMSNs). This introduces practical and
research challenges, because multimedia sensors, particularly image
sensors, generate huge amounts of data to be processed and
distributed within the network, while sensor nodes have restricted
battery power and hardware resources. This book describes how
reconfigurable hardware technologies such as field-programmable
gate arrays (FPGAs) offer cost-effective, flexible platforms for
implementing WMSNs, with a main focus on developing efficient
algorithms and architectures for information reduction, including
event detection, event compression, and multicamera processing for
hardware implementations. The authors include a comprehensive
review of wireless multimedia sensor networks, a complete
specification of a very low-complexity, low-memory FPGA WMSN node
processor, and several case studies that illustrate information
reduction algorithms for visual event compression, detection, and
fusion. The book will be of interest to academic researchers,
R&D engineers, and computer science and engineering graduate
students engaged with signal and video processing, computer vision,
embedded systems, and sensor networks.
Artificial Intelligence, Machine Learning, and Mental Health in
Pandemics: A Computational Approach provides a comprehensive guide
for public health authorities, researchers and health professionals
in psychological health. The book takes a unique approach by
exploring how Artificial Intelligence (AI) and Machine Learning
(ML) based solutions can assist with monitoring, detection and
intervention for mental health at an early stage. Chapters include
computational approaches, computational models, machine learning
based anxiety and depression detection and artificial intelligence
detection of mental health. With the increase in number of natural
disasters and the ongoing pandemic, people are experiencing
uncertainty, leading to fear, anxiety and depression, hence this is
a timely resource on the latest updates in the field.
This edited book will serve as a source of reference for
technologies and applications for multimodality data analytics in
big data environments. After an introduction, the editors organize
the book into four main parts on sentiment, affect and emotion
analytics for big multimodal data; unsupervised learning strategies
for big multimodal data; supervised learning strategies for big
multimodal data; and multimodal big data processing and
applications. The book will be of value to researchers,
professionals and students in engineering and computer science,
particularly those engaged with image and speech processing,
multimodal information processing, data science, and artificial
intelligence.
Wireless sensor networks (WSNs) have many applications ranging from
environmental monitoring, security management, and medical
applications to smart homes. Visual Information Processing in
Wireless Sensor Networks: Technology, Trends and Applications
provides a central source of reference on visual information
processing in wireless sensor network environments and its
technology, application, and society issues. This book is an
important resource for researchers and academics working in the
interdisciplinary domains of wireless sensor network technology and
multimedia technology and its related areas, which include image
processing, pervasive computing, embedded systems, and computer
networks.
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