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Over the last decade, significant progress has been made in 3D
imaging research. As a result, 3D imaging methods and techniques
are being employed for various applications, including 3D
television, intelligent robotics, medical imaging, and
stereovision. Depth Map and 3D Imaging Applications: Algorithms and
Technologies present various 3D algorithms developed in the recent
years and to investigate the application of 3D methods in various
domains. Containing five sections, this book offers perspectives on
3D imaging algorithms, 3D shape recovery, stereoscopic vision and
autostereoscopic vision, 3D vision for robotic applications, and 3D
imaging applications. This book is an important resource for
professionals, scientists, researchers, academics, and software
engineers in image/video processing and computer vision.
This book presents the modern technological advancements and
revolutions in the biomedical sector. Progress in the contemporary
sensing, Internet of Things (IoT) and machine learning algorithms
and architectures have introduced new approaches in the mobile
healthcare. A continuous observation of patients with critical
health situation is required. It allows monitoring of their health
status during daily life activities such as during sports, walking
and sleeping. It is realizable by intelligently hybridizing the
modern IoT framework, wireless biomedical implants and cloud
computing. Such solutions are currently under development and in
testing phases by healthcare and governmental institutions,
research laboratories and biomedical companies. The biomedical
signals such as electrocardiogram (ECG), electroencephalogram
(EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic
Obstructive Pulmonary (COP), Electrooculography (EoG),
photoplethysmography (PPG), and image modalities such as positron
emission tomography (PET), magnetic resonance imaging (MRI) and
computerized tomography (CT) are non-invasively acquired, measured,
and processed via the biomedical sensors and gadgets. These signals
and images represent the activities and conditions of human
cardiovascular, neural, vision and cerebral systems. Multi-channel
sensing of these signals and images with an appropriate granularity
is required for an effective monitoring and diagnosis. It renders a
big volume of data and its analysis is not feasible manually.
Therefore, automated healthcare systems are in the process of
evolution. These systems are mainly based on biomedical signal and
image acquisition and sensing, preconditioning, features extraction
and classification stages. The contemporary biomedical signal
sensing, preconditioning, features extraction and intelligent
machine and deep learning-based classification algorithms are
described. Each chapter starts with the importance, problem
statement and motivation. A self-sufficient description is
provided. Therefore, each chapter can be read independently. To the
best of the editors’ knowledge, this book is a comprehensive
compilation on advances in non-invasive biomedical signal sensing
and processing with machine and deep learning. We believe that
theories, algorithms, realizations, applications, approaches, and
challenges, which are presented in this book will have their impact
and contribution in the design and development of modern and
effective healthcare systems.
Cognitive sensing systems combined with IoTs and smart technologies
are used in countless applications such as industrial robotics,
computer-aided diagnosis, brain-computer interface (BCI),
human-computer interaction (HCI), telemedicine, driverless cars and
smart energy systems. With contributions from an international team
of experts from a wide range of research areas including sensing,
computer vision, signal processing and device and control
applications, this book highlights the emerging role of cognitive
sensors in a growing number of real time applications including
smart health, smart cities, smart transportation and smart
agriculture. The volume will be suitable for a broad audience of
researchers in the fields of smart sensing, signal processing,
automation and robotics, environmental engineering, energy
engineering, biomedical engineering and allied disciplines where
smart sensors are part of the curriculum.
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