|
Showing 1 - 5 of
5 matches in All Departments
Compressed Sensing (CS) in theory deals with the problem of
recovering a sparse signal from an under-determined system of
linear equations. The topic is of immense practical significance
since all naturally occurring signals can be sparsely represented
in some domain. In recent years, CS has helped reduce scan time in
Magnetic Resonance Imaging (making scans more feasible for
pediatric and geriatric subjects) and has also helped reduce the
health hazard in X-Ray Computed CT. This book is a valuable
resource suitable for an engineering student in signal processing
and requires a basic understanding of signal processing and linear
algebra. Covers fundamental concepts of compressed sensing Makes
subject matter accessible for engineers of various levels Focuses
on algorithms including group-sparsity and row-sparsity, as well as
applications to computational imaging, medical imaging, biomedical
signal processing, and machine learning Includes MATLAB examples
for further development
Compressed Sensing (CS) in theory deals with the problem of
recovering a sparse signal from an under-determined system of
linear equations. The topic is of immense practical significance
since all naturally occurring signals can be sparsely represented
in some domain. In recent years, CS has helped reduce scan time in
Magnetic Resonance Imaging (making scans more feasible for
pediatric and geriatric subjects) and has also helped reduce the
health hazard in X-Ray Computed CT. This book is a valuable
resource suitable for an engineering student in signal processing
and requires a basic understanding of signal processing and linear
algebra. Covers fundamental concepts of compressed sensing Makes
subject matter accessible for engineers of various levels Focuses
on algorithms including group-sparsity and row-sparsity, as well as
applications to computational imaging, medical imaging, biomedical
signal processing, and machine learning Includes MATLAB examples
for further development
This book comprises chapters on key problems in machine learning
and signal processing arenas. The contents of the book are a result
of a 2014 Workshop on Machine Intelligence and Signal Processing
held at the Indraprastha Institute of Information Technology.
Traditionally, signal processing and machine learning were
considered to be separate areas of research. However in recent
times the two communities are getting closer. In a very abstract
fashion, signal processing is the study of operator design. The
contributions of signal processing had been to device operators for
restoration, compression, etc. Applied Mathematicians were more
interested in operator analysis. Nowadays signal processing
research is gravitating towards operator learning - instead of
designing operators based on heuristics (for example wavelets), the
trend is to learn these operators (for example dictionary
learning). And thus, the gap between signal processing and machine
learning is fast converging. The 2014 Workshop on Machine
Intelligence and Signal Processing was one of the few unique events
that are focused on the convergence of the two fields. The book is
comprised of chapters based on the top presentations at the
workshop. This book has three chapters on various topics of
biometrics - two are on face detection and one on iris recognition;
all from top researchers in their field. There are four chapters on
different biomedical signal / image processing problems. Two of
these are on retinal vessel classification and extraction; one on
biomedical signal acquisition and the fourth one on region
detection. There are three chapters on data analysis - a topic
gaining immense popularity in industry and academia. One of these
shows a novel use of compressed sensing in missing sales data
interpolation. Another chapter is on spam detection and the third
one is on simple one-shot movie rating prediction. Four other
chapters cover various cutting edge miscellaneous topics on
character recognition, software effort prediction, speech
recognition and non-linear sparse recovery. The contents of this
book will prove useful to researchers, professionals and students
in the domains of machine learning and signal processing.
Deep Learning is now synonymous with applied machine learning. Many
technology giants (e.g. Google, Microsoft, Apple, IBM) as well as
start-ups are focusing on deep learning-based techniques for data
analytics and artificial intelligence. This technology applies
quite strongly to biometrics. This book covers topics in deep
learning, namely convolutional neural networks, deep belief network
and stacked autoencoders. The focus is also on the application of
these techniques to various biometric modalities: face, iris,
palmprint, and fingerprints, while examining the future trends in
deep learning and biometric research. Contains chapters written by
authors who are leading researchers in biometrics. Presents a
comprehensive overview on the internal mechanisms of deep learning.
Discusses the latest developments in biometric research. Examines
future trends in deep learning and biometric research. Provides
extensive references at the end of each chapter to enhance further
study.
The field of magnetic resonance imaging (MRI) has developed rapidly
over the past decade, benefiting greatly from the newly developed
framework of compressed sensing and its ability to drastically
reduce MRI scan times. MRI: Physics, Image Reconstruction, and
Analysis presents the latest research in MRI technology,
emphasizing compressed sensing-based image reconstruction
techniques. The book begins with a succinct introduction to the
principles of MRI and then: Discusses the technology and
applications of T1rho MRI Details the recovery of highly sampled
functional MRIs Explains sparsity-based techniques for quantitative
MRIs Describes multi-coil parallel MRI reconstruction techniques
Examines off-line techniques in dynamic MRI reconstruction Explores
advances in brain connectivity analysis using diffusion and
functional MRIs Featuring chapters authored by field experts, MRI:
Physics, Image Reconstruction, and Analysis delivers an
authoritative and cutting-edge treatment of MRI reconstruction
techniques. The book provides engineers, physicists, and graduate
students with a comprehensive look at the state of the art of MRI.
|
You may like...
Hampstead
Diane Keaton, Brendan Gleeson, …
DVD
R66
Discovery Miles 660
|