77 research outputs found
Sensing and Signal Processing in Smart Healthcare
In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
Quantifying atherosclerosis in vasculature using ultrasound imaging
Cerebrovascular disease accounts for approximately 30% of the global burden
associated with cardiovascular diseases [1]. According to the World Stroke
Organisation, there are approximately 13.7 million new stroke cases annually,
and just under six million people will die from stroke each year [2]. The
underlying cause of this disease is atherosclerosis – a vascular pathology
which is characterised by thickening and hardening of blood vessel walls.
When fatty substances such as cholesterol accumulate on the inner linings of
an artery, they cause a progressive narrowing of the lumen referred to as a
stenosis.
Localisation and grading of the severity of a stenosis, is important for
practitioners to assess the risk of rupture which leads to stroke. Ultrasound
imaging is popular for this purpose. It is low cost, non-invasive, and permits a
quick assessment of vessel geometry and stenosis by measuring the intima
media thickness. Research is showing that 3D monitoring of plaque
progression may provide a better indication of sites which are at risk of
rupture. Various metrics have been proposed. From these, the quantification
of plaques by measuring vessel wall volume (VWV) using the segmented
media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has
been shown to be sensitive to temporal changes in carotid plaque burden.
Thus, methods to segment these boundaries are required to help generate
VWV measurements with high accuracy, less user interaction and increased
robustness to variability in di↵erent user acquisition protocols.ii
This work proposes three novel methods to address these requirements, to
ultimately produce a highly accurate, fully automated segmentation algorithm
which works on intensity-invariant data. The first method proposed was that
of generating a novel, intensity-invariant representation of ultrasound data by
creating phase-congruency maps from raw unprocessed radio-frequency
ultrasound information. Experiments carried out showed that this
representation retained the necessary anatomical structural information to
facilitate segmentation, while concurrently being invariant to changes in
amplitude from the user. The second method proposed was the novel
application of Deep Convolutional Networks (DCN) to carotid ultrasound
images to achieve fully automatic delineation of the MAB boundaries, in
addition to the use of a novel fusion of amplitude and phase congruency data
as an image source. Experiments carried out showed that the DCN produces
highly accurate and automated results, and that the fusion of amplitude and
phase yield superior results to either one alone. The third method proposed
was a new geometrically constrained objective function for the network's
Stochastic Gradient Descent optimisation, thus tuning it to the segmentation
problem at hand, while also developing the network further to concurrently
delineate both the MAB and LIB to produce vessel wall contours. Experiments
carried out here also show that the novel geometric constraints improve the
segmentation results on both MAB and LIB contours.
In conclusion, the presented work provides significant novel contributions to
field of Carotid Ultrasound segmentation, and with future work, this could lead
to implementations which facilitate plaque progression analysis for the end�user
Mobile Robots
The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
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