7,461 research outputs found
Developement of real time diagnostics and feedback algorithms for JET in view of the next step
Real time control of many plasma parameters will be an essential aspect in
the development of reliable high performance operation of Next Step Tokamaks.
The main prerequisites for any feedback scheme are the precise real-time
determination of the quantities to be controlled, requiring top quality and
highly reliable diagnostics, and the availability of robust control algorithms.
A new set of real time diagnostics was recently implemented on JET to prove the
feasibility of determining, with high accuracy and time resolution, the most
important plasma quantities. With regard to feedback algorithms, new
model–based controllers were developed to allow a more robust control of
several plasma parameters. Both diagnostics and algorithms were successfully
used in several experiments, ranging from H-mode plasmas to configuration with
ITBs. Since elaboration of computationally heavy measurements is often
required, significant attention was devoted to non-algorithmic methods like
Digital or Cellular Neural/Nonlinear Networks. The real time hardware and
software adopted architectures are also described with particular attention to
their relevance to ITER.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004,
Nice (France
Investigating High Speed Localization Microscopy Through Experimental Methods, Data Processing Methods, and Applications of Localization Microscopy to Biological Questions
Fluorescence Photoactivation Localization Microscopy(FPALM) and other super resolution localization microscopy techniques can resolve structures with nanoscale resolution. Unlike techniques of electron microscopy, they are also compatible with live cell and live animal studies, making FPALM and related techniques ideal for answering questions about the dynamic nature of molecular biology in living systems. Many processes in biology occur on rapid sub second time scales requiring the imaging technique to be capable of resolving these processes not just with a high enough spatial resolution, but with an appropriate temporal resolution. To that end, this Dissertation in part investigates high speed FPALM as an experimental technique showing images can be reconstructed with effective temporal resolutions of 0.1s. Using fluorescent proteins attached to an influenza viral protein, hemagglutinin(HA), questions of protein clustering and cluster dynamics on the host cell membrane are explored. The results indicate that these HA clusters may be more dynamic than previously thought. The principle disadvantage of the increased speed of imaging is the reduction in information that comes through collecting fewer photons to localize each molecule, and fewer molecules overall. As the molecules become dimmer, they also become harder to identify using conventional identification algorithms. Tools from machine learning and computer vision such as artificial neural networks(ANNs) have been shown to be adept at object identification. Here a method for repeatedly training an ANN is investigated. This method is shown to have exceptional performance on simulations indicating that it can be regarded as a method of high fidelity, even in the presence of weakly fluorescent molecules. Development of this technique can be used to recover more molecules from data sets with weaker molecular fluorescence, such as those obtained with high speed imaging, allowing for higher sampling, and overall higher spatial resolution of the final image. The combination of a high speed experimental technique coupled with a sensitive and robust identification algorithm allow FPALM and related techniques to probe questions of fast biological processes while limiting the sacrifice to spatial resolution inherent in high speed techniques
A Review on Data Fusion of Multidimensional Medical and Biomedical Data
Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods
Towards a cyber physical system for personalised and automatic OSA treatment
Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database
Two-photon imaging and analysis of neural network dynamics
The glow of a starry night sky, the smell of a freshly brewed cup of coffee
or the sound of ocean waves breaking on the beach are representations of the
physical world that have been created by the dynamic interactions of thousands
of neurons in our brains. How the brain mediates perceptions, creates thoughts,
stores memories and initiates actions remains one of the most profound puzzles
in biology, if not all of science. A key to a mechanistic understanding of how
the nervous system works is the ability to analyze the dynamics of neuronal
networks in the living organism in the context of sensory stimulation and
behaviour. Dynamic brain properties have been fairly well characterized on the
microscopic level of individual neurons and on the macroscopic level of whole
brain areas largely with the help of various electrophysiological techniques.
However, our understanding of the mesoscopic level comprising local populations
of hundreds to thousands of neurons (so called 'microcircuits') remains
comparably poor. In large parts, this has been due to the technical
difficulties involved in recording from large networks of neurons with
single-cell spatial resolution and near- millisecond temporal resolution in the
brain of living animals. In recent years, two-photon microscopy has emerged as
a technique which meets many of these requirements and thus has become the
method of choice for the interrogation of local neural circuits. Here, we
review the state-of-research in the field of two-photon imaging of neuronal
populations, covering the topics of microscope technology, suitable fluorescent
indicator dyes, staining techniques, and in particular analysis techniques for
extracting relevant information from the fluorescence data. We expect that
functional analysis of neural networks using two-photon imaging will help to
decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress
in Physic
A Deep Learning Framework for Automated Vesicle Fusion Detection
Quantitative analysis of vesicle-plasma membrane fusion events in the fluorescence microscopy, has been proven to be important in the vesicle exocytosis study. In this paper, we present a framework to automatically detect fusion events. First, an iterative searching algorithm is developed to extract image patch sequences containing potential events. Then, we propose an event image to integrate the critical image patches of a candidate event into a single-image joint representation as the input to Convolutional Neural Networks (CNNs). According to the duration of candidate events, we design three CNN architectures to automatically learn features for the fusion event classification. Compared on 9 challenging datasets, our proposed method showed very competitive performance and outperformed two state-of-the-arts
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
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