9 research outputs found

    Pattern Recognition

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    A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

    Advances in Solid State Circuit Technologies

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    This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields

    Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases

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    Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to affect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have been shown to have a mortality hazard ratio of 1.42 compared to a control population [2], and an unadjusted death rate of 3.35 per 100 person-years compared to 1.23 per 100 person-years in a control population [97]. Current methods for the detection of arterial disease are generally impractical for large scale screening, expensive, or both. If an inexpensive method for the detection of both stenosis and aneurysm is created, that minimises the need for invasive measurements, the cost effectiveness of large scale screening could be improved making both continuous monitoring and screening feasible. One such method is to use easily acquirable haemodynamic measurements at accessible peripheral locations within the circulatory system for diagnosis. Within this thesis an initial exploratory study into the potential of using machine learning classification algorithms to detect arterial disease from such measurements is presented.It is likely that the indicative biomarkers of arterial disease held within pressure and flow-rate profiles consist of micro inter- and intra- measurement details. To facilitate the use of a data driven approach to the discovery of any biomarkers a framework for the creation of virtual patients, through the employment of a mathematical model of blood flow, is presented. This framework is utilised to create a series of virtual patient databases, as the balance between simplicity and realism progresses through the thesis. The most realistic of these databases is made publicly available (https://doi.org/10.5281/zenodo.4549764). The aforementioned framework for the creation of virtual patients is a major contribution of this thesis, and can be applied to a wide range of biological systems given a mathematical description.The synthetic data sets are used to train and subsequently test a series of machine learning classifiers, to predict the presence of both stenosis and aneurysm, using various combinations of pressure and flow-rate measurements. It is shown that the inclusion of a diseased vessel (either stenosis or aneurysm) produces consistent and significant biomarkers in haemodynamic profiles, irrespective of a patients unique underlying arterial network. These biomarkers are found to be differentiable from the natural variability present across a large cohort of patients, showing that arterial disease has a clear and unique effect on pressure and flow-rate profiles. This suggests strong potential in the use of haemodynamic measurements to detect arterial disease

    Microscopy Conference 2017 (MC 2017) - Proceedings

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    Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2017", die vom 21. bis 25.08.2017, in Lausanne stattfand

    Microscopy Conference 2017 (MC 2017) - Proceedings

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    Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2017", die vom 21. bis 25.08.2017, in Lausanne stattfand

    MC 2019 Berlin Microscopy Conference - Abstracts

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    Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2019", die vom 01. bis 05.09.2019, in Berlin stattfand
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