203 research outputs found

    Deep Learning Techniques for Multi-Dimensional Medical Image Analysis

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    2022 Review of Data-Driven Plasma Science

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    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

    Deep Learning Techniques for Multi-Dimensional Medical Image Analysis

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    Rethinking auto-colourisation of natural Images in the context of deep learning

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    Auto-colourisation is the ill-posed problem of creating a plausible full-colour image from a grey-scale prior. The current state of the art utilises image-to-image Generative Adversarial Networks (GANs). The standard method for training colourisation is reformulating RGB images into a luminance prior and two-channel chrominance supervisory signal. However, progress in auto-colourisation is inherently limited by multiple prerequisite dilemmas, where unsolved problems are mutual prerequisites. This thesis advances the field of colourisation on three fronts: architecture, measures, and data. Changes are recommended to common GAN colourisation architectures. Firstly, removing batch normalisation from the discriminator to allow the discriminator to learn the primary statistics of plausible colour images. Secondly, eliminating the direct L1 loss on the generator as L1 will limit the discovery of the plausible colour manifold. The lack of an objective measure of plausible colourisation necessitates resource-intensive human evaluation and repurposed objective measures from other fields. There is no consensus on the best objective measure due to a knowledge gap regarding how well objective measures model the mean human opinion of plausible colourisation. An extensible data set of human-evaluated colourisations, the Human Evaluated Colourisation Dataset (HECD) is presented. The results from this dataset are compared to the commonly-used objective measures and uncover a poor correlation between the objective measures and mean human opinion. The HECD can assess the future appropriateness of proposed objective measures. An interactive tool supplied with the HECD allows for a first exploration of the space of plausible colourisation. Finally, it will be shown that the luminance channel is not representative of the legacy black-and-white images that will be presented to models when deployed; This leads to out-of-distribution errors in all three channels of the final colour image. A novel technique is proposed to simulate priors that match any black-and-white media for which the spectral response is known

    Lossy Compression of Climate Data Using Convolutional Autoencoders

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    A sense of self for power side-channel signatures: instruction set disassembly and integrity monitoring of a microcontroller system

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    Cyber-attacks are on the rise, costing billions of dollars in damages, response, and investment annually. Critical United States National Security and Department of Defense weapons systems are no exception, however, the stakes go well beyond financial. Dependence upon a global supply chain without sufficient insight or control poses a significant issue. Additionally, systems are often designed with a presumption of trust, despite their microelectronics and software-foundations being inherently untrustworthy. Achieving cybersecurity requires coordinated and holistic action across disciplines commensurate with the specific systems, mission, and threat. This dissertation explores an existing gap in low-level cybersecurity while proposing a side-channel based security monitor to support attack detection and the establishment of trusted foundations for critical embedded systems. Background on side-channel origins, the more typical side-channel attacks, and microarchitectural exploits are described. A survey of related side-channel efforts is provided through side-channel organizing principles. The organizing principles enable comparison of dissimilar works across the side-channel spectrum. We find that the maturity of existing side-channel security monitors is insufficient, as key transition to practice considerations are often not accounted for or resolved. We then document the development, maturation, and assessment of a power side-channel disassembler, Time-series Side-channel Disassembler (TSD), and extend it for use as a security monitor, TSD-Integrity Monitor (TSD-IM). We also introduce a prototype microcontroller power side-channel collection fixture, with benefits to experimentation and transition to practice. TSD-IM is finally applied to a notional Point of Sale (PoS) application for proof of concept evaluation. We find that TSD and TSD-IM advance state of the art for side-channel disassembly and security monitoring in open literature. In addition to our TSD and TSD-IM research on microcontroller signals, we explore beneficial side-channel measurement abstractions as well as the characterization of the underlying microelectronic circuits through Impulse Signal Analysis (ISA). While some positive results were obtained, we find that further research in these areas is necessary. Although the need for a non-invasive, on-demand microelectronics-integrity capability is supported, other methods may provide suitable near-term alternatives to ISA

    Machine Learning for Multimedia Communications

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    Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

    Fully Unsupervised Image Denoising, Diversity Denoising and Image Segmentation with Limited Annotations

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    Understanding the processes of cellular development and the interplay of cell shape changes, division and migration requires investigation of developmental processes at the spatial resolution of single cell. Biomedical imaging experiments enable the study of dynamic processes as they occur in living organisms. While biomedical imaging is essential, a key component of exposing unknown biological phenomena is quantitative image analysis. Biomedical images, especially microscopy images, are usually noisy owing to practical limitations such as available photon budget, sample sensitivity, etc. Additionally, microscopy images often contain artefacts due to the optical aberrations in microscopes or due to imperfections in camera sensor and internal electronics. The noisy nature of images as well as the artefacts prohibit accurate downstream analysis such as cell segmentation. Although countless approaches have been proposed for image denoising, artefact removal and segmentation, supervised Deep Learning (DL) based content-aware algorithms are currently the best performing for all these tasks. Supervised DL based methods are plagued by many practical limitations. Supervised denoising and artefact removal algorithms require paired corrupted and high quality images for training. Obtaining such image pairs can be very hard and virtually impossible in most biomedical imaging applications owing to photosensitivity and the dynamic nature of the samples being imaged. Similarly, supervised DL based segmentation methods need copious amounts of annotated data for training, which is often very expensive to obtain. Owing to these restrictions, it is imperative to look beyond supervised methods. The objective of this thesis is to develop novel unsupervised alternatives for image denoising, and artefact removal as well as semisupervised approaches for image segmentation. The first part of this thesis deals with unsupervised image denoising and artefact removal. For unsupervised image denoising task, this thesis first introduces a probabilistic approach for training DL based methods using parametric models of imaging noise. Next, a novel unsupervised diversity denoising framework is presented which addresses the fundamentally non-unique inverse nature of image denoising by generating multiple plausible denoised solutions for any given noisy image. Finally, interesting properties of the diversity denoising methods are presented which make them suitable for unsupervised spatial artefact removal in microscopy and medical imaging applications. In the second part of this thesis, the problem of cell/nucleus segmentation is addressed. The focus is especially on practical scenarios where ground truth annotations for training DL based segmentation methods are scarcely available. Unsupervised denoising is used as an aid to improve segmentation performance in the presence of limited annotations. Several training strategies are presented in this work to leverage the representations learned by unsupervised denoising networks to enable better cell/nucleus segmentation in microscopy data. Apart from DL based segmentation methods, a proof-of-concept is introduced which views cell/nucleus segmentation from the perspective of solving a label fusion problem. This method, through limited human interaction, learns to choose the best possible segmentation for each cell/nucleus using only a pool of diverse (and possibly faulty) segmentation hypotheses as input. In summary, this thesis seeks to introduce new unsupervised denoising and artefact removal methods as well as semi-supervised segmentation methods which can be easily deployed to directly and immediately benefit biomedical practitioners with their research

    UAVs for the Environmental Sciences

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    This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application
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