144 research outputs found

    AI for time-resolved imaging: from fluorescence lifetime to single-pixel time of flight

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    Time-resolved imaging is a field of optics which measures the arrival time of light on the camera. This thesis looks at two time-resolved imaging modalities: fluorescence lifetime imaging and time-of-flight measurement for depth imaging and ranging. Both of these applications require temporal accuracy on the order of pico- or nanosecond (10−12 − 10−9s) scales. This demands special camera technology and optics that can sample light-intensity extremely quickly, much faster than an ordinary video camera. However, such detectors can be very expensive compared to regular cameras while offering lower image quality. Further, information of interest is often hidden (encoded) in the raw temporal data. Therefore, computational imaging algorithms are used to enhance, analyse and extract information from time-resolved images. "A picture is worth a thousand words". This describes a fundamental blessing and curse of image analysis: images contain extreme amounts of data. Consequently, it is very difficult to design algorithms that encompass all the possible pixel permutations and combinations that can encode this information. Fortunately, the rise of AI and machine learning (ML) allow us to instead create algorithms in a data-driven way. This thesis demonstrates the application of ML to time-resolved imaging tasks, ranging from parameter estimation in noisy data and decoding of overlapping information, through super-resolution, to inferring 3D information from 1D (temporal) data

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    University of Windsor Undergraduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1023/thumbnail.jp

    University of Windsor Undergraduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1020/thumbnail.jp

    University of Windsor Undergraduate Calendar 2022 Winter

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1017/thumbnail.jp

    University of Windsor Undergraduate Calendar 2022 Fall

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1019/thumbnail.jp

    Virginia Commonwealth University Graduate Bulletin

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    Graduate bulletin for Virginia Commonwealth University for the academic year 2022-2023. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for graduate programs

    2022-2023 Boise State University Undergraduate Catalog

    Get PDF
    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Deep Learning in Medical Image Analysis

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    The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis
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