178 research outputs found

    Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second

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    To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae

    Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

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    Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

    ISCR Annual Report: Fical Year 2004

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    Temporal trend evaluation in monitoring programs with high spatial resolution and low temporal resolution using geographically weighted regression models

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    Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained in a monitoring program of around 4800 Swedish lakes observed once every 6 years between 2008 and 2021. On applying the methods developed here, we identified nonlinear changes in TOC from consistent negative trends over most of Sweden around 2010 to positive trends during later years in parts of the country

    베이지안 네트워크를 활용한 교통상태의 확률론적 예측

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    학위논문 (박사)-- 서울대학교 대학원 공과대학 건설환경공학부, 2017. 8. 고승영.Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent flow breakdown. Therefore, it is necessary to perform traffic state predictions that reflects the stochastic process of traffic flow. However, traffic state transition is affected complexly and simultaneously by many factors, which lead to a lack of understanding and accurate prediction. Meanwhile, the Bayesian network is a methodology that not only is suitable for a problem with uncertainty but also can improve the understanding of a problem. Also, it is possible to derive fair probability with incomplete information, which allows the analysis of various situations. In this study, we developed a traffic state prediction model using the Bayesian network to reflect dynamic and stochastic traffic flow characteristics. In order to improve the structure of the Bayesian network, which has been used simply in transportation problems, we proposed a modeling procedure using mixture of Gaussians (MOGs). Also, spatially extended variables were used to consider the spatiotemporal evolution of traffic flow pattern. In particular, traffic state identification was performed by estimating the link speed in order to consider the spatial propagation of congestion. In the performance evaluation, the Bayesian network has better performance than logistic regression and has the same level of performance as artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provided the understanding of traffic state prediction and the guidelines for model improvement. Therefore, the Bayesian network developed in this study can be considered as a traffic state prediction model with good prediction accuracy and provides insights for traffic state prediction.Chapter 1. Introduction 1 1.1 Research background and purpose 1 1.2 Research scope and procedure 4 Chapter 2. Literature Review 8 2.1 Characteristics of traffic state 8 2.2 Traffic state estimation and prediction 14 2.3 Bayesian network 37 2.4 Originality of this research 41 Chapter 3. Data Collection and Preparation 46 3.1 Data collection and validity check 46 3.2 Traffic state identification 47 3.3 Data Description 63 Chapter 4. Bayesian Network Modeling 66 4.1 Modeling procedure 66 4.2 Description of interface mechanism 69 4.3 Module design 74 4.4 Eliciting the structure 81 4.5 Verification 81 4.6 Parameter learning 85 Chapter 5. Model Evaluation 87 5.1 Evaluation results 87 5.2 Comparison with other methodologies 92 5.3 Sensitivity analysis 104 Chapter 6. Conclusions 127 6.1 Summary 127 6.2 Guidelines for traffic state prediction 128 6.3 Limitations of the study 129 6.4 Applications and future research 130 References 135Docto

    Level Set Methods for MRE Image Processing and Analysis

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    Ph.DDOCTOR OF PHILOSOPH

    Intelligent Sensor Networks

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    In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

    Development of High-speed Photoacoustic Imaging technology and Its Applications in Biomedical Research

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    Photoacoustic (PA) tomography (PAT) is a novel imaging modality that combines the fine lateral resolution from optical imaging and the deep penetration from ultrasonic imaging, and provides rich optical-absorption–based images. PAT has been widely used in extracting structural and functional information from both ex vivo tissue samples to in vivo animals and humans with different length scales by imaging various endogenous and exogenous contrasts at the ultraviolet to infrared spectrum. For example, hemoglobin in red blood cells is of particular interest in PAT since it is one of the dominant absorbers in tissue at the visible wavelength.The main focus of this dissertation is to develop high-speed PA microscopy (PAM) technologies. Novel optical scanning, ultrasonic detection, and laser source techniques are introduced in this dissertation to advance the performance of PAM systems. These upgrades open up new avenues for PAM to be applicable to address important biomedical challenges and enable fundamental physiological studies.First, we investigated the feasibility of applying high-speed PAM to the detection and imaging of circulating tumor cells (CTCs) in melanoma models, which can provide valuable information about a tumor’s metastasis potentials. We probed the melanoma CTCs at the near-infrared wavelength of 1064 nm, where the melanosomes absorb more strongly than hemoglobin. Our high-speed PA flow cytography system successfully imaged melanoma CTCs in travelling trunk vessels. We also developed a concurrent laser therapy device, hardware-triggered by the CTC signal, to photothermally lyse the CTC on the spot in an effort to inhibit metastasis.Next, we addressed the detection sensitivity issue in the previous study. We employed the stimulated Raman scattering (SRS) effect to construct a high-repetition-rate Raman laser at 658 nm, where the contrast between a melanoma CTC and the blood background is near the highest. Our upgraded PA flow cytography successfully captured sequential images of CTCs in mouse melanoma xenograft model, with a significantly improved contrast-to-noise ratio compared to our previous results. This technology is readily translatable to the clinics to extract the information of a tumor’s metastasis risks.We extended the Raman laser technology to the field of brain functional studies. We developed a MEMS (micro-electromechanical systems) scanner for fast optical scanning, and incorporated it to a dual-wavelength functional PAM (fPAM) for high-speed imaging of cerebral hemodynamics in mouse. This fPAM system successfully imaged transient changes in blood oxygenation at cerebral micro-vessels in response to brief somatic stimulations. This fPAM technology is a powerful tool for neurological studies.Finally, we explored some approaches of reducing the size the PAM imaging head in an effort to translate our work to the field of wearable biometric monitors. To miniaturize the ultrasonic detection device, we fabricated a thin-film optically transparent piezoelectric detector for detecting PA waves. This technology could enable longitudinal studies on free-moving animals through a wearable version of PAM

    Rekonstruktion, Analyse und Editierung dynamisch deformierter 3D-Oberflächen

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    Dynamically deforming 3D surfaces play a major role in computer graphics. However, producing time-varying dynamic geometry at ever increasing detail is a time-consuming and costly process, and so a recent trend is to capture geometry data directly from the real world. In the first part of this thesis, I propose novel approaches for this research area. These approaches capture dense dynamic 3D surfaces from multi-camera systems in a particularly robust and accurate way. This provides highly realistic dynamic surface models for phenomena like moving garments and bulging muscles. However, re-using, editing, or otherwise analyzing dynamic 3D surface data is not yet conveniently possible. To close this gap, the second part of this dissertation develops novel data-driven modeling and animation approaches. I first show a supervised data-driven approach for modeling human muscle deformations that scales to huge datasets and provides fine-scale, anatomically realistic deformations at high quality not attainable by previous methods. I then extend data-driven modeling to the unsupervised case, providing editing tools for a wider set of input data ranging from facial performance capture and full-body motion to muscle and cloth deformation. To this end, I introduce the concepts of sparsity and locality within a mathematical optimization framework. I also explore these concepts for constructing shape-aware functions that are useful for static geometry processing, registration, and localized editing.Dynamisch deformierbare 3D-Oberflächen spielen in der Computergrafik eine zentrale Rolle. Die Erstellung der für Computergrafik-Anwendungen benötigten, hochaufgelösten und zeitlich veränderlichen Oberflächengeometrien ist allerdings äußerst arbeitsintensiv. Aus dieser Problematik heraus hat sich der Trend entwickelt, Oberflächendaten direkt aus Aufnahmen der echten Welt zu erfassen. Dazu nötige 3D-Rekonstruktionsverfahren werden im ersten Teil der Arbeit entwickelt. Die vorgestellten, neuartigen Verfahren erlauben die Erfassung dynamischer 3D-Oberflächen aus Mehrkamera-Aufnahmen bei hoher Verlässlichkeit und Präzision. Auf diese Weise können detaillierte Oberflächenmodelle von Phänomenen wie in Bewegung befindliche Kleidung oder sich anspannende Muskeln erfasst werden. Aber auch die Wiederverwendung, Bearbeitung und Analyse derlei gewonnener 3D-Oberflächendaten ist aktuell noch nicht auf eine einfache Art und Weise möglich. Um diese Lücke zu schließen beschäftigt sich der zweite Teil der Arbeit mit der datengetriebenen Modellierung und Animation. Zunächst wird ein Ansatz für das überwachte Lernen menschlicher Muskel-Deformationen vorgestellt. Dieses neuartige Verfahren ermöglicht eine datengetriebene Modellierung mit besonders umfangreichen Datensätzen und liefert anatomisch-realistische Deformationseffekte. Es übertrifft damit die Genauigkeit früherer Methoden. Im nächsten Teil beschäftigt sich die Dissertation mit dem unüberwachten Lernen aus 3D-Oberflächendaten. Es werden neuartige Werkzeuge vorgestellt, die eine weitreichende Menge an Eingabedaten verarbeiten können, von aufgenommenen Gesichtsanimationen über Ganzkörperbewegungen bis hin zu Muskel- und Kleidungsdeformationen. Um diese Anwendungsbreite zu erreichen stützt sich die Arbeit auf die allgemeinen Konzepte der Spärlichkeit und Lokalität und bettet diese in einen mathematischen Optimierungsansatz ein. Abschließend zeigt die vorliegende Arbeit, wie diese Konzepte auch für die Konstruktion von oberflächen-adaptiven Basisfunktionen übertragen werden können. Dadurch können Anwendungen für die Verarbeitung, Registrierung und Bearbeitung statischer Oberflächenmodelle erschlossen werden
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