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Filtering by: Alternative title ssim Special report 572 Remove constraint Alternative title ssim: Special report 572 Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Committee Member Raich, Raviv Remove constraint Committee Member: Raich, Raviv Degree Name Doctor of Philosophy (Ph.D.) Remove constraint Degree Name: Doctor of Philosophy (Ph.D.)

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  • Position estimation in indoor localization with trilateration

    Creator:
    Su, Zhenqiang
    Abstract:
    Triangulateration uses geometric distance to estimate the location of user by employing techniques like received signal strength (RSS), time-of-arrival (TOA), time-difference-of-arrival (TDOA) and image processing. Radio frequency (RF) signal positioning using TOA or TDOA techniques generally requires timing synchronization of the anchors and/or the anchors and targets. If the desired...
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    Dissertation
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    constraints. To avoid the special problems and difficulties in RF signal positioning, such as the

  • Secure Data Analytics under Data Integrity Attacks

    Creator:
    De Silva, Shashini A.
    Abstract:
    There has been tremendous growth in using data analytic and machine learning algorithms to make critical decisions, such as in the national power grid, healthcare operations, and autonomous vehicles. Employing data analytic for decision-making allows cyber attackers to manipulate the decisions of these algorithms through data falsification. Hence, the trustworthiness...
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    Dissertation

  • Learning with Partial Supervision for Clustering and Classification

    Creator:
    Pei, Yuanli
    Abstract:
    In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
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    Dissertation

  • Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

    Creator:
    Lei, Peng
    Abstract:
    This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
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    Dissertation

  • Learning ranking functions for efficient search

    Creator:
    Xu, Yuehua
    Abstract:
    This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...
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    the search space relative to I. 7 Greedy search is a special case of beam search with b = 1, which

  • Optimal load and resource balance in internet distributed systems

    Creator:
    Nishida, Hiroshi
    Abstract:
    A distributed system is a network of multiple autonomous computational nodes designed primarily for performance scalability and robustness. The performance of a distributed system depends critically on how tasks and resources are distributed among the nodes. Thus, a main thrust in distributed system research is to design schemes for distributing...
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    my wife. Considering my special conditions, working full time in Salem and studying part time at OSU

  • Machine Learning Methods for Computational Sustainability

    Creator:
    Liu, Liping
    Abstract:
    Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
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    express my special gratitude to my advisor, Thomas G. Dietterich. As an exemplary advisor, he provides me

  • Anomaly Detection: Theory, Explanation and User Feedback

    Creator:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
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    explanation of why it was considered anomalous. Specifically, the explanation can report the rare patterns

  • Network Coding, Random Matrices, and Their Applications to Communication Systems

    Creator:
    Nguyen-Huu, Duong
    Abstract:
    In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
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    here enjoyable and memorable. Last but not least, I would like to express my special thanks to my

  • From multitarget tracking to event recognition in videos

    Creator:
    Brendel, William
    Abstract:
    This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from a host of sources, including motion blur, occlusions, and dynamic cluttered backgrounds. We show that these challenges can be successfully addressed by using a multiscale,...
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    Bipartite Matching problem and Hungarian Algorithm The Bipartite Matching (BM) problem is a special case of