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Filtering by: Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad File Format pdf Remove constraint File Format: pdf

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  • Supporting branch-and-bound computations on parallel machines

    Creator:
    Mannan, Mohammed A.
    Abstract:
    We have developed SearchPak, a machine independent parallel searching tool on shared and distributed memory machines. It can be used for combinatorial optimiza­tion problems and OR-parallel computations as well. Both depth-first and best-first search of the state space can be performed using the SearchPak. With SearchPak, a user just provides...
    Resource Type:
    Capstone Project

  • Selling and buying in a distributed virtual market place

    Creator:
    Bella, Ravi
    Abstract:
    We present a model for a distributed virtual market place that can be con­structed on the Internet to support selling and buying requests, such as those found as classified advertisements. One requirement for a transaction to take place in the virtual market place is that a sell request and a...
    Resource Type:
    Capstone Project

  • Power saving in WAP

    Creator:
    Potti, Girish
    Abstract:
    Wireless Application Protocol (WAP) is an industry standard aimed to bring the web to handheld devices. The handheld devices are constrained by battery life and it becomes important that power is conserved across web transactions. The power consumed by the handheld device is directly proportional to the time taken for...
    Resource Type:
    Capstone Project

  • Heuristic search in symbolic probability inference

    Creator:
    Pan, Yonghong
    Abstract:
    I present a new heuristic search approach to compute approximate answers for the probability query in belief nets. This approach can compute the 'best' bounds for a query in a period of any given time (if time permitted, it will get an exact value). It inherits the essence of Symbolic...
    Resource Type:
    Capstone Project

  • Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

    Creator:
    Aziere, Nicolas
    Abstract:
    This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
    Resource Type:
    Dissertation