Now showing items 646-665 of 1071

    • Navigation among movable obstacles in unknown environments 

      Levihn, Martin (Georgia Institute of Technology, 2011-04-05)
      This work presents a new class of algorithms that extend the domain of Navigation Among Movable Obstacles (NAMO) to unknown environments. Efficient real-time algorithms for solving NAMO problems even when no initial ...
    • Near field deniable communication 

      Narain, Abhinav (Georgia Institute of Technology, 2017-07-20)
      There is an increasing interest of companies and government agencies to snoop on people's daily lives the increasing difficulty for people to handle such scenarios. The need for private communications is perhaps greater ...
    • Network Data Streaming: Algorithms for Network Measurement and Monitoring 

      Kumar, Abhishek (Georgia Institute of Technology, 2005-11-18)
      With the emergence of computer networks as one of the primary modes of communication, and with their adoption for an increasingly wide range of applications, there is a growing need to understand and characterize the ...
    • Network Design and Routing in Peer-to-Peer and Mobile Ad Hoc Networks 

      Merugu, Shashidhar (Georgia Institute of Technology, 2005-07-19)
      Peer-to-peer networks and mobile ad hoc networks are emerging distributed networks that share several similarities. Fundamental among these similarities is the decentralized role of each participating node to route ...
    • Network support for adaptive applications 

      Cao, Zhiruo (Georgia Institute of Technology, 2000)
    • Network-based visual analysis of tabular data 

      Liu, Zhicheng (Georgia Institute of Technology, 2012-04-04)
      Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a ...
    • A neurally based vision model for line extraction and attention 

      Ungruh, Joachim (Georgia Institute of Technology, 1992)
    • Neuro-general computing an acceleration-approximation approach 

      Yazdan Bakhsh, Amir (Georgia Institute of Technology, 2018-07-30)
      A growing number of commercial and enterprise systems rely on compute and power intensive tasks. While the demand of these tasks is growing, the performance benefits from general-purpose platforms are diminishing. Without ...
    • New abstractions and mechanisms for virtualizing future many-core systems 

      Kumar, Sanjay (Georgia Institute of Technology, 2008-07-08)
      To abstract physical into virtual computing infrastructures is a longstanding goal. Efforts in the computing industry started with early work on virtual machines in IBM's VM370 operating system and architecture, continued ...
    • New approaches to integer programming 

      Chandrasekaran, Karthekeyan (Georgia Institute of Technology, 2012-06-28)
      Integer Programming (IP) is a powerful and widely-used formulation for combinatorial problems. The study of IP over the past several decades has led to fascinating theoretical developments, and has improved our ability to ...
    • New constructions of cryptographic pseudorandom functions 

      Banerjee, Abhishek (Georgia Institute of Technology, 2015-07-02)
      Pseudorandom functions (PRFs) are the building blocks of symmetric-key cryptography. Almost all central goals of symmetric cryptography (e.g., encryption, authentication, identification) have simple solutions that make ...
    • New formulations for active learning 

      Ganti Mahapatruni, Ravi Sastry (Georgia Institute of Technology, 2014-01-10)
      In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework ...
    • New insights on the power of active learning 

      Berlind, Christopher (Georgia Institute of Technology, 2015-07-22)
      Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to ...
    • New paradigms for approximate nearest-neighbor search 

      Ram, Parikshit (Georgia Institute of Technology, 2013-07-02)
      Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new paradigms for nearest-neighbor search (along with ...
    • New results on discrete-time time-varying linear systems 

      Hafiz, Khaled Mohammad (Georgia Institute of Technology, 1975)
    • New support vector machine formulations and algorithms with application to biomedical data analysis 

      Guan, Wei (Georgia Institute of Technology, 2011-06-13)
      The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript ...
    • New tools for unsupervised learning 

      Xiao, Ying (Georgia Institute of Technology, 2014-08-21)
      In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data. Such problems often arise in machine learning and ...
    • Non-photorealistic rendering with coherence for augmented reality 

      Chen, Jiajian (Georgia Institute of Technology, 2012-07-16)
      A seamless blending of the real and virtual worlds is key to increased immersion and improved user experiences for augmented reality (AR). Photorealistic and non-photorealistic rendering (NPR) are two ways to achieve this ...
    • Nonnegative matrix and tensor factorizations, least squares problems, and applications 

      Kim, Jingu (Georgia Institute of Technology, 2011-11-14)
      Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative ...
    • Nonnegative matrix factorization for clustering 

      Kuang, Da (Georgia Institute of Technology, 2014-07-01)
      This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and efficient clustering method. Clustering is one of the fundamental tasks in machine learning. It is useful for unsupervised ...