Browsing by Subject "Optimal sampling"

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    Innovation based transmission in AI-enabled sensor networks for 6G IoT scenario
    (2022-09) Atalık, Ahmet Arda
    Goal-oriented signal processing and communications are assured to play a key role in developing the next generation of sensor devices and networks, e.g., 6G IoT networks. A critical task of semantic signal processing is the detection of innovation and transmission scheduling based on the innovation in AI-enabled sensor networks and IoT for 6G. This thesis proposes efficient and optimal sampling and transmission strategies for goal-oriented sensor networks for various data models, investigates their performances both analytically and numerically; and introduces the use of dimensionality reduction algorithms in semantic signal processing and highlights its effectiveness in real life case studies. That is, the proposed methods are explained rigorously and demonstrated through simulations and case studies based on real-world computer vision examples with recorded video signals. Numerical results indicate that the next generation sensor devices and networks can benefit significantly from the proposed methods in terms of energy efficiency and semantic innovation detection performances.
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    Optimal sampling of multidimensional periodic band-limited signals
    (IEEE, 2005) Korkmaz, Sayit
    In this paper, we present an algebraic description of the aliasing phenomena evident in the linear sampling process of multidimensional periodic band limited signals. Opposed to the classical Shannon sampling, periodic band limited signals underlie a different aliasing structure providing further freedom in the sampling strategy due to the discreteness of the spectrum. An algebraic formulation of the optimal sampling problem is also presented.