The Experts below are selected from a list of 39402 Experts worldwide ranked by ideXlab platform

Michael B Wakin - One of the best experts on this subject based on the ideXlab platform.

  • An Introduction To Compressive Sampling
    IEEE Signal Processing Magazine, 2008
    Co-Authors: Emmanuel J Candes, Michael B Wakin
    Abstract:

    Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the Maximum Frequency Present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon rePresentation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.

Emmanuel J Candes - One of the best experts on this subject based on the ideXlab platform.

  • An Introduction To Compressive Sampling
    IEEE Signal Processing Magazine, 2008
    Co-Authors: Emmanuel J Candes, Michael B Wakin
    Abstract:

    Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the Maximum Frequency Present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon rePresentation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.

Akçakaya Mehmet - One of the best experts on this subject based on the ideXlab platform.

  • Tensor Completion from Regular Sub-Nyquist Samples
    'Institute of Electrical and Electronics Engineers (IEEE)', 2019
    Co-Authors: Kanatsoulis Charilaos, Fu Xiao, Sidiropoulos, Nicholas D., Akçakaya Mehmet
    Abstract:

    Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the Maximum Frequency Present in the signal. Unfortunately a large number of signals of interest are far from being band-limited. This motivated research on reconstruction from sub-Nyquist samples, which mainly hinges on the use of random / incoherent sampling procedures. However, uniform or regular sampling is more appealing in practice and from the system design point of view, as it is far simpler to implement, and often necessary due to system constraints. In this work, we study regular sampling and reconstruction of three- or higher-dimensional signals (tensors). We show that reconstructing a tensor signal from regular samples is feasible. Under the proposed framework, the sample complexity is determined by the tensor rank---rather than the signal bandwidth. This result offers new perspectives for designing practical regular sampling patterns and systems for signals that are naturally tensors, e.g., images and video. For a concrete application, we show that functional magnetic resonance imaging (fMRI) acceleration is a tensor sampling problem, and design practical sampling schemes and an algorithmic framework to handle it. Numerical results show that our tensor sampling strategy accelerates the fMRI sampling process significantly without sacrificing reconstruction accuracy

Kanatsoulis Charilaos - One of the best experts on this subject based on the ideXlab platform.

  • Tensor Completion from Regular Sub-Nyquist Samples
    'Institute of Electrical and Electronics Engineers (IEEE)', 2019
    Co-Authors: Kanatsoulis Charilaos, Fu Xiao, Sidiropoulos, Nicholas D., Akçakaya Mehmet
    Abstract:

    Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the Maximum Frequency Present in the signal. Unfortunately a large number of signals of interest are far from being band-limited. This motivated research on reconstruction from sub-Nyquist samples, which mainly hinges on the use of random / incoherent sampling procedures. However, uniform or regular sampling is more appealing in practice and from the system design point of view, as it is far simpler to implement, and often necessary due to system constraints. In this work, we study regular sampling and reconstruction of three- or higher-dimensional signals (tensors). We show that reconstructing a tensor signal from regular samples is feasible. Under the proposed framework, the sample complexity is determined by the tensor rank---rather than the signal bandwidth. This result offers new perspectives for designing practical regular sampling patterns and systems for signals that are naturally tensors, e.g., images and video. For a concrete application, we show that functional magnetic resonance imaging (fMRI) acceleration is a tensor sampling problem, and design practical sampling schemes and an algorithmic framework to handle it. Numerical results show that our tensor sampling strategy accelerates the fMRI sampling process significantly without sacrificing reconstruction accuracy

Fu Xiao - One of the best experts on this subject based on the ideXlab platform.

  • Tensor Completion from Regular Sub-Nyquist Samples
    'Institute of Electrical and Electronics Engineers (IEEE)', 2019
    Co-Authors: Kanatsoulis Charilaos, Fu Xiao, Sidiropoulos, Nicholas D., Akçakaya Mehmet
    Abstract:

    Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the Maximum Frequency Present in the signal. Unfortunately a large number of signals of interest are far from being band-limited. This motivated research on reconstruction from sub-Nyquist samples, which mainly hinges on the use of random / incoherent sampling procedures. However, uniform or regular sampling is more appealing in practice and from the system design point of view, as it is far simpler to implement, and often necessary due to system constraints. In this work, we study regular sampling and reconstruction of three- or higher-dimensional signals (tensors). We show that reconstructing a tensor signal from regular samples is feasible. Under the proposed framework, the sample complexity is determined by the tensor rank---rather than the signal bandwidth. This result offers new perspectives for designing practical regular sampling patterns and systems for signals that are naturally tensors, e.g., images and video. For a concrete application, we show that functional magnetic resonance imaging (fMRI) acceleration is a tensor sampling problem, and design practical sampling schemes and an algorithmic framework to handle it. Numerical results show that our tensor sampling strategy accelerates the fMRI sampling process significantly without sacrificing reconstruction accuracy