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Showing 1 to 10 of 10 for “"Compressive Sampling"”.

  1. Methods to reduce perturbation effects in compressive sampling

    With compressive sampling (CS), few measurements or samples will be enough for signal reconstruction as long as the signal can be represented in a basis domain and the coefficients are sparse. Fortunately, many signals in nature can be expressed with sparse bases. However, there arise the CS …

    aus-cath Repository record for Methods to reduce perturbation effects in compressive sampling (opens in a new tab)

  2. Methods to reduce perturbation effects in compressive sampling

    With compressive sampling (CS), few measurements or samples will be enough for signal reconstruction as long as the signal can be represented in a basis domain and the coefficients are sparse. Fortunately, many signals in nature can be expressed with sparse bases. However, there arise the CS …

    anu Repository record for Methods to reduce perturbation effects in compressive sampling (opens in a new tab)

  3. Low power data acquisition for microImplant biometric monitoring of tremors

    … of data needing to acquire a signal by applying compressive sampling thereby alleviating the demand on the energy source. A low energy SAR ADC is designed using adiabatic charging to further reduce energy usage. This application is ideal for adiabatic techniques because of the low frequency of …

    mit Repository record for Low power data acquisition for microImplant biometric monitoring of tremors (opens in a new tab)

  4. Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array

    Compressive sensing (CS) also known as compressive sampling is a technique used to reconstruct or recover the full-length of a signal with only a few non-adaptive measurements. It is a model-based framework for data acquisition and signal recovery that is based on the principles of sparsity and …

    cape-town Repository record for Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array (opens in a new tab)

  5. Efficient and guaranteed algorithms for sparse inverse problems

    … hold under ideal assumptions. For example, the sampling from the frame needs to be independent and identically distributed with the uniform distribution, and the frame must be tight. In practice though, one or more of the ideal assumptions is typically violated and none of the existing …

    uiuc Repository record for Efficient and guaranteed algorithms for sparse inverse problems (opens in a new tab)

  6. Advanced imaging via multiplexed sensing and compressive sensing

    … imaging systems using multiplexed sensing and compressive sensing (CS). Conventional cameras (e.g., pin-hole and lens cameras) follow the one-object-point-to-one-image-point or one-to-one (OTO) mapping model. Multipled sensing and compressive sensing attempt to improve conventional OTO cameras …

    uiuc Repository record for Advanced imaging via multiplexed sensing and compressive sensing (opens in a new tab)

  7. Selection of Step Size for Total Variation Minimization in CT

    <p>Medical image reconstruction by total variation minimization is a newly developed area in computed tomography (CT). In compressed sensing literature, it hasbeen shown that signals with sparse representations in an orthonormal basis may be reconstructed via l1-minimization. Furthermore, if an …

    gsu Repository record for Selection of Step Size for Total Variation Minimization in CT (opens in a new tab)

  8. Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction

    … inverse problems associated with multi-channel sampling and reconstruction that pertain to parallel magnetic resonance imaging (pMRI). The first part of this dissertation addresses adaptive design of spatio-temporal acquisition and reconstruction in model-based pMRI wherein the signal model is a …

    uiuc Repository record for Distortion-optimal parallel MRI with sparse sampling: from adaptive spatio-temporal acquisition to self-calibrating reconstruction (opens in a new tab)