Browsing by Author "Takhar, Dharmpal"

Now showing 1 - 7 of 7
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    Atomic-level investigation of fluorinated single-wall carbon nanotubes
    (2005) Takhar, Dharmpal; Kelly, Kevin F.
    There is a great deal of interest in the functionalization, in particular fluorination, of single-wall carbon nanotubes (SWNTs) for the purposes of solvation and subsequent chemical reaction. Towards this end, this thesis reports the investigation of fluorinated SWNTs (fluorotubes) performed by scanning tunneling microscopy (STM). This research was performed for various compositions of fluorinated nanotubes. In addition, the atomic-scale fluorine coverage on the fluorotubes with composition was observed as a function of annealing temperature. Upon heating of the fluorotubes, we observe the subsequent desorption of the fluorine initiated around 240°C. At higher temperatures all the fluorine desorbs from the tubes revealing a few "defects" still remaining. Further heating leads to cutting of the fluorotubes which we believe is initiated at these defect locations. Through these studies we gain important information on the local chemistry as well as the electronic structure of the functionalized carbon nanotubes.
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    Compressed Sensing for Imaging Applications
    (2008) Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G.; Yin, Wotao
    Compressed sensing is a new sampling theory which allows reconstructing signals using sub-Nyquist measurements. This can significantly reduce the computation re­quired for both image and video whether during acquisition or encoding, especially at the sensor. Compressed sensing works on the assumption of sparsity of the sig­nal in some known domain, which is incoherent with the measurement domain. We exploit this technique to build a single pixel camera using an optical modulator and a single photosensor. Random projections of the signal (image) are applied to the optical modulator, which has a random matrix displayed on it corresponding to the measurement domain (random noise). This random projected signal is focused and summed at the photosensor and will be later used for reconstructing the signal. In this scheme, a tradeoff between the spatial extent of sampling array and a sequential sampling over time with a single detector is performed. In addition to the single sensor method, we will also demonstrate a new design which allows compressive im­
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    Method and apparatus for compressive imaging device
    (2014-09-30) Baraniuk, Richard G.; Baron, Dror Z.; Duarte, Marco F.; Kelly, Kevin F.; Lane, Courtney C.; Laska, Jason N.; Takhar, Dharmpal; Wakin, Michael B.; Rice University; United States Patent and Trademark Office
    A new digital image/video camera that directly acquires random projections of the incident light field without first collecting the pixels/voxels. In one preferred embodiment, the camera employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with only a single detection element while measuring the image/video fewer times than the number of pixels or voxels—this can significantly reduce the computation required for image/video acquisition/encoding. Since the system features a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.
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    Method and apparatus for compressive imaging device
    (2012-06-12) Baraniuk, Richard G.; Baron, Dror Z.; Duarte, Marco F.; Kelly, Kevin F.; Lane, Courtney C.; Laska, Jason N.; Takhar, Dharmpal; Wakin, Michael B.; Rice University; United States Patent and Trademark Office
    A new digital image/video camera that directly acquires random projections of the incident light field without first collecting the pixels/voxels. In one preferred embodiment, the camera employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with only a single detection element while measuring the image/video fewer times than the number of pixels or voxels—this can significantly reduce the computation required for image/video acquisition/encoding. Since the system features a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.
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    Multiscale random projections for compressive classification
    (2007-09-01) Duarte, Marco F.; Davenport, Mark A.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G.
    We propose a framework for exploiting dimension-reducing random projections in detection and classification problems. Our approach is based on the generalized likelihood ratio test; in the case of image classification, it exploits the fact that a set of images of a fixed scene under varying articulation parameters forms a low-dimensional, nonlinear manifold. Exploiting recent results showing that random projections stably embed a smooth manifold in a lower-dimensional space, we develop the multiscale smashed filter as a compressive analog of the familiar matched filter classifier. In a practical target classification problem using a single-pixel camera that directly acquires compressive image projections, we achieve high classification rates using many fewer measurements than the dimensionality of the images.
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    Single-pixel imaging via compressive sampling
    (2008-03-01) Duarte, Marco F.; Davenport, Mark A.; Takhar, Dharmpal; Laska, Jason N.; Sun, Ting; Kelly, Kevin F.; Baraniuk, Richard G.
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    The smashed filter for compressive classification and target recognition
    (2007-01-01) Davenport, Mark A.; Duarte, Marco F.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G.
    The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including object and target recognition, we are ultimately interested in making a decision about an image rather than computing a reconstruction. We propose here a framework for compressive classification that operates directly on the compressive measurements without first reconstructing the image. We dub the resulting dimensionally reduced matched filter the smashed filter. The first part of the theory maps traditional maximum likelihood hypothesis testing into the compressive domain; we find that the number of measurements required for a given classification performance level does not depend on the sparsity or compressibility of the images but only on the noise level. The second part of the theory applies the generalized maximum likelihood method to deal with unknown transformations such as the translation, scale, or viewing angle of a target object. We exploit the fact the set of transformed images forms a low-dimensional, nonlinear manifold in the high-dimensional image space. We find that the number of measurements required for a given classification performance level grows linearly in the dimensionality of the manifold but only logarithmically in the number of pixels/samples and image classes. Using both simulations and measurements from a new single-pixel compressive camera, we demonstrate the effectiveness of the smashed filter for target classification using very few measurements.