Browsing by Subject "Engineering, System Science"
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Item Open Access Computational spectral microscopy and compressive millimeter-wave holography(2010) Fernandez, Christy AnnThis dissertation describes three computational sensors. The first sensor is a scanning multi-spectral aperture-coded microscope containing a coded aperture spectrometer that is vertically scanned through a microscope intermediate image plane. The spectrometer aperture-code spatially encodes the object spectral data and nonnegative
least squares inversion combined with a series of reconfigured two-dimensional (2D spatial-spectral) scanned measurements enables three-dimensional (3D) (x, y, λ) object estimation. The second sensor is a coded aperture snapshot spectral imager that employs a compressive optical architecture to record a spectrally filtered projection
of a 3D object data cube onto a 2D detector array. Two nonlinear and adapted TV-minimization schemes are presented for 3D (x,y,λ) object estimation from a 2D compressed snapshot. Both sensors are interfaced to laboratory-grade microscopes and
applied to fluorescence microscopy. The third sensor is a millimeter-wave holographic imaging system that is used to study the impact of 2D compressive measurement on 3D (x,y,z) data estimation. Holography is a natural compressive encoder since a 3D
parabolic slice of the object band volume is recorded onto a 2D planar surface. An adapted nonlinear TV-minimization algorithm is used for 3D tomographic estimation from a 2D and a sparse 2D hologram composite. This strategy aims to reduce scan time costs associated with millimeter-wave image acquisition using a single pixel receiver.
Item Open Access Sampling and Signal Estimation in Computational Optical Sensors(2007-12-14) Shankar, MohanComputational sensing utilizes non-conventional sampling mechanisms along with processing algorithms for accomplishing various sensing tasks. It provides additional flexibility in designing imaging or spectroscopic systems. This dissertation analyzes sampling and signal estimation techniques through three computational sensing systems to accomplish specific tasks. The first is thin long-wave infrared imaging systems through multichannel sampling. Significant reduction in optical system thickness is obtained over a conventional system by modifying conventional sampling mechanisms and applying reconstruction algorithms. In addition, an information theoretic analysis of sampling in conventional as well as multichannel imaging systems is also performed. The feasibility of performing multichannel sampling for imaging is demonstrated using an information theoretic metric. The second system is an application of the multichannel system for the design of compressive low-power video sensors. Two sampling schemes have been demonstrated that utilize spatial as well as temporal aliasing. The third system is a novel computational spectroscopic system for detecting chemicals that utilizes the surface plasmon resonances to encode information about the chemicals that are tested.