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Articles 1 - 7 of 7
Full-Text Articles in Physics
Mult-Spectral Imaging Of Vegetation With A Diffractive Plenoptic Camera, Tristan R. Naranjo
Mult-Spectral Imaging Of Vegetation With A Diffractive Plenoptic Camera, Tristan R. Naranjo
Theses and Dissertations
Snapshot multi-spectral sensors allow for object detection based on its spectrum for remote sensing applications in air or space. By making these types of sensors more compact and lightweight, it allows drones to dwell longer on targets or the reduction of transport costs for satellites. To address this need, I designed and built a diffractive plenoptic camera (DPC) which utilized a Fresnel zone plate and a light field camera in order to detect vegetation via a normalized difference vegetation index (NDVI). This thesis derives design equations by relating DPC system parameters to its expected performance and evaluates its multi-spectral performance. …
Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy, Hesam Mazidi, Tianben Ding, Arye Nehorai, Matthew D. Lew
Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy, Hesam Mazidi, Tianben Ding, Arye Nehorai, Matthew D. Lew
Electrical & Systems Engineering Publications and Presentations
We present a computational method, termed Wasserstein-induced flux (WIF), to robustly quantify the accuracy of individual localizations within a single-molecule localization microscopy (SMLM) dataset without ground- truth knowledge of the sample. WIF relies on the observation that accurate localizations are stable with respect to an arbitrary computational perturbation. Inspired by optimal transport theory, we measure the stability of individual localizations and develop an efficient optimization algorithm to compute WIF. We demonstrate the advantage of WIF in accurately quantifying imaging artifacts in high-density reconstruction of a tubulin network. WIF represents an advance in quantifying systematic errors with unknown and complex distributions, …
Frequency-Modulated Continuous-Wave Lidar Compressive Depth-Mapping, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Frequency-Modulated Continuous-Wave Lidar Compressive Depth-Mapping, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. Moreover, by efficiently storing only 2m data points from m < n measurements of an n pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods.
System Optimization And Iterative Image Reconstruction In Photoacoustic Computed Tomography For Breast Imaging, Yang Lou
McKelvey School of Engineering Theses & Dissertations
Photoacoustic computed tomography(PACT), also known as optoacoustic tomography (OAT), is an emerging imaging technique that has developed rapidly in recent years. The combination of the high optical contrast and the high acoustic resolution of this hybrid imaging technique makes it a promising candidate for human breast imaging, where conventional imaging techniques including X-ray mammography, B-mode ultrasound, and MRI suffer from low contrast, low specificity for certain breast types, and additional risks related to ionizing radiation. Though significant works have been done to push the frontier of PACT breast imaging, it is still challenging to successfully build a PACT breast imaging …
Fast Hadamard Transforms For Compressive Sensing Of Joint Systems: Measurement Of A 3.2 Million-Dimensional Bi-Photon Probability Distribution, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Fast Hadamard Transforms For Compressive Sensing Of Joint Systems: Measurement Of A 3.2 Million-Dimensional Bi-Photon Probability Distribution, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
We demonstrate how to efficiently implement extremely high-dimensional compressive imaging of a bi-photon probability distribution. Our method uses fast-Hadamard-transform Kronecker-based compressive sensing to acquire the joint space distribution. We list, in detail, the operations necessary to enable fast-transform-based matrix-vector operations in the joint space to reconstruct a 16.8 million-dimensional image in less than 10 minutes. Within a subspace of that image exists a 3.2 million-dimensional bi-photon probability distribution. In addition, we demonstrate how the marginal distributions can aid in the accuracy of joint space distribution reconstructions.
Compressive Wavefront Sensing With Weak Values, Gregory A. Howland, Daniel J. Lum, John C. Howell
Compressive Wavefront Sensing With Weak Values, Gregory A. Howland, Daniel J. Lum, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
We demonstrate a wavefront sensor that unites weak measurement and the compressive-sensing, single-pixel camera. Using a high-resolution spatial light modulator (SLM) as a variable waveplate, we weakly couple an optical field’s transverse-position and polarization degrees of freedom. By placing random, binary patterns on the SLM, polarization serves as a meter for directly measuring random projections of the wavefront’s real and imaginary components. Compressive-sensing optimization techniques can then recover the wavefront. We acquire high quality, 256 × 256 pixel images of the wavefront from only 10,000 projections. Photon-counting detectors give sub-picowatt sensitivity.
Photon Counting Compressive Depth Mapping, Gregory A. Howland, Daniel J. Lum, Matthew R. Ware, John C. Howell
Photon Counting Compressive Depth Mapping, Gregory A. Howland, Daniel J. Lum, Matthew R. Ware, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
We demonstrate a compressed sensing, photon counting lidar system based on the single-pixel camera. Our technique recovers both depth and intensity maps from a single under-sampled set of incoherent, linear projections of a scene of interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional reconstructions are required to image a three-dimensional scene. We demonstrate intensity imaging and depth mapping at 256 × 256 pixel transverse resolution with acquisition times as short as 3 seconds. We also show novelty filtering, reconstructing only the difference between two instances of a scene. Finally, we acquire 32 × 32 pixel real-time video for …