August 1, 2014 at 3:00pm - M.S. Thesis Defense - Gregory J. Fertig - Evaluation of MOSFETs for Terahertz Detector Arrays

Carlson Bldg. (76) - Room 3215 (DIRS Lab)
August 1, 2014 at 3:00pm
Gregory J. Fertig
Evaluation of MOSFETs for Terahertz Detector Arrays
M.S. Thesis Defense

Dr. Zoran Ninkov
Dr. Emmett Ientilucci


The terahertz (THz) region of the electromagnetic spectrum is one of the last remaining regions that has yet to be fully characterized.  THz imaging is one of the foremost drivers of this technology gap and has the potential to push development in the near term to a similar capability level as infrared (IR).  Interest in array based imaging of THz radiation (T-Rays) has gained traction lately, specifically using a CMOS process due to its ease of manufacturability and the use of MOSFETs as a detection mechanism.  Incident terahertz radiation on to the gate channel region of a properly configured MOSFET can be related to plasmonic response waves which change the electron density and potential across the channel producing a photoinduced response.  The 0.35um silicon CMOS MOSFETs tested in this work contains varying structures, providing a range of detectors to analyze.  Included are individual test transistors for which various operating parameters and modes are studied and results presented.  A focus on single transistor-antenna testing provides a path for discovering the most efficient combination for coupling 0.2THz band energy.  Sensitivity analysis and responsivity are described, in parallel with theoretical expectations of the plasmonic response in room temperature conditions. A maximum responsivity of 40,000V/W and corresponding NEP of 10pW/Hz^1/2 (+-10% uncertainty) is demonstrated.

August 1, 2014 at 10:00am - M.S. Thesis Defense - Jordyn Stoddard - Toward Three-Dimensional Reconstruction from Cubesat Imagery: Impacts of Spatial Resolution and SNR on Point Cloud Quality

Carlson Bldg. (76) - Room 3215 (DIRS Lab)
August 1, 2014 at 10:00am
Jordyn Stoddard
Toward Three-Dimensional Reconstruction from Cubesat Imagery: Impacts of Spatial Resolution and SNR on Point Cloud Quality
M.S. Thesis Defense

Advisor: Dr. David W. Messinger


The adoption of cube-satellites (cubesats) by the space community has drastically lowered the cost of access to space and reduced the development lifecycle from the hundreds of millions of dollars spent on traditional decade-long programs.  Rapid deployment and low cost are attractive features of cubesat-based imaging that are conducive to applications such as disaster response and monitoring.  One proposed application is 3D surface modeling through a high revisit rate constellation of cubesat imagers. This work begins with the characterization of an existing design for a cubesat imager based on ground sampled distance (GSD), signal-to-noise ratio (SNR), and smear.  From this characterization, an existing 3D workflow is applied to datasets that have been degraded within the regime of spatial resolutions and signal-to-noise ratios anticipated for the cubesat imager.  The fidelity of resulting point clouds are assessed locally for both an urban and a natural scene.  The height of a building and normals to its surfaces are calculated from the urban scene, while quarry depth estimates and rough volume estimates of a pile of rocks are produced from the natural scene.  Though the reconstructed scene geometry suffers noticeably from the degraded imagery, results indicate that useful information can still be extracted using some of these techniques up to a simulated GSD of 2 meters.

May 27, 2014 at 10:00am - Ph.D. Thesis Defense - Jiashu Zhang - Analytical Modeling and Performance Assessment of Micropulse Photon-counting Lidar System

Carlson Bldg. (76) - Room 3215 (DIRS Lab)
May 27, 2014 at 10:00am
Jiashu Zhang
Analytical Modeling and Performance Assessment of Micropulse Photon-counting Lidar System
Ph.D. Thesis Defense

Advisor: Dr. John Kerekes


The melting of polar ice sheets and evidence of global warming continue to remain prominent research interests among scientists. To better understand global volumetric change of ice sheets, as well as changes in vegetation, laser and radar altimetry measurements from satellites are required. NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2018, is specifically intended to quantify the amount of change in ice sheets and sea ice. Onboard ICESat-2 is a discrete lidar system known as the Advanced Topographic Laser Altimeter Sys- tem (ATLAS) instrument, which employs a high frequency photon-counting laser altimeter with single photon detectability. This instrument will provide significantly greater spatial resolution in both the along-track and cross-track directions. However, the combined effects of sub-beam complex surfaces, as well as system effects on returning photon distribution have not been systematically studied. To better understand the effects of various system attributes and to help improve theory behind lidar sensing of complex surfaces, an analytical model using a first principles 3D Monte Carlo approach is developed to predict system performance.

Based on the latest ICESat-2 design, this analytical model simulates photons which propagate from the laser transmitter to the scene model, and finally reflected to the detector model. A radiometric model using a bidirectional reflectance distribution function (BRDF) model is also applied in the synthetic scene. Such an approach allows the study of surface elevation retrieval accuracy for landscapes which have different shapes, as well as reflectivities. Comparing the results of returning photon detection for example surfaces, it is found that ICESat-2 will have a higher precision on a smoother surface, and a surface with smaller diffuse albedo will on average result in smaller bias.

Furthermore, an adaptive density-based algorithm is developed to detect the surface returns without any geometrical knowledge. This proposed approach is implemented using aforementioned simulated data set as well as airborne Multiple Altimeter Beam Experiment Lidar (MABEL) laser altimeter measurement. Qualitative and quantitative results are presented to show that smaller laser footprint, smoother surface and lower noise rate will result in improved accuracy of ground height estimation. Meanwhile, reasonable detection accuracy can also be achieved in estimating both ground and canopy returns for data generated using Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. This proposed approach is found to be generally applicable for surface and canopy finding from photon-counting laser altimeter data.

Ph.D. Dissertation Defense - Viraj Adduru - Automated brain segmentation methods for clinical quality MRI and CT images

CAR 76-3215 DIRS Lab
Viraj Adduru
Automated brain segmentation methods for clinical quality MRI and CT images
Ph.D. Dissertation Defense



Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with brain tissue loss. Accurate estimation of this loss is critical for the diagnosis, prognosis, and tracking the progression of AD.  Structural magnetic resonance imaging (sMRI) and X-ray computed tomography (CT) are widely used imaging modalities that help to in vivo map brain tissue distributions. As manual image segmentations are tedious and time-consuming, automated segmentation methods are increasingly applied to head MRI and head CT images to estimate brain tissue volumes. However, existing automated methods can be applied only to images that have high spatial resolution and their accuracy on heterogeneous low-quality clinical images has not been tested. Further, automated brain tissue segmentation methods for CT are not available, although CT is more widely acquired than MRI in the clinical setting. For these reasons, large clinical imaging archives are unusable for research studies. In this work, we identify and develop automated tissue segmentation and brain volumetry methods that can be applied to clinical quality MRI and CT images. In the first project, we surveyed the current MRI methods and validated the accuracy of these methods when applied to clinical quality images. We then developed CTSeg, a tissue segmentation method for CT images, by adopting the MRI technique that exhibited the highest reliability. CTSeg is an atlas-based statistical modeling method that relies on hand-curated features and cannot be applied to images of subjects with different diseases and age groups. Advanced deep learning-based segmentation methods use hierarchical representations and learn complex features in a data-driven manner. In our final project, we develop a fully automated deep learning segmentation method that uses contextual information to segment clinical quality head CT images. The application of this method on an AD dataset revealed larger differences between brain volumes of AD and control subjects. This dissertation demonstrates the potential of applying automated methods to large clinical imaging archives to answer research questions in a variety of studies.