Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system.

The ASCE confers an overall D+ grade to American infrastructure, while the NAE lists the restoration and improvement of urban infrastructure as one of its grand engineering challenges for the 21st century, indicating that infrastructure renovation and development is a major challenge in the US. Furthermore, according to the UN World Urbanization Prospects, about 55% of the world's population lives in urban areas and this percentage is set to grow, especially in Africa and Asia. The growth of urban population poses challenges to the expansion of underground infrastructure, such as water, sewage, electricity and telecommunications. Localization and mapping of underground infrastructure are fundamental for infrastructure maintenance and development. Ground penetrating radar (GPR) is a remote sensing method capable of detecting subsurface assets that has been used in the localization and mapping of underground utilities. This thesis contributes improvements of GPR systems and imaging algorithms towards smarter infrastructure, specifically: Application of GPR imaging algorithm to improve GPR data readability and generate augmented reality (AR) content; Use of photogrammetric methods to improve GPR positioning for underground infrastructure localization and mapping.

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is based on the Viola-Jones theory for face and mouth detection and the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the yawning state as a sign of a driver’s drowsiness.

In this thesis, coda waves recorded by local seismographic networks will be analyzed to estimate the three-dimensional spatial distribution of scatterers (SDS). This will be done by using the single scattering approximation. This approach leads to a huge system of equations that can not be solved by traditional methods. For the first time, we will use the Simultaneous Iterative Reconstructive Technique (SIRT) to solve this kind of system in seismological applications. SIRT is slow but provides a means to carry out the inversion with greater accuracy. There is also a very fast non-iterative method that allows to carry out the inversion 102 times faster, with a higher resolution and reasonable accuracy: the Filtered Back-Projection (FBP). If one wishes to use this technique it is necessary to adapt it to the geometry of our problem. This will be done for the first time in this thesis. The theory necessary to carry out the adaptation will be developed and a simple expression will be derived to carry out the inversion.FBP and SIRT are then used to determine the SDS in southern India. Results are almost independent of the inversion method used and they are frequency dependent. They show a remarkably uniform distribution of the scattering strength in the crust around GBA. However, a shallow (0-24 km) strong scattering structure, which is only visible at low frequencies, seems to coincide with de Closepet granitic batholith which is the boundary between the eastern and western parts of the Dharwar craton.Also, the SDS is estimated for the Galeras volcano, Colombia. Results reveal a highly non-uniform SDS. Strong scatterers show frequency dependence, which is interpreted in terms if the scale of the heterogeneities producing scattering. Two zones of strong scattering are detected: the shallower one is located at a depth from 4 km to 8 km under the summit whereas the deeper one is imaged at a depth of ~37 km from the Earth's surface. Both zones may be correlated with the magmatic plumbing system beneath Galeras volcano. The second strong scattering zone may be probably related to the deeper magma reservoir that feeds the system.

Compton cameras localize γ-ray sources in 3D space by observing evidence of Compton scattering with detectors sensitive to ionizing radiation. This thesis proposes a software system for operating a novel Compton camera device comprised of Timepix3 detectors and Katherine readouts. To communicate with readouts using UDP-based protocol, a dedicated hardware library was developed. The presented software can successfully control the acquisition of multiple Timepix3 detectors and simultaneously process their measurements in a real-time setting. To recognize instances of Compton scattering among observed interactions, a chain of algorithms is applied with explicit consideration for a possibly high volume of measured information. Unlike alternate approaches, the presented work uses a recently published charge drift time model to improve its spatial resolution. In order to achieve localization of γ-ray sources, the software performs conical back projection into a discretized cuboid volume. Results of randomized evaluation with simulated data indicate that the presented implementation is correct and constitutes a viable method of γ-ray source localization in 3D space. Experimental verification with a prototype model is in progress.

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system.

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is based on the Viola-Jones theory for face and mouth detection and the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the yawning state as a sign of a driver’s drowsiness.

Hlavním cílem této práce je navrhnout novu metodu rekonstrukce obrazu z dat fotoakustického snímkování. Fotoakustické snímkování je velmi populární neinvazivní metoda snímkování založená na detekování ultrazvukových vln vyvolaných laserovým paprskem. Proces snímkování generuje velké množství dat, a kvůli tomu je proces rekonstrukce obrazu velmi časově náročný. Táto práce demonstruje proces rekonstrukce obrazu pomocí zpětné projekce, algoritmu který je dostatečně jednoduchý na přizpůsobení moderním architekturám procesorů umožňující různé způsoby optimalizovaného výpočtu. Dvě různé variantu algoritmu byly navrženy: z pohledu pixelu a z pohledu senzoru, který detekuje ultrazvukové vlny. Obě varianty byly implementovány třemi různými způsoby: pomocí vektorového paralelismu, vláknového paralelismu a paralelismu na grafické karetě (GPU). Všechny 3 implementace obou variant algoritmu byly testovány a výsledky byly srovnány s výsledkem rekonstrukce algoritmu reverzního času, přesnějšího ale mnohokrát pomalejšího algoritmu. Výsledky ukázaly, že GPU paralelismus nabízí nejrychlejší výpočet, cca. 200 krát rychlejší než u algoritmu reverzního času, a proto se dá použit i v aplikacích pracující v reálném čase.

Synthetic Aperture Radar (SAR) is an emerging technique in remote sensing. The technology is capable of producing high-resolution images of the earth surface in all-weather conditions. Thesis work describes the present available methods for positioning and imaging targets using M-sequence UWB (Ultra-Wideband) radar signals with moving antennas and SAR algorithm to retrieve position and image of the target. M-sequence UWB radar technology used as signal source for transmission and receiving echoes of target. Pseudo random binary sequence is used as a transmitted signal. These radars have an ability to penetrate signal through natural and unnatural objects. It offers low cost and quality security system. Among a number of techniques of image retrieval in Synthetic Aperture Radar, study of Global back projection (GBP) algorithm is presented. As a time domain algorithm, GBP possesses inherent advantages over frequency domain algorithm like ability to handle long integration angle, wider bandwidth and unlimited aperture size. GBP breaks the full synthesis aperture into numbers of sub-apertures. These sub-apertures are treated pixel by pixel. Each sub-aperture is converted to a Cartesian image grid to form an image.  During this conversion the signal is treated with linear interpolation methods in order to achieve the best quality of the images. The objective of this thesis is the imaging of target using M-sequence UWB radar and processing SAR raw data using Global back projection algorithm.

<p>Synthetic antenna aperture (SAR) is a pulses focusing airborne radar which can achieve high resolution radar image. A number of image process algorithms have been developed for this kind of radar, but the calculation burden is still heavy. So the image processing of SAR is normally performed “off-line”.</p><p>The Fast Factorized Back Projection (FFBP) algorithm is considered as a computationally efficient algorithm for image formation in SAR, and several applications have been implemented which try to make the process “on-line”.</p><p>CELL Broadband Engine is one of the newest multi-core-processor jointly developed by Sony, Toshiba and IBM. CELL is good at parallel computation and floating point numbers, which all fit the demands of SAR image formation.</p><p>This thesis is going to implement FFBP algorithm on CELL Broadband Engine, and compare the results with pre-projects. In this project, we try to make it possible to perform SAR image formation in real-time.</p>