No description available.

Learning-based multi-view stereo (MVS) has shown promising results in the domain of general 3D reconstruction. However, no work before this thesis has applied learning-based MVS to urban 3D reconstruction from satellite images. In this thesis, learning-based MVS is used to infer depth maps from satellite images. Models are trained on both synthetic and real satellite images from Las Vegas with ground truth data from a high-resolution aerial-based 3D model. This thesis also evaluates different methods for reconstructing digital surface models (DSM) and compares them to existing satellite-based 3D models at Maxar Technologies. The DSMs are created by either post-processing point clouds obtained from predicted depth maps or by an end-to-end approach where the depth map for an orthographic satellite image is predicted.  This thesis concludes that learning-based MVS can be used to predict accurate depth maps. Models trained on synthetic data yielded relatively good results, but not nearly as good as for models trained on real satellite images. The trained models also generalize relatively well to cities not present in training. This thesis also concludes that the reconstructed DSMs achieve better quantitative results than the existing 3D model in Las Vegas and similar results for the test sets from other cities. Compared to ground truth, the best-performing method achieved an L1 and L2 error of 14 % and 29 % lower than Maxar's current 3D model, respectively. The method that uses a point cloud as an intermediate step achieves better quantitative results compared to the end-to-end system. Very promising qualitative results are achieved with the proposed methods, especially when utilizing an end-to-end approach.

This application software system of Tree Alignment and Reconstruction (TAAR) is an X Window based interactive system developed mainly for sequence analysis. It provides a user-friendly graphical interface and convenient operations for performing Pairwise Alignment, 3-Star Alignment, Phylogeny Reconstruction and Generalized Tree Alignment algorithms. The algorithms for Phylogeny Reconstruction and Generalized Tree Alignment are designed based on heuristic stepwise addition and internal node sequence alignment induction methods. All above algorithms are space-efficiently implemented. For each algorithm, both fast and optimal versions are provided for user's convenience. TAAR can be used for DNA, RNA and PROTEIN sequences analysis. In general, as long as the sequence characters are in the range of a-z and A-Z, and the score matrix alphabet contains the sequence alphabet, the sequence would be accepted by this application and all calculations could be carried out. This software system is programmed in C, Motif and X Window Library functions. It is intended to be running in a UNIX and X Window environment. / Thesis / Master of Science (MS)

No / A new casting of diasporas, exiles and returnees as potentially transformative agents in post-conflict polities is the topic of this article. ‘Return of Qualified Expatriates’ programmes have recently been launched by international agencies in a number of post-conflict countries in an attempt to promote better capacity-building within post-conflict states institutions. This article argues that the ostensible technical orientation of these programmes is misleading, and they have a political significance which is noted and contested locally. In political terms, they represent attempts to smuggle Western hierarchies of knowledge into post-conflict reconstruction efforts under the cover of ethnic solidarity, to the detriment of local participation and empowerment. The article argues further that this is always contested by interested parties locally, often by mobilising alternative capacities, labelled ‘authentic’, in opposition. As such, strategies that attempt to use ethnic ties to overcome this local contestation are placing a significant burden on ethnic categories that are slippery, malleable and contested in post-conflict contexts. These points are demonstrated with reference to the cases of Cambodia and Timor-Leste.

The decays, <img width=119 height=32 align=center src=https://vtechworks.lib.vt.edu/bitstream/handle/10919/28846/image006.gif> and <img width=119 height=29 align=center src=https://vtechworks.lib.vt.edu/bitstream/handle/10919/28846/image008.gif> are studied using data collected at the CLEO II detector at the Cornell Electron Storage Ring. Both decays are identified using a partial reconstruction method where the <I>D<SUP>*</SUP></I> is detected only through a pion daughter from the decay <img width=64 height=25 align=center src=https://vtechworks.lib.vt.edu/bitstream/handle/10919/28846/image010.gif>. Because of the similarities in the analyses of the two modes, the ratio of the rates is measured in a way that is independent of the decay model, limited mainly by the uncertainty in the relative efficiency for detecting neutral and charged pions. This ratio is equivalent to the ratio of the product of production fraction and lifetime for charged and neutral B mesons, <img width=31 height=29 align=center src=https://vtechworks.lib.vt.edu/bitstream/handle/10919/28846/image012.gif>. It is combined with measurements of the lifetime ratio to obtain the ratio of charged and neutral B meson production at the Y(4S) resonance, <img width=175 height=29 align=center src=https://vtechworks.lib.vt.edu/bitstream/handle/10919/28846/image014.gif>. / Ph. D.

One of the most important data elements recorded in the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) is the vehicle change in velocity, or ?V. ?V is the vector change in velocity experienced by a vehicle during a collision, and is widely used as a measure of collision severity in crash safety research. The ?V information in NASS/CDS is used by the U.S. National Highway Traffic Safety Administration (NHTSA) to determine research needs, regulatory priorities, design crash test procedures (e.g., test speed), and to determine countermeasure effectiveness. The WinSMASH crash reconstruction code is used to compute the ?V estimates in the NASS/CDS. However, the reconstruction accuracy of the current WinSMASH version has not previously been examined for side impacts. Given the importance of side impact crash modes and the widespread use of NASS/CDS data, an assessment of the program's reconstruction accuracy is warranted. The goal of this thesis is to quantify the accuracy of WinSMASH ?V estimations for side impact crashes, and to suggest possible means of improving side impact reconstruction accuracy. Crash tests provide a wealth of controlled crash response data against which to evaluate WinSMASH. Knowing the accuracy of WinSMASH in reconstructing crash tests, we can infer WinSMASH accuracy in reconstructing real-world side crashes. In this study, WinSMASH was compared to 70 NHTSA Moving Deformable Barrier (MDB) - to - vehicle side crash tests. Tested vehicles were primarily cars (as opposed to Light Trucks and Vans, or LTVs) from model years 1997 - 2001. For each test, the actual ?V was determined from test instrumentation and this ?V was compared to the WinSMASH-reconstructed ?V of the same test. WinSMASH was found to systemically over-predict struck vehicle resultant ?V by 12% at time of vehicle separation, and by 22% at time of maximum crush. A similar pattern was observed for the MDB ?V; WinSMASH over-predicted resultant MDB ?V by 6.6% at separation, and by 23% at maximum crush. Error in user-estimated reconstruction parameters, namely Principal Direction Of Force (PDOF) error and damage offset, was controlled for in this analysis. Analysis of the results indicates that this over-prediction of ?V is caused by over-estimation of the energy absorbed by struck vehicle damage. In turn, this ultimately stems from the vehicle stiffness parameters used by WinSMASH for this purpose. When WinSMASH was forced to use the correct amount of absorbed energy to reconstruct the crash tests, systemic over-prediction of ?V disappeared. WinSMASH accuracy when reconstructing side crash tests may be improved in two ways. First, providing WinSMASH with side stiffness parameters that are correlated to the correct amount of absorbed energy will correct the systemic over-prediction of absorbed energy when reconstructing NHTSA side crash tests. Second, providing some treatment of restitution in the reconstruction process will correct the under-prediction of ?V due to WinSMASH's assumption of zero restitution. At present, this under-prediction partially masks the over-prediction of ?V caused by over-prediction of absorbed energy. If the over-prediction of absorbed energy is corrected, proper treatment of restitution will correct much of the remaining error observed in WinSMASH reconstructions of NHTSA side crash tests. / Master of Science

The development and improvement of 3-D immersive audio is gaining momentum through the growing interest in virtual reality. Possible applications reach from recreating real world environments to immersive concerts and performances to exploiting big data acoustically. To improve the immersive experience several measures can be taken. The recording of the sound field, the spatialization and the development of the loudspeaker arrays are some of the greatest challenges. In this thesis, these challenges for improving immersive audio will be explored. First, there will be a short introduction about 3D audio and a review about the state of the art technology and research. Next, the thesis will provide an introduction to 3D loudspeaker arrays and describe the systems used during this research. Furthermore, the development of a new 16-element 3rd order sound field microphone will be described. Afterwards, different spatial audio algorithms such as higher order ambisonics, wave field synthesis and vector based amplitude panning will be described, analyzed and compared. For each spatialization algorithm, the quality of soundfield reproduction will be quantified using listener perception tests for clarity and sound source localization. / Master of Science

Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. The method proposed in this study is part of the planned joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI platform. However, since their image data are highly undersampled, analytical methods, such as filtered backprojection, are unable to generate images of sufficient quality. To overcome this drawback, we resort to compressed sensing techniques, which employ sparse priors that result from an application of L₁-norm minimization. To utilize multimodal information, a projection distance is introduced and is tuned to tailor the texture and pattern of final images. Specifically CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. This method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The improved performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI body images and a pair of undersampled CT-MRI head images. These images are tested using joint reconstruction, analytical reconstruction, and independent reconstruction without using multimodal imaging information. Results show that the proposed method improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity measurements compared to independent reconstruction methods. It offers a similar quality as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and a 30% sampling rate for MRI. It is concluded that structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction. / Ph. D. / Medical imaging techniques play a central role in modern clinical diagnoses and treatments. Consequently, there is a constant demand to increase the overall quality of medical images. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. Multimodal imaging techniques can provide more detailed diagnostic information by fusing medical images from different imaging modalities, thereby allowing clinical results to be more comprehensive to improve clinical interpretation. A new form of multimodal imaging technique, which combines the imaging procedures of computed tomography (CT) and magnetic resonance imaging (MRI), is known as the “omnitomography.” Both computed tomography and magnetic resonance imaging are the most commonly used medical imaging techniques today and their intrinsic properties are complementary. For example, computed tomography performs well for bones whereas the magnetic resonance imaging excels at contrasting soft tissues. Therefore, a multimodal imaging system built upon the fusion of these two modalities can potentially bring much more information to improve clinical diagnoses. However, the planned omni-tomography systems face enormous challenges, such as the limited ability to perform image reconstruction due to mechanical and hardware restrictions that result in significant undersampling of the raw data. Image reconstruction is a procedure required by both computed tomography and magnetic resonance imaging to convert raw data into final images. A general condition required to produce a decent quality of an image is that the number of samples of raw data must be sufficient and abundant. Therefore, undersampling on the omni-tomography system can cause significant degradation of the image quality or artifacts after image reconstruction. To overcome this drawback, we resort to compressed sensing techniques, which exploit the sparsity of the medical images, to perform iterative based image reconstruction for both computed tomography and magnetic resonance imaging. The sparsity of the images is found by applying sparse transform such as discrete gradient transform or wavelet transform in the image domain. With the sparsity and undersampled raw data, an iterative algorithm can largely compensate for the data inadequacy problem and it can reconstruct the final images from the undersampled raw data with minimal loss of quality. In addition, a novel “projection distance” is created to perform a joint reconstruction which further promotes the quality of the reconstructed images. Specifically, the projection distance exploits the structural similarities shared between the image of computed tomography and magnetic resonance imaging such that the insufficiency of raw data caused by undersampling is further accounted for. The improved performance of the proposed approach is demonstrated using a pair of undersampled body images and a pair of undersampled head images, each of which consists of an image of computed tomography and its magnetic resonance imaging counterpart. These images are tested using the proposed joint reconstruction method in this work, the conventional reconstructions such as filtered backprojection and Fourier transform, and reconstruction strategy without using multimodal imaging information (independent reconstruction). The results from this work show that the proposed method addressed these challenges by significantly improving the image quality from highly undersampled raw data. In particular, it improves about 5dB in signal-to-noise ratio and nearly 10% in structural similarity measurements compared to other methods. It achieves similar image quality by using less than 5% of the X-ray dose for computed tomography and 30% sampling rate for magnetic resonance imaging. It is concluded that, by using compressed sensing techniques and exploiting structural similarities, the planned joint computed tomography and magnetic resonance imaging system can perform imaging outstanding tasks with highly undersampled raw data.

Visual perception has been a significant subject matter of robotics research for decades but has accelerated in recent years as both technology and community are more prepared to take on new challenges with autonomous systems. In this thesis, a framework for 3D reconstruction using a stereo camera for the purpose of obstacle detection and mapping is presented. In this application, a UAV works collaboratively with a UGV to provide high level information of the environment by using a downward facing stereo camera. The approach uses frame to frame SURF feature matching to detect candidate points within the camera image. These feature points are projected into a sparse cloud of 3D points using stereophotogrammetry for ICP registration to estimate the rigid transformation between frames. The RTK-GPS constrained pose estimate from the UAV is fused with the feature matched estimate to align the reconstruction and eliminate drift. The reconstruction was tested on both simulated and real data. The results indicate that this approach improves frame to frame registration and produces a well aligned reconstruction for a single pass compared to using the raw UAV position estimate alone. However, multi-pass registration errors occur on the order of about 0.6 meters between parallel passes, and approximately 2 degrees of local rotation error when compared to a reconstruction produced with Agisoft Metashape. However, the proposed system performed at an average frame rate of about 1.3 Hz compared to Agisoft at 0.03 Hz. Overall, the system improved obstacle registration and can perform online within existing ROS frameworks. / Master of Science / Visual perception has been a significant subject matter of robotics research for decades but has accelerated in recent years as both technology and community are more prepared to take on new challenges with autonomous systems. In this thesis, a framework for 3D reconstruction using cameras for the purpose of obstacle detection and mapping is presented. In this application, a UAV works collaboratively with a UGV to provide high level information of the environment by using a downward facing stereo camera. The approach uses features extracted from camera images to detect candidate points to be aligned. These feature points are projected into a sparse cloud of 3D points using stereo triangulation techniques. The 3D points are aligned using an iterative solver to estimate the translation and rotation between frames. The RTK (Real Time Kinematic) GPS constrained position and orientation estimate from the UAV is combined with the feature matched estimate to align the reconstruction and eliminate accumulated errors. The reconstruction was tested on both simulated and real data. The results indicate that this approach improves frame to frame registration and produces a well aligned reconstruction for a single pass compared to using the raw UAV position estimate alone. However, multi-pass registration errors occur on the order of about 0.6 meters between parallel passes that overlap, and approximately 2 degrees of local rotation error when compared to a reconstruction produced with the commercial product, Agisoft. However, the proposed system performed at an average frame rate of about 1.3 Hz compared to Agisoft at 0.03 Hz. Overall, the system improved obstacle registration and can perform online within existing Robot Operating System frameworks.

Andrew Johnson returned from the Presidency to a harsh political environment in Tennessee. Immediately upon his return, he set out to win the Senate in his state. Although unsuccessful, he attempted office two more times, finally achieving success in 1874. His motivation lay in vindication for his impeachment, which destroyed and ruined his Presidency. However, other issues emerged as well, particularly that of the ex-Confederate military leaders who dominated the state's political scene from during the 1870s. Johnson successfully subverted them twice. As a spoiler in 1872 he stopped Confederate General Cheatham from winning the congressional at-large and when he won the Senate seat in 1874. Johnson died after only one appearance in the Senate in 1875. / Master of Arts