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Existing lidar-based semantic segmentation algorithms and datasets focus on autonomous vehicles operating in urban environments. This has greatly improved the safety and reliability of these autonomous vehicles in predictable scenery. A new dataset provides lidar data focusing on off-road environments as seen by autonomous ground vehicles, ushering in a new era of off-road exploration capabilities. To the best of our knowledge, no new algorithms have been developed specifically for this unstructured environment. To gain an understanding of how existing algorithms perform in an off-road environment, we assess the baseline performance of four algorithms, KPConv, SalsaNext, Cylinder3D, and SphereFormer, on a commonly used on-road dataset, SemanticKITTI. We then compare the results with an off-road dataset, RELLIS-3D. We discuss the degradation of each algorithm on the off-road dataset and investigate potential causes such as class imbalance, inconsistencies in the labeled data, and the inherent difficulty of segmenting off-road environments. We present the strengths and weaknesses of each algorithm’s segmentation abilities and provide a comparison of the runtime of each algorithm for real-time capabilities. This is crucial for identifying what network architecture features are potentially the most beneficial for unstructured scenes. A robust, open-source software implementation via docker containers and bash scripts provides simple, repeatable execution of all algorithm training and evaluations. All code is publicly available at https://github.com/UA-Lidar-Segmentation-Research.

Existing lidar-based semantic segmentation algorithms and datasets focus on autonomous vehicles operating in urban environments. This has greatly improved the safety and reliability of these autonomous vehicles in predictable scenery. A new dataset provides lidar data focusing on off-road environments as seen by autonomous ground vehicles, ushering in a new era of off-road exploration capabilities. To the best of our knowledge, no new algorithms have been developed specifically for this unstructured environment. To gain an understanding of how existing algorithms perform in an off-road environment, we assess the baseline performance of four algorithms, KPConv, SalsaNext, Cylinder3D, and SphereFormer, on a commonly used on-road dataset, SemanticKITTI. We then compare the results with an off-road dataset, RELLIS-3D. We discuss the degradation of each algorithm on the off-road dataset and investigate potential causes such as class imbalance, inconsistencies in the labeled data, and the inherent difficulty of segmenting off-road environments. We present the strengths and weaknesses of each algorithm’s segmentation abilities and provide a comparison of the runtime of each algorithm for real-time capabilities. This is crucial for identifying what network architecture features are potentially the most beneficial for unstructured scenes. A robust, open-source software implementation via docker containers and bash scripts provides simple, repeatable execution of all algorithm training and evaluations. All code is publicly available at https://github.com/UA-Lidar-Segmentation-Research .

In this paper we present a novel hardware architecture for real-time image compression implementing a fast, searchless iterated function system (SIFS) fractal coding method. In the proposed method and corresponding hardware architecture, domain blocks are fixed to a spatially neighboring area of range blocks in a manner similar to that given by Furao and Hasegawa. A quadtree structure, covering from 32 × 32 blocks down to 2 × 2 blocks, and even to single pixels, is used for partitioning. Coding of 2 × 2 blocks and single pixels is unique among current fractal coders. The hardware architecture contains units for domain construction, zig-zag transforms, range and domain mean computation, and a parallel domain-range match capable of concurrently generating a fractal code for all quadtree levels. With this efficient, parallel hardware architecture, the fractal encoding speed is improved dramatically. Additionally, attained compression performance remains comparable to traditional search-based and other searchless methods. Experimental results, with the proposed hardware architecture implemented on an Altera APEX20K FPGA, show that the fractal encoder can encode a 512 × 512 × 8 image in approximately 8.36 ms operating at 32.05 MHz. Therefore, this architecture is seen as a feasible solution to real-time fractal image compression.

Retention and graduation rates for engineering disciplines are significantly lower than desired, and research literature offers many possible causes. Engineering learning communities provide the opportunity to study relationships among specific causes and to develop and evaluate activities designed to lessen their impact. This paper details an engineering learning community created to combat three common threats to academic success of engineering students: financial difficulties, math deficiencies, and the lack of a supportive engineering culture. The project tracks participants in the learning community from first year through graduation to assess the effectiveness of its activities in improving retention and graduation rates. Scholarships were made available to address the financial difficulties; tutors, mentors, study groups, and a “freshman-to-sophomore bridge” summer program were provided to address math deficiencies; cohort engineering courses, active learning techniques, required group meetings, required group study sessions, dedicated study space, and dedicated faculty advisors were used to promote a sense of community. Quantitative retention and graduation rates for the cohort are compared to other engineering groups at the same institution. Qualitative results collected via student surveys and interviews, and lessons learned by project administrators are also presented. Retention and graduation rates of the cohort are better than those of comparable groups at the same institution. Graduation rates based upon freshman math placement are also higher than comparable groups. 

This thesis presents a new, cost effective multiprocessing architecture which can be constructed using off-the-shelf components. The proposed architecture extends the current generation of single-board-computer systems to include a low cost, supplemental interprocessor communication network. The resulting extended single-board-computer multiprocessor (ESBCM) architecture is shown to have improved scalability characteristics and is much more flexible than current multiprocessor designs. It also directly supports many real-time and fault-tolerant constructs not previously supported. Extensive empirical analysis using discrete event simulations and Monte Carlo techniques indicate that the ESBCM architecture will generally outperform standard bus-based multiprocessors.

Application of three-dimensional (3D) woven composites is growing as an alternative to the use of ply-based composite materials. However, the design, analysis, modeling, and optimization of these materials is more challenging due to their complex and inherently multiscale geometries. Herein, a multiscale modeling procedure, based on efficient, semi-analytical micromechanical theories rather than the traditional finite element approach, is presented and applied to a 3D woven carbon–epoxy composite. A crack-band progressive damage model was employed for the matrix constituent to capture the globally observed nonlinear response. Realistic microstructural dimensions and tow-fiber volume fractions were determined from detailed X-ray computed tomography (CT) and scanning electron microscopy data. Pre-existing binder-tow disbonds and weft-tow waviness, observed in X-ray CT scans of the composite, were also included in the model. The results were compared with experimental data for the in-plane tensile and shear behavior of the composite. The tensile predictions exhibited good correlations with the test data. While the model was able to capture the less brittle nature of the in-plane shear response, quantitative measures were underpredicted to some degree.