Lateral Control of a Vehicle Platoon

Multiple lateral control systems are analyzed for use in a vehicle platoon system. In order to ensure the safety of the vehicle platoon, the system must operate under three constraints: (1) accurate path following, (2) string stability, and (3) functionality in the presence of noise. Computer simulations are employed to analyze candidates according to the safety requirements. Actual vehicle kinematics and nonlinearities - limits on turn radius, velocity, and acceleration - are included in this analysis. Successful control system candidates are implemented in a platoon of five differential-steer vehicles. The sensing and communication requirements of the control system are discussed. Experimental results are compared to the computer simulations. This analysis results in an implementation of a control system which functions according to the previously listed constraints

Investigating directed differentiation strategies in hiPSCs to model cell type-specific vulnerability in ALS

The concept of vulnerability is highly relevant to neurodegenerative diseases, whereby specific subsets of neurons display marked and devastating disease-related pathologies, but neighbouring cells may not. Amyotrophic lateral sclerosis (ALS) provides a perfect example, where spinal pathology manifests in lower motor neurons (MNs), with neighbouring cells remaining relatively unaffected, at least until late-stage disease. Interestingly, spinal MNs display selective vulnerability, with larger and more heavily myelinated alpha motor neurons degenerating the earliest. Additionally, the role of cell types surrounding MNs in contributing to ALS pathogenesis have become more evident over recent years. This includes non-cell-autonomous toxicity mediated by astrocytes and the denervation of Renshaw interneurons (INs) from MNs. Subsequent elucidation of mechanisms underlying cell type-specific vulnerability in ALS would drastically improve our understanding of ALS, the spectrum of cell types affected and provide alternative and tractable cellular targets for therapeutic intervention. The advent of human induced pluripotent stem cells (hiPSCs) has revolutionised disease modelling, providing a virtually inexhaustible source of patient-specific material. As a consequence, a variety of cell types has been generated using ontogeny-driven directed differentiation strategies. However, there is a pressing need for deeper phenotyping and further refinement of differentiation strategies, in order to generate more enriched and disease-relevant populations. With this in mind, I employed an established hiPSC-derived MN protocol and manipulated extrinsic signalling cues during two distinct developmental phases; patterning and terminal differentiation. In this manner, I was able to induce post-mitotic motor columnar diversity, resulting in the specification of lateral motor column phenotype; highly susceptible to degeneration in ALS. Separately, I was able to generate hiPSC-derived dorsal spinal INs arising from dI4-6, which subserve pain, temperature, itch and touch sensations (dI4&5) or indeed those regulating left-right coordination (dI6). Importantly, these INs retain the same axial identity, but are positioned more dorsally in the absence of sonic hedgehog signalling. Lastly, hiPSC-derived INs and MNs were investigated in a valosin-containing protein (VCP) mutant model of ALS. This revealed key differences relating to cell type vulnerability between MNs and INs, including RNA binding protein mislocalisation and alternative splicing events. Overall, the results of this PhD present novel ontogeny-driven directed differentiation strategies of hiPSC-derived cell types and a robust platform for modelling mechanisms of selective vulnerability in ALS. The experiments contained herein also demonstrate that iPSC models can capture neuronal subtype-selective vulnerability

Deep submarine silicic volcanism: Conduit and eruptive dynamics of the 2012 Havre eruption

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Ground Vehicle Platooning Control and Sensing in an Adversarial Environment

The highways of the world are growing more congested. People are inherently bad drivers from a safety and system reliability perspective. Self-driving cars are one solution to this problem, as automation can remove human error and react consistently to unexpected events. Automated vehicles have been touted as a potential solution to improving highway utilization and increasing the safety of people on the roads. Automated vehicles have proven to be capable of interacting safely with human drivers, but the technology is still new. This means that there are points of failure that have not been discovered yet. The focus of this work is to provide a platform to evaluate the security and reliability of automated ground vehicles in an adversarial environment. An existing system was already in place, but it was limited to longitudinal control, relying on a steel cable to keep the vehicle on track. The upgraded platform was developed with computer vision to drive the vehicle around a track in order to facilitate an extended attack. Sensing and control methods for the platform are proposed to provide a baseline for the experimental platform. Vehicle control depends on extensive sensor systems to determine the vehicle position relative to its surroundings. A potential attack on a vehicle could be performed by jamming the sensors necessary to reliably control the vehicle. A method to extend the sensing utility of a camera is proposed as a countermeasure against a sensor jamming attack. A monocular camera can be used to determine the bearing to a target, and this work extends the sensor capabilities to estimate the distance to the target. This provides a redundant sensor if the standard distance sensor of a vehicle is compromised by a malicious agent. For a 320×200 pixel camera, the distance estimation is accurate between 0.5 and 3 m. One previously discovered vulnerability of automated highway systems is that vehicles can coordinate an attack to induce traffic jams and collisions. The effects of this attack on a vehicle system with mixed human and automated vehicles are analyzed. The insertion of human drivers into the system stabilizes the traffic jam at the cost of highway utilization

Ground Vehicle Platooning Control and Sensing in an Adversarial Environment

The highways of the world are growing more congested. People are inherently bad drivers from a safety and system reliability perspective. Self-driving cars are one solution to this problem, as automation can remove human error and react consistently to unexpected events. Automated vehicles have been touted as a potential solution to improving highway utilization and increasing the safety of people on the roads. Automated vehicles have proven to be capable of interacting safely with human drivers, but the technology is still new. This means that there are points of failure that have not been discovered yet. The focus of this work is to provide a platform to evaluate the security and reliability of automated ground vehicles in an adversarial environment. An existing system was already in place, but it was limited to longitudinal control, relying on a steel cable to keep the vehicle on track. The upgraded platform was developed with computer vision to drive the vehicle around a track in order to facilitate an extended attack. Sensing and control methods for the platform are proposed to provide a baseline for the experimental platform. Vehicle control depends on extensive sensor systems to determine the vehicle position relative to its surroundings. A potential attack on a vehicle could be performed by jamming the sensors necessary to reliably control the vehicle. A method to extend the sensing utility of a camera is proposed as a countermeasure against a sensor jamming attack. A monocular camera can be used to determine the bearing to a target, and this work extends the sensor capabilities to estimate the distance to the target. This provides a redundant sensor if the standard distance sensor of a vehicle is compromised by a malicious agent. For a 320×200 pixel camera, the distance estimation is accurate between 0.5 and 3 m. One previously discovered vulnerability of automated highway systems is that vehicles can coordinate an attack to induce traffic jams and collisions. The effects of this attack on a vehicle system with mixed human and automated vehicles are analyzed. The insertion of human drivers into the system stabilizes the traffic jam at the cost of highway utilization

Intussusception in Children.

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Monitoring in a grid cluster

The monitoring of a grid cluster (or of any piece of reasonably scaled IT infrastructure) is a key element in the robust and consistent running of that site. There are several factors which are important to the selection of a useful monitoring framework, which include ease of use, reliability, data input and output. It is critical that data can be drawn from different instrumentation packages and collected in the framework to allow for a uniform view of the running of a site. It is also very useful to allow different views and transformations of this data to allow its manipulation for different purposes, perhaps unknown at the initial time of installation. In this context, we present the findings of an investigation of the Graphite monitoring framework and its use at the ScotGrid Glasgow site. In particular, we examine the messaging system used by the framework and means to extract data from different tools, including the existing framework Ganglia which is in use at many sites, in addition to adapting and parsing data streams from external monitoring frameworks and websites

Analysis and improvement of data-set level file distribution in Disk Pool Manager

Of the three most widely used implementations of the WLCG Storage Element specification, Disk Pool Manager[1, 2] (DPM) has the simplest implementation of file placement balancing (StoRM doesn't attempt this, leaving it up to the underlying filesystem, which can be very sophisticated in itself). DPM uses a round-robin algorithm (with optional filesystem weighting), for placing files across filesystems and servers. This does a reasonable job of evenly distributing files across the storage array provided to it. However, it does not offer any guarantees of the evenness of distribution of that subset of files associated with a given "dataset" (which often maps onto a "directory" in the DPM namespace (DPNS)). It is useful to consider a concept of "balance", where an optimally balanced set of files indicates that the files are distributed evenly across all of the pool nodes. The best case performance of the round robin algorithm is to maintain balance, it has no mechanism to improve balance.<p></p> In the past year or more, larger DPM sites have noticed load spikes on individual disk servers, and suspected that these were exacerbated by excesses of files from popular datasets on those servers. We present here a software tool which analyses file distribution for all datasets in a DPM SE, providing a measure of the poorness of file location in this context. Further, the tool provides a list of file movement actions which will improve dataset-level file distribution, and can action those file movements itself. We present results of such an analysis on the UKI-SCOTGRID-GLASGOW Production DPM

Florida.

1846 Mitchell S. Augustus map of Florida, relief shown by hachures and depth shown by soundings. Includes tables of mileage along various routes. Map denotes forts and battlefields. Prime meridian: Washington D.C. Map scale [ca. 1:3,218,688]and Includes insets of Pensacola and Tallahassee, scale [ca. 1:24,000] Saint Augustine Harbour, scale [ca. 1:72,412].https://stars.library.ucf.edu/cfm-images/1823/thumbnail.jp