The UltraCommuter : a viable and desirable solar-powered commuter vehicle

The University of Queensland UltraCommuter project is the demonstration of an ultra-light weight, low drag, energy efficient and low polluting, electric commuter vehicle equipped with a 2.5m2 on-board solar array. A key goal of the project is to make the vehicle predominantly self-sufficient from solar power for normal driving purposes , so that it does not require charging or refuelling from off-board sources. This paper examines the technical feasibility of the solar-powered commuter vehicle concept, as it applies the UltraCommuter project. A parametric description of a solar-powered commuter vehicle is presented. Real solar insolation data is then used to predict the solar driving range for the UltraCommuter and this is compared to typical urban usage patterns for commuter vehicles in Queensland. A comparative analysis of annual greenhouse gas emissions from the vehicle is also presented. The results show that the UltraCommuter’s on-board solar array can provide substantial supplementation of the energy required for normal driving, powering 90% of annual travel needs for an average QLD passenger vehicle. The vehicle also has excellent potential to reduce annual greenhouse gas emissions from the private transport sector, achieving a 98% reduction in CO2 emissions when compared to the average QLD passenger vehicle. Lastly, the vehicle battery pack provides for tolerance to consecutive days of poor weather without resorting to grid charging, giving uninterrupted functionality to the user. These results hold great promise for the technical feasibility of the solar-powered commuter vehicle concept.

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented

Riding out of the storm: How to deal with the complexity of grid and cloud management

Over the last decade, Grid computing paved the way for a new level of large scale distributed systems. This infrastructure made it possible to securely and reliably take advantage of widely separated computational resources that are part of several different organizations. Resources can be incorporated to the Grid, building a theoretical virtual supercomputer. In time, cloud computing emerged as a new type of large scale distributed system, inheriting and expanding the expertise and knowledge that have been obtained so far. Some of the main characteristics of Grids naturally evolved into clouds, others were modified and adapted and others were simply discarded or postponed. Regardless of these technical specifics, both Grids and clouds together can be considered as one of the most important advances in large scale distributed computing of the past ten years; however, this step in distributed computing has came along with a completely new level of complexity. Grid and cloud management mechanisms play a key role, and correct analysis and understanding of the system behavior are needed. Large scale distributed systems must be able to self-manage, incorporating autonomic features capable of controlling and optimizing all resources and services. Traditional distributed computing management mechanisms analyze each resource separately and adjust specific parameters of each one of them. When trying to adapt the same procedures to Grid and cloud computing, the vast complexity of these systems can make this task extremely complicated. But large scale distributed systems complexity could only be a matter of perspective. It could be possible to understand the Grid or cloud behavior as a single entity, instead of a set of resources. This abstraction could provide a different understanding of the system, describing large scale behavior and global events that probably would not be detected analyzing each resource separately. In this work we define a theoretical framework that combines both ideas, multiple resources and single entity, to develop large scale distributed systems management techniques aimed at system performance optimization, increased dependability and Quality of Service (QoS). The resulting synergy could be the key 350 J. Montes et al. to address the most important difficulties of Grid and cloud management.

Vehicle integration and evaluation of advanced restraint systems. Phase B - test report: Torino-to-Volvo tests.

National Highway Traffic Safety Administration, Washington, D.C.

Baseline test of compact vehicle side structure, 25 mph, 60 degree impact, Torino-to-Volare test no. 1. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Baseline test of compact vehicle side structure 25 mph, 60 degree impact, Torino-to-Volare test no. 2. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 25 mph, 60 degree impact, Torino-to-Volare, test no. 3. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 90 degree impact, Torino-to-Volare, test no. 4. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 60 degree impact, Torino-to-Volare, test no. 5. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 60 degree impact, Impala-to-Volare, test no. 10. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 90 degree impact, Impala-to-Volare, test no. 11. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 60 degree impact, Torino-to-Volare, test no. 6. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 60 degree impact, Torino-to-Volare, test no. 7. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Impact test of compact vehicle with modified side structure, 35 mph, 90 degree impact, Torino-to-Volare, test no. 8. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Baseline test of compact vehicle side structure, 35 mph, 90 degree impact, Torino-to-Volare, test no. 9. Final report.

National Highway Traffic Safety Administration, Washington, D.C.