Recent projects tackling VRU safety, such as ASPECCS [1], have mainly focused on detecting the pedestrians and avoiding accidents by utilizing on-board sensors, such as camera or radar.
In light of the benefits of cloud computing, cloud-based high performance computing (HPC) has the potential to enable users to not only accelerate computationally expensive tasks, but also to reduce costs by utilizing on-demand, ubiquitous, seamless, and user-friendly access to remote engineering application packages as well as remote HPC resources.
These issues are addressed in the current controller scheme by utilizing the on-line adaptation capabilities of the neural networks.
Meanwhile, by utilizing the on-line estimating information of the unknown upper bound and the unknown parameters of necessary FLSs, a new adaptive fuzzy fault-tolerant controller with the adjustable parameter updated laws is designed to achieve robust tracking performance.
By utilizing an on-body relay node with the joint optimal relay location and power allocation, the transmission range in WBANs can be extended effectively and the power consumption can be transferred from the sensor node to the relay node, in which the lifetime of the sensor node can be prolonged significantly, particularly for the around-torso and in-body scenarios.
By utilizing an on-body relay node with the joint optimal RL and PA, the transmission range in WBANs can be extended effectively and the power consumption can be transferred from the sensor node to the relay node, in which the lifetime of the sensor node can be prolonged significantly compared to direct transmission.
This paper summarizes current NASA efforts to validate the explosion model in this size range by utilizing both on-orbit data (collected by the SSN, Haystack, and Haystack Auxiliary (HAX) radars) and ground-test data, and presents the mathematical formalisms describing the model as a function of mass and size.
Researchers are also studying new and innovative ways to optimize road assessments following natural disasters.13 This new research agenda, funded by the U.S. Department of Transportation, utilizes on-the-ground information culled from social media to identify possible areas of road damage.
Moreover, their experimental results were obtained by utilizing only one independent run on each data set and not based on average results, causing that they could not have statistically meaningful conclusions.
The tree species-specific models were estimated on a subpixel scale by utilizing information on the proportions of each species within a plot.
