Through fully utilizing the supper capabilities on computation and mobility of autonomous underwater vehicle, the energy consumption for fixed sensor nodes can be extremely reduced, so that the lifetime of underwater networks is extended.
Through fully utilizing the supper capabilities on computation and mobility of autonomous underwater vehicles, the energy consumption for fixed sensor nodes can be extremely reduced, so that the lifetime of networks is extended.
The capabilities of the computation model are evaluated through a detailed comparison with the experimental databases gathered on the ONERA Mascotte test bench.
Utilizing the capabilities of symbolic computations within modern mathematical software, the effect of the most significant geometrical and cross-sectional beam properties on the free vibration characteristics of the beam are established and presented in tabular and graphical form.
This analysis allows to stress the capabilities of RANS computations to correctly predict tip phenomena and to provide guidelines in order to obtain reliable numerical results, having in mind the necessity of a compromise between calculation accuracy and computational time, essential in the case of a systematic application of the presented approach for design purposes.
Wireless Sensor Networks (WSNs) consist of thousands of tiny nodes having the capability of sensing, computation, and wireless communications.
For future research, this study developed a more reliable evaluation approach in examining the generalization capability of evolutionary computation based decision-making system.
To demonstrate the capability of distributed computation of the method, we have implemented the method in a distributed way to solve the market split problem with (p=6) and (q=50).
However, in addition to many of the capabilities of a computer programming language (i.e., data processing, computations, communicating with other computer devices, and decision making), the robot language also includes statements specifically designed for robot control.
The massive increase in data volume with the development of computation capability has outmoded compute-intensive clusters for the analysis of large-scale datasets due to the network bottleneck caused by the large amount of data transferred over the network.
