No abstract

No abstract

No abstract

Mobility has been identified to be a major characteristic of living things. Humans who are deprived of efficient mobility either by natural or man-made factors loose significant relationship with their environment. The growing demand to produce effective rehabilitation devices for the aged population and disabled individuals, have spurred us to develop a reliable and easy to use biosignal based auto control wheelchair. This is to ensure independent mobility of persons with disabilities and the aged. In this paper, a Recurrent Neural Network (RNN) architecture called Long Short Term Memory (LSTM) is engaged for the classification EMG signals to the corresponding hand-gesture category. The LSTM model in this study yielded a validation accuracy that provides a basis for an improved solution towards real-time deployment.

A significant challenge in 802.11p based vehicular ad hoc networks (VANETs) is that the cooperative awareness messages (CAMs) tend to experience collisions. In this paper, we propose an adaptive CAM messaging algorithm based on the emerging methodology of the age of information (AoI). Our objective is to minimize an age-penalty function in a trajectory prediction application. In our design, each vehicle will compute a local penalty which serves as an indicator on whether the CAM messaging frequency is appropriate for its mobility status; and at the same time, calculates an appropriate penalty associated with all its neighbors which serves as an indicator regarding the impact of network congestion on the trajectory prediction quality. The aggregated penalty score integrating both the local and neighboring parts will be used to adaptively control the CAM sending frequency. We are to present simulation results demonstrating that our adaptive messaging method can indeed mitigate network congestion while meet the driving safety requirements.