Abstract: Routing in adhoc networks is a challenge as nodes are
mobile, and links are constantly created and broken. Present ondemand
adhoc routing algorithms initiate route discovery after a path
breaks, incurring significant cost to detect disconnection and
establish a new route. Specifically, when a path is about to be broken,
the source is warned of the likelihood of a disconnection. The source
then initiates path discovery early, avoiding disconnection totally. A
path is considered about to break when link availability decreases.
This study modifies Adhoc On-demand Multipath Distance Vector
routing (AOMDV) so that route handoff occurs through link
availability estimation.
Abstract: Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.