There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that

In order to select the optimal tracking of fast time variation of multipath fast time variation Rayleigh fading channel, this paper focuses on the recursive least-squares (RLS) and Extended recursive least-squares (E-RLS) algorithms and reaches the conclusion that E-RLS is more feasible according to the comparison output of the simulation program from tracking performance and mean square error over five fast time variation of Rayleigh fading channels and more than one time (send/receive) reach to 100 times to make sure from efficiency of these algorithms.

The grasping stability of robotic manipulators is crucial to enable autonomous manipulation in an environment where robots are facing obstacles in their route, where abrupt changes in the robot’s speed are induced. These speed variations will produce forces affecting the robotic manipulator, hence its grasping stability. In this research, the grasping stability of a robotic manipulator that functions according to a frictional self-locking mechanism is investigated statically and dynamically. Both theoretical and experimental results showed that the grasped object size, weight, and its orientation inside the gripper have a great effect on grasping stability. Both the theoretical and experimental results indicated that the grasping object p