No abstract

One of the newest methods of identification system is finger vein recognition which is a unique and successful way to identify human based on the physical characteristics of finger vein patterns. In this paper, a new type of classifier called Local Mean based K-nearest centroid neighbor (LMKNCN) is applied to classify finger vein patterns. Finally, the significance of the proposed method is proven by comparing the results of LMKNCN classifier with traditionally used K nearest neighbor classifier (KNN). The experimental results indicate that the proposed method in this research confidently merits the performance of the finger vein recognition method, as the gained accuracy using the proposed method is higher than that of the traditionally used method KNN. The maximum obtained accuracy of LMKNCN test with 2040 number of finger vein images is 100% while for KNN is 98.53%.

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems.

No abstract

This research work elaborates the investigation of lateral flight control system as point of unmanned aerial vehicle control.To evaluate our work, we have used rigid flying object model that is 10 meters in lengthand studythe effects of number of forces were consideredsuch as inertia, fin force and wind to design the heading controller,Fuzzy logic controller more specifically Proportional Integral Derivative (PID) controller is used,wherepros and cons of fuzzy logic controller is considered. To justify our proposed work, simulation is been implemented to model controller designs and dynamics of the airship.Furthermore comparative study have been done between the outcome of the system to be design and the latest research literature. Experimentalresultsillustrate that our method is efficient, is more reliable and effective.