Papers by Author: Vaci Istanda

Abstract: Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

Abstract: This paper aimed at constructing a two-tier on-line testing system. Questions were designed based on integration of concept cartoons and multimedia in hopes that the unexplainable contents of an object can be appropriately represented and interpreted through the means of concept cartoons, and that student’s understanding of the questions and interests in test taking can be effectively enhanced through pictorial presentation of concept cartoons. This system was to construct a testing tool through which tests were conducted to explore third graders’ magnetism misconception. The outcomes indicated that incorporation of concept cartoons in multimedia on-line testing can improve accuracy than traditional written test on paper. During the study, 14 magnetism misconceptions entertained by the pupils were identified and classified. Further, the on-line testing tool can attain satisfactory results in system utilization, animation design, attracting students’ attention and enhancing their understanding of the questions. It demonstrated the fact that integration of concept cartoons with two-tier on-line testing can improve question comprehension and students’ attention.

Abstract: As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.