Abstract With the rise of social media and mobile computing, hedonic components and gamification play a more and more important role in the success of e-business systems. In addition, as a majority of users of e-business have game play experiences, understanding their behavior is essential. This research attempts to investigate user behavior in computer game play and the findings will likely shed lights on... With the rise of social media and mobile computing, hedonic components and gamification play a more and more important role in the success of e-business systems. In addition, as a majority of users of e-business have game play experiences, understanding their behavior is essential. This research attempts to investigate user behavior in computer game play and the findings will likely shed lights on how to design gamification features in e-business systems. Based on a conceptual framework of computer game play proposed by Fang, Chan, and Nair [5], an online survey was designed and conducted to investigate the relationship between personality trait and players’ choice of games. In addition, we also attempted to examine the hardcore game players who are alleged to be the main driving force behind the gaming industry [4]. Results suggest that the personality trait, emotional stability, has an impact on how players choose games. Findings and their implications were discussed. Read More Read Less

Abstract Adaptive algorithms play a major role in all adaptive processing systems. One of the most important and wellknown algorithms is the least mean square (LMS) algorithm due to simplicity, stability and fast convergence rate. In this paper, two different types of the LMS algorithm were reviewed and analyzed: adapted and unadapted variable step size. The adapted step size varies its value according to... Adaptive algorithms play a major role in all adaptive processing systems. One of the most important and wellknown algorithms is the least mean square (LMS) algorithm due to simplicity, stability and fast convergence rate. In this paper, two different types of the LMS algorithm were reviewed and analyzed: adapted and unadapted variable step size. The adapted step size varies its value according to signal features while unadapted step size varies its value with a fixed predefined constant. The performance of each algorithm measured in the scope of adaptive noise canceling system. Performance quality was determined with respect to mean square error (MSE), convergence rate and algorithm stability. According to the obtained simulation results the Adapted variable step size VSS-LMS algorithm was performed very well and converged rapidly also it was stable through the adaptation process, as a result, it behaves better than unadapted VSS-LMS algorithm. And both adapted and unadapted VSS-LMS algorithms give an enhanced performance for the ordinary fixed step size LMS algorithm. Read More Read Less

Abstract Generating 3D animation scenes in computer graphics requires applying a 3D transformation on the vertices of the objects. These transformations consume most of the execution time. Hence, for high-speed graphics systems, acceleration of vertex transform is very much sought for because it requires many matrices operations that to be performed at a real-time, so the execution time is essential for such processing. In... Generating 3D animation scenes in computer graphics requires applying a 3D transformation on the vertices of the objects. These transformations consume most of the execution time. Hence, for high-speed graphics systems, acceleration of vertex transform is very much sought for because it requires many matrices operations that to be performed at a real-time, so the execution time is essential for such processing. In this paper, the acceleration of 3D object transformation is achieved using parallel techniques such as Multicore Core Central Processing Unit (MC CPU) or Graphic Processing Unit (GPU) or even both. Multiple geometric transformations are concatenated together at a time in any order with interactive manner. The performance results are presented for a number of 3D objects with paralleled implementations of the affine transform on the NVIDIA GPU series. The maximum execution time was about 0.508 seconds to transform 100 million vertices. Other results also showed the significant speedup compared to (CPU and MC CPU) computations for the same object complexity. Read More Read Less