Mobile learning is the ability to obtain or deliver learning materials on personal pocket electronic devices such as mobile phones, PDAs or tablets. Mobile devices support multimedia elements, such as video conferencing and can be accessed by all learners via WiFi/WiMAX network. In this paper, we evaluate the quality of network traffic when video conferencing is being used by number of mobile devices over Wi-Fi/WiMAX network. A network testbed is designed with the E-Learning portal and PRTG is used to monitor the traffic; i.e. web traffic and multimedia traffic. The experimental result shows that the quality of traffic while using video conferencing in mobile learning is dependant on the number of users, accessing time & duration. It also shows that the technologies used are performing at desired level.

This study analyzes the performance of a combined Shewhart-EWMA control chart based on double median ranked set sampling (DMRSS), for efficient monitoring of changes in process mean level. The numerical performances of the proposed scheme were evaluated in terms of the average run length (ARL), standard deviation of run length, the average ratio ARL and average extra quadratic loss. Results show that the use of a well-structured sampling method on the combined scheme has greatly enhanced the performance of the scheme in detecting all kinds of shifts in a process. We present a comparison of the proposed scheme with some location charts for monitoring changes in a process. Practical application of the proposed scheme is also demonstrated using real industrial data.

This paper presents a supervised classification task on gait biometric of siblings’ data sets. This task, which we refer to as matched-pair classification, evaluates the within pair differences interms of the data set via jackknifing. A misclassification rate (MCR), which measures the percentage of misclassification of one sib compared to the other, gives an estimate on the potential uniqueness of gait for a person, particularly in twins. By this approach, the MCR values are mostly in the range of 90% for a data set of twins and in the range of 80% for a data set of non-twin siblings. When compared to the standard Leave-One-Out (LOO) classification, the MCR values of the proposed approach are higherthan the LOO classification, which may suggest its potential use in machine learning with regard to biometric-based systems.