Wireless power transmission (WPT) is a hot topic today. However, researches on magnetic coupling resonant WPT system are mostly conducted on the system with equal coil size of the transmitter and receiver. Practically, coil size different condition will certainly exist due to the space constraint on the target receiving device. Researches being done on different coil size system were often reported with degraded performance. This work uses an impedance based quality factor tuning technique to design the WPT system to work with the system with coil size disparity. A prototype has proven to transfer the power wirelessly over a distance up 15cm with a near constant efficiency of 73%. The introduced method shows improved efficiency and it is comparable to those equal coil size systems.

In the recent years, DNA microarray has been widely used to investigate genes that cause genetic diseases. Since information from DNA microarray could reveal some interesting relationships between genes and diseases, it has been employed by a number of researchers to classify Acute Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). As microarray gene expression involves high dimensional features, feature reduction or feature selection is required to ensure efficient classification of ALL and AML. This paper proposes a multi-population particle swarm optimization (MPSO) feature selection approach to identify the most significant subsets of genes for classification of ALL and AML. In this research, MPSO is used to increase the search diversity of conventional particle swarm optimization (PSO). It is combined with the Support Vector Machine (SVM) classifier to form a wrapper feature selection model that can capture the interactions between the classifier and the features. The proposed model is evaluated using 10-fold cross validation. Results showed that MPSO gives a more consistent classification performance than the conventional PSO in ALL and AML classification.

The Moving Least Square (MLS) Method is an approach which is used in meshfree solutions and data approximation. In the formulation of MLS, the exponential weight function is applied to influence the formation of continuous approximated functions. The governing factor in the weight function is the shape parameter, yet the effect of shape parameter to the data approximation is unclear. Therefore the objective of the paper is to investigate the effect of shape parameter to the equation formed via MLS. The three examples are studied and the results show that there is no direct relationship between shape function and prediction accuracy.