This paper proposes an approach using dynamic time wrapping (DTW) to improve the classification performance of the SVM separation hyperplane. The presented method by incorporating the distance information derived from DTW template matching calculations into SVM separation hyperplane training will be able to effectively control the balance between mar-gin maximization and the amount of misclassifications, and therefore the recognition accuracy of the SVM classifier on speaker recognition will further be increased. Experimental results demonstrated the effectiveness and efficiency of the developed approach.

In this research, a contact lens design for myopia and astigmatism eyes was proposed. With two spherical surfaces assembled in the optical system, the design could take advantage of compacting overall volume size. But in this design, the value of spherical aberration (SA) at wide radius of contact lenses seems lower. If we corrected the design to improve the value of SA, Modulation Transfer Function (MTF) and coma aberration (TCO) value would become lower relatively. In this study, we integrated the Taguchi method and principal component analysis (PCA) to optimize the multiple quality characteristics (SA, TCO and MTF) of the contact lenses. With the combination of the methods, a set of optimum design parameters was well selected to balance the values of SA, TCO and MTF that improve the SA 25.63%, TCO 91.88% and MTF 2.4%. It was concluded that the integration of the Taguchi method and PCA succeeded in optimizing the SA, TCO and MTF values, and the contact lenses could be well designed without sacrificing system performance.

In this paper, a Kinect-based gesture recognition scheme for the application of operating a vending machine is presented where the popular smart phone device with the android platform is used as a simulator of the vending machine. Compared to the conventional human-computer interface (HCI) of vending machines by pushbutton operations, the developed gesture recognition scheme that employs the gesture action made by a personal operator as the operational command is a new type of HCI to control the vending machine. Presented Kinect-based gesture recognition for vending machine control sufficiently takes use of the released Kinect software development kit (Kinect SDK) to develop the system where the human skeleton information to represent the corresponding operator's gesture action is extracted and then recognized. For further increasing the recognition accuracy of gesture command recognition, a time interval between the current recognition decision and the next recognition decision is investigated and analyzed. A proper value of decision time-interval will then be provided to the gesture recognition system for avoiding the imperfect recognition accuracy that results from an arbitrary and improper setting of the decision time-interval. Experimental results showed that presented Kinect-based gesture command recognition for vending machine operations is feasible and also competitive on recognition performances. On the use of the released Kinect SDK for establishing the gesture recognition system, gesture recognition with an appropriately-designed time interval of decision making will perform apparently much better than that without any investigation information available about decision time-interval settings.