Network slicing enables a single physical network be sliced into multiple virtual networks. Each is customized and optimized according to specific 5G services and business goals. It can accommodate QoS heterogeneity of different 5G vertical services with isolation. In this research, we realize 5G mobile core network slicing based on the NFV (Network Functions Virtualisation) MANO (MANagement and Orchestration) architecture. Our prototype leverages open sources from both OpenStack Tacker and NCTU free5GC to creates 5G core network slicing based on its Network Service Descriptor (NSD). We evaluate and compare the performance between the multiple network slicing system and the one-slice-fits-all single slicing system using three eMBB (enhanced Mobile BroadBand) services of different QoS levels. Our test results show that the former can achieve better throughput and response time than the latter with the tradeoff of increasing its CPU consumption.

The proposed system aims at providing a novel framework of speaker authentication using lip-motion data. The system is divided into three steps: feature extraction and modeling, model synthesis, and probabilistic model matching. First, the visual features are obtained by locating a region-of-interest and extracting geometric and textural features from the region. The extracted feature vectors are then classified by K-means clustering to obtain a reduced number of observation vectors. These observation vectors are fed into a set of Hidden Markov Models (HMMs) classifiers to capture the temporal characteristics of the features. The main contribution of this paper lies in the introduction of using random passwords in performing speaker verification. Our results have demonstrated that random passwords provide useful information for speaker verification. Also, by using the proposed method, we observe a significant improvement in verification rate.

Deformable model have been widely used to approximate objects from collected data points, but most algorithms based on the deformable model can only handle geometrically and topologically simple objects. They are inadequate for objects with deep cavities or multi-part objects. Furthermore, they always assume there is only one underlying object for the collected data, which means the segmentation has been done ahead of time. Unlike most deformable algorithms which approximate one object at a time, our proposed approach can apply simultaneously more than one curve to approximate multiple objects. Using (1) the residual data points, (2) the bad parts of the fitting curve, and (3) appropriate Boolean operations, our approach is able to detect patterns with holes or cavities, and can perform segmentation by itself for more than one underlying object. We currently present experiments mainly on 2D data. These 2D algorithms can be extended to 3D without theoretical difficulties. An experiment on 3D data, composed of two genus I toruses, is also presented. Also, a new method for defining the external energy is presented, which helps capture the shape more accurately with low time and reasonable space complexities, and a method to prevent self-intersection of the curve during evolution is also introduced.>

Presents an implementation of deformable models for approximation of 3-D surfaces. It is an extension of work on B-snakes (Menet, Saint-Marc, and Gerard (1990), which approximate curves using B-splines. The user provides an initial simple surface, such as a cylinder or a plane, which is subject to internal forces and external forces which attracts it toward features. The problem is cast in terms of energy minimization, and is solved iteratively. The authors choice of basis functions leads to reasonable complexity and good numerical stability. The authors give results on real range images to illustrate the applicability of their approach. The advantages of this approach are that it provides a compact representation of the approximated data, it gives a C/sub 1/ continuous analytical description of the data, which allows computation of differential properties, and lends itself to applications such as non-rigid motion tacking and object recognition.>