| Summary: | 博士 === 國立中央大學 === 電機工程研究所 === 98 === The next generation network (NGN) is a communication infrastructure designed to address the needs of the coming age. A technical feature of the NGN is that it takes “IP convergence” network architecture, meaning that IP technology developed on the Internet is applied to the NGN. As a communication infrastructure, the NGN needs to provide carrier-grade qualities in terms of reliability, durability, and quality of service (QoS), while providing ease of new service creation. Time and frequency accuracy is critical to the efficiency of network. Thus, underling technologies of NGN needs more precise and accuracy on timing and frequency.
On the other hand, the mobile device is a terminal of NGN. Smart phone is a personal digital client, which is set with those usual functions of traditional mobile phone, PDA and computer and is mixed with features of business, entertainment, mobile and network. The more application we want to use, the more interact between human and device we need to build mobile devices in.
A time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to forecast future events based on known past events, to predict data points before they are measured.
This dissertation addresses on some signal processing of time series using the methods of computational intelligence to finding a novel , effective, stable and convenient ways in network of frequency calibration and mobile device of user interface areas.
In this work, chapter 2, a novel method is proposed to solving frequency calibration. The control signal is collected every two second for getting a time series sequences in this control system. Fuzzy and ANFIS controller using those sequences to control slave device. Chapter 3 and 4, two novel methods are proposed to solving 3D handwriting gesture recognition on a smart phone. The mobile device collects accelerations using accelerometer when a user does a gesture with holding the device. The accelerations is a set of time series sequences. To train those sets to get some models for gesture recognition.
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