臺灣博碩士論文加值系統

兩裝置之間時脈（clock rate）的微小差異稱為時脈偏移，由於各裝置的時脈偏移不同，因此適合用於裝置識別技術。但是本研究觀察到無線網路等環境下所蒐集的時戳數據，會因為高延遲抖動而產生出低於數據主群體的離群值。若使用蒐集最小延遲值或線性規劃法的方式來測量這類數據的時脈偏移，則會因為這些離群值的干擾而得到不安定的測量結果。
因此本研究提出基於霍氏轉換法的方式，建立數據的主群體篩選機制及其參數設定，並發展出三階段的測線機制來將裝置所傳送的時戳數據篩出主要特徵，最後才使用線性規劃法進行測量。如此可避免高延遲抖動產生的離群值影響時脈偏移量測的準確度。
若透過本論文之方法與直接使用蒐集最小延遲值及線性規劃法等傳統方法量測下進行比較，在僅使用300個時戳的情況下時脈偏移值誤差間隔可減少2ppm以上，而若以數據整體與各分段時脈偏移值之差值平均於最佳情況下也可減少2ppm以上，因此本研究發展的方法能更穩定地估量裝置的特徵。

The difference between two devices’ ticking frequencies is called clock skew, which is considered useful as a physical characteristic for device identification. In this study, it is observed that timestamps collected under high delay jitter communication channels, when mapped to a two-dimensional graph, would have some outliers below the line-shaped mass of samples. This kind of data is different from classic ones, and the clock skews when measured by traditional methods, e.g. quick piecewise minimum or linear programming, tend to be unstable due to the interference from these lower outliers.
In this research, we developed a method based on Hough Transform method to precisely identify the slope of the line-shaped mass of samples. The proposed method includes a data filtering mechanism with parameter settings suggestions and a three-step line-fitting process to filter out all outliers. The remaining samples are then used to measure the clock skew by linear programming. In this way, we may prevent lower outliers from interfering the precision of measurement.
We compared our method with three classical ones on high delay jitter samples collected from 7 devices. The experimental results show that the maximal differences between clock skews derived from 300 timestamps for 8 times of all devices by our method is at least 2ppm smaller than other methods. Also the maximal differences between short-term measurements and long-term measurement by our method are 2ppm smaller. We can thus conclude that the proposed method contributes more precise and stable measurement comparing to other approaches.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第1章 緒論 1
1.1研究背景 1
1.2研究動機與目的 2
1.3論文架構 3
第2章 背景知識與相關研究 4
2.1運用時脈偏移之裝置識別技術 4
2.2估測時脈偏移 4
2.2.1定義及測量方法 4
2.2.2離群值（Outlier） 5
2.2.3線性規劃法（Linear Programming Method, LPM） 6
2.3霍氏轉換法（Hough Transform, HT） 7
第3章 研究方法 10
3.1方法描述 10
3.1.1基於霍氏轉換法的主群體篩選機制 10
3.1.2參數設定 12
3.1.3三階段測線機制 14
3.2方法流程 17
3.2.1主群體篩選機制流程 17
3.2.2三階段測線機制流程 18
第4章 實驗結果與分析 19
4.1實驗環境 19
4.2實驗設計 19
4.3實驗結果與比較 20
4.3.1 使用本研究方法之數據篩選結果分析 20
4.3.2 對照各演算法在數據分段下所得之時脈偏移值比較 28
4.3.3 對照各演算法在以數據整體與各分段時脈偏移值之差值比較 31
第5章 結論與未來研究方向 34
5.1結論 34
5.2未來研究方向 34
參考文獻 35

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