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.

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