本論文以蘭姆波和壓電換能器實現無參考訊號之超聲波觸控面板技術；此技術使用玻璃作為觸控面板材料，於玻璃上下表面放置壓電換能器 (Piezoelectric Transducer) 形成一對壓電傳感器，做為聲波訊號的發送端與接收端。首先藉由同時輸出正負電壓於發送端的二個傳感器，可以激振出A-mode或S-mode的蘭姆波 (Lamb Wave) 於薄板中；透過接收端之一對傳感器所接收到之蘭姆波訊號的疊加或相減，可以實現單一模態蘭姆波的激振和接收。比較手指未觸控與觸控面板對蘭姆波能量的衰減變化，即可即時獲知手指是否觸碰玻璃板以及觸碰的正確位置。此一系統無需事先存取一組基礎訊號進行比對，因此可以達到無參考 (Baseline Free) 訊號的工作模式，並結合人工智慧使用深度學習 (Deep Learning) 演算法的卷積神經網路 (Convolution Neural Networks, CNN) 進行資料分析，根據大量的衰減特徵波形做為訓練數據，產生一種最佳的演算法模型，實現即時觸控辨識。
針對本研究所完成之以蘭姆波和壓電換能器結合卷積神經網路應用於玻璃面板無參考訊號之觸控定位系統，以真實的人體手指進行實測後，能在感測面積150×150 mm2、觸控解析度為1×1 cm2的玻璃面板上達到95.5%觸碰準確度，反應時間約為50至55 ms，即具有20 Hz的更新速率，驗證此設計與架構的可行性與實用價值。

This study implemented a Lamb wave-based ultrasonic touch panel technology in a baseline-free manner. A glass plate was chosen as the touch panel, and piezoelectric transducers were utilized as the signal transmitters and receivers. Pairs of piezoelectric transducers are mounted on both sides of the glass plate so that pure anti-symmetrical mode or symmetrical mode Lamb waves can be excited and detected in the glass plate by applying positive and negative voltages to the paired piezoelectric transducers. By directly comparing the attenuation of Lamb wave energy caused by finger touch on the touch panel without accessing a set of reference signals for comparison, a reference-free signal was achieved. A convolutional neural network (CNN) was employed as the deep learning algorithm, using a large number of attenuation feature waveforms for data and training to generate an optimal algorithm model for real-time touch recognition.
This thesis demonstrated the feasibility and practical value of employing Lamb wave-based ultrasonic touch panel technology in a wide range of applications. Real finger testing on a sensing area of 150×150 mm² achieved a touch accuracy of 95.5% with a touch resolution of 1×1 cm². The response time was approximately 50-55 ms, and the update rate was 20 Hz. These results highlight the potential of this design and architecture and validate the effectiveness of the deep learning algorithm.

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