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系統識別號 U0026-2001201515464600
論文名稱(中文) 壓力感測及平滑追蹤演算法實現於電容式觸控面板
論文名稱(英文) Implementation of a Pressure Sensing and Smooth Tracking Algorithms for Capacitive Touch Panels
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 103
學期 1
出版年 104
研究生(中文) 張益銘
研究生(英文) Yi-Ming Chang
學號 N28981426
學位類別 博士
語文別 英文
論文頁數 70頁
口試委員 指導教授-林志隆
口試委員-劉柏村
口試委員-甘廣宙
口試委員-林宗志
口試委員-陳建富
口試委員-朱聖緣
口試委員-鄭銘揚
口試委員-戴政祺
中文關鍵字 電容式觸控面板  壓力感測筆  筆跡重建演算法  粒子濾波器  卡爾曼濾波器  模糊邏輯系統  強跟蹤卡爾曼濾波器 
英文關鍵字 Capacitive type panel  pressure sensing stylus  stroke reconstruction algorithm  particle filter  Kalman filter  fuzzy logic system  strong tracking Kalman filter 
學科別分類
中文摘要 電容式觸控面板近年來大量引進消費性電子市場中並引起許多關注,歸因於其優異的靈敏度、耐久度以及多點觸控功能。然而,電容式觸控面板容易受雜訊影響,而造成雜訊的原因可能為觸控手指抖動、環境磁場干擾、顯示器產生的雜訊干擾和製程變異。此外,當使用者以不同的速度畫電容式觸控面板時,容易使感測晶片產生量測上的雜訊,進而導致錯誤的觸控位置和鋸齒軌跡,特別是在速度慢的時候最嚴重。雖然移動平均濾波器(MAF)經常被用來降低量測雜訊,然而其需要大量的取樣點也只能濾除高頻雜訊,並且會導致觸控軌跡延遲與振幅衰減。
本論文提出三種新穎的觸控演算法,並藉由實驗與量測結果來分析其效能。第一種演算法為卡爾曼濾波演算法(KF),其被用來降低雜訊,並與三維筆跡重建演算法結合,在不需要額外增加硬體負擔或是觸控筆電源的情況下來偵測觸控的壓力。分析該演算法應用於電容式觸控面板的實驗結果可以得知所提出觸控筆搭配三維筆跡重建演算法的有效性。第二種演算法為使用粒子濾波器(PF)的強跟蹤演算法,該演算法被用來解決卡爾曼濾波器演算法的模型誤差問題,使其能在不限制使用者於觸控面板上任意畫軌跡為快速及非線性的情況下,都可以準確的預估觸碰位置與軌跡。實驗結果顯示無論觸控軌跡為線性或非線性,相較於卡爾曼濾波器,粒子濾波器有較低的均方根誤差(RMSE)。另外,為了減少運算量與保持觸控軌跡平滑度,本文以卡爾曼濾波器演算法為基礎來提出第三種模糊自適應強跟蹤卡爾曼濾波器(FLASTKF),該演算法能緩解量測雜訊變異,並精準的預估觸控位置。本文也提出一新穎的方法來量測及量化觸控軌跡的平滑度。實驗結果皆證明無論移動速度快或慢,本文提出的模糊自適應強跟蹤卡爾曼濾波器方法皆能成功達到平滑跟蹤效果,並與移動平均濾波器方法相比,平均追蹤誤差更減少了85.4%。
英文摘要 The capacitive touch panel (CTP) has attracted a significant amount of interest and achieved considerable penetration of the consumer electronics products market in recent years owing to its sensitivity, excellent durability and multi-touch functionality. However, the CTP is easily affected by noise produced by the trembling of a finger, environmental magnetic interference, display noise, or process variation. Moreover, when a user draws with different speeds, the measurement noise caused by the sensor IC induces an error in the touched position and zigzagged trajectory, especially when the motion is slow. Although the well-known moving average filter (MAF) method is frequently used to reduce the measurement noise, it needs a large number of points in a specific interval to filter out a significantly high frequency noise, leading to trajectory delay and amplitude decay.
This dissertation proposes three novel touch algorithms and verifies their effectiveness by experiment and measurement results. The touch algorithm of Kalman filter (KF) firstly is adopted to reduce the noise effect, and is combined with the stroke reconstruction algorithm to detect touch pressure without increasing hardware costs or the need for a power source for the stylus. The results of experiments on the proposed CTP system were analyzed, demonstrating the effectiveness of the proposed stylus and its stroke reconstruction algorithm. Moreover, the robust tracking algorithm of the particle filter (PF) as second touch algorithm is utilized to overcome the problem of modeling error in the KF method, which accurately estimates the touched position and trajectory when the touch movement changes rapidly with a nonlinear trajectory. Experimental results demonstrate that regardless of linear and nonlinear scenarios, the PF offers better root mean square error (RMSE) of linear and nonlinear tracking trajectories than that of KF. Furthermore, in the third touch algorithm, to reduce the computation cost and maintain the trajectory smoothness, the algorithm based on KF of the mixed strategy is proposed by using the fuzzy logic-based adaptive strong tracking Kalman filter (FLASTKF), which effectively mitigates the effect of variation of measurement noise and supplies accurate estimation of the touched position. In particular, this work also provides a novel method to measure and quantify the "smoothness" of a touched trajectory. The experimental results indicate that the proposed FLASTKF method successfully achieves the a smooth tracking trajectory, regardless of speed, as well as decreases the mean tracking error by 85.4% over that achieved using the MAF.
論文目次 摘要 i
Abstract iii
誌謝 v
Contents vi
List of figures viii
List of tables xi
CHAPTER 1 Introduction 1
1.1. Background 1
1.2. Motivation 3
1.3. Dissertation organization 10
CHAPTER 2 Stroke reconstruction algorithm based on Kalman filter for touchscreen panel 11
2.1. Introduction 11
2.2. Proposed touchscreen panel configuration 13
2.3. Stroke reconstruction algorithm 16
2.4. Experiment results 19
2.5. Summary 23
CHAPTER 3 Touch position tracking based on particle filter for capacitive touch panels 24
3.1. Introduction 24
3.2. Particle filter for tracking on CTP system 26
3.3. Experimental results 30
3.4. Summary 35
CHAPTER 4 Position estimation and smooth tracking with a fuzzy logic-based adaptive strong tracking Kalman filter for capacitive touch panels 36
4.1. Introduction 36
4.2. Signal processing for CTP system 38
4.3. FLASTKF for smooth tracking algorithm 41
4.4. Experimental results 50
4.5. Summary 61
CHAPTER5 Conclusion and future work 62
5.1. Conclusion 62
5.2. Future work 64
Reference 65
Publication List 69
參考文獻 [1] R. N. Aguilar and G. C. M. Meijer, “Fast interface electronics for a resistive touch screen,” in Proc. IEEE Sensors, vol. 2, pp.1360–1363, 2002.
[2] Y. Park, J. Bae, E. Kim, and T. Park, “Maximizing responsiveness of touch sensing via charge multiplexing in touchscreen devices,” IEEE Trans. Consumer Electron., vol. 56, no. 3, pp. 1905–1910, Aug. 2010.
[3] S. Kim, W. Choi, W. Rim, Y. Chun, H. Shim, H. Kwon, J Kim, I. Kee, S. Kim, S. Y. Lee, and J. Park, “A highly sensitive capacitive touch sensor integrated on a thin-film-encapsulated active-matrix OLED for ultrathin displays,” IEEE Trans. Electron Devices, vol. 58, no. 10, pp. 3609–3615, Oct. 2011.
[4] R. Adler and P. J. Desmares, “An economical touch panel using SAW absorption,” IEEE Trans. Ultrasonics, Ferroelectrics, and Frequency Control, vol. 34, no 2, pp. 195–201, Mar. 1987.
[5] S. H. Bae, B. C. Yu, S. Lee, H. U. Jang, J. Choi, M. Sohn, I. Ahn, and I. Kang, “Integrating multi-touch function with a large-sized LCD,” in Proc. SID Tech. Dig., pp. 178–181, 2008.
[6] T. H. Hwang, W. H. Cui, I. S. Yang, and O. K. Kwon, “A highly area-efficient controller for capacitive touch screen panel systems,” IEEE Trans. Consumer Electron., vol. 56, no. 2, pp. 1115–1122, May 2010.
[7] I. S. Yang and O. K. Kwon, “A touch controller using differential sensing method for on-cell capacitive touch screen panel systems,” IEEE Trans. Consumer Electron., vol. 57, no. 3, pp. 1027–1032, Aug. 2011.
[8] K. Lim, K. S. Jung, C. S. Jang, J. S. Baek, and I. B. Kang, “A fast and energy efficient single-chip touch controller for tablet touch applications,” IEEE/OSA J. Display Technol., vol. 9, no. 7, pp. 520–526, Jul. 2013.
[9] H. Shin, S. Ko, H. Jang, I. Yun, and K. Lee, “A 55dB SNR with 240Hz frame scan rate mutual capacitor 30×24 touch-screen panel read-out IC using code-division multiple sensing technique,” ISSCC Dig. Tech. Papers, pp.338–339, Feb. 2013.
[10] C. L. Lin, Y. M. Chang, U C. Lin, and C. S. Li, “Kalman filter smooth tracking based on multi-touch for capacitive panel,” in Proc. SID Tech. Dig., pp. 1845–1847, 2011.
[11] C. L. Lin, C. S. Li, Y. M. Chang, T. C. Lin, J. F. Chen, and U C. Lin “Pressure sensitive stylus and algorithm for touchscreen panel,” IEEE/OSA J. Display Technol., vol. 9, no. 1, pp. 17–23, Jan. 2013.
[12] H. R. Kim, Y.K. Choi, S. H. Byun, S. W. Kim, K. H. Choi, H. Y. Ahn, J. K. Park, D. Y. Lee, Z. Y. Wu, H. D. Kwon, Y. Y. Choi, C. J. Lee, H. H. Cho, J. S. Yu, and M. Lee, “A mobile-display-driver IC embedding a capacitive-touch-screen controller system,” ISSCC Dig. Tech. Papers, pp.114–116, Feb. 2010.
[13] C. Wen and C. H. Huang, “A paperless fax machine with a single-touch panel,” IEEE Trans. Consumer Electron., vol. 54, no.4, pp. 1488–1491, Nov. 2008.
[14] S. Ko, H. Shin, J. Lee, H. Jang, B. C. So, I. Yun, and K. Lee, “Low noise capacitive sensor for multi-touch mobile handset’s applications,” in Proc. IEEE Asian Solid-State Circuits Conference, pp. 1–4, Nov. 2010.
[15] R. Wimmer and P. Baudisch, “Modular and deformable touch-sensitive surfaces based on time domain reflectometry,” in Proc. User Interface Software and Technology, pp. 517–526, Oct. 2011.
[16] Y. Nakai and N. Matsuo, “Portable device, method of detecting operation, and computer-readable storage medium storing program for detecting operation,” U.S Patent 13/126,075. Aug. 25, 2011.
[17] J. S. Wang, Y. L. Hsu, and J. N. Liu, “An inertial-measurement-unit-based pen with a trajectory reconstruction algorithm and its applications,” IEEE Trans. Industrial Electronics, vol. 57, no. 10, pp. 3508–3521, Oct. 2010.
[18] H. Li, Y. Wei, H. Li, S. Young, D. Convey, J. Lewis and P. Maniar, “Late-news paper: Multitouch pixilated force sensing touch screen,” in Proc. SID Tech. Dig, pp. 455-458, 2009.
[19] A. Doucet, S. Godsill and C. Andrieu, “On sequential Monte Carlo sampling methods for Bayesian filtering,” Statistics and Computing, vol. 10, no. 3, pp. 197-208, Oct. 2010.
[20] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Processing, vol. 50, no. 2, pp. 174-188, Feb. 2002.
[21] I. Kukenys and B. McCane, “Touch tracking with a particle filter,” Springer J. Machine Vision and Applications, vol. 24, no. 7, pp. 1501-1521, Oct. 2013.
[22] C. M. Oh, M. Z. Islam, and C. W. Lee, “MRF-based particle filters for multi-touch tracking and gesture likelihoods,” in Proc. IEEE Computer and Information Technology, pp.144–149, 2011.
[23] Y. j. Zhao and S. l. Dai, “A robust and fast monocular-vision-based hand tracking method for virtual touch screen,” in Proc. IEEE Image and Signal Processing, pp.1–5, 2009.
[24] S. Rogers, J. Williamson, C. Stewart and R. Murray-Smith, “Anglepose: robust, precise capacitive touch tracking via 3d orientation estimation,” in Proc. Annual Conference on Human Factors in Computing Systems, pp. 2575–2584, 2011.
[25] S. P. Won, W. W. Melek, Senior, and F. Golnaraghi, “A Kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system,” IEEE Trans. Industrial Electronics, vol. 57, no. 5, pp. 1787–1798, May 2010.
[26] R. K. Mehra, “On the identification of variances and adaptive Kalman filtering,” IEEE Trans. Automatic Control, vol. AC-15, no. 2, pp. 175–184, Apr. 1970.
[27] K. Nam, S. Oh, H. Fujimoto, and Y. Hori, “Estimation of sideslip and roll angles of electric vehicles using lateral tire force sensors through RLS and Kalman filter approaches,” IEEE Trans. Industrial Electronics, vol. 60, no. 3, pp. 988–1000, Mar. 2013.
[28] Z. Xu and M. F. Rahman, “Comparison of a sliding observer and a Kalman filter for direct-torque-controlled IPM synchronous motor drives,” IEEE Trans. Industrial Electronics, vol. 59, no. 11, pp. 4179–4188, Nov. 2012.
[29] X. Gao, D. You, and S. Katayama, “Seam tracking monitoring based on adaptive Kalman filter embedded Elman neural network during high-power fiber laser welding,” IEEE Trans. Industrial Electronics, vol. 59, no. 11, pp. 4315–4325, Nov. 2012.
[30] F. Jiancheng and Y. Sheng, “Study on innovation adaptive EKF for in-flight alignment of airborne POS,” IEEE Trans. Instrumentation and Measurement, vol. 60, no. 4, pp. 1378–1388, Apr. 2011.
[31] A. Chatterjee and F. Matsuno, “A neuro-fuzzy assisted extended Kalman filter-based approach for simultaneous localization and mapping (SLAM) problems,” IEEE Trans. Fuzzy Systems, vol. 15, no. 5, pp. 984–997, Oct. 2007.
[32] W. Abdel-Hamid, A. Noureldin, and N. El-Sheimy, “Adaptive fuzzy prediction of low-cost inertial-based positioning errors,” IEEE Trans. Fuzzy Systems, vol. 15, no. 3, pp. 519–529, Jun. 2007.
[33] D. J. Jwo and S. H. Wang, “Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation,” IEEE Sensor Journal, vol. 7, no. 5, pp. 778–789, May 2007.
[34] X. He, Z. Wang, X. Wang, and D. H. Zhou, “Networked strong tracking filtering with multiple packet dropouts: algorithms and applications,” IEEE Trans. Industrial Electronics, vol. 61, no. 3, pp. 1454–1463, Mar. 2014.
[35] T. Xu, Q. Ge, X. Feng, and C. Wen, “Strong tracking filter with bandwidth constraint for sensor networks,” in Proc. IEEE Control and Automation, pp. 596–601, Jun. 2010.
[36] S. E. Beid and S. Doubabi, “DSP-based implementation of fuzzy output tracking control for a boost converter,” IEEE Trans. Industrial Electronics, vol. 61, no. 1, pp. 196–209, Jan. 2014.
[37] C. H. Wang and D. Y. Huang, “A new intelligent fuzzy controller for nonlinear hysteretic electronic throttle in modern intelligent automobiles,” IEEE Trans. Industrial Electronics, vol. 60, no. 6, pp. 2332–2345, Jun. 2013.
[38] C. M. Lin and H. Y. Li, “A novel adaptive wavelet fuzzy cerebellar model articulation control system design for voice coil motors,” IEEE Trans. Industrial Electronics, vol. 59, no. 4, pp. 2024–2033, Apr. 2012.
[39] H. H. Choi and J. W. Jung, “Discrete-time fuzzy speed regulator design for PM synchronous motor,” IEEE Trans. Industrial Electronics, vol. 60, no. 2, pp. 600–607, Feb. 2013.
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