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研究生:黃士倫
研究生(外文):Shih-Lun Huang
論文名稱:觸控積體電路之多點觸碰追蹤演算法及雜訊抑制演算法
論文名稱(外文):Multi-Touch Tracking Algorithms and Noise Reduction Algorithms for Touch Controller ICs
指導教授:陳中平陳中平引用關係
指導教授(外文):Chung-Ping Chen
口試委員:吳明賢賴飛羆張瑞峰江介宏
口試委員(外文):Ming-Shiang WuFei-Pi LaiRuey-Feng ChangJie-Hong Jiang
口試日期:2019-06-27
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:電子工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:102
中文關鍵詞:觸碰多點觸碰追蹤演算法權重匹配叢聚雜訊平行驅動跳頻重複積分
DOI:10.6342/NTU201901146
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由於電容式感測器可以讓使用者介面變得更直覺和方便且更具互動性,所以已廣泛地應用在許多消費性電子產品上。這些感測器已經被用來取代機械式的按鈕和切換器,此外,他們還提供了多點觸碰的功能,這增加了許多新的操作方式,所以過去十年許多的智慧型手機和平板電腦因此誕生。在我們的論文中,主要是去解決觸控IC最困難的問題,像是多觸碰點的追蹤問題和充電器雜訊的問題。
多觸碰點的演算法必須給予精確的點位置和追蹤軌跡。當觸碰點增加的時候,正確的追蹤軌跡就變得更難處理。我們是第一個考慮到大於10個觸碰點的追蹤問題。這個問題可以轉化成權重最小的匹配問題,進而我們提出前置處理的方法去避免錯誤的追蹤並加速。微控制器廣泛地應用在消費性電子產品,因為他們體積小且便宜和低功耗。任何很耗時的演算法和佔用大量儲存器的程式碼都不適合微控制器。我們發現傳統的觸控演算法是不適合大型觸碰面板去使用的。雖然更高階的微控制器也許可以改善,但是會增加製造成本和功耗。多觸碰點追蹤是最耗時的一個部分,所以為了加速,我們採用了叢聚和計算幾何的技巧。除此之外,我們還證明了我們的方法在一般的情形下可以達到很快的速度而且不失去準確性。實驗結果顯示針對20個觸碰點的時候,我們可以減少70%的時間,另外我們的演算法用低階的微控器可以處理到80點觸控點。
充電器雜訊會讓觸碰點的位置不精準以及假的觸碰點出現,這會使得裝置運作不正確。充電器雜訊的強度比原本的觸碰點的訊號大很多,而且雜訊的頻率會隨著不同的充電器改變,因此在手機市場上這個問題非常的重要。我們證明了重複積分和跳頻的組合是很有效解決這問題的方法。除此之外,我們提出了離散傅立葉轉換為基礎的演算法去找到一個有效的感測頻率。我們還提出平行驅動結合隨機延遲的方法去增加訊雜比。我們也提出了軟硬體共同協作的概念將我們的演算法整合到觸控IC裡。實驗結果顯示我們的方法可以提高訊雜比45 db以上而且可以動態且很快地找到有效的感測頻率。
Capacitive sensors are extensively applied in many consumer electronics because they make user interfaces become more intuitive, more convenient, and more interactive. These types of sensors could be used as electrical buttons and switches to replace the traditional mechanical ones. Besides, they also provide the multi-touch functionality, which enables rich ways to control devices, so a large number of smartphones and tablets have appeared in the past ten years. In this dissertation, we focus on the most critical problems of touch controller ICs, such as the multi-touch tracking problem and the charger noise problem.
Multi-touch algorithms must give the accurate point positions and correct tracking identifications. As the number of touch points increases, the correct tracking identifications become more important and more difficult. To the best of our knowledge, we are the first to consider the tracking algorithm for multi-touch with more than 10 touch points. The multi-touch tracking problem is formulated as a minimum weighted bipartite matching problem. Furthermore, we will propose a pre-processing method to prevent the wrong tracking and to have a significant speed-up. Microcontrollers (MCUs) are extensively used in consumer devices for specific purposes because they are tiny, cheap, and low-power. Any time-consuming algorithm and any large-size program are not suited for MCUs. Recently, we found that the conventional multi-touch algorithm becomes computationally expensive to handle the applications of large-sized touch panels. Although a more high-end MCU can obtain an improvement on speed, it would increase manufacturing cost and operating power consumption as well. In the whole multi-touch algorithm flow, point tracking is the most computationally expensive part. To accelerate tracking, we employ techniques, such as clustering, to speed up our multi-touch algorithm. Besides, we prove that the tracking problem would be solved in O(n) time for practical cases and without losing its accuracy after clustering. Furthermore, we apply computational geometry techniques to develop an efficient clustering method. Experimental results show that clustering is efficient and effective. For the necessary requirement of large-area touch panels having 20 touch points, we can reduce the runtime by up to 70%. Besides, our multi-touch algorithm may support up to 80 touch points accompanied by a low-cost MCU.
Charger noise can cause inaccurate touch points and fake touch points to appear; this can cause a device to behave incorrectly. The intensity of charger noise could be much larger than the intensities of the original touch signals. Furthermore, the frequency of charger noise varies for each different charger. Therefore, industry experts have identified charger noise as the most difficult problem in capacitive touch applications. The demand for a solution to this problem has become crucial for the mobile market. We prove that a particular combination of frequency hopping and repeated integration is an effective method to handle the problem. In addition, we propose an efficient discrete Fourier transform (DFT)-based algorithm to select an effective sensing frequency. We propose parallel driving with random delay to enhance signal-to-noise ratio (SNR). We show an efficient hardware-software co-design that facilitates the application of our methods in touch controller ICs. Experimental results show that our methods can increase SNR by over 45 dB and can find an effective sensing frequency fast and dynamically.
Acknowledgements i
Abstract (Chinese) ii
Abstract iv
List of Tables x
List of Figures xi
Chapter 1. Introduction 1
1.1 Capacitive Touch Sensing Mechanisms 1
1.2 Architecture of a Capacitive Touch Panel System . . . . . . . . . . . . . 6
1.3 Multi-Touch Algorithm Flow . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Driving Methods in the Mutual-Capacitance Sensing . . . . . . . . . . . . 9
1.5 Our Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Chapter 2. Multi-Touch Tracking Algorithm Based on the Hungarian Method 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Chapter 3. Clustering-Based Multi-Touch Tracking Algorithm 25
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.1 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1.2 Our Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 Architecture of a Large-Sized Touch Panel System . . . . . . . . . 29
3.2.2 Point Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.3 Observation on Independent Groups after Pre-Processing . . . . . 31
3.2.4 Clustering for Point Tracking . . . . . . . . . . . . . . . . . . . . . 31
3.3 Multi-Touch Algorithm Framework . . . . . . . . . . . . . . . . . . . . . 32
3.3.1 Multi-Touch Algorithm Flow . . . . . . . . . . . . . . . . . . . . . 33
3.3.2 The Effect of Clustering on Tracking . . . . . . . . . . . . . . . . . 34
3.3.3 The Selection-Based Clustering Algorithm . . . . . . . . . . . . . . 35
3.3.4 The Geometric Clustering Algorithm . . . . . . . . . . . . . . . . . 38
3.4 Experimental Results and Discussion . . . . . . . . . . . . . . . . . . . . 41
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Chapter 4. Frequency Hopping and Repeated Integration for Noise Reduction 46
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2 Modeling of Charger Noise . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 Theorems and an Effective and Efficient Algorithm . . . . . . . . . . . . 53
4.3.1 Theorems for the Charger Noise Reduction Problem . . . . . . . . 53
4.3.2 DFT for Finding Where the Noises Are . . . . . . . . . . . . . . . 57
4.3.3 The Whole Algorithm Flow for Noise Reduction . . . . . . . . . . 58
4.3.4 Hardware-Software Co-Design . . . . . . . . . . . . . . . . . . . . . 60
4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.5.1 The Improvements of SNRs . . . . . . . . . . . . . . . . . . . . . . 68
4.5.2 The Effectiveness of the Repeating Time . . . . . . . . . . . . . . . 68
4.5.3 The Appropriate Execution Time . . . . . . . . . . . . . . . . . . . 69
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Chapter 5. Parallel Driving for Noise Reduction 71
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 Parallel Driving and Random Delay . . . . . . . . . . . . . . . . . . . . . 73
5.3 Integration of Parallel Driving and Repeated Integration . . . . . . . . . 81
5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
Chapter 6. Conclusions and Future Work 92
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