Data identified Time Extension Driving Method

机译：数据识别时间延长驱动方法

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A new liquid crystal display (LCD) Data identifiedrnTime Extension (DiTEX) driving scheme with a highrncharged voltage is proposed. The different chargedrnvoltage owing to the differential charging time andrnvarious initial pixel-potential can be eliminated orrndiminished under this method. It is compatible with arn2-row inversion and can be realized into therncommercial dual-sided gate circuits.

机译：提出了一种新的具有高充电电压的液晶显示（LCD）数据识别时间延长（DiTEX）驱动方案。用这种方法可以消除由于充电时间不同而引起的不同的充电电压和各种初始像素电位。它与arn2行反转兼容，可实现为商用双面门电路。

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《》|2006年|1247-1250|共4页
会议地点 Daegu Exhibition Convention Center (EXCO)(KR);Daegu Exhibition Convention Center (EXCO)(KR);Daegu Exhibition Convention Center (EXCO)(KR);Daegu Exhibition Convention Center (EXCO)(KR)
作者
L.Lin; B.J.Liang; C.M.Huang; S.P.Chiang;
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The Graduate Institute of Electrical and Communications engineering Department of Electrical engineering,Feng Chia University,Seatwen,Taichung,Taiwan 40724,R.O.C.Tel: 886-4-25318899 Ext.7736 E-mail: lin-lin@wintek.com.tw;

The Graduate Institute of Electrical and Communications engineering Department of Electrical engineering,Feng Chia University,Seatwen,Taichung,Taiwan 40724,R.O.C.;

Wintek Corporation,No. 10,Chien-Kuo Road,TEPZ Tantzu,Taichung,427,Taiwan,R.O.C.;

Wintek Corporation,No. 10,Chien-Kuo Road,TEPZ Tantzu,Taichung,427,Taiwan,R.O.C.;

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机译：使用自然驾驶数据和时间序列预测来确定Covid-19大流行对驾驶行为的影响
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4. Data identified Time Extension Driving Method [C] . International Display Manufacturing Conference . 2006

机译：数据识别的时间扩展驱动方法
5. Methods to Explore Driving Behavior Heterogeneity Using SHRP2 Naturalistic Driving Study Trajectory-Level Driving Data [D] . Hammit, Britton Elaine 2018

机译：使用SHRP2自然驾驶研究轨迹级驾驶数据探索驾驶行为异质性的方法
6. Identifying the Best Times for Cognitive Functioning Using New Methods: Matching University Times to Undergraduate Chronotypes [O] . M. D. R. Evans, Paul Kelley, Jonathan Kelley 2017

机译：使用新方法识别认知功能的最佳时间：将大学时间与本科时标相匹配
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

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Data identified Time Extension Driving Method

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