A Gray-Level Dependent Pre-Emphasis Column Driver With Fast Settling for Active-Matrix LCD Applications

Son, Young‐Suk; Son, Hong-Hee; Kim, Yong-Suk; Oh, Hyeong-Seog; Cho, Gyu‐Hyeong

doi:10.1109/tcsii.2007.905362

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Generally, this relationship is represented in structure by a repetitive or slowly changing arrangement relationship. Texture features are obtained through the gray level co-occurrence matrix (GLCM) [ 17 ], the gray level run length matrix (GLRLM) [ 18 ], the gray level size zone matrix (GLSZM) [ 19 ], the neighboring gray tone difference matrix (NGTDM) [ 20 ] and the gray level dependence matrix (GLDM) [ 21 ] five ways to calculate these. In the feature extraction process, the image types used are all unfiltered original images, the bin width is set to 25, the label is set to 1, the interpolator is set to ‘sitkBSpline’, and the resampled pixel spacing and weighting norm are both set to ‘None.’…”

Section: Methodsmentioning

confidence: 99%

Ischemic stroke subtyping method combining convolutional neural network and radiomics

Chen

Jiang

et al. 2023

XST

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BACKGROUND: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics. METHODS: Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model. RESULTS: The experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People’s Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively. CONCLUSION: The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients.

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Section: Methodsmentioning

confidence: 99%

Ischemic stroke subtyping method combining convolutional neural network and radiomics

Chen

Jiang

et al. 2023

XST

View full text Add to dashboard Cite

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14‐2: Data‐Line Driver with Gray‐Level‐Dependent Far‐End Auxiliary Driving for Large AMOLED Display Panel

Lam

Jiao

Zhang

et al. 2021

Symp Digest of Tech Papers

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A gray‐level dependent far‐end auxiliary driving method is proposed for driving a data line of a large AMOLED display panel. The proposed method adopts the both‐end simultaneous driving to reduce the settling time of a data line. The duration of the far‐end auxiliary driving depends on the gray level difference of current and previous display data. The extra high voltage design required for the implementation of the proposed method is only a PMOS transistor, an NMOS transistor, and two level‐shifters. The simulation results show that the proposed driving method can reduce the settling time of the data line by 20% ~ 59% when varying the parasitic load of the data line by ± 20%.

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11.7 A load-aware pre-emphasis column driver with 27% settling-time reduction in ±18% panel-load RC delay variation for 240Hz UHD flat-panel displays

Bang

Kim

Yoon

et al. 2016

2016 IEEE International Solid-State Circuits Conference (ISSCC)

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A Gray-Level Dependent Pre-Emphasis Column Driver With Fast Settling for Active-Matrix LCD Applications

Cited by 5 publications

References 17 publications

Ischemic stroke subtyping method combining convolutional neural network and radiomics

Ischemic stroke subtyping method combining convolutional neural network and radiomics

14‐2: Data‐Line Driver with Gray‐Level‐Dependent Far‐End Auxiliary Driving for Large AMOLED Display Panel

11.7 A load-aware pre-emphasis column driver with 27% settling-time reduction in ±18% panel-load RC delay variation for 240Hz UHD flat-panel displays

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