{"title":"信息显示学会期刊","authors":"Abhishek Kumar Srivastava","doi":"10.1002/msid.1476","DOIUrl":null,"url":null,"abstract":"<p>This year is off to an impressive start with the release of volume 32 of the <i>Journal of the Society for Information Displays</i> (<i>JSID</i>). The first two issues are published already, and in the coming issues, we will feature the best of the International Display Workshops (IDW) 2023 and International Conference on Display Technology (ICDT) 2024. Issue 5 of volume 32 will include the best articles from Display Week 2024, with more than 30 nominations for best papers. We are working on two special issues that focus on augmented, virtual, and mixed reality (AR/VR/MR) and quantum dots (QDs) regarding their applications in displays. The issue on AR/VR/MR will be featured in the July issue, and the QDs issue will appear in the third quarter.</p><p>To read the latest exciting display-related research, visit the <i>JSID</i> website: https://sid.onlinelibrary.wiley.com/journal/19383657.</p><p><b>Highly reliable a-Si:H gate driver on array with complementary double-sided noise-eliminating and dual voltage levels for TFT-LCD applications</b> | Guang-Ting Zheng <i>et al</i>. | https://doi.org/10.1002/jsid.1263</p><p><b>Dual-view integral imaging display with adjustable optimal viewing distance</b> | Bai-Chuan Zhao <i>et al</i>. | https://doi.org/10.1002/jsid.1267</p><p><b>Fast neural network for TV super resolution scaling-up system</b> | Shih-Chang Hsia <i>et al</i>. | https://doi.org/10.1002/jsid.1266</p><p>The authors propose a modified architecture to reduce the computational demands of the generative adversarial network for super-resolution image generation. They incorporated depth-wise and point-wise convolution into the convolution layer.</p><p>This reduced computational complexity and improved network structure. They used a dataset of 900 image pairs with resolutions of 480 × 270 and 1,920 × 1,080 for training and validation. They successfully reduced computational operators by 63 percent compared to the original network while maintaining the quality of super-resolution images. The architecture with a light model was subsequently deployed on a GPU processor to enable real-time implementation. The network effectively produced output with 16× greater resolution without introducing any blurring or obvious artifacts.</p><p><b>Special Issue:</b></p>","PeriodicalId":52450,"journal":{"name":"Information Display","volume":"40 2","pages":"54-55"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msid.1476","citationCount":"0","resultStr":"{\"title\":\"Journal of the Society for Information Display\",\"authors\":\"Abhishek Kumar Srivastava\",\"doi\":\"10.1002/msid.1476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This year is off to an impressive start with the release of volume 32 of the <i>Journal of the Society for Information Displays</i> (<i>JSID</i>). The first two issues are published already, and in the coming issues, we will feature the best of the International Display Workshops (IDW) 2023 and International Conference on Display Technology (ICDT) 2024. Issue 5 of volume 32 will include the best articles from Display Week 2024, with more than 30 nominations for best papers. We are working on two special issues that focus on augmented, virtual, and mixed reality (AR/VR/MR) and quantum dots (QDs) regarding their applications in displays. The issue on AR/VR/MR will be featured in the July issue, and the QDs issue will appear in the third quarter.</p><p>To read the latest exciting display-related research, visit the <i>JSID</i> website: https://sid.onlinelibrary.wiley.com/journal/19383657.</p><p><b>Highly reliable a-Si:H gate driver on array with complementary double-sided noise-eliminating and dual voltage levels for TFT-LCD applications</b> | Guang-Ting Zheng <i>et al</i>. | https://doi.org/10.1002/jsid.1263</p><p><b>Dual-view integral imaging display with adjustable optimal viewing distance</b> | Bai-Chuan Zhao <i>et al</i>. | https://doi.org/10.1002/jsid.1267</p><p><b>Fast neural network for TV super resolution scaling-up system</b> | Shih-Chang Hsia <i>et al</i>. | https://doi.org/10.1002/jsid.1266</p><p>The authors propose a modified architecture to reduce the computational demands of the generative adversarial network for super-resolution image generation. 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This year is off to an impressive start with the release of volume 32 of the Journal of the Society for Information Displays (JSID). The first two issues are published already, and in the coming issues, we will feature the best of the International Display Workshops (IDW) 2023 and International Conference on Display Technology (ICDT) 2024. Issue 5 of volume 32 will include the best articles from Display Week 2024, with more than 30 nominations for best papers. We are working on two special issues that focus on augmented, virtual, and mixed reality (AR/VR/MR) and quantum dots (QDs) regarding their applications in displays. The issue on AR/VR/MR will be featured in the July issue, and the QDs issue will appear in the third quarter.
To read the latest exciting display-related research, visit the JSID website: https://sid.onlinelibrary.wiley.com/journal/19383657.
Highly reliable a-Si:H gate driver on array with complementary double-sided noise-eliminating and dual voltage levels for TFT-LCD applications | Guang-Ting Zheng et al. | https://doi.org/10.1002/jsid.1263
Dual-view integral imaging display with adjustable optimal viewing distance | Bai-Chuan Zhao et al. | https://doi.org/10.1002/jsid.1267
Fast neural network for TV super resolution scaling-up system | Shih-Chang Hsia et al. | https://doi.org/10.1002/jsid.1266
The authors propose a modified architecture to reduce the computational demands of the generative adversarial network for super-resolution image generation. They incorporated depth-wise and point-wise convolution into the convolution layer.
This reduced computational complexity and improved network structure. They used a dataset of 900 image pairs with resolutions of 480 × 270 and 1,920 × 1,080 for training and validation. They successfully reduced computational operators by 63 percent compared to the original network while maintaining the quality of super-resolution images. The architecture with a light model was subsequently deployed on a GPU processor to enable real-time implementation. The network effectively produced output with 16× greater resolution without introducing any blurring or obvious artifacts.
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