Dataset Information

Transparent organic upconversion devices displaying high-resolution, single-pixel, low-power infrared images perceived by human vision.

ABSTRACT: Crystalline photodiodes remain the most viable infrared sensing technology of choice, yet the opacity and the limitation in pixel size reduction per se restrict their development for supporting high-resolution in situ infrared images. In this work, we propose an all-organic non-fullerene-based upconversion device that brings invisible infrared signal into human vision via exciplex cohost light-emissive system. The device reaches an infrared-to-visible upconversion efficiency of 12.56% by resolving the 940-nm infrared signal (power density of 103.8 μW cm^-2). We tailor a semitransparent (AVT, ~60%), large-area (10.35 cm²), lightweight (22.91 g), single-pixel upconversion panel to visualize the infrared power density down to 0.75 μW cm², inferring a bias-switching linear dynamic range approaching 80 dB. We also demonstrate the possibility of visualizing low-intensity infrared signals from the Face ID and LiDAR, which should fill the gap in the existing technology based on pixelated complementary metal-oxide semiconductors with optical lenses.

SUBMITTER: Shih CJ

PROVIDER: S-EPMC10132748 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Transparent organic upconversion devices displaying high-resolution, single-pixel, low-power infrared images perceived by human vision.

Shih Chun-Jen CJ Huang Yu-Ching YC Wang Tai-Yung TY Yu Chang-Wei CW Hsu I-Sheng IS Akbar Abdul Khalik AK Lin Jai-Yi JY Biring Sajal S Lee Jiun-Haw JH Liu Shun-Wei SW

Science advances 20230426 17

Crystalline photodiodes remain the most viable infrared sensing technology of choice, yet the opacity and the limitation in pixel size reduction per se restrict their development for supporting high-resolution in situ infrared images. In this work, we propose an all-organic non-fullerene-based upconversion device that brings invisible infrared signal into human vision via exciplex cohost light-emissive system. The device reaches an infrared-to-visible upconversion efficiency of 12.56% by resolvi ...[more]

PMID: 37126555

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Project description:BackgroundNucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed.ResultsWe analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance.ConclusionsWe conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

Dataset Information

Transparent organic upconversion devices displaying high-resolution, single-pixel, low-power infrared images perceived by human vision.

Publications

Transparent organic upconversion devices displaying high-resolution, single-pixel, low-power infrared images perceived by human vision.

Similar Datasets

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