In contrast to traditional OLED displays found in mobile phones and TVs, OLED microdisplays present distinct challenges. Despite their reduced panel size, achieving high resolution is crucial, resulting in pixel sizes of only a few micrometers and gaps between pixels less than 1 micrometer. In this presentation, we will delve into OLED device technology, focusing on achieving high luminance and a wide color gamut, while considering the unique characteristics of microdisplays. A noteworthy demonstration involved a 3000 pixels-per-inch (ppi) OLED microdisplay with a color gamut of 130.2% based on the sRGB standard. This achievement is attributed to a thoughtful combination of light-emitting materials, OLED device structure, and subpixel arrangement.
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2022-0-00026, Near-eye light field device technology development for hyper-realistic metaverse service)
In this work, we demonstrate fully uniform blue fluorescence graphene anode OLEDs, which have an emission area of
10×7 mm2. Catalytically grown multilayered graphene films have been used as the anode material. In order to
compensate the current drop, which is due to the graphene’s electrical resistance, we have furnished metal bus lines on
the support. Processing and optical issues involved in graphene anode OLED fabrications are presented. The fabricated
OLEDs with graphene anode showed comparable performances to that of ITO anode OLEDs. Our works shows that
metal bus furnished graphene anode can be extended into large area OLED lighting applications in which flexibility and
transparency is required.
KEYWORDS: Mammography, Tissues, Breast cancer, Mathematical morphology, Breast, Halftones, Computer aided diagnosis and therapy, CAD systems, Image processing, Chemical elements
Detecting early symptoms of breast cancer is very important to enhance the possibility of cure. There have been active researches to develop computer-aided diagnosis(CAD) systems detecting early symptoms of breast cancer in digital mammograms. An expert or a CAD system can recognize the early symptoms based on microcalcifications appeared in digital mammographic images. Microcalcifications have higher gray value than surrounding regions, so these can be detected by expanding a region from a local maximum. However the resultant image contains unnecessary elements such as noise, holes and valleys. Mathematical morphology is a good solution to delete regions that are affected by the unnecessary elements. In this paper, we present a method that effectively detects microcalcifications in digital mammograms using a combination of local maximum operation and the region-growing operation.
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