We offer various frame grabbers and IP cores! They are designed in the form of add-ons for CoaXPress 2.0 specifications.
"CoaXPress-over-Fiber" is an extension of the existing CoaXPress specification that supports transmission using optical fiber. It is designed as an add-on to the CoaXPress 2.0 specification. It provides a means to run the CoaXPress protocol unchanged over standard Ethernet connections that include optical fiber. 【Product Lineup】 ■ 4-connection CoaXPress-over-Fiber frame grabber "Coaxlink QSFP+" ■ CoaXPress-over-Fiber IP core "CoaXPress-over-Fiber IP Core" *For more details, please refer to the PDF document or feel free to contact us.
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【Product Features】 ＜Coaxlink QSFP+＞ ■ QSFP+ port compatible with 40 Gbps optical modules ■ Camera bandwidth of 5,000 MB/s ■ PCIe 3.0 (Gen3) x8 bus: bus bandwidth of 6,700 MB/s ■ 20 versatile digital I/O lines ■ A wide variety of camera control functions *For more details, please refer to the PDF document or feel free to contact us.
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For more details, please refer to the PDF document or feel free to contact us.
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Euresys is a leading company in the manufacturing of image, acquisition COTS boards, and image processing software. Euresys Japan Co., Ltd. serves as a base for sales and support in Japan, providing high-quality solutions and support to OEMs and system integrators around the world. As an innovative high-tech leader, Euresys designs and provides image and video acquisition components, frame grabbers, FPGA IP cores, and image processing software. Euresys operates in computer vision, machine vision, factory automation, and medical imaging. Euresys's capabilities are further enhanced by the expertise in imaging fields such as GigE Vision, USB3 Vision, CoaXPress, Camera Link, and GenICam, which has been cultivated over 30 years by the recently acquired Sensor to Image. In terms of image analysis, Euresys offers blob detection, subpixel measurement, pattern matching, color analysis, optical character recognition, barcode reading and verification, and 3D verification and classification using deep learning.
