Provides a series of IP cores and development frameworks to build an FPGA-based transmitter!
We would like to introduce the "CoaXPress Device IP Core for FPGA" that we handle. CoaXPress (CXP) is a standard communication protocol for vision applications that uses widely used coaxial cables, facilitating the connection between cameras and frame grabbers, and supports standard GenICam software. It is compatible with AMD 7 series and Intel Cyclone 10 devices. It also has provisional compatibility with Microchip PolarFire, is compact and customizable. It supports link speeds from 1 Gbps to over 50 Gbps and comes with practical reference designs. 【Overview】 ■ Compatible with AMD 7 series and Intel Cyclone 10 devices ■ Provisional compatibility with Microchip PolarFire ■ Compact and customizable ■ Supports link speeds from 1 Gbps to over 50 Gbps ■ Comes with practical reference designs *For more details, please download the PDF or feel free to contact us.
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【Usage】 ■Vision Application *For more details, please download the PDF 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.
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