Includes practical reference designs! A wide variety of lineups are available.
This catalog introduces the "IP cores" handled by Euresys Japan Co., Ltd. It includes various products such as the compact and customizable "GigE Vision IP core," the "USB3 Vision IP core" with practical reference designs, and the "CoaXPress IP core." Please use this for selecting products. 【Featured Products (partial)】 ■ CoaXPress-over-Fiber Bridge IP core ■ GigE Vision IP core ■ USB3 Vision IP core ■ CoaXPress IP core ■ IMX Pregius IP core, etc. *For more details, please refer to the PDF document or feel free to contact us.
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basic information
【Featured Product Characteristics】 <IMX Pregius IP Core> ■ Sub-LVDS readout and decoding block ■ SPI-based sensor operation configuration module ■ Sensor operation configuration software library ■ Supports free-running or trigger readout modes *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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Company information
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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