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2-port PCI Express Intelligent GigE Vision Frame Grabber

  • PCI Express® x4 compliant
  • GoE (GigE Vision Offload Engine), FPGA image reconstruction, No packet loss, No CPU workload
  • GigE Vision, GENiCAM and GENTL Compliant
  • Automatic IP configuration
  • 100 m cable length
  • PoE, IEEE 802.3af compliant
  • Direct power from PCIe slot (Total Max. 18W)
  • ToE (Trigger over Ethernet)
The PCIE-1172/1174 series is a PCI Express® dual/quad channel frame grabber for two/four independent GigE Vision cameras. They feature GoE(GigE Vision Offload Engine), PoE(Power over Ethernet) and ToE(Trigger over Ethernet) for high performance, robust and reliable machine vision applications. Unlike conventional NICs (network interface controller) GigE Vision protocol is implemented in software and executed on the host CPU, the processor must spend more resource to handle the network traffic and incoming frames rather than the machine vision algorithms, especially in high bandwidth, multiple camera applications. The GoE feature significantly off-loads the GigE Vision protocols into dedicated FPGA(Field Programmable Gate Array), reconstruct the image then transmit to the Host PC via DMA(Direct Memory Access) in real time, release the host processor resource to execute algorithm and applications and there is no frame or packet loss during the image acquisition. The comprehensive ToE/PoE features can lower the installation/maintenance effort through the single cable connection, and reduce image acquisition latency, with the Ad hoc network feature, the connection between GigE Vision cameras and frame grabber are self-configuring, the user define IP address is no longer necessary, and significantly reduces the installation and maintenance cost and effort.

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Featured Highlights

  • Keep Your Edge in Machine Vision Analytics

    Keep Your Edge in Machine Vision Analytics

    Time management, ease of maintenance, and accuracy are always major demands for machine vision solutions at the edge. Edge computing solutions facilitate data processing at or near the source of data generation and serve as a decentralized extension of the cloud, data center, or campus networks.

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