Investigators at National Center of Scientific Research Report Findings in Machine Vision (Real-Time Machine Vision FPGA Implementation for Microfluidic Monitoring on Lab-on-Chips)
By a News Reporter-Staff News Editor at Biotech Week -- Fresh data on Machine Vision are presented in a new report. According to news reporting originating from Athens, Greece, by NewsRx correspondents, research stated, "A machine vision implementation on a field-programmable gate array (FPGA) device for real-time microfluidic monitoring on Lab-On-Chips is presented in this paper. The machine vision system is designed to follow continuous or plug flows, for which the menisci of the fluids are always visible."
Our news editors obtained a quote from the research from the National Center of Scientific Research, "The system discriminates between the front or 'head' of the flow and the back or 'tail' and is able to follow flows with a maximum speed of 20 mm/sec in circular channels of a diameter of 200 mu m (corresponding to approx. 60 mu l/sec). It is designed to be part of a complete Point-of-Care system, which will be portable and operate in non-ideal laboratory conditions. Thus, it is able to cope with noise due to lighting conditions and small LoC displacements during the experiment execution. The machine vision system can be used for a variety of LoC devices, without the need for fiducial markers (such as redundancy patterns) for its operation. The underlying application requirements called for a complete hardware implementation. The architecture uses a variety of techniques to improve performance and minimize memory access requirements. The system input is 8 bit grayscale uncompressed video of up to 1 Mpixel resolution."
According to the news editors, the research concluded: "The system uses an operating frequency of 170 Mhz and achieves a computational time of 13.97 ms (worst case), which leads to a throughput of 71.6 fps for 1 Mpixel video resolution."
For more information on this research see: Real-Time Machine Vision FPGA Implementation for Microfluidic Monitoring on Lab-on-Chips. IEEE Transactions on Biomedical Circuits and Systems, 2014;8(2):268-277. IEEE Transactions on Biomedical Circuits and Systems can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Biomedical Circuits and Systems - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4156126)
The news editors report that additional information may be obtained by contacting C.L. Sotiropoulou, Micro2Gen Ltd, New Technol Pk NCSR Demokritos, Athens, Greece. Additional authors for this research include L. Voudouris, C. Gentsos, A.M. Demiris, N. Vassiliadis and S. Nikolaidis (see also Machine Vision).
Keywords for this news article include: Athens, Greece, Europe, Machine Vision, Machine Learning, Emerging Technologies
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