The Effect of Parameterization on a Reconfigurable Implementation of PIV

This paper presents PARPIV, the design and prototyping of a highly parameterized digital Particle Image Velocimetry (PIV) system implemented on reconfigurable hardware. Despite many improvements to PIV methods over the last twenty years, PIV postprocessing remains a computationally intensive task. It becomes a serious bottleneck as camera acquisition rates reach 1000 frames per second. Besides, in different engineering applications, different PIV parameters are required. Up to now, there is no PIV system that combines both flexible parameterization and high computational performance. In our research we are creating such a system. This implementation is highly parameterized, supporting adaptation to a variety of setups and application domains. The circuit is parameterized by the dimensions of the captured images as well as the dimensions of the interrogation windows and sub-areas, pixel representation, board memory width, displacement and overlap. Through this work a parameterized library of different VHDL components was built. To the best of the authors’ knowledge, this is the first highly parameterized PIV system implemented on reconfigurable hardware reported in the literature. We report the speedup in hardware over a standard software implementation of 10 different implementations with different parameters.

[1]  Tsutomu Maruyama,et al.  An Approach to Real-Time Visualization of PIV Method with FPGA , 2001, FPL.

[2]  L. Lourenço.  Particle Image Velocimetry , 1989 .

[3]  D. Hart.  Super-resolution PIV by recursive local-correlation , 2000 .

[4]  R. Adrian.  Particle-Imaging Techniques for Experimental Fluid Mechanics , 1991 .

[5]  Nathalie Bochard,et al.  An adaptive and predictive architecture for parameterised PIV algorithms , 2006, 2006 IEEE International Conference on Field Programmable Technology.

[6]  M. Sholl,et al.  A fast Lagrangian PIV method for study of general high-gradient flows , 1997 .

[7]  Martyn F. Guest,et al.  An overview of FPGAs and FPGA programming; initial experiences at Daresbury , 2006 .

[8]  S. Leigh,et al.  Probability and Random Processes for Electrical Engineering , 1989 .

[9]  Kenji Fujimoto,et al.  A Real-Time Visualization System for PIV , 2003, FPL.

[10]  María José Moure,et al.  Features, Design Tools, and Application Domains of FPGAs , 2007, IEEE Transactions on Industrial Electronics.

[11]  Miriam Leeser,et al.  Real-Time Particle Image Velocimetry for Feedback Loops Using FPGA Implementation , 2005, J. Aerosp. Comput. Inf. Commun..

[12]  S. Honda,et al.  Real time particle image velocimetry using liquid crystal display , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[13]  Miriam Leeser,et al.  Smart camera based on reconfigurable hardware enables diverse real-time applications , 2004, 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines.

[14]  J. Carr,et al.  Digital particle image velocimetry system for real-time wind tunnel measurements , 1997, ICIASF'97 Record. International Congress on Instrumentation in Aerospace Simulation Facilities.

[15]  M. Tull,et al.  Redundant binary based fixed-point divider , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[16]  Shelly L. Miller,et al.  Particle Image Velocimetry of Human Cough , 2011 .

[17]  I Grant,et al.  The use of neural techniques in PIV and PTV , 1997 .

[18]  R. Adrian.  Twenty years of particle image velocimetry , 2005 .