An adaptive embedded architecture for real-time Particle Image Velocimetry algorithms

TL;DR

This paper presents an adaptive FPGA-based architecture for real-time Particle Image Velocimetry that can be customized for various applications, achieving high-speed processing through parallelization without hardware changes.

Contribution

It introduces a generic, adaptable FPGA architecture for real-time PIV algorithms that can be easily modified for different applications using cross-correlation techniques.

Findings

Achieves speeds exceeding a thousand vectors per second.

Supports reusability across different PIV applications.

Enables fast and reliable design flow for real-time processing.

Abstract

Particle Image Velocimetry (PIV) is a method of im-aging and analysing fields of flows. The PIV tech-niques compute and display all the motion vectors of the field in a resulting image. Speeds more than thou-sand vectors per second can be required, each speed being environment-dependent. Essence of this work is to propose an adaptive FPGA-based system for real-time PIV algorithms. The proposed structure is ge-neric so that this unique structure can be re-used for any PIV applications that uses the cross-correlation technique. The major structure remains unchanged, adaptations only concern the number of processing operations. The required speed (corresponding to the number of vector per second) is obtained thanks to a parallel processing strategy. The image processing designer duplicates the processing modules to distrib-ute the operations. The result is a FPGA-based archi-tecture, which is easily adapted to algorithm specifica-tions without any hardware requirement. The design flow is fast and reliable.