No abstract

Recent advances in Field-Programmable Gate Arrays (FPGAs) and innovations in firmware design have allowed more complex image processing algorithms to be implemented entirely within the FPGA devices while substantially improving performance and reducing development time. Firmware innovations include a unique memory buffer architecture and the use of floating-point math. The design discussed takes advantage of these advances and innovations to implement a geometric transformation algorithm with bilinear interpolation for applications such as distortion correction. The firmware and hardware developed in this effort support image sizes of up to 1024x1024 pixels at 200 Hz and pixel rates of 216 MHz with versions available that support oversized input images.

This paper describes the Hébert-Mackin Parameter Identification Method (HMPIM).This methodology is applicable to testing both hardware and software and enables identification of system or algorithm performance modeling parameters through minimal dynamic stimulation of the hardware or software. Exposing hardware to extensive operation and testing to determine salient system or component level modeling parameters is both costly, time consuming, and potentially risky. Classical test waveforms such as steps, ramps, or sinusoids expose the asset being tested to continuous probing and shaking and each test by itself does not drive out the entire set of essential modeling parameters. The HMPIM, utilizing persistent spectral excitation and data processing, allows the analyst or modeler to determine all the essential system performance and modeling parameters with a single 5 or 10 second excitation of the hardware or software algorithm.

No abstract

No abstract