Garrett Kenyon

Presented is a vision-based algorithm for extracting physical properties from melt pools. The bandwidth requirements for traditional high speed video are too high for real time analysis so silicon retinas are used. This method of imaging has a very fine temporal resolution, high dynamic range, and low bandwidth requirements. The ability to monitor melt pools in real time would improve the quality of laser printed parts and welds because it would allow automatic control systems to recognize and correct imperfections during the printing and welding processes. By measuring the change of intensity within a melt pool then applying blind source separation techniques, spatiotemporal data can be extracted. First a circular membrane model is evaluated to validate the technique. Then the separation technique is performed with a traditional camera on gallium pools of different depths and various lighting conditions. Finally, silicon retina data is used to show that the technique can be applied for this type of imager.

This article presents an overview of various computational approaches to the study of the vertebrate retina. Topics include the unique advantages of the retina as a target system for computational modeling; significant differences with respect to computational models of other neural systems; quantitative models of linear receptive field properties based on lateral inhibition or a difference of Gaussians; quantitative models of contrast gain control based on a cascade of temporal filters; linear–nonlinear models of responses to naturalistic stimuli; models of nonlinear subunits based on rectified input from transient bipolar cells; biophysical models of single cells with reconstructed dendritic morphology; computational models of extended retinal networks; information theory and the retinal code; and quantitative and biophysical models of light and dark adaptation.