Neuromorphic Data Processing for Event-Driven Imagery for Acoustic Measurements

Event-driven silicon retina imagers are useful for structural dynamics applications as they are low-power, high-information bandwidth, and high-dynamic range devices. Currently, event-driven imagers can detect light intensity changes associated with an LED blinking at 18 kHz, which indicates event-driven imagers are capable of capturing dynamic motion associated with the commonly accepted frequency range of human hearing, 20 Hz–20 kHz. As the development and utilization of event-driven imagers advances, it is reasonable to expect the upper bound of measurable frequency to increase. Therefore, event-driven imagers have the potential to be a viable replacement for high-speed imagers in the field of structural dynamics. The majority of statistical techniques currently developed for structural dynamics assume data is captured with a uniform sampling rate. This convention is problematic when using event-based imagers because event-based sensors generate send-on-delta data. The widespread use of these imagers will require the development of an acceptable technique for converting event data to time-series data, which can then be sampled uniformly. Another challenge with utilizing event-driven imagers is the inherent and significant variations exhibited between pixels on the same imager during the generation of an event. These variations need to be better understood and examined. Investigative research has revealed that certain aspects of the two problems, data conversion and pixel variation, have been addressed by the neuroscience community. The spike-based data analysis techniques that have been developed in the neuroscience community may have applicability to structural dynamics in the context of event-driven imagery. These practices will then be used as inspiration for developing event-based data processing techniques to tie event-based data to existing structural dynamics analysis frameworks.