The Role of Actively Created Doppler shifts in Bats Behavioral Experiments and Biomimetic Reproductions

Yin, Xiaoyan

The Role of Actively Created Doppler shifts in Bats Behavioral Experiments and Biomimetic Reproductions

dc.contributor.author	Yin, Xiaoyan	en
dc.contributor.committeechair	Mueller, Rolf	en
dc.contributor.committeemember	Socha, John J.	en
dc.contributor.committeemember	Leonessa, Alexander	en
dc.contributor.committeemember	Abaid, Nicole	en
dc.contributor.committeemember	Roan, Michael J.	en
dc.contributor.department	Mechanical Engineering	en
dc.date.accessioned	2021-01-20T09:00:32Z	en
dc.date.available	2021-01-20T09:00:32Z	en
dc.date.issued	2021-01-19	en
dc.description.abstract	Many animal species are known for their unparalleled abilities to encode sensory information that supports fast, reliable action in complex environments, but the mechanisms remain often unclear. Through fast ear motions, bats can encode information on target direction into time-frequency Doppler signatures. These species were thought to be evolutionarily tuned to Doppler shifts generated by a prey's wing beat. Self-generated Doppler shifts from the bat's own flight motion were for the most part considered a nuisance that the bats compensate for. My findings indicate that these Doppler-based biosonar systems may be more complicated than previously thought because the animals can actively inject Doppler shifts into their input signals. The work in this dissertation presents a novel nonlinear principle for sensory information encoding in bats. Up to now, sound-direction finding has required either multiple signal frequencies or multiple pressure receivers. Inspired by bat species that add Doppler shifts to their biosonar echoes through fast ear motions, I present a source-direction finding paradigm based on a single frequency and a single pressure receiver. Non-rigid ear motions produce complex Doppler signatures that depend on source direction but are difficult to interpret. To demonstrate that deep learning can solve this problem, I have combined a soft-robotic microphone baffle that mimics a deforming bat ear with a CNN for regression. With this integrated cyber-physical setup, I have able to achieve a direction-finding accuracy of 1 degree based on a single baffle motion.	en
dc.description.abstractgeneral	Bats are well-known for their intricate biosonar system that allow the animals to navigate even the most complex natural environments. While the mechanism behind most of these abilities remains unknown, an interesting observation is that some bat species produce fast movements of their ears when actively exploring their surroundings. By moving their pinna, the bats create a time-variant reception characteristic and very little research has been directed at exploring the potential benefits of such behavior so far. One hypothesis is that the speed of the pinna motions modulates the received biosonar echoes with Doppler-shift patterns that could convey sensory information that is useful for navigation. This dissertation intends to explore this hypothetical dynamic sensing mechanism by building a soft-robotic biomimetic receiver to replicate the dynamics of the bat pinna. The experiments with this biomimetic pinna robot demonstrate that the non-rigid ear motions produce Doppler signatures that contain information about the direction of a sound source. However, these patterns are difficult to interpret because of their complexity. By combining the soft-robotic pinna with a convolutional neural network for processing the Doppler signatures in the time-frequency domain, I have been able to accurately estimate the source direction with an error margin of less than one degree. This working system, composed of a soft-robotic biomimetic ear integrated with a deep neural net, demonstrates that the use of Doppler signatures as a source of sensory information is a viable hypothesis for explaining the sensory skills of bats.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:29097	en
dc.identifier.uri	http://hdl.handle.net/10919/101965	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Bats	en
dc.subject	Biosonar	en
dc.subject	Pinna Motions	en
dc.subject	Doppler Shifts	en
dc.subject	Direction finding	en
dc.subject	Biomimetics	en
dc.subject	Deep learning (Machine learning)	en
dc.title	The Role of Actively Created Doppler shifts in Bats Behavioral Experiments and Biomimetic Reproductions	en
dc.type	Dissertation	en
thesis.degree.discipline	Mechanical Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en