PhD Dissertation Defense: Jacob Isbell

Wednesday, February 24, 2021
10:00 a.m.
Maria Hoo
301 405 3681

ANNOUNCEMENT:  PhD Dissertation Defense

Name: Jacob Isbell

Professor Timothy Horiuchi, Chair/Advisor
Professor Pamela Abshire
Professor Behtash Babadi
Professor Cornelia Fermuller
Professor Daniel Butts, Dean's Representative  

Date/Time:  Wednesday, February 24, 2021 at 10:00AM

Location:  Zoom


Abstract:  Bats are known for their unique ability to sense the world through echolocation. This allows them to perceive the world in a way that few animals do, but not without some difficulties.  This dissertation explores two such tasks using a bio-inspired sonar system: tracking a target object in cluttered environments, and echo view recognition. The use of echolocation for navigating in dense, cluttered environments can be a challenge due to the need for rapid sampling of nearby objects in the face of delayed echoes from distant objects. If long-delay echoes from a distant object are received after the next pulse is sent out, these “aliased” echoes appear as close-range phantom objects. This dissertation presents three reactive strategies for a high pulse-rate sonar system to combat aliased echoes: (1) changing the interpulse interval to move the aliased echoes away in time from the tracked target, (2) changing positions to create a geometry without aliasing, and (3) a phase-based, transmission beam-shaping strategy to illuminate the target and not the aliasing object. While this task relates to immediate sensing needs and lower level motor loops, view recognition is involved in higher level navigation and planning. Neurons in the mammalian brain (specifically in the hippocampus formation) named “place cells” are thought to reflect this recognition of place and are involved in implementing a spatial map that can be used for path planning and memory recall.  We propose hypothetical “echo view cells” that could contribute (along with odometry) to the creation of place cell representations actually observed in bats. We strive to recognize views over extended regions that are many body lengths in size, reducing the number of places to be remembered for a map. We have successfully demonstrated some of this spatial invariance by training feed-forward neural networks (traditional neural networks and spiking neural networks) to recognize 66 distinct places in a laboratory environment over a limited range of translations and rotations. We further show how the echo view cells respond in between known places and how the population of cell outputs can be combined over time for continuity.

Audience: Graduate  Faculty 

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