A computational framework for studying energetics and resource management in sea turtle migration and autonomous systems.

Sea turtles complete migrations across vast distances, covering entire ocean basins. To track these migrations, satellite tracking tags are attached to their shells. The impact of these tags must be considered to ensure that turtles' natural behavior is not artificially and adversely impacted through tag-related drag, and that the data collected by a small sample of sea turtles accurately represents the larger population. Additionally, it can be difficult to study animal energetics in the field over large migration distances. In this work, we modify a computational behavior model to study how satellite tracking tags affect turtle migration behavior. Our agent based model contains synthetic magnetic field environments that are used for navigation cues, an ocean current, resource distributions that represent locations of food, and an agent that attempts to migrate to several different goals. The agent loses energy as it progresses, and searches for the resource distributions to replenish itself. Our novel simulation framework demonstrates the relationship between an agent's available energy capacity, its energy consumption based on mechanical power expended, and its ability to navigate to all migratory goal points. This study can be utilized to (1) probe the impacts of an animal's energy capacity and foraging behavior on its resulting navigation and ecology, (2) guide future satellite tag designs, and (3) develop usage recommendations for a suitable tracking tag based on the type of experiment being conducted. Our model can be expanded beyond sea turtles to study other marine species (e.g., sharks, whales). Additionally, this model could be expanded to other domains within the marine environment. For example, it could be modified to examine design trade-offs in remotely operated vehicles (ROVs), which share many of the same operational constraints as sea turtles and other migratory species.

System identification for the contribution of light variations to pupil diameter change.

The pupil diameter (PD) is adjusted by muscles under the control of the sympathetic and parasympathetic divisions of the Autonomic Nervous System (ANS). It is well known that a major mechanism for PD adjustment is the pupillary light reflex (PLR), by which the PD decreases as a response to increased illumination on the retina. However, it has also been found that PD is modified by ANS changes due to affective variations in the subject. Ultimately, we pursue the reproduction and cancellation of PLR-driven PD changes from the PD signal as measured during human-computer interaction, using an Adaptive Interference Canceller (AIC), so that the PD changes not driven by PLR may be used to gauge the affective changes of the computer user. As a preliminary step towards that goal we studied the actual performance of an AIC in modeling changes in measured PD caused exclusively by changes in illumination, which were simultaneously recorded by a light sensor. Our results confirm that the AIC was able to converge, minimizing its error to acceptable levels for all of our 8 subjects. Furthermore, the impulse response model implicitly formed in the weights of the adapted AIC seemed to generally coincide with the impulse response that previous research by others would predict for the transfer function mediating between changes in illumination and changes of pupil diameter.