4 research outputs found
Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks
Magnetic navigation (MagNav) is a rising alternative to the Global
Positioning System (GPS) and has proven useful for aircraft navigation.
Traditional aircraft navigation systems, while effective, face limitations in
precision and reliability in certain environments and against attacks. Airborne
MagNav leverages the Earth's magnetic field to provide accurate positional
information. However, external magnetic fields induced by aircraft electronics
and Earth's large-scale magnetic fields disrupt the weaker signal of interest.
We introduce a physics-informed approach using Tolles-Lawson coefficients for
compensation and Liquid Time-Constant Networks (LTCs) to remove complex, noisy
signals derived from the aircraft's magnetic sources. Using real flight data
with magnetometer measurements and aircraft measurements, we observe up to a
64% reduction in aeromagnetic compensation error (RMSE nT), outperforming
conventional models. This significant improvement underscores the potential of
a physics-informed, machine learning approach for extracting clean, reliable,
and accurate magnetic signals for MagNav positional estimation.Comment: Accepted at the NeurIPS 2023 Machine Learning and the Physical
Sciences workshop, 7 pages, 4 figures, see code here:
https://github.com/fnerrise/LNNs_MagNav
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive
loss of postural reflexes, which eventually leads to gait difficulties and
balance problems. Identifying disruptions in brain function associated with
gait impairment could be crucial in better understanding PD motor progression,
thus advancing the development of more effective and personalized therapeutics.
In this work, we present an explainable, geometric, weighted-graph attention
neural network (xGW-GAT) to identify functional networks predictive of the
progression of gait difficulties in individuals with PD. xGW-GAT predicts the
multi-class gait impairment on the MDS Unified PD Rating Scale (MDS-UPDRS). Our
computational- and data-efficient model represents functional connectomes as
symmetric positive definite (SPD) matrices on a Riemannian manifold to
explicitly encode pairwise interactions of entire connectomes, based on which
we learn an attention mask yielding individual- and group-level explainability.
Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals
with PD, xGW-GAT identifies functional connectivity patterns associated with
gait impairment in PD and offers interpretable explanations of functional
subnetworks associated with motor impairment. Our model successfully
outperforms several existing methods while simultaneously revealing
clinically-relevant connectivity patterns. The source code is available at
https://github.com/favour-nerrise/xGW-GAT .Comment: Accepted by the 26th International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI 2023). MICCAI
Student-Author Registration (STAR) Award. 11 pages, 2 figures, 1 table,
appendix. Source Code: https://github.com/favour-nerrise/xGW-GA
Project Proposal: UFC District Use Case
Class of 2021The Final Deliverable Package includes a complete conceptual design and techno-economic analysis of a proposed interconnected solar PV plus battery electric storage system that maximizes energy offset and savings over the system’s contracted (if PPA or lease) or useful (if cash purchase) lifetime for the division district, given its use case parameters and conditions.U.S. Department of Energy Solar District Cup Collegiate Design Competition; reACT ThinkTan
User-Driven Support for Visualization Prototyping in D3
Templates have emerged as an effective approach to simplifying the
visualization design and programming process. For example, they
enable users to quickly generate multiple visualization designs even
when using complex toolkits like D3. However, these templates are
often treated as rigid artifacts that respond poorly to changes made
outside of the template’s established parameters, limiting user creativity. Preserving the user’s creative flow requires a more dynamic
approach to template-based visualization design, where tools can
respond gracefully to users’ edits when they modify templates in
unexpected ways. In this paper, we leverage the structural similarities revealed by templates to design resilient support features
for prototyping D3 visualizations: recommendations to suggest
complementary interactions for a users’ D3 program; and code
augmentation to implement recommended interactions with a
single click, even when users deviate from pre-defined templates.
We demonstrate the utility of these features in Mirny, a designfocused prototyping environment for D3. In a user study with 20
D3 users, we find that these automated features enable participants
to prototype their design ideas with significantly fewer programming iterations. We also characterize key modification strategies
used by participants to customize D3 templates. Informed by our
findings and participants’ feedback, we discuss the key implications
of the use of templates for interleaving visualization programming
and design.https://doi.org/10.1145/3581641.358404