ORCID ID
0000-0002-2297-6181
Date Awarded
2019
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.)
Department
Applied Science
Advisor
Mark K. Hinders
Committee Member
Hannes Schniepp
Committee Member
Walter Silva
Committee Member
Nathanael Kidwell
Abstract
We describe a framework for global shipping container monitoring using machine learning with multi-sensor hubs and infrared catadioptric imaging. A wireless mesh radio satellite tag architecture provides connectivity anywhere in the world which is a significant improvement to legacy methods. We discuss the design and testing of a low-cost long-wave infrared catadioptric imaging device and multi-sensor hub combination as an intelligent edge computing system that, when equipped with physics-based machine learning algorithms, can interpret the scene inside a shipping container to make efficient use of expensive communications bandwidth. The histogram of oriented gradients and T-channel (HOG+) feature as introduced for human detection on low-resolution infrared catadioptric images is shown to be effective for various mirror shapes designed to give wide volume coverage with controlled distortion. Initial results for through-metal communication with ultrasonic guided waves show promise using the Dynamic Wavelet Fingerprint Technique (DWFT) to identify Lamb waves in a complicated ultrasonic signal.
DOI
https://doi.org/10.21220/9j2v-ek78
Rights
© The Author
Recommended Citation
Trujillo, Victor Esteban, "Global Shipping Container Monitoring Using Machine Learning with Multi-Sensor Hubs and Catadioptric Imaging" (2019). Dissertations, Theses, and Masters Projects. William & Mary. Paper 1582642585.
https://doi.org/10.21220/9j2v-ek78
