Li, Yuqi
(2025)
Optimizing Fiber-Optic Sensor Networks through Innovative Signal Processing and Embedded System Design.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Fiber optic sensors offer numerous advantages, including their versatility and high resistance to temperature, pressure, and electromagnetic interference, making them ideal for measuring physical parameters such as temperature, pressure, and vibration in harsh environments. However, widespread adoption has been hindered by challenges such as high system costs, complex real-time data processing, and scalability limitations for large deployments. This dissertation addresses these barriers by proposing innovative algorithmic and hardware solutions designed to enhance the performance, scalability, and cost-efficiency of fiber-optic sensor systems. The first major contribution is the development of novel signal processing algorithms that reduce performance demands on optical components, particularly for high-speed and high-resolution sensing applications. These algorithms enable precise measurements using lower-cost components, significantly lowering system costs while maintaining accuracy, thus making fiber-optic systems more accessible for commercial applications. Secondly, a restructured optical system architecture is introduced, eliminating unnecessary redundancies typical of traditional systems. By streamlining the design and focusing on essential functionalities, this architecture operates more efficiently and further reduces costs, making the technology attractive for industries like aerospace, automotive, and industrial diagnostics. The third contribution is the implementation of a real-time pedestrian recognition system using fiber-optic sensor networks, powered by FPGA-based high-level synthesis (HLS) acceleration. This system rapidly extracts valuable information from large volumes of optical data, reducing the computational burden on data storage and transmission, while improving scalability for handling the increased data loads in larger sensor networks. These advancements pave the way for cost-effective, high-performance optical sensing systems that address key limitations in performance, cost, and scalability, contributing significantly to the field of optical sensing and large-scale sensor networks.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
7 January 2025 |
| Date Type: |
Publication |
| Defense Date: |
1 November 2024 |
| Approval Date: |
7 January 2025 |
| Submission Date: |
22 October 2024 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
121 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Fiber-optic sensor |
| Date Deposited: |
07 Jan 2026 13:20 |
| Last Modified: |
08 Jan 2026 13:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/47035 |
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