Project
Underwater Direction Finding Using a Single Hydrophone
REU Scholar: Steven Cohen
REU Scholar Home Institution: 吃瓜网
REU Mentor: Dr. Georgios Sklivanitis
Underwater direction-of-arrival (DoA) estimation traditionally relies on hydrophone arrays or specialized vector sensors that impose substantial size, weight, power, and cost (SWaP-C) constraints on small autonomous underwater vehicles. AMULET addresses these limitations by surrounding a single hydrophone with a bio-inspired, air-filled spiral acoustic metastructure that introduces direction-dependent signatures into received signals. In this project, we reproduce and evaluate the AMULET calibration and classification process using software-defined radios, GNU Radio, an automated stepper-motor turntable, and a custom signal-processing pipeline. Two 360掳 calibration sweeps were collected in a water tank at 0.9掳 angular resolution. At each calibrated angle, the received wideband chirp was deconvolved from the known transmitted chirp in the frequency domain, with time-of-flight compensation and impulse-response truncation used to isolate the metastructure's direction-dependent acoustic signature. For DoA estimation, an unknown response was cross-correlated with every normalized signature in the calibration dictionary over possible propagation delays, and the arrival direction was assigned to the angle producing the largest correlation peak. DoA estimates produced a mean angular error of 2.46掳, a median error of 1.80掳, and a 90th-percentile error of 5.40掳. These results validate single-hydrophone DoA sensing with passive acoustic metastructures and demonstrate the importance of automated calibration, robust template construction, and controlled experimental conditions.
Slide Presentation
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Video Presentation
Project
TIDE: Testing Intermittent Devices for Energy Harvesting
REU Scholar: Ashlynn Vick
REU Scholar Home Institution: University of 吃瓜网
REU Mentor: Dr. Georgios Sklivanitis
Long-term environmental sensing in remote locations is constrained by the limited lifetime and maintenance requirements of conventional batteries. This project characterizes the runtime and energy consumption of a batteryless sensing platform designed for intermittent energy-harvesting operation. The system integrates an ADP5092 ultra-low-power energy-harvesting power management board, an MSP430FR5994 microcontroller, an NTC thermistor, and a supercapacitor charged to approximately 3.6 V. Using a controlled Analog Discovery input, the platform was discharged to the ADP5092 shutdown threshold of 2.0 V under three firmware configurations: Low Power Mode 3 without logging, Direct Memory Access logging, and DMA logging with active CPU temperature calculations. Average runtimes were 520.17, 490.40, and 469.67 seconds, respectively. The corresponding mean powers were 0.135, 0.633, and 0.875 mW. Results quantify the energy costs of sensing, logging, and computation and provide practical benchmarks for designing energy-aware firmware. Future work will apply these findings to federated, intermittent batteryless environmental sensing networks.
Slide Presentation
For a downloadable version of this presentation, email I-SENSE@FAU.
Video Presentation