Code & Resources – SmartDATA Lab

Spread Spectrum Reflectometry on Complex Transmission Lines Repository

Transmission lines aren’t always simple—especially in complex systems like aircraft wiring, industrial cabling, or sensor networks. As these systems age, hidden faults, impedance mismatches, and parasitic elements can quietly degrade performance. Detecting those issues without disrupting the system? That’s the challenge.

This CodeOcean capsule implements a Spread Spectrum Time Domain Reflectometry (SSTDR) algorithm for detecting and localizing lumped elements—like capacitors and resistors—on asymmetric transmission lines, developed by Ayobami Edun, based on the work of Sabeti, Leckey, De Marchi, and Harley.

Joel B. Harley July 29, 2025 0

Long-Term Guided Wave Dataset Under Real-World Conditions Dataset

The Dataset on Guided Waves from Long-Term Structural Health Monitoring under Uncontrolled and Dynamic Conditions, created by Kang Yang and the SmartDATA Lab, offers one of the first comprehensive public resources for doing just that. This large-scale, real-world dataset captures guided ultrasonic waves over two years of continuous monitoring, encompassing millions of waveforms, changing environmental conditions, and true operational variability.

Joel B. Harley July 23, 2025 0

Sparse Wavenumber Recovery in Anisotropic Composites Repository

Guided wave imaging is a cornerstone technique in structural health monitoring (SHM), especially for composite materials. But composites are anisotropic—meaning wave speeds and behaviors vary with direction—which makes interpreting wave propagation challenging.

This CodeOcean capsule presents the algorithm and tools for Sparse Wavenumber Recovery (SWR) developed by Soroosh Sabeti, which leverage compressed sensing and sparse signal processing to efficiently extract anisotropic wavenumber content from limited measurements.

Joel B. Harley July 15, 2025 0

Fast Transmission Line Simulation with Graphical Models Repository

Transmission lines are the backbone of modern electronic systems—from printed circuit boards to power grids—but simulating how electrical signals move through complex, multi-segment transmission lines is notoriously time-consuming.

This CodeOcean capsule offers a fast and scalable algorithm for simulating transient signals in multi-segment transmission lines using an algebraic graphical model—a breakthrough that reduces computation time while maintaining high fidelity.

Joel B. Harley July 1, 2025 0

Statistical Partial Wavefield Imaging Repository

Statistical Partial Wavefield Imaging (SPWI) is a powerful signal processing algorithm designed to detect and localize structural damage using sparse ultrasonic sensor arrays. Unlike traditional imaging methods that require dense sensor grids or full wavefield measurements, SPWI extracts high-quality damage localization images from partial and limited measurements, making it ideal for real-world Structural Health Monitoring (SHM) applications.

Joel B. Harley June 24, 2025 0

Dynamic Time Warping for Temperature Compensation Repository

Guided wave Structural Health Monitoring (SHM) systems are powerful tools for detecting damage in structures like aircraft fuselages, pipelines, and bridges. However, one major obstacle persists: temperature variation. Even small temperature changes can significantly distort guided wave signals, often leading to false positives or missed damage.

Enter Dynamic Time Warping (DTW) Temperature Compensation — a robust, data-driven algorithm developed by Alexander Douglass and Dr. Joel B. Harley that aligns guided wave signals distorted by temperature, improving the accuracy and reliability of damage detection without requiring physical models or manual calibration.

Joel B. Harley June 17, 2025 0

K-SVD Dictionary Learning for Damage Detection Repository

K-SVD Dictionary Learning for Damage Detection is a baseline-free, data-driven approach for detecting structural damage using guided ultrasonic waves. Developed by Supreet Alguri and Dr. Joel B. Harley, this method eliminates the need for pristine baseline measurements—a major limitation in many real-world Structural Health Monitoring (SHM) systems.

This CodeOcean capsule provides a reproducible implementation of the algorithm, as described in the paper:

Joel B. Harley June 10, 2025 0

Temporal Sparse Wavenumber Analysis Repository

Temporal Sparse Wavenumber Analysis (TSWA) is a novel technique that reconstructs high-resolution spatiotemporal wavefields using fewer temporal samples than traditional methods typically require. Developed by Soroosh Sabeti and Dr. Joel B. Harley, TSWA makes it possible to retrieve accurate guided wave information from temporally undersampled data, which is crucial in applications where high-speed sensing, data storage, or power consumption are limiting factors.

Joel B. Harley June 3, 2025 0

Data-Driven Matched Field Processing (DDMFP) Repository

Data-Driven Matched Field Processing (DDMFP) is an innovative signal processing framework designed for localizing acoustic sources in complex environments, such as those encountered in structural health monitoring (SHM) using Lamb waves. Traditional matched field processing (MFP) techniques rely heavily on accurate physical models of the propagation medium, which can be challenging to obtain in real-world scenarios. DDMFP circumvents this limitation by constructing localization models directly from measured data, enhancing robustness and accuracy in complex, multimodal propagation environments.

Joel B. Harley May 17, 2025 0

Sparse Wavenumber Analysis (SWA) Repository

Sparse Wavenumber Analysis (SWA) is a signal processing algorithm designed to extract high-resolution wavenumber information from spatial wavefield measurements—especially when that data is sparse, noisy, or irregularly sampled. Developed by Dr. Joel B. Harley and collaborators, SWA enables researchers and engineers to analyze wave propagation with unprecedented clarity, even in situations where traditional Fourier-based methods fall short.

This CodeOcean capsule contains a complete, reproducible implementation of the SWA algorithm, based on the foundational work in:

Joel B. Harley May 16, 2025 0

Category: Code & Resources