Author: Joel B. Harley

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.

July 15, 2025 0

When AI Runs Dry: The Challenge of Training Models on Sparse Medical & Biomechanical Data

We all love the idea of AI diagnosing diseases from a single MRI scan or powering exoskeletons that move as naturally as we do. But guess what? These applications often falter because there’s simply not enough data—or the data is imbalanced, messy, and hard to collect. In medicine and biomechanics, training robust AI models is more like playing chess blindfolded: with limited pieces, incomplete vision, and a big risk of making the wrong moves.

July 9, 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.

July 1, 2025 0
Biomechanics Research

When AI Says “Maybe”: The Quest for Meaningful Uncertainty in Machine Learning

In the realm of artificial intelligence (AI), particularly within machine learning (ML), the ability to quantify uncertainty is paramount. As AI systems increasingly influence critical decisions in fields like healthcare, engineering, and finance, understanding the confidence of these systems becomes essential. Yet, translating the abstract probabilities of AI models into actionable insights remains a significant challenge.

June 25, 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.

June 24, 2025 0

The Next Frontier in AI: Merging Physics with Data for Smarter Models

In the realm of artificial intelligence (AI), a new paradigm is emerging—one that marries the empirical rigor of physics with the adaptability of data-driven models. This hybrid approach is poised to revolutionize industries and scientific research by addressing the limitations inherent in purely data-driven or purely physics-based models.

June 18, 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.

June 17, 2025 0

Why AI Can’t Yet Grow a Perfect Crop Model

Imagine trying to beat Elden Ring with only half the map, no health potions, and a sword that breaks every few swings. That’s roughly the challenge agricultural scientists face when applying artificial intelligence (AI) to crop modeling.

Crop models—like the venerable Decision Support System for Agrotechnology Transfer (DSSAT)—simulate plant growth, soil chemistry, and environmental interactions. They’re essential tools for predicting yields, managing fertilizers, and preparing for climate change. But these models are only as good as their inputs, and in agriculture, data is often scarce, noisy, or incomplete.

June 11, 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:

June 10, 2025 0

Why AI Struggles to Crack the Code of Nondestructive Testing

There’s a common trope in science fiction: an all-seeing machine, unblinking and exact, scanning the world for flaws we can’t detect. In the real world of nondestructive testing (NDT)—the science of using sensors to find cracks, corrosion, and hidden damage in everything from aircraft wings to oil pipelines—that dream still feels frustratingly out of reach.

We have artificial intelligence that can beat world champions at Go, generate Hollywood-quality dialogue, and diagnose rare diseases from pixelated scans. And yet, when we point these same tools at ultrasonic signals or thermographic images from NDT inspections, the results are… inconsistent at best.

June 4, 2025 0

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