Joel B. Harley – Page 2

Why Everyone Should Learn a Bit of Signal Processing

If you’ve ever adjusted an Instagram filter, used noise cancellation on a plane, or asked Siri to play Taylor Swift, then you’ve used signal processing—the mathematical art of massaging, analyzing, and extracting meaning from data that changes over time. It’s behind your music, your fitness tracker, your MRI scan, and maybe even your job application. And while it might sound like an electrical engineer’s pet topic, signal processing is actually a foundational—and surprisingly flexible—tool across tech, science, and modern careers.

So if you’re a student wondering what to do with that Fourier Transform assignment, or why your professor keeps talking about “filtering out noise,” read on. Signal processing isn’t just useful—it’s everywhere.

Joel B. Harley August 22, 2025 0

Hot Tech for Cold Cancers: How Microwave Imaging Is Reinventing Breast Screening

Picture your chest as a crowded subway car: packed, complicated, and full of different signals—some harmless, others alarming. Traditional mammography snapshots it like a grainy train schedule. Enter microwave imaging, which floods this crowded space with gentle electromagnetic pulses (1–10 GHz), listens to how they bounce back, and pieces together a map of tissue properties. It’s like a radar that detects suspicious riders without shaming them for squeezing on too tight.

Microwave imaging for breast cancer combines electromagnetics, inverse problems, and signal processing—a playground for math nerds who want to turn reflections into medical breakthroughs. And behind much of this progress are longtime efforts at McGill University and newer advances from the University of Utah. Let’s unpack how it all works—and why it’s still struggling to go mainstream today.

Joel B. Harley August 20, 2025 0

When Grain Growth Models Don’t Grow Real Grains

Picture a bustling medieval city: houses of all shapes, roads interweaving unpredictably, and gates that won’t budge because of stubborn gatekeepers. That’s exactly what modeling mesoscale grain growth feels like—chaotic, unpredictable, and utterly maddening. Sure, we have tools like phase-field, Monte‑Carlo Potts, and cellular automata to simulate this thermal dance at the grain level. But each has quirks that make them fall short of mimicking real-world materials.

Joel B. Harley August 13, 2025 0

When Matrices Bend Reality: Unlocking Waves with Metric Spaces and Pseudo‑Hermitian Algebra

Think of a symphony where each instrument plays in perfect harmony. Now imagine that hall bending and warping the music—notes stretch, shift, harmonics twist. That’s akin to how metric spaces, pseudo-symmetric, and pseudo-Hermitian matrices are transforming how we understand wave dynamics in warped environments—from quantum realms to engineered metamaterials.

Joel B. Harley August 6, 2025 0

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

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.

Joel B. Harley 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.

Joel B. Harley July 1, 2025 0

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.

Joel B. Harley June 25, 2025 0

Author: Joel B. Harley