Joel B. Harley – SmartDATA Lab

How to Keep a System from Spiraling Out: Why Every ECE Student Should Care About Control Systems

Take a moment to think about anything that moves or adjusts itself—like a drone flying in the wind, a robot arm reaching for a part, or even your bedroom thermostat switching off the heat. What do they all have in common?

They’re all part of a control system, and they all need to behave in a way that’s stable—meaning they don’t freak out when something changes. And that’s where your signals and systems knowledge starts turning into real-world impact.

Control systems are what allow us to design machines that respond to changes, make decisions, and settle down safely. They’re the tools engineers use to keep things running smoothly in a world that’s constantly pushing and pulling in different directions.

Joel B. Harley September 10, 2025 0

The Hidden Framework Behind Your Favorite Tech: Why Linear Time-Invariant Systems Still Rule the World

There’s a secret lurking beneath your noise-canceling headphones, your AI-powered photo filter, and even some of the tools used to predict stock markets and brain activity. It’s not AI. It’s not quantum computing. It’s something far older—and, in its way, far more elegant.

We’re talking about linear, time-invariant systems—or LTIs, if you’ve seen them scrawled across lecture boards in engineering classes. If that phrase makes your eyes glaze over, you’re not alone. But here’s the twist: understanding LTIs doesn’t just help electrical engineers or control theorists. It gives you a mental model for how inputs become outputs—in machines, in math, even in thought.

This article is a case for why students—and really, anyone interested in how the modern world works—should give LTIs the credit (and curiosity) they deserve.

Joel B. Harley September 3, 2025 0

The Inner Product: The Most Important Math You’re Already Using Without Realizing It

If you’ve ever used Spotify, trained a neural network, filtered an image, or Googled anything—you’ve relied on an idea so fundamental it barely gets named in everyday conversation: the inner product.

It’s not flashy. It’s not mysterious. It’s certainly not trending on TikTok. But the inner product—also known as the dot product—is quietly powering the smartest algorithms and signal tools in existence. It’s the mathematical handshake that lets one signal say, “Hey, you look a lot like me.”

If you’re a student wondering what separates hand-wavy intuition from real-world results in signal processing, data science, or machine learning, this article is your invitation to take the inner product seriously—and see how it shows up almost everywhere.

Joel B. Harley August 27, 2025 0

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

Author: Joel B. Harley