Agate Sensors is changing that with the world’s first fully software-defined hyperspectral chip that brings lab-grade spectral intelligence to an ultra-compact, mass-manufacturable form factor. By shifting intelligence from rigid, costly hardware to software, the team is making what was once inaccessible far more accessible.

“By bringing lab-grade hyperspectral capabilities to an ultra-compact, fully software-controlled chip, we are redefining spectral measurement and defining a new category of capabilities that simply did not exist before,” says Mikael Westerlund, Chief Business Officer and Co‑founder of Agate Sensors.

Breaking the limits of conventional sensors

Most sensors used in consumer and industrial devices today are built around fixed optical architectures that rely on filters or specialized optics to separate incoming signals before detection. While effective for traditional imaging, they impose hard trade-offs in terms of size, cost, and flexibility.

Agate Sensors takes a fundamentally different approach. “We are not using any filters or diffractive elements to separate wavelengths before detection,” explains Westerlund. “Because we use all available signal energy, the signal-to-noise ratio at the detector level is dramatically better. And without filters, the platform becomes inherently smaller, cheaper to manufacture, and scalable in ways conventional architectures cannot match.”

By eliminating the need for specialized optics, Agate Sensors enables a solid‑state sensing platform that is compact, robust, and highly programmable. Instead of designing a new sensor for every application, device manufacturers can adapt the same platform through software, shifting sensing behavior as requirements change.

This shift is especially important because innovation in conventional sensing has largely plateaued. As Westerlund notes, traditional imaging systems have focused on incremental improvements, such as higher pixel counts, rather than fundamentally new capabilities. “That development has stagnated totally,” he says. “It’s been years since we’ve seen something really new on these devices.”

By moving innovation into software, teams can explore new applications without re‑engineering hardware, reducing development time and lowering the barrier to experimentation.

As a result, a single sensing platform can support a wide range of use cases, from health monitoring and material identification to machine vision and environmental sensing.

From raw data to real‑time intelligence

At the heart of Agate Sensors’ platform is the ability to extract meaningful insight from rich spectral data. “Spectral data is an underutilized natural resource today because current technology is big, bulky, expensive, and impossible to miniaturize,” says Westerlund. “By miniaturizing this technology and making it affordable, it can be integrated into devices like mobile phones, wearables, cars, drones, and even satellites.”

This is exactly what Agate Sensors has achieved by enabling spectral data to be captured and interpreted in real time across many domains.

In wearables, for example, hyperspectral sensing could analyze biochemical signals without needles or test strips. In other applications, devices could identify materials or detect hazards in the field.

Agate Sensors develops technology to bring lab-grade hyperspectral capabilities to ultra-compact, fully software-controlled chips. (Image courtesy of Agate Sensors)

This approach also aligns naturally with artificial intelligence. “Current sensors are built to replicate how humans see the world,” Westerlund explains. “What we provide is much richer data that AI can use far more efficiently than humans ever could.”

Rather than producing images meant for people to look at, Agate Sensors’ platform generates measurement data designed for machines to analyze. Instead of being limited to red, green, and blue channels, devices gain access to deeper information that AI models can use to identify materials, detect signals, and make real-time decisions.

“This technology is really a match made in heaven between machine vision and AI‑driven applications,” Westerlund says. Turning this kind of data into reliable, real‑time intelligence depends not just on sensing, but on the algorithms that interpret it.

Using MATLAB to accelerate algorithm development

MATLAB plays a central role in Agate Sensors’ workflow, particularly during the algorithm development and validation phases, providing the accuracy and analytical depth needed to work with large, complex datasets.

As prototypes generate large volumes of complex data, MATLAB enables the team to explore, analyze, and iterate quickly. Agate Sensors relies on toolboxes such as Signal Processing, Image Processing, and Computer Vision to support these workflows. “MathWorks is a central tool for our algorithm development,” explains Tommi Leino, CEO and Co-founder of Agate Sensors. “We use MATLAB for spectral reconstruction, signal processing, and analyzing measurement data during development”.

MATLAB also helps bridge the gap between research and deployment. Once the algorithms are developed, the team uses MATLAB Coder to generate C code that runs on the CPU in their chip.

For a startup building custom chips, that continuity matters. “It definitely speeds up algorithm development because you have ready‑made toolsets and functions instead of coding everything from scratch,” Leino adds.

Moving fast from research to silicon

The core innovation behind Agate Sensors originated in academic research but turning that breakthrough into a scalable product requires both speed and technical rigor.

Like many deep‑tech startups, Agate Sensors operates under intense time pressure. Hardware development demands significant upfront investment, while customers expect rapid progress toward real‑time deployment. “Time is essential in the startup world,” says Leino. “We need to execute fast and still deliver high‑quality output for customers.”

Tommi Leino, CEO and Mikael Westerlund, CBO of Agate Sensors. (Image courtesy of Agate Sensors)

That urgency has shaped how the team approaches development. By focusing on software-defined sensing and a streamlined path from algorithms to silicon, Agate Sensors moves from research concepts to deployable hardware without sacrificing accuracy or flexibility.

Looking ahead

Agate Sensors is approaching a major milestone: receiving its first silicon back from the foundry. From there, the focus shifts to validation, customer proofs of concept, and preparing for mass production.

The longer‑term vision is clear. By embedding software-defined sensing into everyday devices, Agate Sensors aims to enable a new generation of intelligent systems that can perceive, classify, and understand the physical world in ways previously impossible.

For researchers and engineers considering a similar leap from academia to industry, the team’s advice reflects their own journey. “Close your eyes and jump,” concludes Westerlund. “You’ll never know if you don’t do it.”

Sometimes, the biggest breakthroughs come not from adding more hardware, but from rethinking where intelligence belongs.

The Challenge: Complexity Is Outpacing Traditional Methods

In Aerospace and Defense, mission complexity is growing faster than the ability to manage trade‑offs effectively. UAV operations are evolving from single-vehicle missions to multi-asset, coordinated systems operating in dynamic environments, while satellite architectures are shifting towards large constellations that require true system-of-systems thinking. At the same time, program timelines are shrinking, even as expectations for performance, resilience, and interoperability continue to rise.

The fundamental question facing engineering teams is no longer “Can we build it?” It is increasingly becoming, “Can we make the right decisions early enough?”

This is where Mission Engineering plays a critical role. It helps teams connect requirements, architecture, analysis, and verification to enable better decisions earlier in the lifecycle.

Mission Engineering: Enabling Better Decisions, Earlier

Mission Engineering focuses on evaluating systems in the context of real mission outcomes, rather than isolated component performance. By leveraging Model‑Based and digital engineering approaches, teams can:

→  Explore design alternatives quickly

→  Analyze trade‑offs before costly decisions are locked in

→  Validate concepts earlier using executable models

→  Reduce rework caused by late discovery of requirements or integration issues

As Aerospace systems grow more complex, engineering teams are rethinking how they design, analyze, and validate missions. Traditional component-level approaches are often insufficient for understanding system behavior, trade-offs, and risk at the mission level, especially early in the lifecycle.

Modern aerospace missions are no longer defined by individual platforms, they are defined by how systems interact across communications, sensing, modeling, autonomy, and AI to achieve mission‑level outcomes.

Across aerospace programs, a common set of patterns is emerging in how teams approach mission‑level modeling and decision‑making. The following themes reflect those patterns and highlight where mission engineering is having the greatest impact.

1. Fidelity Needs to Be Intentional

Not every decision requires high‑fidelity models. In fact, insisting on maximum fidelity at every stage can slow progress.

An effective mission engineering approach applies fidelity intentionally:

•  Use lower‑fidelity models for early exploration and rapid trade studies

•  Increase fidelity as design decisions narrow and risk areas become clearer

•  Define clear success criteria to understand when “good enough” is sufficient

This approach enables teams to move faster while still making informed decisions.

2. Digital Continuity Is Critical

Disconnected tools and handoffs remain a major cause of delays and rework in aerospace programs.

A strong Mission Engineering approach is built on digital continuity, connecting:

•  Requirements

•  System architecture

•  Analysis and simulation

•  Verification and validation

When these elements are digitally linked, teams can assess the impact of changes instantly, maintain traceability, and keep stakeholders aligned throughout the lifecycle.

3. Satellite Constellations Are Communication‑Driven

For satellite constellations, mission success is no longer defined by the performance of a single spacecraft.

The focus shifts to:

•  Coverage and revisit rates

•  Network resilience

•  End‑to‑end communication performance

•  Behavior under constraints such as link failures or congestion

Mission-level modeling allows teams to evaluate constellation behavior holistically, ensuring that system-level objectives are met, even under non-ideal conditions.

4. UAV Missions Demand Interoperability

Modern UAV missions involve multiple platforms, ground systems, and stakeholders. As a result, interoperability becomes a core design consideration.

This requires:

•  Alignment between mission requirements and system architecture

•  Shared models that span disciplines and organizations

•  Verification strategies that reflect operational realities, not just nominal cases

Mission Engineering helps ensure that all elements of a UAV ecosystem work together as intended.

5. Resilience Matters More Than Nominal Performance

Optimizing solely for ideal conditions is no longer sufficient for complex aerospace missions.

Teams need to evaluate system behavior under disruption, including:

•  Degraded communications

•  Asset loss or failures

•  Environmental uncertainty

•  Adversarial or contested scenarios

Mission‑level analysis helps uncover vulnerabilities early and guides more resilient system designs.

Looking Ahead

For aerospace startups and innovators, Mission Engineering is quickly becoming a competitive advantage. By focusing on early insight, intentional fidelity, and connected digital workflows, teams can reduce risk, accelerate development, and deliver systems that perform not only on paper but in real missions.

As mission complexity continues to increase, the ability to decide early, model wisely, and design for resilience will define the next generation of aerospace innovation.

Innovating Mission Engineering for Tomorrow Webinar Recordings

If you want to explore these ideas in more depth, recordings from the Innovating Mission Engineering for Tomorrow series are available here:

• Engineering UAV Missions: Digital Tools for Complex Scenarios
• Mission Engineering for Satellite Constellations

Each session includes practical examples, workflows, and demonstrations using MATLAB® and Simulink®, showing how digital tools can support mission‑level decision‑making from concept through validation.

This was the reality facing Raptee.HV, an India-based startup developing the country’s first high-voltage electric motorcycle. While high-voltage architecture has become common in electric cars, it is not the standard in the two-wheeler market. There is no established ecosystem to build upon, no off-the-shelf components, no reference designs, and no proven playbook.

The team did what many deep‑tech startups must do: they started from the ground up.

Designing a Motorcycle From Scratch

Raptee.HV’s goal is to deliver a technologically advanced motorcycle that elevates the everyday commuting experience. Pursuing a high‑voltage architecture means the team has to design and validate every major subsystem themselves. From the powertrain and battery pack to the motor controller and suspension, each component is engineered, modeled, and tested in-house.

For a small team working under constrained timelines and resources, relying on traditional build‑and‑test cycles is not an option.

Moving Development into the Virtual World

To keep pace, Raptee.HV has adopted a Model-Based Design workflow using MATLAB and Simulink. By shifting early development into a virtual environment, the team can explore ideas, test assumptions, and uncover issues long before hardware is involved.

Using Simulink, engineers create detailed digital models of the motorcycle’s key systems. These models allow them to experiment and iterate before machining parts.

Just as importantly, modeling helps the team manage complexity. High‑voltage systems demand tight coordination between controls, power electronics, and energy storage. MATLAB enables engineers to develop and validate complex algorithms for the battery pack and motor controller while continuously evaluating efficiency and performance.

The result is faster iterations and better design decisions earlier in the process.

One Engineer, One Workflow: Traction Inverter Development

One clear example of this approach is the development of the motorcycle’s traction inverter.

In a traditional setup, this process would involve multiple handoffs. Control engineers, embedded programmers, and test engineers each working in different tools. Instead, MATLAB and Simulink enable a single, end-to-end workflow. One engineer designs schematics, models system behavior, generates code, and deploys it directly to the target hardware for testing on the bike.

With Embedded Coder, Raptee.HV generates production-ready C code straight from their Simulink models. This approach eliminates delays and reduces the risk of translation errors between design and implementation.

Faster Development, Higher Confidence

For Raptee.HV, Model-Based Design isn’t just about speed; it is about confidence.

By identifying and fixing issues in simulation, the team has reduced overall development time. They can analyze control‑loop stability, run Hardware‑in‑the‑Loop (HIL) tests, and validate system behavior across operating conditions before those systems ever reach customers’ hands.

High-fidelity models also play an unexpected role beyond engineering. For the startup, demonstrating validated system behavior helps the team clearly communicate technical progress to investors during early development.

As Phunith Kumar V, Co‑founder at Raptee.HV, concludes, “I believe that the pace of innovation is gated by the pace of iteration. What MATLAB helps us to do is iterate fast, even without going to hardware, which helps us reach new levels of product development.”

For a startup building something the market has not seen before, that ability to iterate quickly and with confidence can make all the difference.

Hear more from the Raptee.HV team:

Learn more about Raptee.HV: https://www.rapteehv.com

Read more on how Raptee.HV uses Model-Based Design: https://www.mathworks.com/company/mathworks-stories/designing-indias-first-high-voltage-electric-motorcycle-with-model-based-design-and-code-generation.html 

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

The Problem: Data Silos Hold Engineers Back

Engineering organizations generate tons of data, but much of it is scattered across laptops, servers, test rigs, and disconnected tools. Mike Rosam, CEO of Quix, explains, “That really prevents engineers from using more modern analytical techniques like data science, machine learning, and AI at scale.” The result is an undesirable, slower time to market, lower product quality, and missed opportunities for automation.

Organizations trying to overcome this have a few decisions. They can build a custom in-house system, which can be expensive and complex to maintain, hire consultants for a digital transformation, again costly, or buy an enterprise platform, requiring customization and long deployment timelines. “Every R&D organization in the world has its unique processes,” Rosam notes. “It’s really hard to buy a standardized product that you can just purchase like a CRM for marketing data.”

The Solution: Quix – A Platform Built for Engineers

Quix flips the script with a developer platform designed for engineers. “We’re a Python-native development environment, so engineers can build their own workflows in languages they already use,” Rosam describes. “There’s no DevOps. Engineers write Python scripts, deploy them, and they’re up and running.”

The platform handles two major jobs:

1. Data Ingestion: Quix makes it easy to build data pipelines from test rigs, labs, and simulation tools, normalizing and enriching data for analytics. Engineers customize connectors using AI‑assisted code generation, then route data into a centralized warehouse. Metadata from configuration systems is automatically merged, allowing downstream tools to consume analytics-ready datasets. Mechanical and test engineers can set up pipelines themselves without IT tickets or delays.
2. Analysis and Automation: Once data is centralized, engineers can pull it into the tool they prefer. From MATLAB and Simulink to Jupyter Notebooks or custom tooling, the open structure enables hybrid toolchains rather than locking teams into one environment. A powerful use case is to automate event-driven analysis, triggering simulations, validation routines, or model-based workflows as soon as new test data arrives. Rosam explains, “We’re really trying to automate all of those steps in the engineering workflow and let engineers build their own workflows.”

“A big differentiator for Quix is it’s very open,” Rosam notes. “We can get data from any R&D tool, any physical system, into a consolidated cloud and let engineers pull the data into any tool.”

Seamless Integration with MATLAB and Simulink

Quix’s platform is deeply integrated with MATLAB and Simulink. “We use MathWorks tools every day to help our customers solve problems,” Rosam says. “The integrations we’ve built make it easy for engineers to acquire data from simulations and serve models in the cloud.” Engineers can use a Simulink block to stream data from Simulink models directly into Quix Cloud. They can run MATLAB and Simulink models inside Quix against live or historical data streams. Or parameterize models dynamically for real-time digital twin applications.

The Quix.IO platform seamlessly integrates data into Simulink. (Image courtesy of Quix)

Quix’s approach is already penetrating industries from motorsport to manufacturing. For example, a Formula One team uses the platform to run digital twin models in real time as the car is driving. When the car changes the front wing angle, engineers update the parameter, and the digital model running in Quix adjusts instantly. This keeps the virtual system aligned with reality, which is critical for verification and validation.

“We work in a very practical way,” Rosam emphasizes. “We identify a key bottleneck in the R&D process and fix that quickly, sometimes within a month or two. This isn’t a years-long, million-euro digital transformation. It’s pragmatic, high-impact problem-solving.” This model has helped customers accelerate simulation workflows, improve validation cycles, and close data loops between physical and digital environments.

Partnering with MathWorks Startup Program

For Quix, MathWorks Startup Program has been a foundational partner. The startup joined the program early to obtain access to MATLAB to help a customer. This quickly grew into a much more collaborative partnership. “Startups are cash-constrained, so the Startup Suites is a no-brainer,” Rosam shares. “But the support has been unrivalled. MathWorks went the extra mile, from account support, engineering support, even marketing support. We haven’t seen this level of support from other tech vendors.”

For a lean startup managing product development, customer success, and operations, this support saves both time and capital.

What’s Next for Quix

Quix is growing rapidly. They are looking forward to opening new offices in Prague and London. The team is hiring, launching new initiatives, and looking ahead to future funding rounds. Their mission remains the same, to remove data friction so engineers can focus on engineering. Rosam concludes, “When a customer says, ‘You changed the way we work,’ that’s the reward. We want to deliver that every day.”

Learn more about Quix: https://quix.io/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

This is the backstory behind the startup company weg//weiser. While working in a laboratory focusing on drive technology, some friends, Christian Klöffer and Philipp Degel, faced a consistent problem. “Testing new motors without the necessary information was always a major headache,” recalls Philipp Degel, CEO of weg//weiser. “We started looking into the possibility of automatically measuring electric motors, and eventually decided to build a company around solving this problem.” The years of pain were inspiration to come up with a solution for fellow engineers.

Electric vehicles (EVs) have become a backbone of the energy transition. The core of every EV is a complex electric motor. Getting these motors from the lab to the road is a challenging process. For engineers, testing and commissioning new motor designs can be a time-consuming, costly, and expertise-heavy process. Weg//weiser aims to change that with a system that automates and accelerates electric motor characterization, delivering test results faster with exceptional performance.

The Problem: Electric Motor Testing is Slow, Expensive, and Complex

The global shift to electromobility is reshaping how people and goods are moved. As new types of electric machines are developed, testing and measuring of these motors’ performance remains a critical bottleneck. Information is required to set operating points for certain conditions, such as the torque to be achieved under a specific speed, voltage, and temperature. This leads to the question of how the necessary complex information should be obtained. Companies are suddenly confronted with new, highly technical tasks.

The current situation is usually such that internally developed solutions have to be constantly modified and adapted to new requirements, or are underutilized. A lack of automation and complex integration increases the time required for commissioning, leading to slower innovation and rising costs for the use of expensive laboratories over a longer period of time

The Solution: weg//weiser’s Automated Universal Motor Test System

Weg//weiser’s solution is a universal test system for all types of electric motors. The software provides a fully automated evaluation of all measurement data across the entire speed range, deriving all relevant control parameters for optimal and safe motor operation. The flexible environment can be integrated into existing testbed systems, while allowing for customer-specific adaptations to meet tailored process solutions and expandable to future needs.

“Our mission is to empower our customers to complete their tests in record time with exceptional performance,” says Degel. “We want anyone to operate and analyze any electrical machine without needing expert-level knowledge.” To face testing complexity for operators, the platform incorporates a simple interface with “one-click” for commissioning, characterization, and evaluation of electric machines.

As Degel puts it, “Automated measurement routines, combined with detailed data evaluation, achieve the commissioning of electric motors in the shortest time possible.” This approach saves customers significant time and money when bringing new products to market, while delivering reliable, repeatable results.

weg//weiser’s operator interface for testing and commissioning of engines. (Image courtesy of weg//weiser)

Accelerating Innovation with MATLAB

A key part of weg//weiser’s rapid development of their platform was integrating MATLAB into their engineering foundation. “As an engineer, you often have the false perception that you could solve the problem better yourself. You must learn that nobody pays you money to reinvent the wheel,” says Degel. “MATLAB gives us the opportunity to focus our energy and time on innovation.”

The company developed sophisticated algorithms for evaluating measurement data in real-time with MATLAB and Simulink. To streamline the transition from development to deployment, weg//weiser uses Simulink Coder to generate code for their dSPACE system. Finally, the team leverages MATLAB Compiler to create a front-end for their parameterization and data evaluation tools. Stateflow automates even the most complex test routines.

Degel points out that using MATLAB and Simulink enabled weg//weiser to deliver their new platform to market faster and with reduced R&D costs by an estimated 50%. He adds, “Thanks to MathWorks tools, we as a small company can still develop very quickly and generate a high output of innovation. As we must be technically on a par with global corporations, this is one of the keys to the [startup] company’s success.”

Lessons for Founders

Weg//weiser’s journey hasn’t been without unique challenges. As an engineer turned CEO, Degel realizes the need to keep the overall product and customer in mind without getting lost in the technical details of the solution. His advice for other founders comes from his own experience. “Never stop learning and always meet people with an open mind. Treating customers with respect and trust is the most important thing. If the customer knows they can rely on us, you build long-term and reliable business relationships,” concludes Degel.

By making electric motor testing faster, easier, and more accessible, weg//weiser is helping drive the future of electromobility.

Learn more about weg//weiser: https://future-of-tomorrow.com/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

For startup Reflex Aerospace, the successful launch secured its place in the “New Space” era. It was the first step to proving that satellites can be designed, built, and deployed in under 12 months, all without sacrificing quality or reliability.

Speed Matters in Space

For decades, satellite development has meant long lead times, massive budgets, and little room for iteration. “The old space approach was to build satellites capable of achieving lifetimes of 10 to 15 years, but that’s not needed anymore,” explains Gabriele Palumbo, Altitude and Orbit Control System (AOCS) Engineer at Reflex Aerospace.

Space technology is moving rapidly. What’s cutting-edge today could be obsolete in two to three years. This urgency is driven by the rapid evolution of payloads, including cameras, sensors, and communication systems that are installed on satellites. Levi Vuylsteke, Lead Altitude and Orbit Control System (AOCS) Engineer at Reflex Aerospace, draws an analogy to how technology improves so rapidly in smartphones: “It’s the same thing with satellites. If you have a lead time of more than four years, it doesn’t make sense. The market craves fast satellites.”

Over the past two decades, the CubeSat approach has revolutionized, in part, the design of satellites. Makers use off-the-shelf components to reduce delivery times. However, the market was wary about the lack of reliability and performance with these platforms.

Reflex is on a mission to deliver highly reliable custom satellites at a pace that matches the rapid evolution of technology on Earth. At its core, Reflex is driven by the belief that the old way of doing things no longer meets the needs of today’s space industry.

A New Approach to Satellite Development

Reflex’s approach to satellite development is fundamentally different than previous generations. They blend the best of both practices: the reliability and customization of traditional “Old Space” with the agility and speed of “New Space.”

“We design a platform that is custom-made for the customer, but we generalize our processes to streamline system design,” Palumbo notes. The team is not constrained by a particular supplier. They source components, even from outside the traditional space supply chain, if it means faster delivery and equal or better reliability. This flexibility extends to manufacturing. Reflex designs satellites to be fast to build and integrate, shortening procurement and assembly times. The result: satellites tailored to mission needs, delivered on timelines that keep pace with technological change.

The ability to deliver satellites quickly is not just a technical achievement for the builder but also advantageous for its customers. Satellite constellations are becoming the norm. Instead of relying on a single, long-lived satellite, operators deploy fleets of smaller satellites that can be refreshed every few years. This approach keeps their services at the highest level, allowing them to adapt to changing market demands.

Simulation at the Core

Central to Reflex’s speed and reliability is its commitment to simulation-driven development. “You only have one try in space. You can’t launch these satellites and have them fail,” Vuylsteke says. “Simulations are very important. We use them across all phases of satellite development.”

The workflow begins with system-level modeling in Simulink, where engineers build digital twins of their satellites. “For our AOCS simulator, we use the MATLAB and Simulink toolchain,” shares Vuylsteke. “It allows us to rapidly develop the simulator, and we benefit from predefined Simulink blocks.”

To accurately represent the space environment and satellite dynamics, Reflex leverages the Aerospace Toolbox and Aerospace Blockset. These toolboxes provide ready-to-use models for orbit propagation, attitude dynamics, and environmental effects such as Earth’s magnetic field.

Stateflow is used to map out the satellite’s operational modes so that every transition and contingency is accounted for. The environment lets them experiment, iterate, and refine designs before any hardware is built. The team leverages Parallel Computing Toolbox to accelerate large-scale simulation campaigns, efficiently exploring thousands of scenarios. As the design matures, Simulink Coder is used to automatically generate C-code, ensuring a seamless transition from model to onboard software.

“The startup license let us experiment with different toolboxes and see what fits our workflow. The support from MathWorks has been invaluable; feature requests, bug fixes, and training resources have all helped us push the boundaries.” – Levi Vuylsteke, Lead Altitude and Orbit Control System (AOCS) Engineer at Reflex Aerospace

Despite their rapid pace, Reflex does not cut corners on verification and validation. “Testing is very important. You have to test early and often,” Vuylsteke emphasizes. The team employs a layered approach to testing, starting with unit tests for every low-level function to system-level functional testing with Simulink Test. Monte Carlo simulations are run to probe the robustness of control algorithms against uncertainties.

Once the software passes the virtual checks, Reflex moves to processor-in-the-loop and hardware-in-the-loop testing. This ensures their satellite will behave exactly as intended when it reaches orbit. “We make sure our generated code behaves the same as in the simulations,” Vuylsteke says. “It’s very satisfying as an engineer to see that your simulation matches reality perfectly.”

Requirements-based test cases are formulated early and reused throughout development, helping catch errors before they reach the hardware stage. As Palumbo explains, “We use Simulink testing capabilities to validate our core libraries and blocks. With every new mission, we start from our heritage, adapt, and revalidate.” With the satellite in space, Curve Fitting Toolbox supports data analysis and post-processing, helping engineers validate that real-world performance matches their predictions.

By weaving these toolboxes into a seamless digital workflow, Reflex Aerospace can adapt to changing requirements and deliver satellites faster than previous generations. The rigorous V&V workflow maintained by Reflex Aerospace ensures that satellites not only launch quickly but also perform reliably in the demanding environment.

Looking Ahead from Launch

The culmination of this development process was the launch of SIGI, Reflex’s first satellite. The whole team was on hand to watch the SpaceX live stream. When SIGI separated from the rocket, the engineers rushed to the mission operations room to see the first data roll in. Vuylsteke recalls, “It was one of the most stressful and exciting moments of my life.”

With SIGI in orbit, Reflex is already looking ahead. “Now that we have a real satellite in space, we can use that data to validate and improve our simulation models,” Vuylsteke concludes. “Our simulations are generic, so we can quickly adapt them for new missions. We’re building modular architectures and libraries of blocks, making it easy to swap components and scale up production.”

Reflex Aerospace aims to democratize access to space. By reducing costs and timelines, they enable organizations, including governments, research institutions, and commercial ventures, to deploy satellites tailored to their specific needs. This has far-reaching potential from new scientific discoveries to improved global communications.

The startup is setting a new standard for the industry. By proving that high-quality satellites can be delivered quickly and reliably, Reflex is challenging legacy builders to rethink their own processes and embrace innovation.

Hear more from the Reflex Aerospace team:

Learn more about Reflex Aerospace: https://www.reflexaerospace.com/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

Simultaneously, the path to commercial value is being accelerated by software, specifically through quantum-inspired algorithms. These solutions apply quantum principles to today’s advanced classical hardware, enabling businesses to solve complex optimization problems without waiting for fully scalable quantum machines. This approach allows enterprises to transition immediately from “quantum-aware” to “quantum-ready.”

Finding a Solution to the Quantum Problem

Problem: In compute-intensive industries like aerospace, energy, and semiconductors, simulation engineers and data scientists face a persistent bottleneck: complex simulations are slow, expensive, and often limited by outdated algorithms. Even with modern high-performance computing (HPC), running thousands of simulations for a single aircraft component can take months, stalling innovation and inflating costs.

Solution: Startup BQP has developed a software platform that leverages quantum-inspired algorithms to supercharge digital twin modeling and simulation. A substantial increase in computational efficiency enables users to conduct complex simulations and AI training with greater speed and fidelity. This allows for exploring larger design spaces, converging on optimal solutions faster, and using more sophisticated physical models that align closely with real-world outcomes. It reduces reliance on costly physical prototypes, uncovers edge cases, and accelerates time-to-market.

BQP’s QIO platform is easily called from within MATLAB (Image courtesy of BQP)

Inspired by Computational Barriers

BQP was founded by innovators who were all experiencing the same critical bottleneck of the “math lag.” “The math that goes behind these tools is outdated. They actually haven’t changed in the past 40 years,” Abhishek Chopra’s, Founder, CEO, and Chief Scientific Officer of BQP explains. While hardware capabilities have accelerated exponentially, the fundamental algorithms driving these simulations have remained stagnant for over forty years. Simply migrating legacy deterministic code to modern high-performance GPUs yields suboptimal results because the underlying logic is outdated. The industry has reached a performance plateau where faster machines can no longer compensate for inefficient, 1980s-era mathematics. This was Chopra’s “aha” moment.

A Platform for Modern Optimization

BQP’s product, BQPhy, is powered by a Quantum-inspired optimization (QIO) solver, designed to bring the benefits of quantum computing to classical hardware. Instead of relying on quantum computers, which are not yet commercially viable, the QIO solver uses quantum-inspired algorithms that mimic the mathematical principles behind quantum computing. These algorithms represent quantum circuits as tensors (multi-dimensional matrices) and execute tensor operations efficiently on traditional HPCs, CPUs, and GPUs.

The approach enables large-scale optimization without the cost or inaccessibility of quantum hardware. By leveraging advanced parallelization and concurrency, QIO scales across HPC environments, allowing simultaneous evaluation of multiple design points during optimization. This results in exploring challenging problems and areas that were prohibitive to the engineers. For example, it enables large-scale battery optimization for electric vehicles and streamlines aerospace scheduling tasks.

The team aims to solve the unsolved problems. “Legacy simulation algorithms are relics of the pre-smartphone era. Our team has rewritten the simulation algorithms from the ground up, to shatter yesterday’s limits,” shares Rut Lineswala, Founder and CTO of BQP. Engineers can achieve up to 10 times speedup on the same architecture for existing use cases, while maintaining compatibility for future integration with quantum hardware.

Integrating with MATLAB

The team chose MATLAB and Simulink to build their platform. Aditya Singh, Founding Member, VP Business & Strategic of BQP says, “Our goal is not to force engineers to learn a completely new, standalone platform. Our vision is to be a ‘backend engine’ that supercharges the tools they already use.” With MATLAB being an industry-standard tool for many engineers, BQP uses MATLAB to develop its technology as a seamless toolbox that plugs directly into the engineer’s existing environment. This integration dramatically lowers the barrier to adoption for their customers.

To provide solutions to engineers in industries such as aerospace and automotive, the BQP team sought access to the same tools they trust. “As a startup, resources are tight. MathWorks Startup Program gives us affordable access to the complete MATLAB and Simulink Suites,” Singh says. “This allows us to experiment, build, and test with the same powerful tools used by our enterprise-level customers without the enterprise-level cost.” For their development, they utilize MATLAB for rigorous testing and validation, ensuring their vision operates seamlessly for each customer.

“MathWorks Startup Program provides essential guidance and one-on-one support from MathWorks application engineers. This close collaboration was instrumental in our development; it’s precisely how our Quantum-Inspired Optimization Toolbox came into being. We were able to get expert advice to build our product in a way that is robust and native to the MathWorks environment.” – Abhishek Chopra, Cofounder, CEO, and Chief Scientific Officer, BQP

MATLAB enables the team to reduce R&D costs and increase productivity. “It is easy to compile source code into MEX format, enabling a common architecture and framework,” says Chopra. With ongoing solutions being added, the integration allows them to rapidly iterate and release new versions based on customer input.

Open Sharing with File Exchange

As Chopra agrees, a significant challenge for any startup is getting its product in front of the right users. For BQP, they wanted to leverage the global community of MathWorks users to amplify their flagship platform. They use MathWorks’ File Exchange to give a limited time of free access to the solver so users can experience QIO.

BQPhy toolbox is seen within MATLAB as an add-on toolbox.

MathWorks File Exchange allows the community to find and share custom applications, scripts, and more. Directly in MATLAB, a user can explore Add-Ons and find a toolbox or script to download that meets their needs. This allows users to leverage external tools while remaining within a coding environment that’s familiar to them. For startups like BQP, it’s a pathway to spread awareness of their platform. Not only does this attract prospective customers, but it also enables them to gather insights and feedback from users, helping to improve their product. BQP has found this to be a successful tactic for their go-to-market strategy.

Adopting a Value Mindset

Chopra explains how his mindshift in building a startup needed to change from his academic background. “In R&D or academia, the goal is often technical perfection or publishing a paper. In a startup, the goal is creating value that someone will pay for,” Chopra describes. “A ‘good enough’ solution that solves 80% of a customer’s problem today is infinitely more valuable than a ‘perfect’ solution that arrives two years too late.” The team moved their benchmark for success from technical elegance to customer traction and revenue.

However, this doesn’t mean the team is satisfied with the status quo. They are focused on continually improving the platform, starting with the addition of advanced physics AI and Multiphysics solvers to their platform. BQPhy’s near-term quantum-inspired solvers extract value for end-users today by utilizing the existing HPC, while also future-proofing with hybrid quantum-native solvers for datacenters where quantum computers will sit next to the HPC.

Preparing for the Quantum Future

The team is dedicated to building a future talent pipeline focused on quantum. Companies can struggle to establish quantum-focused innovation teams because it requires a rare, new blend of expertise in physics, computer science, and engineering. This gap creates a “quantum readiness debt,” leaving companies vulnerable to a steep, multi-year learning curve and competitive disadvantage.

BQP is participating in a pilot program in Upstate New York’s Quantum Valley, designed to expand awareness and fill the education gap. By providing quantum-inspired tools and real-world expertise to college students and local universities, BQP is giving the next generation of engineers and scientists practical, hands-on experience. This initiative creates a new, quantum-ready workforce, bypassing the long learning curve and ensuring a steady flow of talent that can be productive from day one.

All of this in mind, the team continues to fall in love with the problem that started it all. “Engineers and scientists often fall in love with their technology, be it AI, quantum, or a new algorithm. But successful startups are not built on a technology; they are built on a painful, expensive, and urgent problem that a customer has. Be obsessed with solving that customer’s problem. Your technology is just the tool you use to do it,” concludes Chopra.

Learn more about BQP: https://www.bqpsim.com/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

Watch a live demo of BQPhy in MATLAB:

Despite efforts to improve survival rates through the distribution of automated external defibrillators (AEDs) in public spaces like gyms, offices, or airplanes, there has been little improvement in the resuscitation rates for out-of-hospital cardiac arrest (OHCA). A startup company, Defibrio, aims to change this.

A lifesaving idea in your hand

The founding trio of Defibrio wanted to explore how the ubiquity of smartphones could be used to save more lives of people who experience sudden cardiac arrest. Equipped with the perfect combination of skills, one a technologist, one a businessman, and one an emergency room medical doctor, they set out to put lifesaving technology within arm’s reach of everyone.

Their product, the Defibrio AED, is the first of its kind – an AED powered by a smartphone. Josh Hale, Chief Product Officer at Defibrio, explains, “The use of a smartphone as the brains and power of an AED presents significant potential with regard to ease of use, cost, and maintenance compared to current AED solutions.”

The Defibrio AED uses an app and the power of your smartphone to deliver a therapeutic shock to treat sudden cardiac arrest (Image courtesy of Defibrio)

Traditional AEDs can be expensive, large, and unfamiliar to most laypeople. The Defibrio team wanted to design their technology for the everyday person. Most people are comfortable with using an app interface on their phone, making it easier to instruct the user through the app-based platform in an emergency. Along the same lines, the AED relies on the Defibrio app to deliver built-in notifications to test the device and replace the pads on a pre-determined schedule, taking out the forgetfulness of regular maintenance and battery replacement of existing AEDs. A novel aspect is that the power source is the smartphone battery itself – the AED does not have its own battery, as the phone battery has plenty of power to deliver multiple shocks. Finally, to keep the device readily available to people, the founding team centered on the idea of an app for a much lower cost of entry.

The Defibrio AED – making AEDs more accessible

The Defibrio AED system is composed of three parts: a smartphone app, an AED module, and a small device that comes with a USB-C connection and electrode pads attached to the AED module that can be applied to the patient’s chest.

During a cardiac arrest emergency, the user simply connects the module to their phone via USB-C. The Defibrio app will open automatically, contact emergency services, and guide the user with step-by-step instructions to apply the pads to the patient’s chest. The app analyzes the patient’s heart rhythm and delivers a shock (if warranted) to revive the patient. In addition, the app gives CPR instructions and records data that can be shared with medical professionals.

The Defibrio AED is connected to a smartphone and electrode pads are placed onto a patient (Image courtesy of Defibrio)

At a technical level, the Defibrio app manages a multitude of activities during an emergency. Once connected, the app sends commands to the AED module to quickly test that all components are ready to be used. The AED is signaled to begin charging, powered by the smartphone, in the event a shock should be delivered. When the user indicates that the electrode pads are applied to the patient, the app initiates an analysis of the patient’s heart, including receiving the ECG data from the electrode pads, filtering noise out of the signal, and running the data through an algorithm that classifies the type of rhythm and whether it can be treated with a shock. These behind-the-scenes processes, along with finding a way to design the hardware elements in a small, portable form factor, are the core of the engineering focus that Defibrio had to develop.

Partnering for market success

Like many startups tackling ambitious ideas, Defibrio faced challenges in finding the right teams to execute on its vision. They worried that the time it might take to get up and running with a capable team could mean significant delays to the overall program schedule. Leveraging strong networks in Boston, they were able to connect with expert partners who could help bring their idea, a Class III medical device, to market.

“Our philosophy has been to blend our strengths in overall business management and product leadership with third-party skills to develop and deliver the product,” Hale explains, “it was obvious that Andrews Cooper was able to bring to the table a host of experiences that were complementary to Defibrio’s mission.” Andrews Cooper (AC), an engineering services firm specializing in highly complex technology development, has been a core partner from the beginning of the venture.

Engineers from Andrews Cooper work on the Defibrio prototype (Images courtesy of Andrews Cooper)

AC played a crucial role in building the original proof of concept to illustrate that the AED’s fundamental capabilities of working with a smartphone were possible. Hale commends the partnership, “AC has not only led the way on our hardware design, but they have also driven all of our firmware development and were instrumental in the development of our custom rhythm recognition algorithm that runs in the Defibrio app.” Beyond the dedicated groups at AC working to help Defibrio meet critical development goals, they support the startup in identifying partners for new phases of development, such as a contract manufacturer to build the AED, and communication with stakeholders.

Engineering the algorithm with MATLAB

By working with AC, Defibrio was able to access specialized engineering expertise without the delays and overhead of building an in-house team from scratch. A key part of the engineering workflow centered on developing and validating a rhythm recognition algorithm.

The team used MATLAB App Designer to build a custom annotation tool, used by clinicians to tag heart rhythms in a database as shockable or not shockable. This process involved pre-labeling rhythms by residents, a formal review by physicians, and an adjudication step to resolve diagnosis differences. This human-annotated database provided the backbone for assessing the next phase of an engineered algorithm against.

ECG annotation tool using MATLAB App Designer (Image courtesy of Andrews Cooper)

The second phase focused on the development of the recognition algorithms. Engineers used MATLAB to hypothesize a signal processing method to enhance characteristics of the ECG wave that would reliably separate the various heart conditions between those that should be shocked and those that should not be shocked. Large databases of ECG waves were analyzed and plotted to test how effective the signal processing method was at separating out the various heart conditions. The team could then compare human judgment against algorithm judgment to determine if the Defibrio AED was successfully able to identify rhythms as shockable vs. not shockable.

In parallel with the development of the MATLAB modules, the algorithm was implemented in C++ for deployment in the final product. By running these versions side by side, the team can spot discrepancies and fix errors early, reducing defects and speeding up development.

Leveraging MathWorks tools to focus on innovation

The adoption of MathWorks tools played a pivotal role in accelerating Defibrio’s algorithm development process. “The beauty of MATLAB is the speed and efficiency with which one can test an algorithm concept and thereby iterate on various concepts,” says Hale. MATLAB’s language structure with math constructs that allow for easy manipulation and computation of arrays, combined with a rich capability of plotting results, noticeably sped up their algorithm development.

Data integration was also a critical factor in Defibrio’s workflow. The team needed to consolidate rhythm data from multiple sources to build a robust training and testing foundation for their AED’s recognition algorithm. MathWorks tools offered confidence in the ability to interface with a variety of data formats and sources.

“Developing new algorithms with MATLAB dramatically helped to reduce our total R&D cost and the time to market.” – Josh Hale, Defibrio 

For the team comprised of engineers with diverse backgrounds and areas of expertise, having a unified set of familiar tools minimized onboarding time and facilitated seamless communication in comparison to open-source alternatives.

Access to MathWorks Startup Suites enabled the team to focus on the technical challenges of innovation, rather than the logistics of tool integration or data handling, supporting a more efficient and cohesive development cycle.

Healthcare in everyone’s pocket

Breaking from the traditional with a new form of technology can be difficult, both in developing the product itself and successfully bringing it to buyers. Making a new medical device comes with a particular set of challenges, including understanding regulatory requirements, embedding a quality system and its processes into the development workflow, and coordinating test activities across disparate teams. To mitigate risk, Defibrio focused on finding experienced, capable partners like AC and prioritizing structured meetings and processes to facilitate communication across the project.

Reflecting on advice he would share with other founders, Hale concludes, “Building something new is bound to attract both passionate support and ardent skepticism. We use the support to our advantage and try to learn from the skepticism, but also not let it slow us down.” The team has certainly not let any skepticism halt their passion for bringing life-saving technology to everyone. Defibrio is focused on its next major milestone – finalizing development and obtaining regulatory approval for commercial distribution and sale of the device.

With a smartphone-powered AED, Defibrio hopes to make a real difference in saving the lives of sudden cardiac arrest patients, one pocket at a time.

Learn more about Defibrio: https://defibrio.com/

Learn more about Andrews Cooper: https://www.andrews-cooper.com/

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

The Beginning of Impedyme: A Mission to Democratize Real-time Test and Emulation

Impedyme Inc. has a bold vision: to make multi-nodal power electronics test systems accessible to innovators across industrial and academic domains. Dr. Angelos Stavrou, CEO and Co-Founder, explains, “We’re on a mission to democratize CHP technology as an all-in-one solution across diverse industries that use high-power equipment, including EVs, aerospace, and renewables.” Stavrou goes on to say, “By lowering the barrier to entry without sacrificing scalability, we empower innovation in high-power energy and transportation systems worldwide.”

Impedyme CHP-Series Test and Emulation system showcased at The Battery Show 2024, Detroit MI (Image courtesy of Impedyme)

To achieve this goal, Impedyme developed a range of solutions to meet the needs of its customers.

Dr. Ash Kan, Director of Business Development, explains, “Up to now, real-time power hardware-in-the-loop (PHiL) testing of power electronics devices for both functionality and performance has been a pretty expensive and time-consuming endeavor.”

Indeed, real-time simulation systems can easily cost thousands of dollars, especially when a power amplifier is added. “Impedyme’s aim is to lower the cost of entry and enable customers to access turnkey solutions with software and hardware components tailored to a specific project, namely a wide range of real-time hardware-in-the-loop applications, including emulation support for motor, grid components, battery, to name a few,” says Kan.

During a project, the type and number of inputs and outputs signals, processing unit, number of electrical phases, and frequency bandwidth, for example, are designed according to the requirements of the intended device under test (DUT). Kan describes how these tailor-made solutions in the market can suffer from a drawback: If the requirements change, or if there is a parallel project to develop a different part of a powertrain, such as a traction inverter, battery pack, or on-board charger, which is typical for agile R&D teams, the test bench might need to be modified or upgraded. This can be highly costly and time-consuming, making it often impossible to reuse the existing hardware for a completely new application.

In many cases, budget shortages push teams to purchase a simple AC/DC power supply instead of an advanced Power HiL testbench for power testing. This shortcut can leave them with many unanswered questions, such as how to do high-frequency testing, emulate accurate system behaviors, or simulate the fast dynamics of the system. Impedyme’s PHiL products resolve this concern by providing a highly programmable and modular system, enabling reconfigurable testing and application adaptation with minimal cost and time requirements.

Today, Power HiL testing and simulation are still in the hands of relatively few dedicated experts in a company. The cost and complexity of integrating a real-time simulator with power amplifiers prevent regular development engineers from utilizing real-time Power HiL simulators.

At the heart of Impedyme’s platform is the Combined Hardware-in-the-Loop and Power (CHP) system, an advanced test and emulation system, with ultra-fast FPGA real-time processing. Equipped with proprietary stand-alone liquid cooling, CHP offers increased power density and fundamental frequency. The flexible interfaces, including multi-channel analog I/O, digital I/O, and multi-gigabit fiber links, enable CHP to integrate seamlessly with sensors, controllers, and external hardware. Multi-channel signals are directly accessible in MATLAB and Simulink, allowing real-time interaction, parameter tuning, and measurement within the modeling environment.

Impedyme CHP power module scalability for electrical and real-time simulation (Image courtesy of Impedyme)

The result is an affordable, multi-purpose device that can conduct both functional and performance tests. Even engineers who are only familiar with MATLAB and Simulink tools can be up and running with the Impedyme Power HiL solutions in no time.  “Our vision is to provide an all-in-one Power HiL platform that is accessible to all hardware and software engineers, regardless of their level of HiL experience,” concludes Kan.

A Unified Software Ecosystem: PowerHIL Studio and Simulink Integration

Impedyme’s PowerHIL Studio integrates seamlessly with Simulink, enabling real-time control and scripting via MATLAB. Engineers can deploy models to FPGA-based real-time hardware, automate test sequences, and switch between motor emulator, grid emulator, and impedance analyzer modes all within one environment. This reduces development time and eliminates the need for manual reconfiguration, enabling a faster time-to-market.

PowerHIL Studio software for cabinet configuration and FPGA bit-stream deployment (Image courtesy of Impedyme)

Motor Emulator for Inverter Testing and Validation

Impedyme’s Motor Emulator is a powerful real-time testing solution designed to revolutionize the development, validation, and deployment of inverter systems, particularly in the fields of electric mobility, renewable energy, and industrial automation. Built to support a wide range of motor types, including Permanent Magnet Synchronous Machines (PMSMs), Brushless DC Motors (BLDCs), and Induction Machines (IMs), the Motor Emulator simulates the realistic electrical and dynamic behaviors of physical motors under various load conditions. This enables customers to test inverters across a full range of operating scenarios without the need for a physical motor or mechanical setup. This virtualized approach to inverter validation dramatically reduces setup complexity, enhances lab safety, and accelerates development cycles.

Motor Emulator and RCP Box Testing Platform control logic for validation and power testing (Image courtesy of Impedyme)

One of the core advantages of Impedyme’s Motor Emulator is its seamless integration with MATLAB and Simulink and Impedyme’s MotorSim Studio, allowing engineers to apply model-based design methodologies and automate test workflows. Parameters such as inductance, resistance, and inertia can be modified in real-time, enabling rapid testing across a wide range of motor profiles with just a few clicks. Engineers can also inject faults such as short circuits or sensor failures in a controlled environment, gaining insights into system robustness and fault-handling strategies. Whether testing a traction inverter for an EV or tuning a VFD for an HVAC system, Impedyme’s Motor Emulator offers a flexible, cost-effective, and high-fidelity alternative to physical motor testing – empowering innovation while reducing risk and development cost.

Impedyme’s motor emulator testbench deployed for the customer testing inverter (Image courtesy of Impedyme)

The Grid Emulator: Power Testing Meets Smart Grids

The CHP Grid Emulator is a regenerative grid simulation platform designed for PHIL and HIL scenarios. It enables validation of renewable integration, microgrid behavior, and inverter/grid-tied performance. The utility industry is heavily regulated, and adding new technology that touches the power grid must be rigorously tested and de-risked. With standards support like IEEE 1547, UL 1741, and IEC 61000, it meets the compliance needs of research labs and OEMs alike.

Impedyme’s GridSim Studio with intuitive graphical user interface designed to configure, operate, and visualize real-time grid emulation scenarios (Image courtesy of Impedyme)

From Simulation to Scalable Innovation

Impedyme’s partnership with MathWorks empowered them to harness the full range of MATLAB and Simulink capabilities from day one. “The MathWorks Startup Program eliminated the guesswork,” says Stavrou, “having access to MATLAB toolboxes and seamless integration saved months of development. Model-based design (MBD) in MATLAB and Simulink streamlines the entire product and code lifecycle by unifying requirements, design, implementation, and testing in a single model-driven workflow, enabling our team to deliver higher-quality products with reduced cost and time-to-market.”

Global Remote Access for Scaling Worldwide

Impedyme’s PHIL platform has robust support for global remote access and live demonstration, making high-power testing more accessible than ever. Through secure cloud-based connectivity, users can remotely operate the PHIL test bench to run simulations, modify system parameters, monitor waveforms, and even inject faults in real-time without needing to be physically present at the lab. This is especially valuable for OEMs, research institutions, and multi-site engineering teams who require flexible, location-independent access to test resources.

Before deployment, customers are often given remote access to a live system configured with their models or hardware under test, allowing them to validate functionality, explore features, and build confidence in the solution. Impedyme engineers can integrate any customer component, including inverters, converters, or protection relays, into the Power HiL testbench and showcase full-system behavior through a real-time interface. This approach accelerates decision-making, reduces commissioning timelines, and supports virtual training and collaboration, empowering organizations to innovate faster while minimizing travel and setup costs.

Looking Ahead: Impedyme’s Systems in Action

Impedyme’s products now span five continents, advancing their mission to democratize Combined HIL and Power (CHP) technology as an all-in-one solution. Impedyme supports a wide range of industries including EV, eVTOL, aerospace, marine, and renewable energy sectors such as wind, solar, and hydropower, with the goal of making advanced CHP systems accessible, affordable, and scalable, empowering innovators and researchers worldwide to accelerate breakthroughs in sustainable energy, transportation, and grid modernization.

Take a look at Impedyme’s emulation systems in action: https://www.youtube.com/watch?v=YMjE1vIy39I

To learn more about Impedyme, explore the products and request a live demo visit: https://impedyme.com

Learn more about MathWorks Startup Program: https://www.mathworks.com/products/startups.html

Here are some of the notable highlights we took away:

Space: dual-use technologies and new launch hubs

• There is growing interest in surrounding dual-use for space technologies – hardware and solutions that serve both public and private sector needs, with investment resources prioritizing these applications.
• Due to the pending shortage of space launch sites in the US, Texas is prioritizing the development of new launch sites. This could position Texas as a future hub for space startups, offering the infrastructure and ecosystem necessary for growth.

DARPA: connecting startups to accelerators

• Programs like DARPA’s Commercial Accelerators are forging new pathways for startups. By collaborating with accelerators (such as Capital Factory), these programs enable rapid commercialization and scaling of startup technologies.

Drones: evolution and focus shifting

• Drone technology continues to evolve at an unprecedented pace. With platforms becoming outdated in a matter of months, the most successful startups will prioritize components for drone production more so than on drone platforms as a whole. This strategy allows them to stay agile and meet the rapidly changing needs of the market.

Investment: open to more arenas

• There has been a notable shift in the investment landscape in recent years. US venture capital firms are increasingly open to startups working in traditionally government-focused sectors, including international companies. VCs see value in streamlining manufacturing processes, signaling a broader trend toward modernizing and scaling critical infrastructure.

Energy: a rise in nuclear innovation

• Nuclear energy was another popular topic, particularly small modular reactors. These technologies are gaining traction for their potential to provide reliable, on-site energy. One example initiative is Project Pele, an Idaho National Laboratory (INL) project developing a portable nuclear reactor that could have wide-ranging applications.

The conference sparked interesting discussions about the mobilization of resources and the future of national innovation. As the ecosystem continues to evolve, it will be fascinating to watch how public-private partnerships and regional hubs like Texas shape the landscape for startups and emerging technologies.

We’ll be back in Austin in October for Austin Tech Week and we’ll look forward to connecting with more from the ecosystem!