Published on May 25, 2026
In the realm of embedded systems design, traditional sensor integration has often dictated both the complexity and cost of development. Engineers typically rely on physical sensors for data acquisition, leading to larger system footprints and increased power consumption. However, recent advancements are shifting this norm toward more streamlined and efficient solutions.
A new webinar from MathWorks introduces a comprehensive workflow that allows engineers to design, validate, and deploy AI-based virtual sensors. models into the Simulink environment, developers can simulate and verify sensor behavior without physical prototypes, addressing common challenges of scalability and resource management.
This innovative approach employs formal verification techniques to ensure neural networks perform as intended. Additionally, the webinar covers methods to compress AI models, reducing memory requirements and enhancing execution speeds. Participants can also learn to generate efficient C code directly from their AI models and assess the trade-offs involved in their design choices.
The implications of this technology are significant. Companies can expect faster deployment times, lower costs, and improved performance in their embedded applications. -based virtual sensors, engineers not only simplify their design processes but also pave the way for more intelligent and adaptive systems in various industries.
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