ResCon Technologies employs Machine Learning to create digital twins of complex dynamical systems. Our approach achieves stunning fidelity using minimum power and data, making it compatible with edge computing hardware. This enables model training directly on the edge in real-time without the need for a cloud connection or enormous datasets. Edge-hosted digital twins provide data analysis, adaptive control, and predictive health and status.
May 2024 - ResCon is very excited to begin our first collaboration with NASA: a multi-year Space Act Agreement (SAA) project focusing on processing complex SansEC sensor signals using our Reservoir-Augmented Control and Health (ReACH) software. ReACH is ideal for remote environments requiring low-power machine learning using disconnected edge hardware. Initial development for ReACH was funded by the Air Force via an Open Topic Phase I STTR with Ohio University.
December 2023 - ResCon has been awarded a Missile Defense Agency Phase II SBIR focused on the modeling and prediction of Inertial Measurement Unit (IMU) performance characteristics. The team is using the funding to develop and field the RAIN algorithm. Short for "Reservoir Aided Inertial Navigation," RAIN uses computationally efficient machine learning techniques to correct for deterministic errors and improve IMU performance in real-time, all while being hosted on low power embedded microcontroller hardware.
Ongoing - ResCon is seeking a Machine Learning Developer to help create models of complex systems. ML-based models will be deployed to embedded computing hardware to provide adaptive control, predict health and status, and furnish actionable insights to users.
Ongoing - ResCon is seeking an individual with a passion for aerospace that has a background in Guidance, Navigation & Control (GNC), ideally with experience in Machine Learning and/or Physics, to help us develop several software products for commercial and Government customers.
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