Electro Optical Communication

Object Recognition and Classification

Advanced Neural Network

Spin Drift Technologies provides simple elegant solutions to complex problems through the innovation of novel systems.

Thermal Image Enhancement

Counter Unmanned Aerial Systems (UAS)

Enabling low probability of intercept / low probability of detection communications by pioneering data transfer outside of traditional radio, microwave, or infrared spectrums to meet the demands of the modern electronic warfare environments. Ability to transfer updated battle plans, intelligence documents, weather reports, operation manuals, targeting data, and scientific data in fully denied environments.

Long-range object recognition powered by our Novel Neural Network. Dynamic training pipeline enables tailorable object recognition and classification to desired target(s). Distillable to a range of deployment platforms for straight forward system integration.

Spin Drift Technologies employs hybrid machine learning systems of novel architecture neural networks operating in parallel with convolutional neural networks. This enables rapid image processing, application of machine vision, error correction, and object recognition/classification.

Novel neural network architecture integration into existing IRTI sensors to create a cutting-edge soldier borne super resolution and recognition system. In-the-moment image processing, object detection, and pattern recognition increasing the effectiveness of operators in the field.

Multi-modal detection system utilizing radio, microwave, electro optical, and infrared sensors. Combining digital signal processing with Ultra-Fast Neural networks to rapidly analyze, identify, target, and spatially locate drones and swarming events.

SDT IS developing a Hamiltonian Neural Network for tracking space borne C.C.D.M. (camouflage, concealment, deception, or maneuver). the system Leverages Hamiltonian Physics, Neural Networks, Approximate Nearest Neighbors, and Electro-Optical systems to identify uncorrelated tracks (u.c.t) and perform rapid vector comparisons in bulk to identify its orbital parameters and propagate its position forward and backward in time.