SYNCHRONIZING ORBITAL DATA STREAM...
Engineering high-reliability neural architectures, quantized edge vision models, and causal telemetry analytics purpose-built for extreme, non-stationary aerospace and robotic environments.
Deploys convolutional feature extractors and vision transformers directly to edge accelerators on orbital and robotic platforms. Identifies terrain hazards, structural defects, and atmospheric phenomena with zero cloud latency dependency.
Built for deterministic latency, mathematical explainability, and edge hardware execution.
Hybrid ViT architectures designed to ingest 128+ contiguous spectral bands simultaneously without spatial resolution decimation.
Directed Acyclic Graphs mapping causal relationships across 5,000+ telemetry channels to isolate component degradation before physical threshold trips.
Reinforcement-learned task planners capable of scheduling optical target pointings and power budget allocations under strict orbital thermal constraints.
Post-training quantization (PTQ) and quantization-aware training (QAT) compiling massive models into sub-15W silicon compute budgets.
Self-supervised pre-trained foundation weights trained on multi-temporal global satellite imagery for instant zero-shot land-use segmentation.
Formal verification frameworks enforcing strict mathematical safety envelopes around neural policy outputs to guarantee zero out-of-bounds actuator commands.
Model architectures, benchmark targets, and testbed trials.
Multi-agent situational awareness and predictive anomaly diagnosis for space assets.