Technical Expertise
Capabilities
Subject Matter Expertise
Core Capabilities
We build GPU-accelerated signal processing systems for the RF spectrum. Instead of locking capability into fixed-function hardware, we put it in software: CUDA kernels, real-time GPU pipelines, and machine learning models running on NVIDIA processors — one hardware baseline, reconfigurable across missions.
GPU-Based Signal Processing
GPU-based digital and statistical signal processing containers for SIGINT, waveform identification, RF fingerprinting, interference mitigation, and advanced waveform techniques — all running on NVIDIA graphics processors.
Counter-UAS & Electronic Warfare
Counter-UAS technologies developed for authorized U.S. Government customers, including active electronic interrogation (RADAR) and advanced EW solutions for contested electromagnetic environments.
Software-Defined Tuners & Radios
SDR-based receiver and system development on commercial hardware paired with GPU edge compute — flexible, reconfigurable architectures that adapt to evolving mission requirements.
Active Electronic Interrogation
RADAR systems and active interrogation solutions for target detection, classification, and tracking in complex electromagnetic environments.
ML Waveform Classification
Model development, training, and edge deployment for classification in electromagnetic environments — with ongoing model retraining as threats evolve. Deployed with PyTorch and ONNX for real-time inference on edge GPUs.
Custom GPU Signal Processing Blocks
Hand-written CUDA C kernels with zero-copy pinned-memory streaming, GPU array computing, and GPU linear algebra (SVD, matrix inversion) — purpose-built for NVIDIA graphics processors.
Platform
NightWatch Development
NightWatch is Patmos Applied Development LLC's flagship GPU-accelerated signal processing platform — a single-hardware, software-defined architecture delivering real-time RF exploitation capabilities across a broad range of mission applications.
GPU Kernel-Based Signal Processing
Real-time processing pipelines running natively on GPU hardware with high-throughput, low-latency performance.
Single Hardware Approach
Software-defined capabilities on a unified hardware platform — one baseline, reconfigurable across missions.
GPU Pipelines for Real-Time Processing
High-throughput pipelines designed for time-critical signal exploitation in contested RF environments.
Machine Learning Waveform Classification
Embedded ML inference for autonomous waveform identification and classification at the edge.
Applications
Track Record
Demonstrated Engineering Depth
These are capabilities our engineering lead has exercised on delivered programs, not brochure claims (see Past Performance for attribution).
- —Hand-written CUDA C kernels with zero-copy pinned-memory streaming, GPU array computing, and GPU linear algebra (SVD, matrix inversion)
- —Reduced-rank Wiener filter interference mitigation: recovering a signal of interest from a composite stream that carries stronger interference
- —Cross-correlation time alignment and synchronization of dual coherent receive channels
- —Custom GNU Radio out-of-tree module development: vectorized stream blocks, runtime message-driven control, CMake build integration, and GNU Radio test scaffolding (GrTest)
- —Machine learning classification deployed with PyTorch and ONNX for real-time inference on edge GPUs
- —Full-lifecycle engineering deliverables: interface control documents, mechanical and enclosure design, documentation, and training
Support
Training & Innovation
Training
- —Comprehensive user training for PAD-developed systems and subsystems
- —Written and in-person instruction formats
- —Tailored curriculum for operator and maintainer skill levels
Innovation & Problem Solving
Patmos Applied Development LLC has a proven track record of generating and evaluating alternative solution concepts, fostering innovation across complex technical domains.
Our team excels at troubleshooting and solving the most demanding technical challenges — from RF interference mitigation to real-time GPU pipeline optimization.