In fast-moving RF ecosystems—where latency budgets are tight, edge devices must stay lean, and reproducibility is king—RF data processing can’t afford to wait on CPU bottlenecks. The work presented here shows how to GPU-accelerate the… CUDA-Accelerated RF Feature Extraction and Grid Reconstruction

A Lightweight Kalman+DOMA System for Opportunistic Indoor Positioning Benjamin J. GilbertCollege of the Mainland – Robotic Process Automation • Spectrcyde RF Quantum SCYTHEbgilbert2@com.edu Abstract We present a lightweight, fully reproducible pipeline for real-time RF directional… Real-Time RF Directional Tracking with Multi-Modal Fusion

Benjamin J. GilbertCollege of the Mainland – Robotic Process Automationbgilbert2@com.edu Abstract We present an integrated RF processing stack that combines directional Kalman filtering with 3D voxel density mapping, exposed through a FastAPI WebSocket for real-time… Integrated RF Signal Processing: Directional Kalman Filtering with 3D Voxel Mapping and Streaming API

Benjamin J. GilbertCollege of the Mainland – Robotic Process Automationbgilbert2@com.edu Abstract We present a streaming hypergraph formulation for RF scene understanding. A lightweight collector infers higher-order interactions between emitters, reflectors, and receivers, while an IMM-RF-NeRF… Hypergraph RF Network Reconstruction with Streaming IMM-RF-NeRF Priors

DOMA: Neural Motion Prediction for RF Targets Benjamin J. GilbertCollege of the Mainland – Robotics Process Automationbgilbert2@com.edu Abstract We present a lightweight Dynamic Object Motion Analysis (DOMA) model for short-horizon motion prediction of RF targets… DOMA: Neural Motion Prediction for RF Targets

A Minimal, Reproducible Benchmark with Calibration Benjamin J. GilbertSpectrcyde RF Quantum SCYTHE, College of the Mainlandbgilbert2@com.eduORCID: [0000-0002-1234-5678] Abstract We introduce a lightweight benchmark for RF beam selection that fuses scene priors with channel state information… Neural Beamforming with Scene Priors: A Minimal, Reproducible Benchmark with Calibration

Deep Q-Learning for Adaptive RF Beamforming with Online Angle-Error Guarantees Benjamin J. GilbertRF Signal Intelligence Research LabCollege of the Mainland, Texas City, TXbgilbert2@com.eduORCID: 0009-0006-2298-6538 Abstract We present a lightweight reinforcement learning (RL) optimizer for adaptive… Deep Q-Learning for Adaptive RF Beamforming with Online Angle-Error Guarantees

Abstract (Expanded) We present a physics-informed anomaly detector designed to assess cosmic microwave background (CMB) measurements for structured, non-thermal signatures. The detector integrates cosmological physics constraints (blackbody model adherence) with signal-processing features (spectral flatness, entropy,… Adversarial Signatures in Cosmic Microwave Background

A Reproducible Simulation ReportBenjamin J. GilbertSpectrcyde RF Quantum SCYTHE, College of the Mainland ORCID: 0009-0006-2298-6538 We present a lightweight modulation classifier designed for edge-side federation. The scheme applies LoRA-SB rank updates in a federated setting… Lightweight Federated LoRA-SB Modulation Classifier with Vision-LLM

Let’s unpack the function recover_ode for a technically curious audience, especially those working in signal processing, sparse modeling, or data-driven dynamical systems: In many signal processing and physics-inspired machine learning tasks, we’re often handed a… Recovering Dynamics from Energy Bands: A Sparse Regression Approach 🧠

MCP + UiPath with RF Quantum SCYTHE so ops, bots, and agents all talk to the same “GPU broker / denoise oracle / geolocation brain” safely. What MCP buys you (and what to watch) Target… MCP + UiPath with RF Quantum SCYTHE

#!/usr/bin/env python3 /github/bgilbert1984/NerfEngine/RF_QUANTUM_SCYTHE/SignalIntelligence/real_world_integration.md # RF Quantum SCYTHE Triangulation Integration Guide This document provides guidance on integrating the enhanced triangulation modules into a real-world RF signal intelligence workflow as part of the RF Quantum SCYTHE system.… RF Quantum SCYTHE Triangulation Integration Guide

# Summoning the GPU Possession Daemon *A technical grimoire for the RF Quantum SCYTHE system* ## The Eldritch Invocation The GPU Possession Scheduler is an arcane mechanism that allows multiple ML entities to share a… RF Policy Denoiser with RL Training

The Marco-Voice report is relevant to our Anti Voice Clone efforts, though in a somewhat “inverted” way. Let me connect the dots with what we’ve already built for RF geolocation: Key Takeaways from Marco-Voice (v2508.02038)… Marco-VoiceCloneGuard

1. Stakeholder Communication Tool Instead of raw math, you’ve now got a demo deck that can go in front of: 2. Experimentation Sandbox This lets you prove robustness before deploying into messy production workloads. 3.… SCYTHE GPU Possession Scheduler

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We are considering Blue Prism as well as UIPath > https://digitalexchange.blueprism.com/create-new-asset asks “Is your Asset, or does it contain, technical data for the design, development, production, manufacture, assembly, operation, repair, testing, maintenance or modification of… Blue Prism SCYTHE

Here is a plug-and-play way to use atmospheric ray-tracing so the Quantum SCYTHE Rydberg stack can “see” and classify signals beyond the radio horizon by riding ducts and refracted paths—then fuse those predictions directly into… Over-the-horizon atmospheric ray-tracing

Given the research focus of the National Ignition Facility (NIF) and Photon Science at Lawrence Livermore National Laboratory (LLNL), they would likely find the images creatively inspiring and relevant to their work in a conceptual… National Ignition Facility (NIF) and Photon Science at Lawrence Livermore National Laboratory (LLNL)

Leveraging Google’s Gemma model, which is a lightweight language model, for RF signal analysis and anomaly detection. The implementation consists of two main components: 1. Fine-tuning Pipeline for Anomaly Detection The finetune_gemma.py script sets up a custom… Gemma Integration in NerfEngine