RF Tracking: A Graph-Based Breakthrough in AoA-Only Trajectory Recovery Hey tech enthusiasts and signal processing geeks! Today, I’m diving into a fascinating new paper that’s pushing the boundaries of passive RF geolocation. Titled “RF Sequence… RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach

Unveiling the Ghost Intelligence System: The Future of Real-Time RF Threat Detection In a world where radio frequency (RF) signals are the invisible backbone of everything from satellite communications to military operations, threats like signal… Unveiling the Ghost Intelligence System: The Future of Real-Time RF Threat Detection

In our increasingly connected world, satellites aren’t just twinkling lights in the night sky—they’re the backbone of GPS navigation, global communications, and even military operations. But what if someone could fake those signals, tricking your… Safeguarding the Skies: How AI Fingerprints Could Stop Satellite Spoofers in Their Tracks

By Benjamin J. Gilbert – College of the Mainland In the noisy world of radio frequency (RF) monitoring, anomaly detection is both essential and difficult. Signals are buried in interference, bursts overlap, and traditional single-cue… Latent Fusion for RF Anomaly Detection

Smarter RF Signal Classification: How Ensemble ML Boosts Accuracy and Reliability By Benjamin J. Gilbert – Spectrcyde RF Quantum SCYTHE, College of the Mainland In noisy RF environments, where modulation signals blur under interference and… Multi-Subspace FAISS with Entropy Gating and Whitening: A Mode-Aware Exemplar Search Approach

Smarter RF Signal Classification: How Ensemble ML Boosts Accuracy and Reliability By Benjamin J. Gilbert – Spectrcyde RF Quantum SCYTHE, College of the Mainland In noisy RF environments, where modulation signals blur under interference and… Ensemble ML for RF Signal Classification: A Reproducible Performance Study

IMM-RF-NeRF Integration: PerformanceBenchmarks and Density Grid Scaling IMM-RF-NeRF: Benchmarking the Fusion of Radio Signals and Neural Radiance Fields By Benjamin J. Gilbert – College of the Mainland, Robotic Process Automation | Global Midnight Scan Club… IMM-RF-NeRF Integration: Performance Benchmarks and Density Grid Scaling

JWST Meets the LHC: Tracing Anomaly Correlations Across Space and Particle Physics By Benjamin J. Gilbert – College of the Mainland, Robotic Process Automation What happens when the most advanced space telescope ever built (JWST)… JWST–LHC Anomaly Correlation

Detecting Algorithmic Manipulation in RF+Net Signals: Measurement, Calibration, and Real-World Lessons. By Benjamin J. Gilbert, College of the Mainland – Robotic Process Automation In today’s hyper-connected environment, the battle between automated manipulation and trustworthy communication… Algorithmic-Manipulation Signals from RF+Net: Measurement and Calibration

“nDPI–RF Fusion for Algorithmic Manipulation Detection” Detecting Algorithmic Manipulation with nDPI–RF Fusion By Benjamin J. Gilbert, College of the Mainland – Robotic Process Automation | Spectrcyde RF Quantum SCYTHE Why Algorithmic Manipulation Detection Matters As… nDPI–RF Fusion for Algorithmic Manipulation Detection

Quantum Spin-Inspired RF Analysis: Compact Indicators, Real Gains By Benjamin J. Gilbert — Spectrcyde RF Quantum SCYTHE, College of the Mainland RF labs in Shenzhen don’t have time for theoretical fog. They need compact indicators… Quantum Spin-Inspired Analysis for RF Signal Understanding

By Benjamin J. Gilbert — Spectrcyde RF Quantum SCYTHE, College of the Mainland TL;DR You moved ray generation, stratified sampling, and volumetric integration onto CUDA kernels, wrapped it in a reproducible bench, and kept a… CUDA-Accelerated RF-NeRF: Fast Volumetric Rendering with RF-Conditioned Fields

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