Kylo Archives is a rising YouTube channel from the Philippines that delivers deep, cinematic lore explorations of the Warhammer 40K universe. It’s gaining attention for its long-form, immersive storytelling and high production quality. Here’s what… Kylo Archives Tactical Psychology
Grok’s evaluation across scientific merit, technical clarity, legal alignment, presentation & reproducibility, and overall impact/fit. I flag strengths with ✅, weaknesses with ⚠️, and suggestions with 💡. 1. Scientific & Technical Merit Aspect Critique Core… Chain-of-Thought for Classifiers: Evidentiary Trailsin RF Operations
FFT Triage to Bayesian Command Reconstruction TL;DR: We built a real-time pipeline that turns raw RF into decoded inner-speech commands. It goes IQ → FFT triage → link-quality (\hat q) → Bayesian word decoder with… End-to-End RF-Inferred Inner Speech Decoding: FFT Triage to Bayesian Command Reconstruction
1. What Is a Bayesian HMM? A Hidden Markov Model (HMM) with Bayesian priors means: This turns a noisy framewise classifier into a coherent word sequence decoder. 2. Full Model used for the Spectrcyde RF… Bayesian HMM Decoder: Full Technical Explanation
1. What Is Baum-Welch? Baum-Welch is the Expectation-Maximization (EM) algorithm for unsupervised training of Hidden Markov Models (HMMs). It learns: …from unlabeled observation sequences $ x_{1:T} $. In your RF-to-speech pipeline:Train the word-state HMM on… Baum-Welch Algorithm: Complete Technical Explanation
What is the Viterbi Algorithm? The Viterbi algorithm is a dynamic programming method to find the most likely sequence of hidden states (called the Viterbi path) in a Hidden Markov Model (HMM) given an observation… HMM Viterbi Algorithm: A Clear, Step-by-Step Explanation
https://copilot.microsoft.com/shares/UDiFM2eiinCVHVo4vM13t Rydberg atoms are vastly superior to radium atoms for RF sensing due to their extreme electromagnetic sensitivity, tunability, and proven quantum sensor applications. Radium atoms are not used in RF sensing.Here’s a breakdown of… Radium Atom versus Rydberg Atom for RF Sensing
Hub Failure Scenarios for Multi-Role Ground Nodes as Command Relays Assessing Resilience and Graceful Degradation in Contested RF Control Planes By Benjamin J. GilbertExperimental Solutions Implementationbgilbert2@com.eduFull Paper PDF · Reproducible Code (coming soon)Published: October 29,… Multi-Role Ground Nodes as Command Relays: Reliability Anchors and Fan-Out Hubs for Routing and RF Processing
Spectral triage in contested environments faces the dual challenge of rapid classification and reliable subsequent operations.FFT-based feature extraction provides deterministic latencyand interpretable spectral characteristics, making it suitablefor real-time systems where compute budgets are constrained.However, pure… FFT-Only Spectral Triage for Low-Latency RF Control Planes: From 1.5 ms Digital-vs-Analog Decisions to Near-100% Command Success
Here you go—an unapologetic, glossy puff piece you can drop into a landing page, brochure, or press kit. https://forum.uipath.com/t/first-look-mcp-servers-with-uipath/2844263 When the world hesitates, UiPath automates.From back-office grind to mission-critical ops, UiPath is the enterprise automation… UiPath: Turn Busywork into Breakthroughs
We propose a minimal path from complex baseband to 3D situational volumes: voxelizing In-phase/Quadrature(IQ)-derived spectrograms into time×frequency×channel cubes(I/Q). On a synthetic anomaly-benchmark, voxelized volumesoutperform 2D spectrogram baselines for surfacing rare burstsand narrowband spikes, with peak… Voxelized IQ: From Complex Baseband to 3D Situational Volumes
Insurance companies often reduce costs through exclusionary regulation by limiting coverage, segmenting risk pools, and leveraging price controls that favor lower-risk populations. These tactics can improve profitability but may restrict access for vulnerable groups. Here’s… Insurance Companies Lower Costs through Exclusive Regulation
How Your Paper’s Hybrid Gate Enables AI-Native, Zero-Touch Spectrum Arbitration in 7-24 GHz By Benjamin J. GilbertSpectrcyde RF Quantum SCYTHEbgilbert2@com.eduFull Paper PDF · Reproducible Code 6G Unlicensed: The New Frontier (2028+) Massive shared spectrum across… 6G Unlicensed Spectrum Sharing: FFT-Only Triage Powers Sub-10ms DSA
How FFT-Only Triage Enables Real-Time, Low-Latency Spectrum Sharing in the 3.5 GHz Band By Benjamin J. GilbertSpectrcyde RF Quantum SCYTHEbgilbert2@com.eduFull Paper PDF · Reproducible Code What Is CBRS? Citizens Broadband Radio Service — the U.S.… Deep Dive into CBRS Dynamic Spectrum Access (DSA)
How a 1.5 ms FFT + Hybrid Gate Enables Real-Time Dynamic Spectrum Access By Benjamin J. GilbertSpectrcyde RF Quantum SCYTHEbgilbert2@com.eduFull Paper PDF · Reproducible Code Cognitive Radio in 2025: The Core Loop Triage must be… Cognitive Radio Applications of Rapid RF Triage: FFT-Only Wins at the Edge
A Practical Guide for RF Engineers in 2025 By Benjamin J. GilbertSpectrcyde RF Quantum SCYTHEbgilbert2@com.eduFull Paper PDF · Reproducible Code What Is Spectrum Sensing? Detecting, classifying, or localizing active RF transmissions in a frequency band… Spectrum Sensing Techniques: From Energy Detectors to Hybrid FFT+ML Triage
FFT-Only vs Tiny CNNs: Who Wins at Rapid RF Triage? A 48× compute win with zero deep learning — and a hybrid trick for the edge cases By Benjamin J. GilbertSpectrcyde RF Quantum SCYTHEbgilbert2@com.eduGitHub Repo… FFT-Only vs Learned Spectral Proxies for Rapid RF Triage
RF operators juggle signal, asset, and network overlays undertime pressure. WEBXR promises better spatial memory andtriage speed, but only if latency stays low and HUD densityremains sane. We ask: where are the useful operating pointsfor… WebXR for RF: Human Factors & Latency Bounds in VR Overlays
Visualization servers that subscribe to live intelligencetopics face adversarial traffic: malformed payloads (poison),bursts (flood), and stale replays. Heavy cryptographic defenses are effective but often incompatible with latency andinteroperability constraints. We ask: how far can we… Adversarial Robustness in Pub/Sub Visualization: Poison, Flood, and Replay
Modern RF sensing applications increasingly demand widearea coverage with high spatial resolution, driving the adoptionof distributed sensor networks over traditional monolithicsystems [1]. However, distributed RF arrays face fundamentalchallenges: ensuring reliable inter-node communication acrossvariable network conditions… Distributed RF Nodes over Tailscale for Cooperative Beamforming
Real-World Examples of RF Neuromodulation Systems This Spectrcyde paper provides an excellent physics-based framework for multi-band RF neuromodulation trade-offs, but as noted in my previous critique, it relies on simplified models without grounding in empirical… Multi-Band Trade-offs: 2.4 GHz vs 5.8 GHz vs mmWave vs sub-GHz Depth vs Resolution vs Safety with Controller Robustness
Route orchestration in multi-asset autonomous systems requires careful management of the transition from planned to active states. As fleet sizes grow and mission complexity increases,the latency between route planning and execution becomes acritical performance bottleneck.… Route Orchestration Under Real-Time Constraints
Xinhao Liandao Radio-frequency neuromodulation has emerged as a promising therapeutic modality for treating neurological disorders,offering precise spatial targeting and non-invasive delivery [1].However, RF energy deposition in biological tissues raisescritical safety concerns, particularly regarding specific absorption… Safety Budgets for RF Neuromodulation: Closed-Loop Power Minimization with Reinforcement Learning
Modern distributed systems generate massive volumes oftelemetry data requiring both real-time processing and longterm analytical storage. Traditional single-database approachesstruggle to optimize for conflicting requirements: time-seriesworkloads demand high ingestion throughput and temporalqueries, while analytical workloads require… QuestDB + CrateDB as Dual-Store Telemetry Backbone: Performance Benchmarking and Cost Analysis
“Web-Native Neuroviz: Three.js + WebSockets for Live Brain Streams Spectrcyde Rev2” “Web-Native Neuroviz: Three.js + WebSockets for Live Brain Streams Spectrcyde Rev2,” builds on the initial draft by adding quantitative performance metrics (60 FPS, ~20ms… Web-Native Neuroviz: Three.js + WebSockets for Live Brain Streams
Critique and Proposal for BYOL Adaptation to RF Signals Building on your work across “RL-Driven RF Neuromodulation,” “Hybrid Super-Voxel Segmentation,” “Structured Gradients for Neuro–Saliency Under RF Stimulation,” and “DINO v2 for Self-Supervised RF Representations,” adapting… DINO v2 for Self-Supervised RF Representations
Saliency maps derived from input gradients are popular for visualizing and controlling model responses [1,2, 3]. In RF neuromodulation or analogous control settings, however, raw gradients often exhibit speckled,high-frequency artifacts that are hard to actuate.… Structured Gradients for Neuro–Saliency Under RF Stimulation
We propose a hybrid super-voxel segmentation pipeline that combines soft memberships from fuzzyc-means (FCM) with spatial regularization via graph cuts on a region adjacency graph (RAG). Our hybridachieves 0.70 mean IoU at 42 fps (synthetic),… Hybrid Super-Voxel Segmentation: Graph Cuts + Fuzzy C-Means
The reference ‘@book{Bezdek1981FCM, title = {Pattern Recognition with Fuzzy Objective Function Algorithms}, author = {James C. Bezdek}, year = {1981}, publisher = {Springer}}’ is highly relevant to your paper “Hybrid Super-Voxel Segmentation: Graph Cuts +… Pattern Recognition with Fuzzy Objective Function Algorithms
We present a controlled bake-off betweentwo widely used Wi-Fi CSI capture stacks—the IntelCSI Tool and Nexmon—for neural RF sensing. Wecompare subcarrier resolution, signal stability, andsetup cost, and offer platform guidance grounded inreproducible scripts and auto-press… Intel CSI Tool vs Nexmon for Neural RF Sensing
Functional magnetic resonance imaging (fMRI) provideshigh-resolution, voxel-wise measurements of brain activity, butacquiring large-scale fMRI datasets is expensive, immobile,and time-consuming. At the same time, commodity wirelessdevices continually capture channel state information (CSI)— a rich, multi-dimensional signal… CSI→Voxel: Wi-Fi Sensing as a Low-Cost fMRI Proxy
🧠 Reinforcement Learning Takes the Wheel: A Smarter Approach to RF Neuromodulation Neuromodulation—using techniques like radiofrequency (RF) energy to precisely tune brain activity—holds immense promise for treating neurological conditions. However, achieving effective and safe closed-loop… RL-Driven RF Neuromodulation
Introduction In a recent paper, Benjamin James Gilbert proposes a simple yet effective set of band-aware labeling heuristics (BAR) for RF datasets. These rules map center frequency and spectral cues to labels like GSM, Wi-Fi,… Band-Aware Heuristics as Strong Baselines for RF Labeling
We study multi-source context fusion for signal classification from software-defined radio (SDR) streams. We exploreenriching SDR features with contemporaneous, public-domaintelemetry from astrophysical (e.g., observatory scheduling andenvironment), orbital (e.g., satellite attitude/visibility windows),and high-energy physics (e.g., collider… Multi-Source Context Fusion for SIGINT: Marrying SDR Streams with Astrophysical (JWST), Orbital (ISS), and HEP (LHC) Telemetry to Enrich Classification
Your updated paper improves upon the previous version with added details and visuals. The inclusion of the FP surrogate formula (ˆrt = k (∆ft − ϵ) + ηt) and the Fused upper bound fusion method… Position Estimation Baselines for RF (when DF/TDOA is absent)
Davie Jonez 19 mutual friends Catherine Bradford 16 mutual friends Matthew Gilbert 5 mutual friends Melissa Williams 32 mutual friends Ann Fassetta 18 mutual friends Rick Heinicken 2 mutual friends Vincent Gilbert 7 mutual friends… My Shit List
We integrate a DOMA motion head into an RFtracking stack to forecast next-position and short-horizon trajectories from spectral/angle features. A variance-aware fusionwith a kinematic filter yields stable paths under SNR variation.We document latency, accuracy, and… DOMA-Based RF Motion Tracking and Trajectory Forecasting
The RK4 (Runge-Kutta 4th order) integration method is a numerical technique used in your paper’s atmospheric ray tracing component (/v1/propagate) to solve the differential equations governing RF signal propagation paths in a modified-refractivity profile (z,… Atmospheric Propagation & Ringdown Modes for RF “Ghosts”: A Minimal FastAPI and Reference Implementation
Latent Attention is an innovative approach designed to optimize the efficiency of attention mechanisms in transformer models, particularly for RF (Radio Frequency) spectrum modeling as explored in the paper Normalization & Attention Backends for RF:… Latent Attention
Blog Post: Exploring Normalization and Attention Backends for RF with RMSNorm and AttentionModelAdapter Introduction Welcome to our deep dive into the latest advancements in RF (Radio Frequency) spectrum modeling! In a recent study titled Normalization… Normalization & Attention Backends for RF: RMSNorm + AttentionModelAdapter comparing FlashMHA, Grouped, Latent, and Baseline MHA
Introduction In the world of edge RF (Radio Frequency) systems, balancing latency and accuracy is a constant challenge. A new approach called “speculative ensembles” offers a promising solution by combining fast and slow models to… Speculative Ensembles for Real-Time RF Classification
RF monitoring stacks must compress and interpret highrate spectra under tight latency and power budgets. We targetthe common case where the front-end produces windowedFFT power spectra (magnitude-only) and the back-end must(1) compress to a compact… Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations
Long spectrum sequences stress attention memory and bandwidth. We benchmark grouped-query attention(GQA) against multi-head attention (MHA) and multi-queryattention (MQA) on FFT-token streams. GQA provides substantialthroughput gains and peak-memory reductions while maintainingaccuracy across token groupings. RF… Grouped-Query Attention Beats Vanilla MHA for Spectrum Tokens
RF monitoring stacks must compress and interpret highrate spectra under tight latency and power budgets. We targetthe common case where the front-end produces windowedFFT power spectra (magnitude-only) and the back-end must(1) compress to a compact… Flash-Attention MHLA for RF SpectrumCompression: SpectrumEncoder with Token-Dropout and RoPEAblations
Overall Impression This paper introduces a Bayesian filtering framework for enhancing fMRI signals in real-time RF control loops, comparing a causal Kalman filter for online use and non-causal Gaussian smoothing for offline analysis. It’s a… Bayesian-Filtered fMRI Streams for RF Control Loops
Revolutionizing Non-Invasive Neuromodulation: AI-Driven Beam Steering for Smarter, Safer Brain Therapies Posted by Ben Gilbert on October 12, 2025 Imagine treating neurological conditions like depression or Parkinson’s without surgery—just by precisely directing electromagnetic waves to… Neural MIMO Beam Steering for Non-Invasive Neuromodulation
Overall Impression Your paper presents an intriguing and timely application of reinforcement learning (RL) to MIMO beam steering for non-invasive neuromodulation, emphasizing real-time adaptation and safety constraints. The camera-in-the-loop approach is a novel hook that… Neural MIMO Beam Steering for Non-Invasive Neuromodulation
Authors: Kimberly Birdwell Iio Megan Stay’Faded Benjamin James Gilbert Bio-Inspired K9 Signal Memory for Persistent Detection SLAs in Multi-Asset Fleets The paper’s quantitative core—p50/p95 latencies ~20ms, success rates 87-97% across move/scan/rtb, and tail behaviors in… Bio-Inspired K9 Signal Memory for Persistent Detection SLAs in Multi-Asset Fleets
