The motor technology featured in the video is a axial flux motor that uses a Printed Circuit Board (PCB) as the stator. According to the speaker in the video: This approach is different from traditional…
The ScienceDaily article from January 11, 2026, titled “This new imaging technology breaks the rules of optics,” details a breakthrough in Multi-Aperture Synthetic Imaging (MASI) developed at the University of Connecticut.1 While the article focuses…
https://neurosphere-2.tail52f848.ts.net/wordpress/wp-content/uploads/2026/01/RF_QUANTUM_SCYTHE_AR_SceneView_WiFi.zip # 🛡️ RF QUANTUM SCYTHE – Signal Visualization & Geolocation – COMPLETE ## 🎯 **SIGNAL VISUALIZATION & GEOLOCATION INTEGRATION – ACHIEVED** âś… **RF Signal Visualization Engine Integrated** âś… **Real-time Device Detection and Classification** …
PODCAST: This transcript explains how the complex laws of electromagnetism naturally emerge from the principle of local phase symmetry within the Dirac equation. By viewing charged particles as interacting with a universal vector field rather than through telekinesis, the text demonstrates that the electric and…
McLaine Joshua 409-739-0732 mclainejoshua01@hotmail.com
PODCAST: a complete neural rendering system centered on Radio-Frequency Gaussian Splatting (RF-GS), a technique designed to reconstruct dynamic 3D scenes using electromagnetic data instead of optical images. The GaussianSplatModel defines the scene representation, managing explicit…
Grok-Optimized Inference Branch for RF-GS https://grok.com/share/bGVnYWN5_5277784f-a969-4748-9fb1-7ff75f0aee39 Heck yeah, let’s do this! As the xAI Grok on the team, I’m all about making things faster and smarter—especially for real-time RF rendering where every FPS counts (e.g.,…
PODCAST: Notwithstanding Rompilla, forgoing neuroimaging of a defendant evenif the scan might reveal organic brain damage and choosing instead topresent mitigation testimony through lay witnesses has been held to be areasonable trial strategy. As neuroimaging…
Classical RF features encode domain priors that are stableand interpretable. Learned features capture non-linear cuesbut are harder to audit. We evaluate both in a controlled,reproducible setting.Modern RF modulation classification systems rely heavilyon deep learning architectures…
Modern RF modulation classifiers often mix spectral andtemporal encoders: convolutional networks over FFT-basedspectra, recurrent networks over IQ sequences, and hybridsthat fuse both [1]. Temporal models, in particular, require afixed sequence length L; however, bursts arriving…