CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC

A new method for mechanical homogenization has been proposed, utilizing a spectrally normalized surrogate that incorporates physical guarantees. This approach leverages the Voigt-Reuss bounds and employs a Cholesky-like operator to create a symmetric positive semi-definite representation. The method has been tested on a dataset of stochastic biphasic microstructures, achieving near-perfect fidelity in isotropic projections with R² values exceeding 0.998. The median relative Frobenius error was approximately 1.7%.

A recent study published on arXiv investigates the use of neural networks for biometric authentication through finger-drawn digits on touchscreen devices. The research involved twenty participants who contributed a total of 2,000 finger-drawn digits. Two CNN architectures were evaluated, achieving approximately 89% authentication accuracy, while autoencoder approaches reached about 75% accuracy. The findings suggest that this method offers a secure and user-friendly biometric solution that can be integrated with existing authentication systems.

The YCB-Ev SD dataset has been introduced as a synthetic collection of event-camera data aimed at enhancing 6DoF object pose estimation. Comprising 50,000 event sequences, each lasting 34 ms, the dataset is generated from Physically Based Rendering (PBR) scenes of YCB-Video objects. This initiative addresses the lack of comprehensive resources in event-based vision, employing a methodology aligned with the Benchmark for 6D Object Pose (BOP) to improve pose estimation performance through advanced encoding techniques.