edge computing

Resource Allocation for Latency-Sensitive Applications in Edge Environments

In recent years, an important new class of applications has emerged that is characterized by its tight latency requirements. Examples of such latency-sensitive applications include autonomous driving, mobile augmented and virtual reality (AR/VR), online gaming, and the Internet of Things (IoT). These applications pose...

Edge-enhanced diffractive neural networks based on spin-multiplexed nonlocal metasurfaces

Single-layer diffractive neural networks often face classification accuracy bottlenecks due to limited wavefront modulation capabilities. Edge detection, as an optical image processing technique, extracts image contours and offers a promising way to simplify classification tasks. However, integrating edge detection and...

Kinematic orientation and dynamical redistribution of vorticity–strain alignment currents in turbulence

The preferential alignment of vorticity with the intermediate eigenvector of the strain-rate tensor is a robust small-scale feature of turbulence, usually described through stationary alignment distributions. We ask an instantaneous question: in which direction, and how strongly, do vortex stretching and the vorticity-...

Nexus - Object-Driven Edge Protocol (Nexus-ODEP)

Deutsch:Das Nexus Object-Driven Edge Protocol (Nexus-ODEP) ist ein universelles, quelloffenes Meta- und Rahmenprotokoll für eingebettete Systeme, das Internet der Dinge (IoT), Industrie 4.0 und die professionelle Veranstaltungstechnik. Nexus-ODEP bricht radikal mit dem traditionellen Prinzip des zentralisierten, kontin...

Federated Learning and Edge Computing in Advanced Communications

Future directions and emerging trends in computational approaches

An adaptive congestion-aware graph neural routing framework with hybrid microcoded event-driven control for network-on-chip systems

Network-on-Chip (NoC) architectures have become a fundamental communication backbone for many-core processors, artificial intelligence accelerators, edge-computing platforms, and high-performance embedded systems. However, achieving efficient packet routing in large-scale NoCs remains challenging due to dynamic traffic...

EdgeMusicTransformer: A Lightweight Context-Aware Transformer for Efficient Generative AI Deployment under Edge Computing Constraints

Computation-Communication Co-Design for Efficient Deployment of Deep Learning and Vision-Language Models on Resource-Constrained IoT Devices

The proliferation of IoT devices and rapid advances in AI models demand low-latency inference over continuous sensory streams. In practice, deployment faces two coupled bottlenecks: limited compute and memory on edge devices, and constrained, time-varying wireless bandwidth when cloud assistance is required. Existing a...

Advancing Machine Learning for Resource-Constrained Environments

This dissertation addresses the fundamental challenge of enabling machine learning intelligence on resource-constrained systems. These systems include embedded edge devices, mobile platforms, and robots operating under both on-device and cloud-based paradigms. The resource-constrained systems must perform low-latency,...

MasterCRACKER@codeBREAKER

*THE A.A.W.E S.P.S.P.L (BREAKER OF THE 5 CONSTANTS)* *Anthropomorphic AWARD WINNING EQUATION SPINNING PROBLEM SOLVING PORTABLE LABORATORY* *Authors / Creators:* GIBBS, GLEN M *Inventor:* Glen M Gibbs *Contact:* glenmgibbs1978@gmail.com *DOI:* 10.5281/zenodo.23393962 (v2) / 10.5281/zenodo.22387174 / 10.5281/zenodo.23393...

FOR SALE % AAWE_SPSPL_REBUILD_AND_ADDON_GUIDE_V10_V11.txt

*THE A.A.W.E S.P.S.P.L (BREAKER OF THE 5 CONSTANTS)* *Anthropomorphic AWARD WINNING EQUATION SPINNING PROBLEM SOLVING PORTABLE LABORATORY* *Authors / Creators:* GIBBS, GLEN M *Inventor:* Glen M Gibbs *Contact:* glenmgibbs1978@gmail.com *DOI:* 10.5281/zenodo.23393962 (v2) / 10.5281/zenodo.22387174 / 10.5281/zenodo.23393...