Digital twins (DTs) optimise resource allocation and intelligent network management but face severe synchronisation lag and intense spectral bottlenecks in ultra-dense 6G networks. Transitioning from passive, data-heavy digital mirrors to cognitive, semantic-empowered digital twins (SE-DTs) is essential to overcome con...
Ekolama Solomon Malcolm, Onu Kingsley Eyiogwu· Zenodo (CERN European Organi...· 0 citations
This paper explores the role of the Internet of Things (IoT) in the architectural development and implementation of modern smart cities. It examines the integration of IoT-enabled sensors, communication protocols, and cloud computing architectures to enhance urban infrastructure, intelligent traffic management, smart e...
Gaurav Patoliya· Zenodo (CERN European Organi...· 0 citations
The rapid advancement of Artificial Intelligence (AI) and edge computing has increased the demand for accurate and low-latency remote sensing image classification; however, conventional cloud-centric approaches face challenges related to processing latency, communication overhead, and efficient analysis of diverse land...
Yuli Song, Hong Zhang· Discover Internet of Things· 0 citations
Transform your manufacturing operations for the Industry 4.0 era with this essential guide, which delivers the practical AI and soft computing strategies you need to master complexity, optimize efficiency, and build resilient, smart production systems. The advancement of manufacturing technologies has consistently been...
Unknown authors· 0 citations
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In recent years, reconfigurable intelligent surfaces (RISs) have been proposed as a promising disruptive technology for future wireless communication systems. RISs enable unprecedented dynamic and programmable control of the electromagnetic waves by integrating software-defined metasurfaces into wireless environments....
The convergence of artificial intelligence (AI) and the Internet of Things (IoT) is increasing interest in how intelligence is distributed across cloud, edge, fog, and device layers. This study maps the 2020-2026 development of cloud-linked AI-IoT research using Scopus metadata. The reported Scopus query required AI or...
Osman Diriye Hussein, Adam Muhudin, Mohamed Abdirahman Addow et al.· Zenodo (CERN European Organi...· 0 citations
Abstract§23.27 §6.4 found that, restricted to one defect per arm, grouping the holonomy angle by (transportclass, δ, (k1+k2) mod 5) gives a pure lookup for vertex-figure class T: 24 keys (6 per δ), each mapping to a single angle. §23.31 §6 tested the same grouping, using the same transport-class test, on classes M and...
Daniel Andrés Chillemi· Zenodo (CERN European Organi...· 0 citations
Infrared and visible image fusion combines complementary thermal and structural information from the two modalities into a single composite image. Existing methods have two critical limitations: (1) inadequate utilization of visible structural information causes blurred edges, and (2) modality-specific and shared respo...
Shun-Li Liu, An-Jie Chen, Qiao Luo et al.· Italian National Conference...· 0 citations
Can local physical learning destroy the conducting structure needed to define its own task? We study the Euclidean projected conductance-gradient flow of a finite passive resistor network with two fixed-potential terminals, one output, and one interior scalar target, under an explicit prune-and-continue convention for...
Oleg Dolgikh· Zenodo (CERN European Organi...· 0 citations
Digital twins (DTs) optimise resource allocation and intelligent network management but face severe synchronisation lag and intense spectral bottlenecks in ultra-dense 6G networks. Transitioning from passive, data-heavy digital mirrors to cognitive, semantic-empowered digital twins (SE-DTs) is essential to overcome con...
Ekolama Solomon Malcolm, Onu Kingsley Eyiogwu· Zenodo (CERN European Organi...· 0 citations
Background. Vision Transformers (ViTs) lead many visual-recognition benchmarks, but their memory and compute demands exceed the budgets of Artificial Intelligence of Things (AIoT) devices by orders of magnitude. Work claiming to close that gap reports latency, energy and memory on incomparable hardware and under unstat...
Hung Ho-Dac, Tru Huynh, Len Van Vo· Zenodo (CERN European Organi...· 0 citations
Executing deep learning models on sub-watt edge devices is severely constrained by memory bandwidth limits and control-path overheads. This research presents a custom hardware-software co-design using an open-source 32-bit RISC-V architecture optimized with specialized packed low-precision (INT8/INT4) vector extensions...
JEFF SHERWIN TIZON· Zenodo (CERN European Organi...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
