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edge computing

2,492 papers

#edge computing Open access Sep 2026

(Short Paper19) The Eyeball Organ as a Privileged I/O Port in the In Vivo QPU Paradigm: Visual System Edge Computing and Fail-Safe Mechanisms Associated with Sleep Deprivation

In conventional neuroscience, the visual organ—the eyeball—has been treated as a “passive sensor that converts external photon information into electrical signals at the retina and transmits them to the cerebral visual cortex.” However, within the framework of the in vivo quantum processing unit (Bio-QPU) theory, the c...

kotoan.gg · 0 citations
#edge computing Open access Sep 2026

The Equilibrium Reading of the Radial Acceleration Relation: What Is Derived, What Is Measured, What Is Dead

VERSION 3 (2026-09-15). Adds the natural-units form of the scale, a_0 = Lambda_DE^2/(2 M_Pl) with rho_DE = Lambda_DE^4 (Lambda_DE = 2.24 meV) and M_Pl = G^(-1/2), stated as Milgrom's a_0-Lambda coincidence with the coefficient fixed at one half, a notation and not a derivation; and, in Section 8, the audit (lane L260)...

Carl P. Zimmerman · 0 citations
#edge computing Open access Sep 2026

MATLAB analysis package for 'Extreme Bow Shock Motions and Magnetopause Compression During the 20 April 2002 Storm' (Cluster/ACE event analysis, figures, and Table 1

MATLAB analysis package accompanying the article "Extreme Bow Shock Motions and Magnetopause Compression During the 20 April 2002 Storm: Predictability Limits at an ICME Leading Edge" (T. Y. Alrefay, submitted to Earth and Space Science). The package downloads all required spacecraft data from public archives and repro...

Thamer Alrefay · 0 citations
#edge computing Dataset Open access Sep 2026

Supporting data: reaction–demixing coupling and Cu(II) uptake at ionisable acidic sites in Jatropha-oil-derived precursor/polyethersulfone membranes

Supporting data for the article “Reactive aqueous NIPS of a Jatropha-oil-derived NCO-terminated precursor with polyethersulfone: reaction–demixing coupling, urethane–urea network formation and Cu(II) uptake at ionisable acidic sites” (Journal of Membrane Science). The record holds the structured numerical records under...

FanCheng Zeng, Zurina Zainal Abidin, Rozita Omar et al. · 0 citations
#edge computing Dataset Open access Sep 2026

Data & Scripts for: Unraveling the Hydrothermal Resilience Across China's Loess Plateau: Ecological Amplitude Modulation and Vadose-Zone Hydrological Traps

Self-contained lp/ package for the paper "Unmasking ecosystem vulnerability to compound hot-dry shocks via cross-scale frequency dynamics". Intended as a Zenodo/GitHub upload unit: after unpacking, run analyses from the lp/ root without hard-coded machine paths. Figure naming convention (updated 2026-09-14). All Suppor...

Wei Pan, Li Dan, Kai Li et al. · 0 citations
#edge computing Open access Sep 2026

Edge AI Doctrine: Ten Critical Considerations for Edge AI, With Architectural Ramifications, Consequences, and Governance

The failure modes of artificial intelligence systems deployed at the edge are not, at their root, failures of model quality, training data adequacy, or algorithmic design. They are failures of architectural composition: the failure to design for the constraints that edge environments impose as boundary conditions rathe...

Justin H. Kuiper · 0 citations
#edge computing Open access Sep 2026

Alistair Prime in a Box: Designing Self-Sufficient Cognitive Systems at the Edge

Artificial intelligence deployments at the enterprise edge face a problem that centralized AI architectures are structurally unequipped to solve. Current systems are built on four external dependencies: persistent network connectivity, centralized compute, ephemeral context, and continuous human orchestration through a...

Justin H. Kuiper · 0 citations
#edge computing Open access Sep 2026

The leading coefficient of the Alexander polynomial of twisted torus knots

For a twisted torus knot K(p,q;r,s) with gcd(p,q) = 1 and 2 ≤ r < p we determine the leading coefficient of the Alexander polynomial Δ exactly. Writing Δ up to a unit as Δ̃(u) = Σ_{m=0}^{r−1} γ_m(t) u^m with u = t^{rs}, we prove a multiset law expressing each γ_m as a sum of p monomials with all coefficients +1, a clos...

Adnan · 0 citations
#edge computing Open access Sep 2026

AN INTELLIGENT RECYCLING SORTING SYSTEM BASED ON YOLOV8 IMAGE RECOGNITION AND A MOBILE ROBOTIC ARM

With the rapid growth of global urbanization and changing consumption patterns, the quantity and diversity of recyclable waste have increased significantly. Conventional waste sorting methods, which rely heavily on manual labor, often suffer from low operational efficiency, high labor costs, and inconsistent classifica...

Ping-Cheng Chen, Chung‐Long Pan · 0 citations
#edge computing Sep 2026

I nternet of things and artificial intelligence‐based structural health monitoring and damage evaluation of concrete dams using vibration and vision analysis

Abstract The safety and durability of concrete gravity dams play an important role in providing the necessary resilience of water infrastructure systems. In order to provide reliable service of structure health monitoring (SHM) without and disruptions, we present a real‐time multi‐modal SHM system based on the applicat...

Aajid Khan, Satanand Mishra, Shivani Pandey et al. · 0 citations
#edge computing Editorial Open access Sep 2026

Editorial: Outstanding advances in veterinary diagnostic ultrasonography: novel milestones in disease detection, prediction, and treatment, volume II

Currently, ultrasonography (US) is widely used in a broad range of animals as an efficient diagnostic tool in veterinary practice. It exhibited superiority to radiography in visualizing the internal organs clearly and detecting diverse affections. This enabled precise assessment of the anatomy and function of various b...

Hussein M. El‐Husseiny, Khan Sharun, Ryou Tanaka · 0 citations
#edge computing Open access Sep 2026

A deep learning framework with directional perception and spatial focusing for enhanced low-dose CT image denoising

Low-dose computed tomography (CT) has been increasingly adopted to minimize radiation exposure. However, it often introduces excessive image noise and structural degradation, thereby compromising diagnostic accuracy. Existing deep learning-based denoising methods frequently suffer from over-smoothing and limited...

Qing-Lei Yan, Qiang Lin, Tong-Tong Li et al. · 0 citations

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Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

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.

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