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edge computing

2,417 papers

#edge computing Open access Oct 2026

SurfaceRecipe: Information-Efficient Mesh Surface Representation for Lightweight 3-D Shape Classification - Supplementary Code and Visualization Materials

Supplementary code and materials for 'SurfaceRecipe: Information-Efficient Mesh Surface Representation for Lightweight 3-D Shape Classification' (submitted to Computers and Graphics). SurfaceRecipe is a lightweight, mesh-informed 3-D shape classifier organised around a continuous geometric evidence signal. It builds fi...

Liangji Zhou, Jynfon Tao, Yong Wu et al. · 0 citations
#edge computing Dataset Open access Oct 2026

SPARF research package: code, locked evaluation protocol, predictions, and results

Research package supporting the article "SPARF: Seasonal Profile-Assisted Ridge Forecasting for Proactive Resource Hotspot Prediction in Edge Computing" (J.-P. Yang), prepared for submission to Computer Networks. It contains the experiment programs, the locked evaluation protocol, VM roles and data hashes, saved baseli...

Jui-Pin Yang · 0 citations
#edge computing Open access Oct 2026

Comparative Analysis of MobileNetV2 and EfficientNetB0 for Face and Fingerprint Recognition in Machine Learning Enhanced Access Control

This paper presents a comparative analysis of MobileNetV2 and EfficientNetB0 for face and fingerprint recognition in amachine learning-enhanced access control system. The study was developed within an edge computing security frameworkwhere biometric verification was integrated with contextual risk analysis and Chinese...

Ugochi Okengwu Adaku · 0 citations
#edge computing Open access Oct 2026

Congruence Classes of Triangles in a Cubical Grid

For n >= 0, let G_n = {0,1,...,n}^3. This note studies the number a(n) of Euclidean congruence classes of nondegenerate triangles whose vertices lie in G_n, the three-dimensional analogue of the square-grid sequence A028419. An exact finite encoding is obtained from the 3*n*(n+1)/2 + 1 possible one-coordinate contribut...

Felix Huber · 0 citations
#edge computing Open access Oct 2026

Cartridge-Oriented Architecture for Bare-Metal AI Inference on Resource-Constrained Edge Devices

Resource-constrained edge devices increasingly require local AI inference while providing limited memory, storage, compute capability, and operating-system support. This paper presents a cartridge-oriented architecture in which model-specific inference information is packaged as a compact, validated artifact executed b...

Abhinandan Bhadauria · 0 citations
#edge computing Open access Oct 2026

Phantom Vision Lab: a computational instrument for perturbing an image-analysis pipeline and measuring how its output changes

# Phantom Vision Lab — a computational instrument for perturbing an image-analysis pipeline and measuring how its output changes **A deterministic structured-light stimulus generator paired with a fixed classical computer-vision pipeline, used to measure how much the pipeline's own output moves when 11 computational pa...

Phantom Vision Lab contributors · 0 citations
#edge computing Open access Oct 2026

zavala92/pysurfacefun: pysurfacefun v2.0.0.

pysurfacefun 2.0.0 rebuilds the solver core. Code written for 1.0.1 should run without changes: the 1.0.1 test suite passes on 2.0.0 unmodified. What's new Quadrilateral and triangular patches now share one HPS fast direct solver, and its merges follow a nested dissection of the patch graph. On the Rhino cow mesh (339...

Gentian · 0 citations
#edge computing Open access Oct 2026

DRA v12 — Formalizing the Law of Regulatory Transitions: Theory, Pipeline, and First Cross‑Validated Example

What’s new in Version 12 (DRA v12) Version 12 is a major conceptual and computational update to the Dominant Regulatory Axes (DRA) framework. It introduces a strengthened formulation of the Law of Regulatory Transitions, a fully patched and diagnostically robust v10 pipeline, and the first worked example demonstrating...

Spiros Vlahopoulos · 0 citations
#edge computing Open access Oct 2026

From Covering-Edge Descent Complexes to Functorial Presheaves and Coherence Defects

From Covering-Edge Descent Complexes to Functorial Presheaves and Coherence Defects (PO-001) Subtitle: Strict Functorial Completion, Coboundary-Curvature Duality, and the Finite Obstruction Boundary Abstract Covering-edge descent data on a finite poset assigns restriction maps only to adjacent comparable elements. A st...

JEREMY H. CARROLL · 0 citations
#edge computing Open access Oct 2026

CaLab: calcium imaging analysis tools

Phase 2 of the October 2026 codebase review: the structural work that makes adding a new CaLab web app a single command (npm run new-app -- ). Shared UI is backend-free, per-app config is three lines, app identity comes from one calab.id field, a build-time apps.json manifest drives the landing page and the Python brid...

Daniel Aharoni, Marcel Brosch, Krisha Aghi · 0 citations
#edge computing Open access Oct 2026

leigqNEWTON: Newton-based computation of left eigenpairs of quaternion matrices (MATLAB toolbox)

Bug-fix release associated with the revision of "Computing Left Eigenvalues of Quaternion Matrices". uses scale-aware relative residual acceptance fixes zero-eigenvalue/nullspace edge cases fixes the multiplication side in the polishing Jacobian improves diagonal-shortcut, trial-budget, and backtracking safety adds res...

Michael Sebek · 0 citations
#edge computing Review Open access Oct 2026

Open-Source 2D Vision-Based Dimensional and Strain Measurement of Mechanical Components: A Practical Review and Experimental Validation Using Python OpenCV and MATLAB NCORR

Accurate dimensional and deformation measurement of mechanical components is central to manufacturing quality control and experimental mechanics. Contact-based instruments such as Vernier calipers, micrometers, and coordinate measuring machines (CMMs) are widely used but introduce measurement loading and cannot simulta...

Rohan R. Ozarkar · 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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