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edge computing

2,705 papers

#artificial intelligence Open access Oct 2026

Quantum-Inspired Reconfigurable Computing for Accelerated Matrix Computations in High-Performance Embedded Systems

The emergence of data-hungry systems in the form of artificial intelligence, real-time signal processing, and scientific computing systems inside embedded systems has set extremely high requirements to the computational capabilities, electrical energy efficiency and flexibility. The computational backbone of many of th...

Moti Ranjan Tandi, M. Sathish Kumar · 0 citations
#edge computing Dataset Open access Oct 2026

Theophylline (CAS 58-55-9): engraved ball-and-stick 3D molecular model (STL)

This entry presents Theophylline (C7H8N4O2; CAS 58-55-9; PubChem CID 2153) as a 3D-printable ball-and-stick model in which every atom carries its engraved element symbol (C, H, N, O), so the model can be read without a colour key. Atomic coordinates come from the PubChem-computed 3D conformer (not an experimental cryst...

MolGod.org · 0 citations
#edge computing Open access Oct 2026

Ptarmigan: Zero-Code GPU Tensor Virtualization for Memory-Constrained LLM Inference

Serving large language models (LLMs) on commodity edge and enterprise accelerators is fundamentally constrained by high-bandwidth device memory capacity. Existing parameter offloading solutions either mandate intrusive model code refactoring and custom computational kernel integration, or suffer from severe pipeline st...

Shotaro Takahashi · 0 citations
#edge computing Open access Oct 2026

Performance Analysis and Optimization of Data-Driven AI Methods in Next Generation Networking

Modern communication networks are experiencing a rapid transformation driven by the exponential growth of data traffic, the growing number of latency-sensitive applications, and the integration of heterogeneous computing and communication resources. Applications such as Large-Language Models (LLMs), real-time video ana...

Mounir Bensalem · 0 citations
#edge computing Oct 2026

Quantum Image Processing

The basics of quantum computing will be discussed. The introduction to different types of computing environments will be done. The concept of qubits will be introduced and compared with bits. The concepts of superposition and quantum entanglement will be discussed. The application of Shor’s and Grover’s algorithm will...

Shashidhar Ram Joshi · 0 citations
#edge computing Oct 2026

CHEGARAVIY HISOBLASHLAR TIZIMLARIDA HISOBLASHLARNI YUKLASH JARAYONIDA BLOKCHEYN TEXNOLOGIYASINING O‘RNI: TIZIMLI ADABIYOTLAR SHARHI

Ushbu maqolada chegaraviy hisoblashlar (Edge Computing), hisoblashlarni yuklash (Computational Offloading) va blokcheyn texnologiyalarining o‘zaro integratsiyasi hamda ushbu yo‘nalishdagi mavjud tadqiqotlar tahlil qilinadi. Dastlab chegaraviy hisoblashlar, hisoblashlarni yuklash va blokcheyn texnologiyasining asosiy xu...

Gulbaxar Umarova, Kuanishbay Sadatdiynov · 0 citations
#edge computing Open access Oct 2026

Machine learning-based prediction of critical load events in airborne wind energy systems

Abstract. Predicting and avoiding critical load events is crucial for the reliable autonomous operation of airborne wind energy (AWE) systems. Such events can lead to actuator saturation, loss of steering authority, and increased structural stresses, eventually resulting in long-term degradation of the kite, tether, an...

Monika Pasquali, Oriol Cayon, Spilios Theodoulis et al. · 0 citations
#edge computing Open access Oct 2026

Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture

The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing da...

Zhen Ma, Xiyuan Chen, Cundeng Wang · 0 citations
#edge computing Open access Oct 2026

Central Algebras and Central Cohomology: Central Actions, Lifting Towers, and Filtered Derived Centres

We develop a centre-first framework for constructing and controlling ambient algebraic objects from a prescribed central object and additional peripheral data. For associative algebras the basic datum is a central map \(C\to A\), not merely the ordinary centre \(Z(A)\). We distinguish central actions, faithful central...

Kianming(Jianming) Wang · 0 citations
#edge computing Open access Oct 2026

Transformer-Convolution Pyramid Network for Multi-Class Kidney, Tumor, and Cyst Segmentation

Background/Objectives: Chronic kidney diseases are fatal disorders, and early treatment is crucial for reducing mortality. Segmenting kidneys, tumors, and cysts from computed tomography scans has a significant role in assessing these diseases’ progression, determining their severity, and planning surgeries. However, ac...

Evgin Göçeri · 0 citations
#edge computing Open access Oct 2026

cgarling/StarFormationHistories.jl: v2.0.0

StarFormationHistories v2.0.0 Diff since v1.3.1 Added cum_sfr_quantiles returns quantiles of the total birth stellar mass of the sampled star formation histories (mstar) and, given per-population surviving mass fractions through the new surviving_fractions keyword (e.g., from surviving_mass_fraction), of their present-...

Chris Garling · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

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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