Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks by Bowen Bao, Hui Yang, Qiuyan Yao, Lin Guan, Jie Zhang, Mohamed Cheriet

“…Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edge-cloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. …”
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5G Indoor/Outdoor Field Trial of Deep Joint Source-Channel Coding Method by Daisuke Hisano, Keigo Matsumoto, Yoshiaki Inoue, Yuko Hara, Kazuki Maruta, Yu Nakayama, Hiroshi Tatsukawa, Yuji Kawai, Yoshinori Shinohara, Hiroki Ikeda

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A Scalable Framework for Deploying AI-Powered Wildlife Monitoring in Resource-Limited Field Environments by Md. Auhidur Rahman, Tesfalem Mehari Berhe, Luca Borgianni, Md Sabbir Ahmed, Cristian Bua, Davide Adami, Stefano Giordano

“…Wildlife monitoring in remote field environments presents significant challenges owing to energy constraints, limited network connectivity, and the need for real-time processing. …”
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Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina by Chao Wang, Conghe Song, Todd A. Schroeder, Curtis E. Woodcock, Tamlin M. Pavelsky, Qianqian Han, Fangfang Yao

“…Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. …”
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ETC-PCR: Coarse-to-Fine Edge-Triangle Compatibility for Robust and Accurate Point Cloud Registration by Mingyuan Zhao, Long Xu

“…Typically, features are first extracted—either through hand-crafted descriptors or deep learning-based methods—to establish a putative correspondence set. …”
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Study of Pyramids with Equal Edges, Which Have Equal Radius of the Circle Described Around Their Base by Toni Chehlarova, Neda Chehlarova

“…Computer models for studies with pyramids with equal edges whose bases are inscribed in a specific circle are presented. …”
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Enhancing Computation-Efficiency of Deep Neural Network Processing on Edge Devices through Serial/Parallel Systolic Computing by Iraj Moghaddasi, Byeong-Gyu Nam

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A2PC: Augmented Advantage Pointer-Critic Model for Low Latency on Mobile IoT With Edge Computing by Rodrigo Carvalho, Faroq Al-Tam, Noelia Correia

“…The nature of this planning problem makes cutting-edge deep reinforcement learning (DRL) techniques useful in solving pertinent issues, such as having to deal with multiple dimensions in the action space while aiming for optimum levels of system performance. …”
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A Network Traffic Characteristics Reconstruction Method for Mitigating the Impact of Packet Loss in Edge Computing Scenarios by Jiawei Ye, Yanting Chen, Aierpanjiang Simayi, Yu Liu, Zhihui Lu, Jie Wu

“…With the rapid expansion of wireless networks driven by edge computing, IoT, and 5G technologies, challenges such as transmission instability, channel competition, and environmental interference have led to significant packet loss rates, adversely impacting deep learning-based network traffic analysis tasks. …”
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Toward 6G: Latency-Optimized MEC Systems with UAV and RIS Integration by Abdullah Alshahrani

“…Multi-access edge computing (MEC) has emerged as a cornerstone technology for deploying 6G network services, offering efficient computation and ultra-low-latency communication. …”
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University Libraries in Türkiye: Past to Present - A Comprehensive Analysis by Güler Demir, Hasan Sacit Keseroğlu

“…From research materials and academic resources to access to cutting-edge technology and expert staff, these libraries are at the forefront of fostering a culture of learning, collaboration, and innovation both within their physical spaces and online. …”
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AI-Enabled Collaborative Distributed Computing in Networked UAVs by Bassem Mokhtar

“…Using a networked UAV system and various application scenarios, numerical simulation studies have been presented. The performance analysis and results show how the proposed distributed computing architecture with collaborative learning outperforms the centralized computing architecture with edge and cloud computing paradigms.…”
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Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks by Wei Zheng, Pengshan Ren, Qing Li

“…Abstract The efficient integration of communication and computation in the internet of things (IoT) presents new opportunities for enhancing system performance but still faces challenges such as interference management, resource allocation and task scheduling. …”
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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…However, deploying deep learning models on edge devices presents significant challenges due to limited computational resources and strict latency constraints. …”
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Enhancing DevOps Practices in the IoT–Edge–Cloud Continuum: Architecture, Integration, and Software Orchestration Demonstrated in the COGNIFOG Framework by Kostas Petrakis, Evangelos Agorogiannis, Grigorios Antonopoulos, Themistoklis Anagnostopoulos, Nasos Grigoropoulos, Eleni Veroni, Alexandre Berne, Selma Azaiez, Zakaria Benomar, Harry Kakoulidis, Marios Prasinos, Philippos Sotiriades, Panagiotis Mavrothalassitis, Kosmas Alexopoulos

“…This paper presents COGNIFOG, an innovative framework under development that is designed to leverage decentralized decision-making, machine learning, and distributed computing to enable autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. …”
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Child-Sum (N2E2N)Tree-LSTMs: An interactive Child-Sum Tree-LSTMs to extract biomedical event by Lei Wang, Han Cao, Liu Yuan

“…Tree-LSTM can update gate and memory vectors from the multiple sub-units. Learning edge features can strengthen the expression ability of graph neural networks. …”
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Artificial Vision Systems for Mobility Impairment Detection: Integrating Synthetic Data, Ethical Considerations, and Real-World Applications by Santiago Felipe Luna-Romero, Mauren Abreu de Souza, Luis Serpa Andrade

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Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano by H. M. Mohan, S. Anitha, Rifai Chai, Sai Ho Ling

“…The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. …”
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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

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FPGA SoC Implementation of Adaptive Deep Neural Network-Based Multimodal Edge Intelligence for Internet of Medical Things by Nikhil B. Gaikwad, Smith K. Khare, Dinesh Mendhe, Hasan Mir, Sokol Kosta, U. Rajendra Acharya

“…To assist medical personnel in intensive care units (ICU), we present the design of a network edge gateway that performs resource-efficient, real-time data analytics. …”
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