10T SRAM Computing-in-Memory Macros for Binary and Multibit MAC Operation of DNN Edge Processors by Van Truong Nguyen, Jie-Seok Kim, Jong-Wook Lee

“…Fabricated in 28 nm CMOS process, this design achieves 409.6 GOPS throughput, 1001.7 TOPS/W energy efficiency, and a 169.9 TOPS/mm² throughput area efficiency. …”
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Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…This process ensures data security at the edge level and eliminates the need for centralized storage. …”
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Global drought tolerant maize research and development: Analysis and visualization of cutting-edge scientific technologies by Muhammad Azrai, Abdul Haris Bahrun, Roy Efendi, Nining Nurini Andayani, Muhammad Jihad, Bahtiar, Bunyamin Zainuddin, Muslimin, Muhammad Aqil

“…The dataset from Scopus was divided into two categories: a unified query and segmented queries, categorizing documents into four specific themes: transgenic, MAS, HTPP, and genome editing. A careful filtering process removed duplicates and irrelevant documents, ensuring dataset accuracy and relevance. …”
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Rip Current Identification in Optical Images Using Wavelet Transform by Hsu-Min Wang, Dong-Jiing Doong, Jian-Wu Lai

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Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures by Edwin Salcedo

“…Alternatively, the emerging paradigm of edge computing, which involves moving computational processes to local devices, offers the potential to reduce latency, enable real-time surveillance, and eliminate reliance on internet connectivity. …”
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Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions by Soroush Heydari, Qusay H. Mahmoud

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Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks by Zhenyu Li, Yuchuan Fu, Mengqiu Tian, Changle Li

“…The combination of integrated sensing and communication (ISAC) with mobile edge computing (MEC) enhances the overall safety and efficiency for vehicle to everything (V2X) system. …”
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Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing by Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos K. Markakis, Athina Bourdena, George Mastorakis

“…The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. …”
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Hierarchical Reinforcement Learning for Multi-Layer Multi-Service Non-Terrestrial Vehicular Edge Computing by Swapnil Sadashiv Shinde, Daniele Tarchi

“…We aim to optimize the overall latency and energy requirements for processing the VU data by selecting the appropriate edge nodes and the offloading amount. …”
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Polara-Keras2c: Supporting Vectorized AI Models on RISC-V Edge Devices by Nizar El Zarif, Mohammadhossein Askari Hemmat, Theo Dupuis, Jean-Pierre David, Yvon Savaria

“…These capabilities position Polara-Keras2c as a transformative tool in real-time, energy-efficient AI processing for edge devices, pushing forward the evolution of edge computing.…”
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Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions by Xuanzheng Wang, Zhipeng Xu, Xingfei Sui

“…Edge computing has emerged as a vital paradigm for processing data near its source, significantly reducing latency and improving data privacy. …”
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Joint Adaptive Resolution Selection and Conditional Early Exiting for Efficient Video Recognition on Edge Devices by Qingli Wang, Chengwu Yu, Shan Chen, Weiwei Fang, Naixue Xiong

“…Furthermore, the superiority of RSEE on resource-constrained edge devices is validated on the NVIDIA Jetson Nano, with processing speeds controlled by hyperparameters ranging from about 12 to 60 Frame-Per-Second (FPS) that well enable real-time analysis.…”
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Query scheduling based on cloud-edge multi-data warehouse architecture and cost prediction model by GAO Xuning, YANG Song, LI Mingzhe, ZHANG Yanfeng

“…With the development of cloud computing and big data, traditional local data warehouses are difficult to expand and have low data processing efficiency, As a result, the data warehouse of cloud edge architecture comes into being. …”
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Query scheduling based on cloud-edge multi-data warehouse architecture and cost prediction model by GAO Xuning, YANG Song, LI Mingzhe, ZHANG Yanfeng

“…With the development of cloud computing and big data, traditional local data warehouses are difficult to expand and have low data processing efficiency, As a result, the data warehouse of cloud edge architecture comes into being. …”
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OPTIMIZATION OF THE CUTTING EDGE GEOMETRICAL PARAMETERS BASED ON FINITE ELEMENT SIMULATION OF TURNING INCONEL 718 by CHENG Xin, LIU QingFeng, WANG NingChang, JIANG Feng

“…Then,the optimal parameters of cutting edge on the processing of nickel-based alloy are selected. …”
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Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices by Md Shahanur Alam, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha

“…An ultralow-power, high-performance online-learning and anomaly-detection system has been developed for edge security applications. Designed to support personalized learning without relying on cloud data processing, the system employs sample-wise learning, eliminating the need for storing entire datasets for training. …”
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Real-time monitoring and optimization methods for user-side energy management based on edge computing by Jisheng Huang, Shanshan Zhou, Guangming Li, Qiang Shen

“…Abstract This paper presents a comprehensive framework for real-time monitoring and optimization of user-side energy management systems leveraging edge computing technology. The proposed approach addresses key challenges in traditional centralized energy management by bringing computation and data processing closer to end devices. …”
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SmartCardio: Advancing cardiac risk prediction through Internet of Things and edge cloud intelligence by S. Durga, Esther Daniel, J. Andrew, Radhakrishna Bhat

“…It consists of two main phases: data acquisition, where sensors measure heart rate, temperature, and blood pressure, and data processing, where the edge cloud processes the data using Haar Wavelet transform, Convolutional Neural Network (CNN), and transfer learning. …”
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U-SCAD: An Unsupervised Method of System Call-Driven Anomaly Detection for Containerized Edge Clouds by Jiawei Ye, Ming Yan, Shenglin Wu, Jingxuan Tan, Jie Wu

“…Container technology is currently one of the mainstream technologies in the field of cloud computing, yet its adoption in resource-constrained, latency-sensitive edge environments introduces unique security challenges. …”
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A tree crown edge-aware clipping algorithm for airborne LiDAR point clouds by Shangshu Cai, Yong Pang

“…Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. …”
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