Reconfigurable and Broadband Analog Computing With Terahertz Metasurface Based on Electrical Tuning of Vanadium-Dioxide Resonators by Mohammad Ali Shameli, Mirko Magarotto, Antonio-D Capobianco, Luca Schenato, Marco Santagiustina, Maria Antonietta Vincenti, Domenico de Ceglia

“…A architecture of reconfigurable analog computing devices is proposed for image processing and advanced wireless communication systems, such as those required for self-driving cars. …”
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Patient Health Monitoring based on Cloud Computing using the Message Queuing Telemetry Transport Protocol by Laith A. Abdul-Rahaim, Shahad W.Jassim

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Efficient implementations of a Born Series for computing photoacoustic field from a collection of erythrocytes by Ujjal Mandal, Navroop Singh, Kartikay Singh, Vinit Nana Hagone, Jagpreet Singh, Anshu S. Anand, Ben T. Cox, Ratan K. Saha

“…Numerical implementation of the Born series procedure is a computationally expensive task. Various computational strategies have been adopted and tested in this work for fast execution of the convergent Born series (CBS) algorithm for solving inhomogeneous Helmholtz equation in the context of biomedical photoacoustics (PAs). …”
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Research on computing resource allocation strategy for cloud radio access network based on demand forecasting by Zhipeng WANG, Bin CAO, Qinyu ZHANG

“…Cloud radio access network(CRAN) is a new network architecture commonly used in 5G network.It uses network function virtualization and software defined network technology to support end-to-end network slicing so that access network can share the same infrastructure.A computing resource allocation scheme based on virtualized network function(VNF) computing resource demand forecasting was proposed for cloud wireless network supporting end-to-end network slicing.Data-driven operation and maintenance framework established by the control plane collects network information and performs data processing to predict the demand for computing traffic in a period of time,which used the discrete particle swarm optimization based on best-fit-decreasing to dynamically and dynamically allocate CRAN computing resources,reducing the migration energy consumption and migration times of VNF.…”
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Architecture and model design of operational management system for computing force network based on intent enhancement by WANG Xiaohan, ZHAO Peng, TANG Guohua, WANG Chen, YUAN Xiangyang

“…Facing the application of intent-driven technology in computing force network, this paper designs the architecture of computing force network operation management system based on intent enhancement, gives the complete intent processing flow. …”
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Design of MobileNetV3 network accelerator based on dynamic adaptive computing engine by Xiang Haobin, Yang Ruimin, Wu Wentao, Li Chunlei, Dong Yan

“…To address this problem, this paper proposes a MobileNetV3 network accelerator based on a dynamic adaptive computing engine. Firstly, a pipeline inference architecture of local perception area convolution is designed to achieve highly parallel processing and buffer scheduling of features and weights. …”
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Edge computing for detection of ship and ship port from remote sensing images using YOLO by Vasavi Sanikommu, Sai Pravallika Marripudi, Harini Reddy Yekkanti, Revanth Divi, R. Chandrakanth, P. Mahindra

“…This deployment will ensure scalability, efficient processing, and reduced reliance on central computing resources, making it especially suitable for maritime settings in which real-time monitoring is vital. …”
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Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach by Bhargavi Krishnamurthy, Sajjan G. Shiva

“…The experimental results ensure that the proposed framework is good with respect to processing delay, energy consumption, load balancing, and service level agreement violation under a finite and infinite fog computing environment. …”
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Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods by David S. Smith, John C. Gore, Thomas E. Yankeelov, E. Brian Welch

“…We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. …”
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Future perspectives on internet of vehicles resource management: digital twin-enabled edge computing frameworks by Jihong Xie, Gui Wu, Xiang Zhou, Shanshan Deng

“…DTs replicate real entities in the virtual domain with real-time control, diagnostics, maintenance, and better management. With edge computing, processing data near the source, these technologies significantly lower response time and optimize system performance. …”
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Flexible hybrid edge computing IoT architecture for low-cost bird songs detection system by Francisco A. Delgado-Rajó, Carlos M. Travieso-Gonzalez

“…This study proposes a flexible hybrid edge computing IoT architecture for a low-cost bird song detection system. …”
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A Low-Cost Flexible Optoelectronic Synapse Based on ZnO Nanowires for Neuromorphic Computing by Yongqing Yue, Zixia Yu, Fangpei Li, Wenbo Peng, Quanzhe Zhu, Yongning He

“…Neuromorphic computing, inspired by the brain, holds significant promise for advancing artificial intelligence. …”
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Computing medial axis of a simple polygon in linear time based on R-L sequence by Hongyu Chen, Xiaodiao Chen, Yizhao Xue

“…Medial axis computation has wide applications in pattern recognition, image processing, finite element mesh generation, and CNC tool path extraction. …”
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Enhancing Property-Based Token Attestation With Homomorphic Encryption (PTA-HE) for Secure Mobile Computing by Thinh Le Vinh, Huan Thien Tran, Samia Bouzefrane

“…This paper proposes PTA-HE, an enhanced Property-based Token Attestation scheme integrated with Homomorphic Encryption (HE), specifically designed to address critical security challenges in mobile cloud computing environments. Traditional Property-based Token Attestation (PTA) protocols, although foundational, inherently lack robust mechanisms to secure sensitive data during active processing stages, exposing data to potential confidentiality and integrity breaches. …”
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Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning by Yuze Zhang, Geming Xia, Chaodong Yu, Hongcheng Li, Hongfeng Li

“…With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. …”
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Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing by Sudheer Mangalampalli, Syed Shakeel Hashmi, Amit Gupta, Ganesh Reddy Karri, K. Varada Rajkumar, Tulika Chakrabarti, Prasun Chakrabarti, Martin Margala

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Advancing Emotionally Aware Child–Robot Interaction with Biophysical Data and Insight-Driven Affective Computing by Diego Resende Faria, Amie Louise Godkin, Pedro Paulo da Silva Ayrosa

“…This paper investigates the integration of affective computing techniques using biophysical data to advance emotionally aware machines and enhance child–robot interaction (CRI). …”
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Efficient Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Joint Optimization Approach by Moein Valitabar, Mohammad Fathi, Keivan Navaie

“…The solution assigns mobile terminals (MTs) to ESs and optimally allocates computational resources at ESs for both task computation and block generation. …”
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Enhancing security in IIoT: RFID authentication protocol for edge computing and blockchain-enabled supply chain by Vikash Kumar, Santosh Kumar Das

“…By utilizing the real-time processing capabilities of edge computing and the decentralization and immutability of blockchain, the protocol enhances the security of data transmitted in the system. …”
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A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

“…However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. …”
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