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distributed optical fiber acoustic sensing system based on laser pulse coding 4

Distributed optical fiber acoustic sensing system based on l...

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4th international Computational Imaging conference, CITA 2024

作者： Bai, Weidong Wang, Yu Jia, Hong Zhao, Yushen Liu, Xin Bai, Qing Jin, Baoquan Key Laboratory of Advanced Transducer and Intelligent Control Systems Ministry of Education and Shanxi Province Taiyuan University of Technology 79 Yingzexi Avenue Taiyuan030024 China College of Physics and Optoelectronic Engineering Taiyuan University of Technology 79 Yingzexi Avenue Taiyuan030024 China College of Electronic Information Engineering Taiyuan University of Technology 79 Yingzexi Avenue Taiyuan030024 China

ISBN: (纸本)9781510688834

the enhancement of signal-to-noise ratio (SNR) represents a significant challenge for distributed optical fiber acoustic sensing systems. this paper presents a distributed optical fiber acoustic sensing system based on laser pulse coding. the generation mechanism of poly-phase coding sequence is elucidated. the decoding method of poly-phase coding sequence based on pulse compression technology is analyzed, and the SNR enhancement result is discussed. the experimental results demonstrate that, in comparison to the traditional single pulse modulation method, the proposed pulse coding method enhances the system SNR by 9.41 dB and the strain resolution by 17 pΕ/√Hz. this pulse coding method represents a viable approach for enhancing the performance of distributed optical fiber acoustic sensing system. © 2025 SPIE.

关键词： Optical fibers

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset 14

Intrusion Detection at the Edge Computing: A Deep Learning A...

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14th IEEE international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Vipin Kumar, Vivek Bhadauria, Abhay Pratap Singh Dixit, Jay Kumar, Amit Haridwar India Department of Computer Engineering & Applications GLA University UP Mathura India

ISBN: (纸本)9798331531935

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. the findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. this study investigates the application of edge computing, Internet of things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. the results demonstrate better performance, showing their suitability for real-time security applications. these findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments. © 2025 IEEE.

关键词： Edge computing

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Design of a Scaled-Vehicle Research Platform for Software-Defined Vehicles

Design of a Scaled-Vehicle Research Platform for Software-De...

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11th international conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Oh, Sung Bhin Do, Young Soo Lim, Se Jeong Choi, Hyeok Jun Cha, Sung Keun Hong, Hyeong Keun Lim, Seong Hyeon Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798331509293

Modern vehicles are integrated systems of complex software that support advanced features such as autonomous driving and over-the-air updates. Software-defined vehicles (SDVs) are emerging as a future automotive paradigm for managing increasing software complexity by centralizing computing resources and enabling flexible software updates and feature deployment. However, most SDV research is conducted in simulation environments because using real vehicles presents many challenges, including high cost, safety concerns, and implementation difficulties. In this paper, we present the design of an SDV research platform using a 1/5-scale vehicle model to overcome these limitations of SDV research. the platform implements a zone architecture and integrates various embedded systems to replicate the distributed computing environment of a modern vehicle. the platform allows researchers to experiment with different SDV concepts such as distributed computing, OTA updates, middleware, and vehicle software management without the constraints of full-scale vehicle testing. Our design demonstrates the feasibility of using extended vehicles for SDV research and provides insights into platform design for SDV research. © 2025 IEEE.

关键词： distributed cloud

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A Prompt Chaining Framework for Long-Term Recall in LLM-Powered intelligent Assistant 25

A Prompt Chaining Framework for Long-Term Recall in LLM-Powe...

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30th international conference on intelligent User Interfaces, IUI 2025

作者： Seo, Seongbum Yoo, Sangbong Jang, Yun Sejong University Seoul Korea Republic of

ISBN: (纸本)9798400713064

intelligent Assistants (IAs) often struggle with maintaining context in extended conversations, limiting their long-term recall capabilities and personalization. this study introduces a prompt chaining framework that integrates Large Language Models (LLMs) into IAs to address these issues. Our approach encompasses: (1) a formative study (N=30) analyzing IA-user interactions and identifying key challenges in long-term memory and personalization, (2) development of an LLM-based system with a novel prompt chaining mechanism for improved context retention, adaptable to various LLM architectures, and (3) implementation of a multi-step reasoning process to enhance context-aware and personalized responses. We evaluated the framework through quantitative analysis on multiple datasets, tested with different LLM backends, and conducted ablation studies to assess component contributions. the results show improvements in long-term recall, context awareness, and personalization across all tested models and datasets. A human evaluation study (N=47) indicated better Sensibleness, Consistency, and Personalization in IA response. this approach enhances IA memory capabilities and can adapt to different LLM backends. © 2025 Copyright held by the owner/author(s).

关键词： Memory architecture

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A Novel Device Based Edge-Cloud Architecture for Vehicular Edge computing 5th

A Novel Device Based Edge-Cloud Architecture for Vehicular E...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Raj, P. Herbert Kumar, P. Ravi Juwono, Filbert H. University of Southampton Malaysia Gelang Patah Malaysia University Technology Brunei Bandar Seri Begawan Brunei Xi’an Jiaotong–Liverpool University Suzhou China

ISBN: (纸本)9789819780426

the rapid advancement of vehicular technology and the proliferation of connected vehicles have given rise to the demand for efficient and responsive computing solutions within the vehicular environment. Vehicular Edge computing (VEC) emerges as a promising paradigm to meet these demands by leveraging the computational resources at the network edge. this paper presents an in-depth exploration of Vehicular Edge computing Architecture (VECA), a novel framework designed to enhance the capabilities of connected vehicles through edge computing. VECA integrates edge computing nodes, vehicle-to-everything (V2X) communication technologies, and intelligent algorithms to create a dynamic and distributed computing environment within the vehicular network. this architecture addresses critical challenges related to latency, bandwidth, and scalability, enabling a wide range of applications, including real-time navigation, autonomous driving, traffic management, and infotainment services. Key components of VECA include edge servers strategically placed at roadside infrastructure and within vehicles, a robust communication infrastructure that supports low-latency data exchange, and machine learning algorithms for predictive analytics and decision-making. the architecture fosters efficient resource allocation, load balancing, and secure data management, ensuring optimal utilization of computational resources while preserving data privacy. this paper provides a comprehensive overview of VECA’s architecture, highlighting its technical specifications, benefits, and potential use cases. this research paper also discusses the integration of the novel Device Based Edge-Cloud Architecture into existing vehicular networks, along with challenges and future research directions. through the adoption of this new architecture, connected vehicles can harness the power of edge computing to enhance safety, efficiency, and user experience, ushering in a new era of intelligent and responsive vehicular

关键词： Decision making

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CS-UNet: A LightWeight UNet Model Based On Context Information 17th

CS-UNet: A LightWeight UNet Model Based On Context Informati...

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17th international conference on intelligent Robotics and Applications, ICIRA 2024

作者： Shen, Chenxin Fang, Yinfeng Yu, Xixia Guo, Chunsheng Ju, Zhaojie Pinghu Digital Technology Innovation Research Institute Hangzhou Dianzi University Zhejiang Pinghu China School of Communication Engineering Hangzhou Dianzi University Zhejiang Hangzhou China Department of Internal Medicine Xinhua Hospital of Zhejiang Province The Second Affiliated Hospital Zhejiang Chinese Medical University Zhejiang Hangzhou310003 China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

ISBN: (纸本)9789819607761

Since the introduction of UNet in 2015,U-shaped architecture has become an important paradigm in the field of medical image segmentation. However, due to the inherent local limitations of convolutions, a fully convolutional segmentation networks with U-shaped architecture cannot effectively utilize global context information. Although the combination of transformers and CNNs can address these issues, it also brings larger computational cost. At the same time, Due to the specificity of medical scene, scarce medical resources will be an obstacle to the application of transformers in the medical field, which is well adapted by the inductive bias in lightweight networks. For this reason we propose CS-UNet, a lightweight fully convolutional model that enables fast image segmentation in real medical scenarios. In this model, we reconstruct the encoder and decoder to generate a dense receptive domain to extract contextual information. We evaluate CS-UNet on five different types of medical image datasets, and found that CS-UNet excelled in segmentation performance. While ensuring high accuracy, CS-UNet has faster computation speed and smaller model parameters, which achieves a better balance between performance and computation cost, and is less constrained by the environment as well as the equipment in practical application scenarios. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Multiscale Temporal-Convolution and Attention Improved Neural Network for Biased Mental State Recognition 17th

Multiscale Temporal-Convolution and Attention Improved Neur...

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17th international conference on Brain Informatics, BI 2024

作者： Zuo, Guoyu Xiao, Erjun Zhang, Tielin Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9789819632930

Accurate detection of mental state is vital for sleep assessment and anesthesia monitoring. However, challenges exist, including 1) limited data sources caused by few modalities, few channels, biased sample distribution, or large individual differences;2) multiscale dynamic feature of mental state signals, containing complex and dynamic fluctuations. In this paper, we proposed a multiscale temporal convolution and attention improved neural network (MTA-NN) to handle these two challenges, including: a preprocessing method using a clustering-first and label-generation approach for data augmentation involves filtering the production samples to mitigate the impact of noise on model performance;multiscale temporal-convolution module for detecting different scales of temporal and frequency features;an attention module for decoding key features in different spatial and temporal channels, and for setting the importance of historical dependencies;a full-connection layer is designed to recognize different sleep stage. the proposed MTA-NN is then verified in three typical sleep-state recognition tasks, including SleepEDF-20, SleepEDF-78, and ISRUC3. Compared to some state-of-the-art algorithms, the proposed MTA-NN achieves higher performance. Further ablation experiments show the necessary and importance of these designed sub modules in MTA-NN. We consider the same algorithm can also be extended to further similar mental state recognition tasks such as anesthesia monitoring. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anesthesiology

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distributed and Personalized Federated Learning in Wireless Ad Hoc Networks 18th

Distributed and Personalized Federated Learning in Wireless...

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18th international conference on Wireless Artificial intelligent computing Systems and Applications, WASA 2024

作者： Huang, Baogui Wang, Bei Li, Xiangqian Ma, Chunmei Li, Guangshun Lai, Qingliang School of Computer Science Qufu Normal University Shandong Rizhao276826 China Huilian Zhongchuang Intelligent Technology Research Institute Shandong Rizhao276826 China

ISBN: (纸本)9783031714665

this paper studies the difficult task of designing federated learning algorithms tailored for wireless ad hoc networks. Federated learning in such networks presents numerous challenges, including signal interference, decentralized infrastructure, dynamic changes in network topology, heterogeneous devices, and diverse data statistics. In response to these challenges, this paper proposes a fully distributed and personalized federated learning algorithm specifically designed for wireless ad hoc networks, named ADDPFed. ADDPFed tackles the issue of wireless interference by leveraging non-orthogonal multiple access technology and successive interference cancellation for enhancing overall communication efficiency. Another key strength of ADDPFed lies in its ability to enable direct local model exchanges among neighboring clients, eliminating the need for central server coordination for model aggregation. For the model aggregation, ADDPFed first calculates the distance between local models using the Tonimoto coefficient, and then assigns suited aggregation weights to these models. this approach emphasizes the contribution of similar models to the global model, effectively tackling the issue of data statistical heterogeneity. Extensive experiments validate the effectiveness of ADDPFed, paving the way for enhanced collaborative and distributed learning paradigms in wireless ad hoc networks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Fast Memory Disaggregation with SwiftSwap 20th

Fast Memory Disaggregation with SwiftSwap

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Zhang, Xiangwei Wang, Desheng Zhang, Weizhe Yu, Zhiji Hao, Meng School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Cyberspace Science Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819628292

With the continuous evolution of cloud computing technology, the memory requirements of cloud servers are increasing. Insufficient memory has become the performance bottleneck of many applications. Memory disaggregation has been proposed to solve this problem, which enables computing nodes to utilize the memory of remote memory nodes, thus not only effectively extending the capacity of logical memory, but also significantly improving the resource utilization and elasticity of the whole cluster, providing a more flexible resource allocation scheme for large-scale parallel computing and data processing. However, most of the existing memory disaggregation systems require modification of hardware, operating systems or applications, which are difficult to use. this paper proposes a high-performance memory disaggregation system SwiftSwap, which can directly access remote memory without any modification of hardware, operating system, or application. We design and implement a high performance cache, cache lookup algorithm LDA and cache elimination strategy RM_AwareLRU on the computing node to improve the performance of the system. through comprehensive evaluation, SwiftSwap has up to 5.1× performance advantage in throughput, 4.8× performance advantage in tail delay, and 6.4× performance advantage against applications running in containers. © IFIP international Federation for Information Processing 2025.

关键词： Resource allocation

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Real-Time Energy Management Based on intelligent Predictive Control for Low-Voltage Electric Power Systems Considering Storage Facility Availability 14th

Real-Time Energy Management Based on Intelligent Predictive ...

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14th international conference on Frontier computing, FC 2024

作者： Pei, Chuanxun Fang, Jiandi Ge, Zhifeng Jiang, Han Yu, Min Ren, Jiaorong State Grid Ninghai Power Supply Company Zhejiang Ningbo China State Grid Ningbo Electric Power Supply Company Zhejiang Ningbo China

ISBN: (纸本)9789819627936

the optimal energy management of a multi-energy system is a complicated optimization task. this work explores the optimal scheduling problem of power systems under the conditions of distributed power sources and energy storage devices. Firstly, model each unit in the system, explain its operating mechanism, provide constraints during the operation process, and consider the objective function of minimizing scheduling costs. A Model Predictive Control (MPC) based solution is developed that includes distributed power sources and energy storage devices. Rolling optimization was carried out based on predicted data, and feedback correction was continuously performed using measured data during the optimization process to form a complete closed-loop control. In addition, this work compares and analyzes the optimization control methods. the results show that the algorithm proposed in this article can more effectively utilize the output of distributed power sources, fully utilize the charging and discharging characteristics of energy storage devices, improve their economic benefits, and reduce the impact and burden on the public power grid. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Charge storage

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