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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

IEEE Wireless Communications Letters 2025年第6期14卷 1748-1752页

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Linköping University Department of Computer and Information Science Linköping Sweden Singapore University of Technology and Design Information Systems Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. © 2012 IEEE.

关键词： Bandwidth

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Refinery: Graph Solver as a Service: Refinement-based Generation and Analysis of Consistent Models 24

Refinery: Graph Solver as a Service: Refinement-based Genera...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Marussy, Kristóf Ficsor, Attila Semeráth, Oszkár Varró, Dániel Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary Linköping University Department of Computer and Information Science Linköping Sweden McGill University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9798400705021

Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https://***/Qy-3udNsWsM. Continuously deployed at: https://***/. © 2024 IEEE Computer Society. All rights reserved.

关键词： Open source software

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Enhancing Learning from Incidents by Reconstruction of Events: Using the SQUARE Tool for Evaluation 20

Enhancing Learning from Incidents by Reconstruction of Event...

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20th Global information systems for Crisis Response and Management Conference, ISCRAM 2023

作者： Radianti, Jaziar Pilemalm, Sofie Steen-Tveit, Kristine Rustenberg, Kjetil Department of Information Systems University of Agder Norway Department of Management and Engineering Linköping university Norway

ISBN: (纸本)9798218217495

Evaluation after exercise and incidents—often called After Action Review or AAR—are important to enhance the emergency management (EM) response capability. However, evaluation support tools for event reconstruction after incidents and exercises are not yet fully available. We engaged EM stakeholders in a tabletop exercise based on snow chaos and car accidents scenarios to evaluate a fully functional prototype. The tool assists information sharing in real-time and enhances debriefing process of any EM response and exercise. Analysis of reflective discussions and an open question survey shows that the EM participants consider the features of the prototype to facilitate better learning from incidents. However, they have different attitudes concerning the adoption, management, and application of the tool in organizations. For instance, more security features are required to comply with regulations. We argue that the tool is an important first step to fill the gap on the need for "event reconstruction-based evaluation." © 2023 information systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： information management

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Cloud ERP systems architectural challenges on cloud adoption in large international organizations: A sociomaterial perspective

Cloud ERP systems architectural challenges on cloud adoption...

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2022 International Conference on ENTERprise information systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care information systems and Technologies, HCist 2022

作者： Paulsson, Victoria Johansson, Björn Department of Management and Engineering Division of Information Systems and Digitalisation LinköpingSE-581 83 Sweden

This combined literature review and case employs a sociomaterial perspective to explore architectural challenges and countermeasures that BioOrganism, the case company, should consider in a decision to adopt a cloud enterprise resource planning (ERP) system. The case has experiences with an on-premises model, but not the cloud model per se. The study is designed with an element of case to provide a solid backdrop toward the sociomaterial perspective, which focuses on a reciprocal enactment between the cloud ERP systems architecture and the BioOrganism contextual surrounding. Six architecture challenges were identified: (1) data security, (2) data privacy, (3) network dependency, (4) system mobility, (5) vendor lock-in, and (6) scalability. Countermeasures that BioOrganism, and other large international organizations of similar nature, can take to mitigate risks from the six challenges are presented in the review. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Cloud computing architecture

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Performance Improvement of Adaptive N-Square PS-QAM Based on LUT for Flexible Coherent PON

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Journal of Lightwave Technology 2025年第12期43卷 5569-5578页

作者： Lin, Gengming Zhu, Yixiao Yan, Zijun Cheng, Ziyu Zhuge, Qunbi Hu, Weisheng Shanghai Jiao Tong University State Key Lab of Advanced Optical Communication Systems and Networks School of Electronic Information and Electrical Engineering Department of Electronic Engineering Shanghai200240 China

Flexible coherent passive optical network (PON) is recognized as an advanced capacity optimization strategy for beyond 100G PON due to its flexibility and adaptability, which are pivotal for the evolution of high-capacity optical access networks. In this study, we propose an adaptive N-square QAM scheme compatible with probabilistic shaping (PS), enabling a more continuous variation of shaping entropy compared to standard 2M-QAM shaping scheme and improved frequency offset estimation (FOE) accuracy in blind 4th-power Fast-Fourier-transform (FFT) algorithm. In experimental demonstrations, dynamic-range and data-rate product (DRDRP) of uniform N2-QAM surpasses that of uniform 2M-QAM by 86 dB·Gbps. The DRDRP of PS N2-QAM also outperforms that of PS 2N-QAM by around 41 dB·Gbps. The development of PON optimization scheme for multi-carrier transmission is achieved through the generation of a look-up table (LUT) based on channel signal-to-noise ratio (SNR) conditions. The feasibility of the LUT is demonstrated under different super-Gaussian filter parameter settings at the back-to-back (B2B) scenario. After 20-km standard single-mode fiber (SSMF) transmission, the normalized generalized mutual information (NGMI) is greater than 0.985 across four subcarriers with varying received optical power (ROP) from -16 to -11 dBm. © 2025 IEEE.

关键词： Single mode fibers

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PBF-AMP-Onto: an ontology for powder bed fusion additive manufacturing processes 1

PBF-AMP-Onto: an ontology for powder bed fusion additive man...

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1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024

作者： Pour, Mina Abd Nikooie Tarafder, Prithwish Wiberg, Anton Li, Huanyu Moverare, Johan Lambrix, Patrick Department of Computer and Information Science Linköping University Linköping Sweden Swedish e-Science Research Centre Linköping University Linköping Sweden Department of Management and Engineering Linköping University Linköping Sweden Wallenberg Initiative Materials Science for Sustainability Linköping University Linköping Sweden Wallenberg AI Autonomous Systems and Software Program Linköping University Linköping Sweden

Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings) or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research and industry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semanticsaware data management, and enhance the understanding and optimization of additive manufacturing processes. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph. © 2024 CEUR-WS. All rights reserved.

关键词： Ontology

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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Computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2519-2547页

作者： Hassen Louati Ali Louati Elham Kariri Slim Bechikh College of Information Technology Kingdom UniversityRiffa40434Bahrain SMART Lab University of TunisISGTunisTunisia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease *** promising avenue involves the use of chest X-Rays,which are commonly utilized in *** fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic ***,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data *** tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image *** approach accurately classifies radiography images and detects potential chest abnormalities and infections,including ***,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting *** method can help reduce the amount of labeled data required for the task and enhance the overall performance of the *** have validated our method via a series of experiments against state-of-the-art architectures.

关键词： Computer-aided diagnosis deep learning evolutionary algorithms deep compression transfer learning

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Man Overboard Detection System Using IoT for Navigation Model

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Computers, Materials & Continua 2022年第6期71卷 4955-4969页

作者： Hüseyin Gürüler Murat Altun Faheem Khan Taegkeun Whangbo Department of Information Systems Engineering Mugla Sitki Kocman UniversityMuglaTurkey Artificial Intelligence Lab Department of Computer EngineeringGachon UniversityKorea

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry,roro,catamaran,frigate,yacht that are the vehicles services for the purpose of logistic and passenger *** this paper,all processes in the event of Man overboard(MOB)are initiated for smart *** MOB the falling person is totally dependent on the person who first saw the falling *** main objective of this paper is to develop a solution to this significant *** a staff member or a passenger does not see the fall into the sea,undesirable situations such as disappearance,injury and death can occur during the period until the absence of the fallen person is *** this paper,a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements,in which human resources are intensively involved like passengers,freight,logistics,fishing,business,yacht,leisure and naval *** this case,if the ship’s personnel or passengers fall into the sea in any way,it detected the fallen person into the sea by the sensors in the portable emergency device,which each person will *** warning system is activated via the in-ship automation system to which the information is transmitted by wireless ***,the case of MOB will be determined *** of things(IoT)has a key role in identifying the location and information of the person falling into the sea through sensors,radio frequency,GPS and connected ***,the alarm system on board will be activated and MOB flag(Oscar)will automatically be *** paper enables the Search and rescue(SAR)operations to be initiated and accelerated without losing time through decision-making process.

关键词： SAR internet of things man overboard arduino wireless communication smart transportation

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Mixed Traffic Flow Performance Evaluation Considering Spatial Distribution of Mixed-Automated Vehicles 27

Mixed Traffic Flow Performance Evaluation Considering Spatia...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Liu, Haoji Mu, Zeyu Wang, Zejiang Park, B. Brian Oak Ridge National Laboratory Oak RidgeTN37932 United States University of Virginia Link Lab and the Department of Systems and Information Engineering CharlottesvilleVA22903 United States University of Virginia Department of Civil and Environmental Engineering CharlottesvilleVA22903 United States

ISBN: (纸本)9798331505929

Adaptive cruise control (ACC) and cooperative ACC (CACC) systems for connected automated vehicles (CAVs) have been extensively developed over decades. However, the impact of the mixed-automated vehicle spatial distribution on mixed traffic flow performance considering different cruise controllers, particularly with the introduction of new CACC systems involving unconnected vehicles between two CAV s (CACCu), remains unclear. In this study, we modeled and evaluated the impact of different spatial distributions on mixed traffic flow. First, we developed car-following models for traditional human-driven vehicles (THV s) incorporating human reaction time, and for CAV s utilizing various cruise controllers including ACC, CACC, and CACCu, accounting for delayed vehicle dynamics. Then, numerical simulations were conducted to analyze the influence of mixed-automated vehicle spatial distribution, CAV penetration rate, and CAV s' desired time gap on traffic flow performance. Evaluation measures for driving comfort, energy consumption, and travel efficiency were employed to assess the outcomes. Our findings indicate that solely pursuing the clustered distribution to form CACC platoons may not always be optimal due to potential traffic disturbances amplified by clustered THV s, contrasting with most existing research. Furthermore, implementing CACCu is proved to improve CAV connectivity utilization, resulting in enhanced traffic performance in terms of safety, comfort, energy efficiency, and travel efficiency. © 2024 IEEE.

关键词： Adaptive control systems

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