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Reconfigurable Intelligent Surface Direct Data Modulation With Adaptive Beam Steering Backscatter Communication

IEEE Internet of Things Journal 2025年第11期12卷 15600-15619页

作者： Amri, Muhammad Miftahul Aziz, Arif Abdul Tran, Nguyen Minh Park, Je Hyeon Kim, Dong In Choi, Kae Won Sungkyunkwan University College of Information and Communication Engineering Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Telkom University University Center of Excellence for Intelligent Sensing-IoT Bandung Indonesia Department of Wireless Communications Hanoi Viet Nam

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for next-generation wireless communications due to its ability to manipulate electromagnetic (EM) waves. This work introduces a novel RIS direct modulation scheme based on the backscatter communication concept. In this work, the RIS acts as a sole information modulator. Unlike conventional RIS systems that relay incoming data-carrying waves, this work enables RIS to encode its own information into unmodulated waves without requiring any hardware modifications. We propose an adaptive algorithm that maximizes modulation order while meeting the quality of service (QoS) requirements by selecting appropriate constellation points to minimize error probability. The proposed scheme was validated through simulations and experiments using a passive 5.8 GHz RIS prototype. The validations demonstrate consistent performance in both distributed and co-located transmitter-receiver scenarios. These results highlight its feasibility as an alternative approach for RIS-assisted communications and contribute to further exploration of RIS-enabled symbiotic radio (SR) and over-the-air (OTA) modulation systems. © 2014 IEEE.

关键词： Adaptive algorithms

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Smart Water Networks as Cyber-Physical-Socio-Environmental Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 95-105页

作者： Eliades, Demetrios G. Malialis, Kleanthis Vrachimis, Stelios Polycarpou, Marios M. University of Cyprus KIOS Research and Innovation Center of Excellence Nicosia1678 Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This position paper introduces a novel Cyber-Physical-Socio-Environmental Systems (CPSES) framework for Smart Water Networks (SWN). The proposed framework introduces environmental aspects as a key constituent, which is important for addressing the interconnected challenges of water systems, such as climate change impacts, contamination risk, and sustainability. The CPSES framework dynamically incorporates interactions between physical components (sensors, actuators), cyber systems (control, monitoring, digital twins), social factors (policy, demand management, crisis response), and environmental impacts (emissions, resource availability). The significance of this framework lies in its potential to enable more resilient, sustainable, and secure SWNs by incorporating feedback loops among the CPSES constituents. Key CPSES research challenges are outlined, such as modeling the impacts of climate change, dynamic risk estimation, and ethical and fairness aspects within SWN. A real-world use case on water contamination crisis management demonstrates the framework's practical application and relevance for researchers. © 2024 The Authors.

关键词： Water pollution control

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Efficient Global Neural Architecture Search

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SN computer Science 2025年第3期6卷 1-12页

作者： Siddiqui, Shahid Kyrkou, Christos Theocharides, Theocharis Electrical and Computer Engineering Department University of Cyprus Nicosia Cyprus KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Neural architecture search (NAS) has shown promise towards automating neural network design for a given task, but it is computationally demanding due to training costs associated with evaluating a large number of architectures to find the optimal one. To speed up NAS, recent works limit the search to network building blocks (modular search) instead of searching the entire architecture (global search), approximate candidates’ performance evaluation in lieu of complete training, and use gradient descent rather than naturally suitable discrete optimization approaches. However, modular search does not determine network’s macro architecture i.e. depth and width, demanding manual trial and error post-search, hence lacking automation. In this work, we revisit NAS and design a navigable, yet architecturally diverse, macro-micro search space. In addition, to determine relative rankings of candidates, existing methods employ consistent approximations across entire search spaces, whereas different networks may not be fairly comparable under one training protocol. Hence, we propose an architecture-aware approximation with variable training schemes for different networks. Moreover, we develop an efficient search strategy by disjoining macro-micro network design that yields competitive architectures in terms of both accuracy and size. Our proposed framework achieves a new state-of-the-art on EMNIST and KMNIST, while being highly competitive on the CIFAR-10, CIFAR-100, and FashionMNIST datasets and being 2-4 times faster than the fastest global search methods. Lastly, we demonstrate the transferability of our framework to real-world computer vision problems by discovering competitive architectures for face recognition applications. © The Author(s) 2025.

关键词： AutoML Convolutional neural networks Deep learning Image classification Neural architecture search

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Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review

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Artificial Intelligence Review 2025年第8期58卷 1-78页

作者： Al-antari, Mugahed A. Salem, Saied Raza, Mukhlis Elbadawy, Ahmed S. Bütün, Ertan Aydin, Ahmet Arif Aydoğan, Murat Ertuğrul, Bilal Talo, Muhammed Gu, Yeong Hyeon Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Artificial Intelligence College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Computer Engineering Faculty of Engineering Fırat University Elazığ Turkey Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey Department of Software Engineering Faculty of Technology Fırat University Elazığ Turkey Department of Neurosurgery Faculty of Medicine Fırat University Elazığ Turkey Department of Computer Science & Engineering University of North Texas DentonTX76205 United States

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of Science, ScienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs). © The Author(s) 2025.

关键词： Explainable artificial intelligence (XAI) Harmonization Large language models (LLMs) LSS prediction Lumbar spinal stenosis (LSS) Spinal LSS indices measurements

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

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IEEE Access 2025年 13卷 86768-86780页

作者： Saleem, Muhammad Asim Javeed, Ashir Benjapolakul, Watit Srisiri, Wattanasak Chaitusaney, Surachai Kaewplung, Pasu Chulalongkorn University Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Bangkok 10330 Thailand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies. © 2013 IEEE.

关键词： Disaster Prediction Feature Extraction Machine Learning SMOTE XGBoost

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MARM-HyLiWi: Mobility-Aware Rate Maximization in Link Aggregation Enabled Hybrid Li-Fi/Wi-Fi Networks 17

MARM-HyLiWi: Mobility-Aware Rate Maximization in Link Aggreg...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Paramita, Saswati Mondal, Rajorshi Bhattacharya, Arani Bohara, Vivek Ashok Srivastava, Anand Delhi Wirocomm Research Group Center of Excellence Li-Fi Department of Electronics and Communication Engineering New Delhi India IIIT Delhi Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798331531195

Light Fidelity (Li-Fi) offers high data rates in confined areas but is limited by susceptibility to signal blockages and constrained coverage. To address these limitations, Li-Fi is often integrated with Wi-Fi in a heterogeneous network (HetNet), leveraging Li-Fi's speed with Wi-Fi's broader coverage. However, hybrid networks introduce frequent handovers between Li-Fi and Wi-Fi access points (APs), especially for users near blind spots or with limited mobility. This paper proposes a link aggregation (LA) approach for a Li-Fi and Wi-Fi HetNet (LA-HLWN), where users receive data from both types of APs simultaneously, enhancing data rates and reducing handover frequency. To manage network load effectively, we develop an algorithm for dynamic data rate allocation, formulated as a multi-dimensional commodity flow problem that considers factors such as user requirements, AP signal quality, and backhaul conditions. Given the NP-hard nature of this problem, our solution models it initially as an integer linear programming (ILP) challenge and then applies a relaxed linear programming (LPP) approach with a rounding technique for real-time allocation. Additionally, to balance throughput and reduce the computational overhead, we implement a strategy to control handover frequency and load distribution across APs, achieving a 54% improvement in throughput. This approach demonstrates the potential of LA in enhancing user satisfaction and system performance in hybrid Li-Fi/Wi-Fi networks. © 2025 IEEE.

关键词： Wireless local area networks (WLAN)

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Azimuthal Equidistant Mapping and Projection of Head CT Scans for Fracture Visualization

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IEEE Access 2025年 13卷 78214-78220页

作者： Hadjittoouli, Nicolas Nikolaou, Christos Pitris, Costas University of Cyprus KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia2109 Cyprus Limassol General Hospital Radiology Department Limassol3304 Cyprus

Skull fractures of the base and facial regions of the skull are challenging to identify, even by expert radiologists. The intricate nature and subtle features of such fractures, demand meticulous attention and ample time allocation to assure effective diagnosis and positive patient outcomes. This paper introduces a novel approach, utilizing azimuthal equidistant mapping, to better visualize the skull and, thus, enhance the diagnostic speed and accuracy of skull fracture evaluation. The proposed method maps the inherently spherical shape of the skull into a planar representation, providing radiologists with an alternative view of each complete hemisphere. Applying azimuthal equidistant mapping to CT volumetric data preserves the skull’s anatomy, while fractures, which are usually perpendicular to the bone’s surface, become more visible in projection views. The enhanced visibility is particularly useful for subtle basilar or facial fractures and increases the likelihood of detection. The effectiveness of the technique was evaluated using the CQ500 dataset. Azimuthal equidistant mapping and projection can become a valuable addition to conventional radiologic evaluation, offering an alternative view for radiologists and emergency department staff, which can increase the effectiveness and time efficiency of skull fracture detection. © 2013 IEEE.

关键词： Diagnostic radiography

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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