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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

science China(Information sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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A reduction scheme of PAPR in CO-OFDM system using time-delay neural networks

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Optoelectronics Letters 2025年

作者： TONG Zhengrong LIU Zhihong WANG Hao ZHANG Weihua ZHANG Tianhao JIA Qiqi Tianjin Key Laboratory of Thin Film Electronics and Communication Devices， Engineering Research Center for Optoelectronic Devices and Communication Technology of the Ministry of Education， School of Integrated Circuit Science and Engineering， Tianjin University of Technology

An iterative partial transmission sequence （IPTS） cascading time-delay neural network （TDNN） scheme is proposed to reduce peak-to-average power ratio （PAPR） in the coherent optical orthogonal frequency division multiplexing （CO-OFDM） systems. The IPTS algorithm further reduces computational complexity based on the partial transmission sequence （PTS） algorithm. TDNN is trained by the iterative clipping filtering （ICF） method. Simulation results show that the proposed scheme achieves a PAPR reduction of 5.84 dB when complementary cumulative distribution function （CCDF） is 10-4and an extra transmission distance of 200 km for 10-3bit error rate （BER） compared to the original signals.

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Terahertz dual-beam leaky-wave antenna based on multi-mode spoof surface plasmon polariton waveguide

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Optoelectronics Letters 2025年

作者： BAI Yukun HAN Yuanqing Tianjin Key Laboratory of Film Electronic and Communication Devices， Engineering Research Center of Optoelectronic Devices and Communication Technology， Ministry of Education， School of Integrated Circuit Science and Engineering， Tianjin University of Technology

In this paper， a terahertz dual-beam leaky-wave antenna (LWA) based on a multi-mode spoof surface plasmon polariton (SSPP) waveguide is proposed. By dispersion engineering of the SSPP transmission line (SSPP TL)， multiple modes are excited on the SSPP TL. A dual-beam LWA is constructed based on the radiation from the first space harmonics of the guided fundamental mode and the first higher-order mode. The simulation results show that the antenna radiates frequency-dependent dual beams in the 1.9 ～ 2.6 THz operating frequency band. The average gains for the two beams are 12.9 dBi and 8.2 dBi， respectively. The compact dual-beam LWA is promising in the application of wireless communication and radar systems.

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Compact wideband filtering antenna based on SIW resonator

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Optoelectronics Letters 2025年

作者： LIU Shaoshuai BAI Yukun Tianjin Key Laboratory of Film Electronic and Communication Devices， Engineering Research Center of Optoelectronic Devices and Communication Technology， Ministry of Education， School of Integrated Circuit Science and Engineering， Tianjin University of Technology

This paper proposes a novel wideband filtering antenna based on a double-mode substrate integrated waveguide (SIW) resonator. By etching butterfly-shaped slots on the upper surface of the SIW resonator， mixed modes based on TE110 and TE120 are introduced， which enhances the antenna's bandwidth. Additionally， the incorporation of a defected ground structure (DGS) suppresses out-of-band radiation and improves the overall filtering performance of the antenna. The final design achieves a center frequency of 2.41 GHz with an impedance bandwidth of 11.71%. Compared to other filtering antennas， the proposed antenna features a compact size， wide bandwidth， excellent filtering performance， and ease of integration， meeting the high-integration and miniaturization requirements in wireless communication systems.

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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

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Computers, Materials & Continua 2025年第3期82卷 4181-4218页

作者： Haydar Abdulameer Marhoon Rafid Sagban Atheer Y.Oudah Saadaldeen Rashid Ahmed Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-Qar64011Iraq College of Computer Sciences and Information Technology University of KerbalaKarbala56001Iraq Enginerring Technical College Al-Ayen UniversityThi-Qar64011Iraq Information Technology College University of BabylonHilla51002Iraq Department of Computer Science College of Education for Pure ScienceUniversity ofThi-QarNasiriyah64001Iraq Artificial Intelligence Engineering Department College of EngineeringAl-Ayen UniversityThi-Qar64001Iraq Department of Computer Science Bayan UniversityErbilKurdistan44001Iraq

In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.

关键词： Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis 4

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvam, L. Dhamotharan, R. Krishnan, R. Santhana Francis Raj, J. Relin Sankar, G. Ram Yamini, G. Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Pottapalayam Sivaganga India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India

ISBN: (纸本)9798331523923

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies. © 2025 IEEE.

关键词： Recurrent neural networks

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Interconnected Through Robotic System in English Foreign Language education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information communication technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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Enhanced Bio-Oil Production from Nannochloropsis Algae via Catalytic Liquefaction: Synthesis and Application of Iron-based Magnetically Recoverable Catalysts

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BioResources 2025年第2期20卷 2745-2764页

作者： Nirmala, Narasiman Arun, Jayaseelan Kumar, Sivaperumal Sanjay Jayamani, Manivannan Sureshkumar, Kumar Jawhari, Ahmed Hussain Palanisamy, Sivasubramanian Ali, Syed Kashif Al-Farraj, Saleh A. Centre for Waste Management Sathyabama Institute of Science and Technology Jeppiaar Nagar Tamil Nadu Chennai600119 India Centre of Excellence for Energy Research Sathyabama Institute of Science and Technology Jeppiaar Nagar Tamil Nadu Chennai600119 India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh Vaddeswaram522302 India Department of Physical Sciences Chemistry Division College of Science Jazan University P.O. Box.114 Jazan45142 Saudi Arabia Department of Mechanical Engineering PTR College of Engineering and Technology Austinpatti Tamil Nadu Madurai625008 India Department of Physical Sciences Chemistry Division College of Science Jazan University Jazan45142 Saudi Arabia Nanotechnology Research Unit College of Science Jazan University Jazan45142 Saudi Arabia Department of Zoology College of Science King Saud University Riyadh Saudi Arabia

Magnetically recoverable iron-based catalysts were developed, with the goal of being cost-effective, reusable, and environmentally friendly. The use of iron-based magnetically recoverable catalyst enhances the catalytic process efficiency for the enhanced bio-oil yield and quality compared to non-catalytic and conventional catalytic methods. The present study aimed to evaluate the hydrothermal liquefaction (HTL) of Nannochloropsis sp. in the presence of Fe3O4 nanostructures for enhanced bio-oil production. The use of a magnetic-supported catalyst that is low-cost, safe, and can be reused many times without requiring a regeneration step (by retrieving it magnetically from the solid material) can serve as a novel strategy to be used at an industrial scale. The maximum bio-oil yield 31.4% was obtained with a Fe3O4 catalyst dosage of 0.3 g at temperatures of 300 °C and biomass to solvent ratio of 100 g/L, respectively. The compositional analysis of the produced bio-oil was performed and showed notable characteristics for biofuel application. The synthesized Fe3O4 catalyst was recyclable for up to five repeated cycles and a fluctuated bio yield was achieved for the last three cycles of procreation capability for HTL of Nannochloropsis sp. Further improvisation in designing the next generation magnetically recoverable catalyst with improved stability, efficiency will pay wave way for cost–effective and scalable bio-oil production systems. © 2025, North Carolina State University. All rights reserved.

关键词： Algae

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A Neutrosophic Logic Ruled Based Machine Learning Approaches for Chronic Kidney Disease Risk Prediction

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Neutrosophic Sets and Systems 2025年 79卷 76-95页

作者： Mohanty, Bhabani S. Krishna Priya, R. Alias, Liya Vijaya Kumar Reddy, R. Raut, Prasanta Kumar Isaac, Samson Manibharathi, D. Saravanakumar, C. Panda, Nihar Ranjan Broumi, Said Department of Statistics and Applied Mathematics Central University of Tamil Nadu India Research and Consultancy Department University of Technology and Applied Sciences P.O. Box: 12 Khasab Governorate of Musandam Musandam811 Oman College of Engineering and Technology University of Technology and Applied Science Shinas Oman Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur AP India Department of Mathematics Trident Academy of Technology Odisha Bhubaneswar India Division of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electronics and Communication Engineering Government College of Engineering Tamilnadu Salem India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Kattankulathur Tamilnadu Chengalpattu India Department of Medical Research IMS & SUM Hospital SOA Deemed to be university India Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

Chronic kidney disease (CKD) represents a significant global health challenge in society, and early detection of risk is essential for on-time treatment and intervention. This research suggests a novel machine-learning technique to create a reliable and accurate CKD risk prediction model by combining neutrosophic logic with various classification algorithms. We use neutrosophic logic to address the inherent imprecision and uncertainty in medical data, resulting in a more realistic portrayal of real-world scenarios. We measure the effectiveness of the proposed neutrosophic logic-based models using various metrics, including precision, specificity, and sensitivity. The results show that the neutrosophic logic method is better than traditional machine learning methods at finding people who are likely to develop CKD because it is more accurate and stable. This study illustrates the potential for incorporating neutrosophic logic into machine learning frameworks to improve risk prediction in medical fields. © 2025, University of New Mexico. All rights reserved.

关键词： Prediction models

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Nafion edge passivation and nitrogen annealing treatment to improve solar cell reliability under light and elevated temperature induced degradation (LeTID) investigation

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Current Applied Physics 2025年 75卷 1-7页

作者： Jony, Jaljalalul Abedin Yousuf, Hasnain Khokhar, Muhammad Quddamah Zahid, Muhammad Aleem Madara, Polgampola Chamani Rahman, Rafi Ur Alamgeer Chu, Mengmeng Sanyal, Simpy Wang, Fucheng Kim, Youngkuk Kim, Kyesoo Dhungel, Suresh Kumar Park, Sangheon Yi, Junsin Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of University Tamilnadu 600124 India Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Faculty of Technology Nepal Academy of Science and Technology 3323 Khumaltar Lalitpur Nepal

This research examines the effectiveness of nitrogen annealing and Nafion treatment in mitigating Light and Elevated Temperature Induced Degradation (LeTID) in Passivated Emitter and Rear Contact (PERC) solar cells, employing a 30-kW photovoltaic (PV) system for evaluation. As a photosensitive device, solar cells are susceptible to damage from laser irradiation, leading to reduced photoelectric conversion efficiency, structural damage, and functional loss. To minimize this loss, nitrogen annealing and Nafion treatments were applied, followed by the analysis of LeTID effects on untreated, Nafion-treated, and combined nitrogen-annealed and Nafion-treated PERC cells. Untreated cells showed an 8 % fall in open-circuit voltage (Voc) and a 7 % fill factor (FF) loss. The combined treatment (Combination of Nitrogen & Nafion Treatment) improved stability, reducing losses to 6 % in Voc and 5 % in FF. The Nafion treatment yielded the best results, limiting deterioration to 4 % in Voc and 3 % in FF. PVsyst simulations, incorporating real-world data, confirmed enhanced stability and efficiency, aiding in optimizing PV system design and maintenance. © 2025 Korean Physical Society

关键词： Annealing

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