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Fluid Antenna-Assisted Simultaneous Wireless Information and Power Transfer Systems

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8285-8290页

作者： Zhou, Liaoshi Yao, Junteng Wu, Tuo Jin, Ming Yuen, Chau Adachi, Fumiyuki Ningbo University faculty of Electrical Engineering and Computer Science Ningbo315211 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Sendai Japan

This paper examines a fluid antenna (FA)-assisted simultaneous wireless information and power transfer (SWIPT) system. Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs. This capability enhances both energy harvesting and communication performance. The system comprises a base station (BS) equipped with multiple FAs that transmit signals to an energy receiver (ER) and an information receiver (IR), both equipped with a single FA. Our objective is to maximize the communication rate between the BS and the IR while satisfying the harvested power requirement of the ER. This involves jointly optimizing the BS's transmit beamforming and the positions of all FAs. To address this complex convex optimization problem, we employ an alternating optimization (AO) approach, decomposing it into three sub-problems and solving them iteratively using first and second-order Taylor expansions. Simulation results validate the effectiveness of our proposed FA-assisted SWIPT system, demonstrating significant performance improvements over traditional FPA-based systems. © 2025 IEEE.

关键词： Convex optimization

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Fingerprint Fraud Explainability Using Grad-Cam for Forensic Procedures 23

Fingerprint Fraud Explainability Using Grad-Cam for Forensic...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Department of Computer & Software Engineering Faculty of Science and Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation-a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model's effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model's performance can be enhanced. Through detailed visual analysis, we uncover the model's focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Slotted V Shaped Compact Wideband Antenna with Partial Ground Plane for Microwave Based Head Imaging Applications 6

Slotted V Shaped Compact Wideband Antenna with Partial Groun...

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6th International Conference on Electrical Information and Communication Technology, EICT 2023

作者： Moue, Sonia Akter Samsuzzaman, Md. Mahmud, Md Zulfiker Siddiquea, Sefat Talukder, Md Siam Majumder, Sarna Patuakhali Science And Technology University Faculty of Computer Science And Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Patuakhali Science and Technology University Faculty of Computer Science and Engineering Dumki Patuakhali Bangladesh

ISBN: (纸本)9798350370836

This study presents a detailed description of a Slotted V Shaped Compact Wideband Antenna designed for the purpose of microwave-based head imaging. The antenna under consideration comprises a radiating patch with a Vshaped slot and a partial ground with slots. Slots have been found to significantly improve the efficiency, dispersion of current, gain, and various other aspects of antennas. The antenna under consideration has a maximum gain of 5.85, operates within the frequency range of 4 to 8.06 GHz, and is deemed suitable for head imaging applications. The dimensions of the antenna under consideration are 0.33λ × 0.667λ × 0.21λ, where λ represents the wavelength, at a lower frequency of 4 GHz. Additionally, the antenna is equipped with a 50 ohm microstrip transmission line. The antenna is fabricated on a cost-effective FR4 substrate with a thickness of 1.5 mm. The antenna under consideration achieves a fractional bandwidth of 67.33% within the frequency range of 4 to 8 GHz. In order to optimize antenna performance, a comprehensive parametric analysis is carried out utilizing the widely recognized software tools HFSS and CST. The experimental testing and analysis of the prototype antenna yielded findings that exhibited a satisfactory level of concurrence with the corresponding calculations. © 2023 IEEE.

关键词： Brain

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Combining Extreme Learning Machine and Linear Discriminant Analysis for Optimized Apple Leaf Disease Classification 4

Combining Extreme Learning Machine and Linear Discriminant A...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Parjito Ahmad, Imam Borman, Rohmat Indra Alexander, Allan D. Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Bhayangkara Jakarta Raya Faculty of Computer Science South Jakarta Indonesia Universitas Muhammadiyah Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350378368

Apple trees are an agricultural commodity with high economic value that often face serious challenges due to various leaf diseases. Early detection and proper treatment are crucial to reducing economic losses and ensuring healthy plant growth. Manual methods for detecting apple leaf diseases tend to be time-consuming and require specialized expertise, making them difficult to apply on a large scale. This study aims to develop an automatic classification system based on image processing to detect apple leaf diseases by combining Extreme Learning Machine (ELM) and Linear Discriminant Analysis (LDA) algorithms. ELM offers high training speed with randomly assigned weights, while LDA performs dimensionality reduction to retain the most relevant features, thereby improving model performance. The study results indicate that the combination of ELM and LDA produces a model capable of classifying apple leaf diseases with an accuracy of 92.50%, an improvement of 6.25% compared to using ELM alone. This demonstrates that the model is highly effective in classifying apple leaf diseases. © 2024 IEEE.

关键词： Fruits

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