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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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All-Polarization-Maintaining Tunable Mode-Locked Er-Doped Fiber Laser with a Compact Reflective Lyot Filter

All-Polarization-Maintaining Tunable Mode-Locked Er-Doped Fi...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Dai, Maolin Liu, Bowen Shirahata, Takuma Sun, Xiangnan Set, Sze Yun Yamashita, Shinji The University of Tokyo Department of Electrical Engineering and Information Systems Tokyo Japan

An all-polarization-maintaining tunable carbon nanotube mode-locked Er:fiber laser is demonstrated based on a compact reflective Lyot filter, with wavelength tuning slope of 0.62 nm/° C, 47.7 times higher than th... 详细信息

ISBN: (纸本)9781665462136

关键词： Carbon nanotubes

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Backup subscription scheme for differential protection using IEC61850-9-2 sampled values

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Energy Informatics 2024年第1期7卷 110页

作者： Katoulaei, Mohammad Khalili Rahmani, Aamir Høidalen, Hans Kristian Oleinikova, Irina Mork, Bruce Department of energy systems Norwegian University of Science and Technology Trondheim Norway Department of electrical power system Michigan Technical University Michigan United States Department of Electrical Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Computer Engineering Michigan Technological University HoughtonMI49931 United States

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection. © The Author(s) 2024.

关键词： Hardware-in-the-loop simulation

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Cross-Layer Management Framework for Enhancing XR-Based System Security in Zero-Trust Wireless Communications

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2011-2024页

作者： Ghourab, Esraa M. Azab, Mohamed Gracanin, Denis Al-Qutayri, Mahmoud Muhaidat, Sami Khalifa University 6G Center Department of Computer Science Abu Dhabi127788 United Arab Emirates Virginia Military Institute Department of Computer and Information Sciences VA United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Khalifa University 6G Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms. © 1983-2012 IEEE.

关键词： Resource allocation

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Recent Progress in Neural Network Signal Processing in Earth-Observation Remote Sensing and Ground Penetrating Radars 3

Recent Progress in Neural Network Signal Processing in Earth...

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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Hirose, Akira The University of Tokyo Department of Electrical Engineering and Information Systems Tokyo Japan

ISBN: (纸本)9798350300604

This keynote discusses recent topics in the theory and applications of neural-network artificial intelligence (AI) in earth observation remote sensing and ground penetrating radars. First, we look over broad general research trends. Then we pick up some topics by focusing on their essence, ideas, and practical effects. Lastly, we look forward into future developments. © 2023 IEEE.

关键词： Self organizing maps

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FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Reader 6

FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Read...

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6th Australian Microwave Symposium, AMS 2025

作者： Lasantha, Likitha Aliasgari, Javad Khan, Shahed I. Ray, Biplob Karmakar, Nemai C. Monash University Department of Electrical and Computer Systems Engineering Clayton Australia Central Queensland University School of Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350379969

Ultra-wideband (UWB) technology has gained traction for applications demanding high bandwidth, precise target localisation, and low power consumption. This paper presents an FPGA-based UWB pulse generator tailored for an Impulse-radio (IR) UWB chipless RFID reader. Using a low-cost FPGA platform, the design generates a square wave signal with a pulse repetition frequency (PRF) of 1-50 MHz. This base signal undergoes pulse sharpening to produce picosecond-scale pulses, achieving the bandwidth exceeding 500 MHz necessary for UWB communication. Such a high-bandwidth signal ensures better range resolution, meeting the requirements of applications of UWB chipless RFID readers. By implementing pulse generation and PRF control on an FPGA, our system combines low cost, flexibility, and scalability, making it suitable for diverse UWB applications. © 2025 IEEE.

关键词： Ultra-wideband (UWB)

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Classification of cervical spine disease using convolutional neural network

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Multimedia Tools and Applications 2024年第41期83卷 88963-88979页

作者： Bezabh, Yohannes Agegnehu Salau, Ayodeji Olalekan Abuhayi, Biniyam Mulugeta Ayalew, Aleka Melese Department of Information Technology University of Gondar Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cervical spondylosis myelopathy (CSM) is a degenerative disorder of the cervical spine’s disks and joints, leading to neurological disability. Typically, this condition causes increasing discomfort and neurologic deterioration. Cervical spine pain disease classification using deep learning can assist medical professionals in making early and more precise medical diagnosis of cervical abnormalities. The main focus of the assessment for doctors is the classification of cervical spine diseases from X-rays images, which is valuable for patients to indicate the various symptoms they are faced with. Identifying cervical spine pain injuries (CPI) is a critical aspect of the diseases segmentation and classification. Manually explaining the high-dimensional feature space makes it challenging to identify the exact category and level of severity. This paper introduces a Convolutional Neural Networks (CNN) concatenation feature extraction method to help in the diagnosis of cervical spine pain disease from X-ray images. An innovative CNN-concatenation model-based fully connected classifier and a four-class system are applied to classify X-ray image data in this research. The CNN approach is employed to automatically classify X-ray images into healthy (normal) and with cervical spine disease (abnormal). CSM is currently diagnosed mostly through clinical evaluation using imaging methods such as X-ray and computed tomography (CT). Due to its inexpensive cost and minimal radiation dose, X-ray is commonly used. The proposed approach achieved a training accuracy of 99.98%, a validation accuracy of 98.29%, and a testing accuracy of 97.82% of classification from experiments. These results indicate that the image preprocessing, data augmentation, and CNN approaches provide an efficient classification method for identifying and accurately classifying cervical spine diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control systems:A Case Study in Level Control Process

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 762-780页

作者： Majid Ghorbani Mahsan Tavakoli-Kakhki Aleksei Tepljakov Eduard Petlenkov Department of Computer Systems Tallinn University of TechnologyTallinn 12618Estonia Faculty of Electrical Engineering K.N.Toosi University of TechnologyTehran ***Iran IEEE

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this *** uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world ***,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)*** the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time *** three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise ***,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

关键词： Interval uncertainty FOPID controller fractional-order systems robust stability analysis smith predictor

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