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5th IEEE International Conference on communication, Computing and Industry 6.0, C2I6 2024

作者： Ramamoorthy, Raghu Velu, Anitha Appala Naidu, R China Asha Kumari, A. Angel Donny, F. Harish Kumar, M. The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Vemana Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798350379884

Using biologically inspired approaches has bonus advantages in offering optimal message distribution for vehicular ad hoc networks (VANETs). Bio-motivated approaches are motivated by the natural behavior of biological entities such as bees and insects. Ant colony optimization (ACO) is the optimal bio-inspired style, suitable for offering optimal routing in VANETs. The communication pattern of ACO and VANETs is similar, such as ants of ACO apply indirect communication among ants for the exchange of information, and VANETs require cooperative routing techniques for message exchange. The goal of the Bio-motivated approach is to attain proficient and accessible communication in a VANET, where vehicles turn as nodes and animatedly form a system to interchange information. In this context, the proposed work discusses the effectiveness and benefits of using a bio-inspired approach such as ACO for VANETs. To demonstrate ACO, a healthcare scenario is considered. In the healthcare system, efficient communication is required between emergency vehicles and hospitals to offer optimal treatment for patients. This work applies ACO to find the optimal path for sending an alarm to the nearest hospital from the emergency vehicle to arrange necessary things to treat patients. Using ACO increases the efficiency of the routing mechanism in all aspects such as throughput, overhead, data delivery, low latency, route discovery time, route repair time, convergence speed, and stagnation issue. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Enhancing the Capability of Single Error Correction and Double Error Detection Method 2

Enhancing the Capability of Single Error Correction and Doub...

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2nd International Conference on Information and communication Technology, ICICT 2024

作者： Sadi, Muhammad Sheikh Molla, M.D. Peyal Asraf, Amanullah Ali, Md. Liakot Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798331508227

This paper proposes a new approach to tolerate double-bit soft errors. Soft error is becoming a matter of concern for computer systems. Critical systems and systems operated in electronic hostile environments are at high risk due to the sudden occurrence of soft errors. This paper proposes a new method for correcting double-bit soft errors in an n-bit dataword where n can be any integer number. The most efficient part of this paper is the temperate use of redundant bits. In this paper, the XOR method is used along with the SECDED (Single Error Correction - Double Error Detection) code to enhance the capability of SECDED toward double-bit error correction. This paper corrects all two-bit errors and can also correct almost every type of triple-bit error. ©2024 IEEE.

关键词： Errors

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks 11th

Dual-Channel MEMS Actuated Tunable Optical Filter Based on P...

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11th EAI International Conference on Advancement of Science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Enhanced Modified Contrast Enhancement and Exposure Fusion: a visionary dehazing approach for clarity in hazy imagery

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Multimedia Tools and Applications 2025年 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In the field of image processing, haze is a type of atmospheric scattering that reduces contrast and clarity of images, frequently masking small details and far-off objects. It is mostly caused by microscopic airborne particles like smoke, dust, and water droplets that scatter incoming light. The scattered light, producing a hazy image, diminishes the direct light that reaches the camera sensor. Many image processing methods have been presened to solve this problem and lessen the impacts of haze. Usually, these methods work by determining how much haze is there in an image, and then using this information to get the image back to its assumed previous quality. In this work, we present the proposed Modified Contrast Enhancement and Exposure Fusion (MCEEF) technique. The MCEEF dehazing technique falls under the umbrella of enhancement-based dehazing techniques. In this technique, hazy frames or images undergo sharpening through a Smoothing-Sharpening Image Filter (SSIF) designed to accentuate the disparity between the haze and objects in images. Subsequently, Gamma Correction (GC) and Color Preserving Adaptive Histogram Equalization (CP-AHE) enhance the sharpened hazy images by augmenting their contrast. Dehazed images are then obtained by fusing the results of the CP-AHE- and GC-enhanced images. The MCEEF dehazing technique is aided with enhanced hazy images or frames as input for the dehazing process. Moreover, essential enhancement tools precede the MCEEF dehazing technique. These tools include homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), which are instrumental in controlling the dynamic range before the dehazing phase. In the proposed approach, a hazy image or frame is initially subjected to homomorphic processing, followed by the application of CLAHE, and finally, the MCEEF dehazing technique is employed. To demonstrate the effectiveness of the proposed approach, we apply it on both visible and Near Infrared (NIR) frames. We

关键词： Demulsification

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Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4915-4929页

作者： Ramani, R. Dhinakaran, D. Edwin Raja, S. Thiyagarajan, M. Selvaraj, D. Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Artificial neural network Classifier Feature selection MapReduce Normalization Regression analysis

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Boosting efficiency and stability of 2D alternating cation perovskite solar cells via rational surface-modification: Marked passivation efficacy of anion

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Journal of Energy Chemistry 2023年第9期84卷 354-362页

作者： Hualin Zheng Xuefeng Peng Tingxi Chen Ting Zhang Shihao Yuan Lei Wang Feng Qian Jiang Huang Xiaodong Liu Zhi David Chen Yanning Zhang Shibin Li State Key Laboratory of Electronic Thin Films and Integrated Devices and School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Department of Electrical&Computer Engineering and Center for Nanoscale Science&EngineeringUniversity of KentuckyLexingtonKY 40506USA Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808GuangdongChina

Two-dimensional(2D) alternating cation(ACI) perovskite surface defects,especially dominant iodine vacancies(V_Ⅰ) and undercoordinated Pb^(2+),limit the performance of perovskite solar cells(PVSCs).To address the issue,1-butyl-3-methylimidazolium trifluoro-methane-sulfonate(BMIMOTF) and its iodide counterpart(BMIMI) are utilized to modify the perovskite surface *** find that BMIMI can change the perovskite surface,whereas BMIMOTF shows a nondestructive and more effective defect passivation,giving significantly reduced defect density and suppressed charge-carrier nonradiative *** mainly attributes to the marked passivation efficacy of OTF-anion on V_Ⅰ and undercoordinated Pb^(2+),rather than BMIMI^(+) *** from the rational surface-modification of BMMIMOTF,the films exhibit an optimized energy level alignment,enhanced hydrophobicity and suppressed ion ***,the BMIMOTF-modified devices achieve an impressive efficiency of 21.38% with a record open-circuit voltage of 1.195 V,which is among the best efficiencies reported for 2D PVSCs,and display greatly enhanced humidity and thermal stability.

关键词： 2D ACI perovskite solar cells Charge-carrier nonradiative recombination Surface defects passivation Energy level alignment Ionic migration Stability

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A Novel Sharding Protocol for Blockchain Scalability Using Random Function Assignment 3

A Novel Sharding Protocol for Blockchain Scalability Using R...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rajesh Sharma, R. Ellappan, V. Sungheetha, Akey Vetrivendan, L. Mahapatra, Sheila Sivakumar, C. Alliance University Department of Computer Sciences and Engineering Bengaluru India Mahendra Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798331518592

Blockchain technology has a major barrier to its expansion, which is scalability. However, current methods have problem of security vulnerabilities and inefficiencies and although sharding has proved to be a good mitigation for this problem, there is no actual implementation of it in any relational database. Our paper explores the improvements to the sharding protocol by integrating the Verifiable Random Functions (VRF) with a feature of reshuffling for optimal distribution on the chain. Verification also showed that our solution provided a transaction through rate that is forty percent higher than the current existing shard handling protocols with only 0.3% error margin on transaction validation standing at 99.7%. Nonetheless, the system effectively addressed the goal of minimizing cross-shard communication by a ratio of 35% though was able to enhance the mean load balancing efficiency by only 28%. Interestingly, our protocol enhances the defense against the takeover of shard from an opponent, an aspect that makes the protocol ideal for large scale application in BFSI and SCM applications. Our results show not only the scalability extension of VRF-based sharding but also the strong security for blockchain system, making sharding a feasible fully-solution for the current enterprise-level blockchain concerns. © 2024 IEEE.

关键词： Distributed ledger

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Multi-Fault Classification Method for Wind Turbine with Class Imbalance Data 14th

Multi-Fault Classification Method for Wind Turbine with Clas...

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14th International Conference on Frontier Computing, FC 2024

作者： Chatterjee, Subhajit Gil, Joon-Min Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

Data-driven fault classification is critical for wind turbine condition monitoring systems. However, imbalanced class distributions, where certain fault types are significantly less frequent than others, pose a challenge for traditional classifiers. This work addresses this issue by investigating the combined effect of oversampling with Synthetic Minority Oversampling Technique (SMOTE) and undersampling with Tomek Links for data cleaning, followed by a stacking ensemble learning approach. We evaluate the proposed method on a multiclass wind turbine fault dataset and demonstrate its effectiveness in improving classification performance compared to baseline methods. Our findings highlight the potential of the proposed approach for enhancing fault classification accuracy in wind turbine systems plagued by imbalanced data. The classification performance is evaluated using metrics like confusion matrix, precision, recall, and F1-score, demonstrating significant improvements compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind turbines

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