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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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Mobile Edge Computing Discovery for Device-to-Device Communication in 5G 8

Mobile Edge Computing Discovery for Device-to-Device Communi...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Benbraika, Mohamed Kamel Bourekkache, Samir Kraa, Okba Himeur, Yassine Telli, Khaled Ouamane, Abdelmalik University of El-Oued Echahid Hamma Lakhdar El-Oued Algeria Mohamed Khider University Smart Computer Science Laboratory Biskra Algeria Mohamed Khider University Energy Systems Modeling Laboratory Biskra Algeria University of Dubai College of Engineering and Information Technology United Arab Emirates Mohamed Khider University Laboratory of LI3C Biskra Algeria

ISBN: (纸本)9798350309249

Device-to-Device (D2D) communications is one of the promising technologies for future fifth-generation cellular networks (5G). The first step to enable D2D communication is to discover neighbor devices and choose which will establish a D2D communication link. The limited battery life, discovery resources and the growing number of devices bind the current discovery protocols, so we need to find a new discovery protocol that goes with the next 5G cellular network. In this paper, we propose a new discovery protocol "MEC Discovery"based on Mobile Edge Computing (MEC) for D2D communication in 5G. The MEC server will discover neighbor devices for a User Equipment (UE) when requested and keep track of discovered devices in case other UEs ask for the same information with D2D mode in the near future. Our simulation results indicate a huge dropping in power consumption by the UE and reduced interferences caused by many devices doing discovery simultaneously in the same area. © 2024 IEEE.

关键词： Mobile edge computing

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Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment 22

Toward Scene Understanding with Depth and Object-Aware Clust...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Bhurtel, Manish Siwakoti, Yuba R. Rawat, Danda B. Sadler, Brian M. Fossaceca, John M. Rice, Daniel O. Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States U.S. Army Research Laboratory AdelphiMD United States Bae Systems Fast Labs BurlingtonMA United States

ISBN: (纸本)9798350345346

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled 'Common Objects in Battlefield (COBA)' which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios. © 2023 IEEE.

关键词： clustering depth estimation object detection scene understanding situational awareness task allocation

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Segment-Adaptive Spread Spectrum Audio Watermarking Using NSGA-Ⅱ

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Chinese Journal of Electronics 2021年第4期30卷 727-735页

作者： SU Zhaopin ZHANG Guofu ZHOU Xianxian LI Wangwang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology

In the field of robust audio watermarking,how to seek a good trade-off between robustness and imperceptibility is challenging. The existing studies use the same embedding parameter for each part of the audio signal, which ignores that different parts may have different requirements for embedding parameters. In this work, the constraints on imperceptibility are first ***, we present a segment multi-objective optimization model of the scaling parameter under the constrained Signal-to-noise ratio(SNR) in Spread spectrum(SS)audio watermarking. Additionally, we adopt the Nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) to solve the proposed model. Finally, we compare our algorithm(called SS-SNR-NSGA-Ⅱ) with the existing methods. The experimental results show that the proposed SS-SNRNSGA-Ⅱ not only provides flexible choices for different application demands but also achieves more and better trade-offs between imperceptibility and robustness.

关键词： Spread spectrum audio watermarking Scaling parameter Segment Constrained SNR NSGA-Ⅱ

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Frontiers of collaborative intelligence systems

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Journal of Information and Intelligence 2024年第1期2卷 14-27页

作者： Maoguo Gong Yajing He Hao Li Yue Wu Mingyang Zhang Shanfeng Wang Tianshi Luo School of Electronic Engineering Key Laboratory of Collaborative Intelligence SystemsMinistry of EducationXidian UniversityXi'an 710071China School of Computer Science and Technology Key Laboratory of Collaborative Intelligence SystemsMinistry of EducationXidian UniversityXi'an 710071China School of Cyber Engineering Key Laboratory of Collaborative Intelligence SystemsMinistry of EducationXidian UniversityXi'an 710071China

The development of information technology has propelled technological reform in artificial intelligence(AI).To address the needs of diversified and complex applications,AI has been increasingly trending towards intelligent,collaborative,and systematized development across different levels and *** on intelligent,collaborative and systematized AI can be divided into three levels:micro,meso,and ***,the micro-level collaboration is illustrated through the introduction of swarm intelligence collaborative methods related to individuals collaboration and decision variables ***,the meso-level collaboration is discussed in terms of multi-task collaboration and multi-party ***,the macro-level collaboration is primarily in the context of intelligent collaborative systems,such as terrestrial-satellite collaboration,space-air-ground collaboration,space-air-ground-air collaboration,vehicle-road-cloud collaboration and end-edge-cloud ***,this paper provides prospects on the future development of relevant fields from the perspectives of the micro,meso,and macro levels.

关键词： Artificial intelligence collaboration mechanism Micro,meso and macro collaboration Artificial intelligence mega application

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Image Desaturation for SDO/AIA Using Mixed Convolution Network

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Research in Astronomy and Astrophysics 2022年第6期22卷 91-100页

作者： Xuexin Yu Long Xu Zhixiang Ren Dong Zhao Wenqing Sun National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng National Laboratory Shenzhen 518000China National Space Science Center Chinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100190China

The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)provides full-disk solar images with high temporal cadence and spatial resolution over seven extreme ultraviolet(EUV)wave ***,as violent solar flares happen,images captured in EUV wave bands may have saturation in active regions,resulting in signal *** this paper,we propose a deep learning model to restore the lost signal in saturated regions by referring to both unsaturated/normal regions within a solar image and statistical probability model of massive normal solar *** proposed model,namely mixed convolution network(MCNet),is established over conditional generative adversarial network(GAN)and the combination of partial convolution(PC)and validness migratable convolution(VMC).These two convolutions were originally proposed for image *** addition,they are implemented only on unsaturated/valid pixels,followed by certain compensation to compensate the deviation of PC/VMC relative to normal *** results demonstrate that the proposed MCNet achieves favorable desaturated results for solar images and outperforms the state-of-the-art methods both quantitatively and qualitatively.

关键词： Sun:activity Sun atmosphere-Sun chromosphere

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation 7

Multi-Layer Perceptron Regression Model for Material Charact...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Chaudhari, Tilesh Sanjay Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Indian Institute of Technology Creative and Advanced Research Based on Nanomaterials Laboratory Department of Chemical Engineering Hyderabad India School of Engineering and Applied Science Bennett University U.P. Greater Noida India

ISBN: (纸本)9798331531836

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an R² of 99.991% and MSE of 6.65 × 10^-5, with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions. © 2025 IEEE.

关键词： Complex Permittivity Cross-Dataset Generation Material Characterization Multi-Layer Perceptron Open-Ended Coaxial Probe Stan-dard Reference Loads

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