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15th International Conference on information and Communication technology Convergence, ICTC 2024

作者： Nduwayezu, Maurice Ofori-Amanfo, Kofi A. Asiedu, Derek K. P. Yun, Ji-Hoon Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul01811 Korea Republic of IMT-Atlantique Lab-STICC Brest29238 France

We solve a sum-rate maximization problem in a downlink vehicular nonorthogonal multiple access (NOMA) system by optimally constructing subarrays and mapping each to a per-vehicle NOMA stream, achieving higher sum-rate... 详细信息

ISBN: (纸本)9798350364637

关键词： Clutter (information theory)

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Scalable parallel ultrafast optical random bit generation based on a single chaotic microcomb

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Light(science & Applications) 2024年第4期13卷 637-644页

作者： Pu Li Qizhi Li Wenye Tang Weiqiang Wang Wenfu Zhang Brent ELittle Sai Tek Chu KAlan Shore Yuwen Qin Yuncai Wang Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 51006China. Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of TechnologyGuangzhou 51006China. Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of TechnologyGuangzhou 51006China. Key Laboratory of Advanced Transducers andIntelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China. State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’an 710119China. Department of Physics and Materials Science City University of Hong KongHong KongChina. School of Electronic Engineering Bangor UniversityBangorWales LL571UTUK

Random bit generators are critical for information security,cryptography,stochastic modeling,and *** and scalability are key challenges faced by current physical random bit ***,we propose a massively parallel scheme for ultrafast random bit generation towards rates of order 100 terabit per second based on a single micro-ring resonator.A modulation-instability-driven chaotic comb in a micro-ring resonator enables the simultaneous generation of hundreds of independent and unbiased random bit streams.A proof-of-concept experiment demonstrates that using our method,random bit streams beyond 2 terabit per second can be successfully generated with only 7 comb *** bit rate can be easily enhanced by further increasing the number of comb lines *** approach provides a chip-scale solution to random bit generation for secure communication and high-performance computation,and offers superhigh speed and large scalability.

关键词： resonator random chaotic

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Mid-infrared computational temporal ghost imaging

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Light(science & Applications) 2024年第6期13卷 1088-1097页

作者： Han Wu Bo Hu Lu Chen Fei Peng Zinan Wang Goery Genty Houkun Liang College of Electronics and Information Engineering Sichuan UniversityChengduSichuan610064China College of Electrical Engineering Sichuan UniversityChengduSichuan610064China Key Lab of Optical Fiber Sensing&Communications University of Electronic Science&Technology of ChinaChengduSichuan611731China Laboratory of Photonics Tampere UniversityFI-33014TampereFinland

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow *** technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal ***,the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns,which are not available in all spectral regions especially in the mid-infrared ***,we demonstrate a frequency downconversion temporal ghost imaging scheme that enables to extend the operation regime to arbitrary wavelengths regions where fast modulators and detectors are not *** approach modulates a signal with temporal intensity patterns in the near-infrared and transfers the patterns to an idler via difference-frequency generation in a nonlinear crystal at a wavelength where the temporal object can be *** a proof-of-concept,we demonstrate computational temporal ghost imaging in the mid-infrared with operating wavelength that can be tuned from 3.2 to 4.3μ*** scheme is flexible and can be extended to other *** results introduce new possibilities for scan-free pump-probe imaging and the study of ultrafast dynamics in spectral regions where ultrafast modulation or detection is challenging such as the mid-infrared and THz regions.

关键词： ghost Ghost scheme

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CLMSI: A Novel Image-Text Named Entity Recognition Method Based on Contrast Learning and Multimodal Semantic Interaction 11

CLMSI: A Novel Image-Text Named Entity Recognition Method Ba...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Ma, Jiangtao Jin, Wenduo Chen, Yonggang Zhang, Fan Qian, Shenyi Qiao, Yaqiong College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The State Information Center China China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin300350 China

ISBN: (纸本)9798331531904

Most existing multimodal named entity recognition (MNER) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNER accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNER benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI. © 2024 IEEE.

关键词： Contrastive Learning

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection 18

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dhimann, Santoresh Kumari Sawhney, Tinny Yadav, Rakesh Kumar Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India University of Jammu Digital Signal Processing Lab Department of Electronics Jammu India

ISBN: (纸本)9789380544519

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not available in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cancer detection CNN Hough lines mammogram pectoral muscle

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Propagating subduction initiation of the Caroline Plate at the Mussau Trench

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science China Earth sciences 2025年第1期68卷 253-269页

作者： Chun-Feng LI Yongkang DENG Tao WU Dongdong DONG Guoliang ZHANG Jiazheng ZHANG Yutao LIU Yaqing LI Fugui TANG Lulu ZHANG Department of Marine Sciences Zhejiang UniversityZhoushan316021China Laboratory of Marine Mineral Resources Laoshan LaboratoryQingdao266237China Key Lab of Marine Geology and Environment Institute of OceanologyChinese Academy of SciencesQingdao266071China Key Lab of Ocean and Marginal Sea Geology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou510301China Science&Technology on Integrated Information System Lab Institute of SoftwareChinese Academy of SciencesBeijing100190China

New seismic imaging from the Mussau Trench confirms that,under horizontal compression on an oceanic plate boundary,it is within the young and weak oceanic plate that a trench will develop,and it is the young plate,not so long after its birth,that starts to subduct toward the old *** Mesozoic Pacific Plate cannot easily bend,under the joint horizontal compression and vertical gravity pull,even with a pre-existing weak zone in place and with a large age offset across the plate *** bending of the Caroline Plate near the northern end of the Mussau Trench started only after a long-time span of trench migration,and/or after a prolonged transition from initial compression to trench *** contrasting trench deformation and sedimentary accretion between two parallel seismic sections support an evident migration of underthrusting or significant longitudinal heterogeneity along the *** swift transition from underthrusting to non-thrusting can be explained by a counter-clockwise rotation of the Caroline *** miniature accretionary wedges develop even before the subduction initiates imply strong compression and uplifting before the underthrusting can *** of the Caroline Plate have been halted as the Mussau Trench is seismically rather quiet and the Lyra Trough to the east shows relaxational extension.

关键词： Mussau Trench Subduction initiation Seismic reflection Caroline Plate

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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Computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

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Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer Security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

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Intelligent Automation & Soft Computing 2023年第8期37卷 1637-1654页

作者： Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2R3Canada CCCC Highway Consultants Co. Ltd.Beijing100101China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm *** paper proposes a method for thunderstorm point charge path *** on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization ***,point charge movement paths are obtained after fitting time series localization ***,AEF data losses make it difficult to fit and visualize ***,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost *** paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered ***,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are *** recovered AEF is then input into the fixed SAE-XGBoost *** paths need to be fitted is determined by the interval to which the output PV *** results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关键词： Thunderstorm point charge atmospheric electric field(AEF) recovery

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