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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Xiaoyu Zhang, Zitian Qin, Xiaohan Zhou, Haibo Zhao, Lian Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou China Toronto Metropolitan University Department of Electrical Computer Biomedical Engineering TorontoONM5B 2K3 Canada

ISBN: (纸本)9798350351255

The ultra-dense low earth orbit (UD-LEO) satellite network is being vigorously developed due to its great potential in providing global coverage and services. For the sake of improved network performance in resource-constrained satellite networks, multipath schemes are being explored. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic satellite network features (i.e., frequent traffic variation, link failures) and fail to exploit the simple grid topology to design fast yet efficient traffic engineering (TE) approaches. In this paper, we propose a novel distributed TE scheme called Flexible Satellite Traffic engineering (FlexSATE), which leverages global path computation coupled with distributed local routing decisions to improve the overall load balancing performance for ultra-dense LEO satellite networks. By constructing a minimum-hop binary tree (MHBT), we propose an MHBT-based k-segment Routing algorithm, which is capable of promptly discovering routing paths with low latency, high diversity, and good load balancing. To further enhance network transmission performance, we employ supervised learning into dynamic rate adaption, where FlexSATE employs centralized offline learning to derive insights from the globally optimal routing strategy and utilizes distributed deployment to predict the optimal distribution of traffic in real time. Our simulation results on a real-world typical Walker-delta type LEO constellation with 720 satellites show that FlexSATE outperforms some existing approaches with superior robustness and flexibility. © 2024 IEEE.

关键词： Binary trees

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A Semi-Supervised Learning Approach for Fault Detection and Diagnosis in Complex Mechanical Systems 19

A Semi-Supervised Learning Approach for Fault Detection and ...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Askari, Bahman Cavone, Graziana Carli, Raffaele Grall, Antoine Dotoli, Mariagrazia The Department of Electrical and Information Engineering The Polytechnic of Bari Italy The Department of Civil Computer Science and Aeronautical Technologies Engineering The University Roma Tre Italy The LIST3N-LM2S University of Technology of Troyes France

ISBN: (纸本)9798350320695

The integration of artificial intelligence in mechanical fault detection and diagnosis (FDD) helps to increase reliability, reduce costs, and improve the overall performance of mechanical systems in Industry 4.0 applications. Most interesting industrial applications nowadays come from dynamic environments where data are generated continuously over time and where the labeled data are scarce and expensive. Therefore, semi-supervised learning (SSL) can be particularly useful in FDD because faults may be rare or difficult to identify, and may not be fully represented in the labeled data. By using a combination of labeled and unlabeled data, SSL can help to identify these rare or difficult-to-detect faults, leading to more effective FDD. In this paper, graph-based SSL relying on label propagation is combined with conventional classification algorithms to detect potential failures in complex mechanical systems. Experimental results on realistic pneumatic and hydraulic systems from the related literature show that the proposed method can effectively enlarge the labeled datasets and interestingly identify different types of non-nominal conditions with higher accuracy compared to baseline methodologies. © 2023 IEEE.

关键词： Graphic methods

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Developing A Low-Cost Open-Source Platform For Conducting Microgravity Research In Space As A Volunteer Student Organization 74

Developing A Low-Cost Open-Source Platform For Conducting Mi...

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74th International Astronautical Congress, IAC 2023

作者： Fløan, Freider E. Steinberg, Jarle Ytterboe, Nora Department of Electronic Systems Norwegian University of Science and Technology O.S. Bragstadsplass 2B Trondheim7030 Norway Systems Engineering ESA Norway Department of Computer Science Norwegian University of Science and Technology O.S. Bragstadsplass 2B Trondheim7030 Norway

Orbit NTNU's vision is to become the best place in Europe to learn about satellites by letting our members get hands-on experience with real-life engineering challenges. Because Orbit aims to be a place for educational, personal, and professional growth, the need for creating something our members believe in and want to work on becomes essential. The BioSat mission aims to make this vision a reality by creating the need for new technology, challenging both old and new members in multiple ways. The BioSat mission consists of a 3U satellite, where 1U is reserved for a payload that can facilitate biological research in microgravity. The primary motivation for the mission is to develop an open-source platform for research that can be utilized for microgravity research in space at an extremely low cost. The technology required to grow plants in one of the harshest conditions known to humanity will also have important implications for our own planet. Our ability to provide food for everyone is becoming increasingly difficult with an evergrowing population. This technology can be used in areas with limited resources, such as water scarcity, extreme temperatures, and poor soil quality. Creating solutions for low-cost plant research that is open-source will make it possible for others to iterate and improve on our design with limited resources creating a catalyst for inspiration. BioSat will not revolutionize the way we perform long-duration missions or grow food back on earth. This is a really complex problem that will need a lot more research, testing, and failure, which no mission can do alone. We are part of an international collaborative effort to which we are contributing. In this paper we will discuss the motivation and need for the BioSat mission and how we are making microgravity research more accessible to everyone by lowering costs and reducing complexity. This paper aims to elucidate the unique contributions of Orbit NTNU's BioSat mission in advancing space-based

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Hybrid Quantum Noise Approximation and Pattern Analysis on Parameterized Component Distributions

Hybrid Quantum Noise Approximation and Pattern Analysis on P...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Chakraborty, Mouli Mukherjee, Anshu Krikidis, Ioannis Nag, Avishek Chandra, Subhash The University of Dublin College Green School of Natural Sciences Trinity College Dublin Dublin 2 Ireland University College Dublin Belfield School of Electrical and Electronic Engineering Dublin 4 Ireland University of Cyprus IRIDA Research Centre for Communication Technologies Department of Electrical and Computer Engineering Nicosia Cyprus University College Dublin Belfield School of Computer Science Dublin 4 Ireland

ISBN: (纸本)9798350376715

Noise is a vital factor in determining the accuracy of processing the information of the quantum channel. One must consider classical noise effects associated with quantum noise sources for more realistic modelling of quantum channels. A hybrid quantum noise model incorporating both quantum Poisson noise and classical additive white Gaussian noise (AWGN) can be interpreted as an infinite mixture of Gaussians with weightage from the Poisson distribution. The entropy measure of this function is difficult to calculate. This research developed how the infinite mixture can be well approximated by a finite mixture distribution depending on the Poisson parametric setting compared to the number of mixture components. The mathematical analysis of the characterization of hybrid quantum noise has been demonstrated based on Gaussian and Poisson parametric analysis. This helps in the pattern analysis of the parametric values of the component distribution, and it also helps in the calculation of hybrid noise entropy to understand hybrid quantum noise better. © 2024 IEEE.

关键词： Quantum noise

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A Composite Dataset of Lumbar Spine Images with Mid-Sagittal View Annotations and Clinically Significant Spinal Measurements 2

A Composite Dataset of Lumbar Spine Images with Mid-Sagittal...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Masood, Rao Farhat Hassan, Taimur Raja, Hina Hassan, Bilal Dias, Jorge Werghi, Naoufel Nust CUST Islamabad & College of Eme Department of Electrical Engineering Rawalpindi Pakistan Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates College of EME Nust Biomisa Lab Department of Computer and Software Engineering Rawalpindi Pakistan Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

The modern computer-aided screening systems re-quire a large amount of well-annotated training data to produce robust and consistent diagnostic performance. Furthermore, the public datasets designed to evaluate automated spinal disorders screening frameworks lack quantitative labels, which are marked by expert radiologists and clinically validated by spinal surgeons. This paper presents a dataset containing high-resolution (and well-labeled) mid-sagittal views of lumbar spine magnetic resonance imaging (MRI) scans. These scans also contain vertebral body masks along with clinically significant spinal measurements, including lumbar height, intervertebral body distances, vertebral body sidewall dimensions, vertebral body superior and inferior end-plates dimensions, lumbar lordotic angles, and lumbosacral angles. The mid-sagittal view MRI scans within the proposed dataset were first procured, and then they were manually marked by the expert radiologists and validated by the expert spinal surgeons. Afterward, different spinal measurements were recorded, which serves as a benchmark to evaluate the autonomous frameworks for predicting spinal misalignments. In addition to this, the proposed dataset is, to the best of our knowledge, the first composite database that contains lumbar spine mid-sagittal images along with spinal attributes and detailed markings of radiologists duly verified by the spinal surgeons. The proposed dataset, unlike its competitors, also introduces a quantitative vote to the clinicians and researchers in the assessment process of lumbar spine disorders. Apart from this, the dataset is publicly available at https://***/datasets/k3b363f3vz/2. © 2022 IEEE.

关键词： Magnetic resonance imaging

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XLM-RoBERTa Based Sentiment Analysis of Tweets on Metaverse and 6G 15

XLM-RoBERTa Based Sentiment Analysis of Tweets on Metaverse ...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Gaurav, Akshat Gupta, Brij B. Sharma, Sachin Bansal, Ritika Chui, Kwok Tai Ronin Institute MontclairNJ United States Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan State Bank of India India Insights2Techinfo India Hong Kong Hong Kong

This study employs the XLM-RoBERTa transformer model to perform sentiment analysis on Twitter data, focusing on discussions around the Metaverse and 6G technologies. Through a comprehensive dataset, we analyzed tweets to discern public sentiment, classifying them into negative, neutral, positive, and a calculated final sentiment score. Our results demonstrate a predominance of neutral and positive sentiments, with negative sentiments less frequent but impactful when present. The analysis, visualized through bar charts, box plots, and histograms, suggests a cautiously optimistic public view towards these cutting-edge technologies. The research highlights the effectiveness of XLM-RoBERTa in processing complex language data and contributes to understanding the public perception that may influence the adoption and development of the Metaverse and 6G. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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A Shallow U-Net with Split-Fused Attention Mechanism for Retinal Vessel Segmentation

A Shallow U-Net with Split-Fused Attention Mechanism for Ret...

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IEEE International Conference on Image Processing

作者： Amit Bhati Samir Jain Neha Gour Pritee Khanna Aparajita Ojha Naoufel Werghi Department of Computer Science and Engineering PDPM IIITDM Jabalpur C2PS Departement of Electrical Engineering and Computer Science Khalifa University UAE

Extraction of retinal vascular parts is an important task in retinal disease diagnosis. Precise segmentation of the retinal vascular pattern is challenging due to its complex structure, overlapping with other anatomical structures, and crucial thin vascular structures. In recent years, complex and heavy deep learning networks have been proposed to segment retinal blood vessels accurately. However, these methods fail to detect the thin vascular structure among different patterns of thick vessels. An attention-based novel architecture is proposed to segment the thin vasculature to address this limitation. The proposed model comprises a shallow U-Net based encoder-decoder architecture with split-fuse attention (SFA) block. The proposed SFA block enables the network to identify the placement of pixels for the tree-shaped vessel patterns at their relative position during the reconstruction phase in the decoder. The attention block aggregates low-level and high-level semantic information, improving the vessel segmentation performance. Experimentation performed on publicly available fundus datasets, DRIVE, HRF, CHASE-DB1, and STARE show that the proposed method performs better than the current state-of-the-art methods. The results demonstrate the adaptability of the proposed model for clinical applications due to its low memory footprint and better performance.

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MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace

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IEEE Transactions on Mobile Computing 2025年

作者： Duan, Sijing Lyu, Feng Zhang, Jing Lu, Huali Yang, Peng Wu, Huaqing Zhang, Yaoxue Shen, Xuemin Central South University School of Computer Science and Engineering Hunan ChangSha410083 China Tsinghua University Department of Computer Science and Technology Beijing China Huazhong University of Science and Technology School of Electronic Information and Communications Hubei Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada Tsinghua University Department of Computer Science and Technology Haidian District Beijing100084 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method named MoCo. In MoCo framework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority of MoCo in comparison with state-of-the-art baselines. Robust experiments show that MoCo can work efficiently in different transportation modes and urban densities. © 2002-2012 IEEE.

关键词： Urban transportation

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Explainable Hybrid Model for Presaging Tumor Stage Classification with Survival Survey

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SN computer science 2025年第4期6卷 1-24页

作者： Manna, Sweta Mistry, Sujoy Zhao, Zhongming Dahal, Keshav Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia W.B Kolkata 741249 India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX United States School of Engineering and Computing University of the West of Scotland Paisley PA1 2BE United Kingdom

Tumor stage classification with a survival survey during detection time is the most crucial part of cancer treatment. The survivability period is directly associated with the early detection of the stage. This study developed a hybrid model to accurately classify the tumor (T) stages with the survivability analysis. The study aims to provide an explainable Artificial Intelligence framework (such as SHAP, and SHAPASH), through which the trustability and interpretability of the proposed model can increase. Along with the XAI, this hybrid approach uses two statistical models, ANOVA and LASSO with the standard TNM (tumor, node, metastasis) method for the T-stage classification. Afterward, the survivability analysis for each T stage is shown by the Kaplan–Meier (KM) method, proportion hazard ratio (Cox). Where KM is used to represent the survivability of individual tumor stages in months and the Cox regression shows the risk factors of the events. Initially, the proposed model was examined on a full dataset that contained malignant and benign data for evaluating the performance of the model, therefore stage classification for T1, T2, and T3 performs for malignant data. The K-fold and repeated-stratified K-fold cross-validation are used to measure the accuracy of the whole dataset for lung and breast cancer. For lung cancer, maximum accuracy was attained by MLP–97.64 in K-fold cross-validation. In breast cancer maximum accuracy achieved by GPC-99.57 in repeated-stratified K-fold cross validation. The results of both datasets are compared with the state-of-the-art models. The study contributed by representing the significance of individual features for tumor staging using the advanced approach of XAI, the SHAPASH interface. The accuracy of the T1, T2, and T3 stages based on malignant data was demonstrated with various classifier models with cross-validation. Finally, it is expected that executing that approach will improve patient outcomes. By using, advanced artificial in

关键词： ANOVA Breast cancer Deep learning Explainable artificial intelligence F-score Gaussian process classification LASSO Lung cancer Machine learning Random-Forest Shapely additive explanation XGBoost

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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