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Sampled-data local exponential stabilization of nonlinear retarded systems by first order approximation methods and spline interpolation of measures 63

Sampled-data local exponential stabilization of nonlinear re...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Pepe, P. Borri, A. Di Ferdinando, M. University of l'Aquila Center of Excellence for Research Dews Department of Information Engineering Computer Science and Mathematics L'Aquila67100 Italy Istituto di Analisi Dei Sistemi Ed Informatica 'A. Ruberti' Consiglio Nazionale Delle Ricerche Rome00185 Italy

ISBN: (纸本)9798350316339

It is proved, for systems described by nonlinear retarded functional differential equations, that (local) exponential stability is guaranteed under fast sampling and spline approximation of stabilizing feedbacks obtained in continuous-time by first-order approximation methods. At the (high) price to consider just the local case, here we make rid of any kind of assumptions such as: the availability and exhibition of Lyapunov-Krasovskii functionals with suitable properties;the availability and exhibition of suitable steepest descent feedbacks of the state with suitable properties;global Lipschitz properties of the function describing the dynamics and of the state feedback. No assumptions are introduced besides standard Fréchet differentiability at the origin of the function describing the dynamics, and the availability of a linear state feedback for the stabilization of the first order approximating linear system, which a huge literature is devoted to with so many results. © 2024 IEEE.

关键词： Continuous time systems

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SDN control plane security: Attacks and mitigation techniques 2021

SDN control plane security: Attacks and mitigation technique...

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4th International Conference on Networking, Information Systems and Security, NISS 2021

作者： Fatima, Kiran Zahoor, Kanwal Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women

ISBN: (纸本)9781450388719

Traditional networks are complex and hard to manage due to many reasons such as manual configuration requirements of dedicated devices, lack of flexibility and a non-dynamic approach. To overcome these limitations and to meet the challenges of modern networks a new networking paradigm software Defined Networking (SDN) has been introduced. SDN presents a centralized and completely dynamic environment which provides flexibility and programmability in networks. It enables the network to be controlled centrally and intelligently using multiple software applications. SDN being in its infancy, brings along new challenges. Standardization of various interfaces, scalability, compatibility of contrasting or divergent networks, and vulnerability issues are few of them. This paper discusses various vulnerabilities and possible attacks on every layer of an SDN and focuses on control plane attacks. Further, it presents a comprehensive survey on numerous attacks on the brain of an SDN i.e. the control plane along with existing solutions. The study concludes that despite the challenges that are worth debating, SDN has many characteristics that make it an ideal candidate for future networks. © 2021 ACM.

关键词： Attacks on control plane SDN security software defined network (SDN) Vulnerabilities of SDN

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Online Detection of Water Contamination Under Concept Drift

Online Detection of Water Contamination Under Concept Drift

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Jin Li Kleanthis Malialis Stelios G. Vrachimis Marios M. Polycarpou KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent calibration. This study introduces the Dual-Threshold Anomaly and Drift Detection (AD&DD) method, an unsupervised approach combining a dual-threshold drift detection mechanism with an LSTM-based Variational Autoencoder (LSTM-VAE) for real-time contamination detection. Tested on two realistic WDNs, AD&DD effectively identifies anomalies with sensor offsets as concept drift, and outperforms other methods. A proposed decentralized architecture enables accurate contamination detection and localization by deploying AD&DD on selected nodes.

关键词： Location awareness Chlorine Concept drift Water quality Water pollution Real-time systems Public healthcare Contamination Monitoring Anomaly detection

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Exploring Unlabeled Data in Multiple Aspects for Semi-Supervised MRI Segmentation

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Health Data Science 2024年第1期4卷 177-190页

作者： Qingyuan He Kun Yan Qipeng Luo Duan Yi Ping Wang Hongbin Han Defeng Liu Radiology Department Peking University Third HospitalBeijingChina Peking University Third Hospital Beijing Key Laboratory of Magnetic Resonance Imaging Devices and TechnologyBeijingChina School of Computer Science Peking UniversityBeijingChina Department of Pain Medicine Peking University Third HospitalBeijingChina School of Software and Microelectronics Peking UniversityBeijingChina National Engineering Research Center for Software Engineering Peking UniversityBeijingChina Key Laboratory of High Conffdence Software Technologies(Peking University) Ministry of EducationBeijingChina Department of Obstetrics and Gynecology Peking University Third HospitalBeijingChina Center for Reproductive Medicine Peking University Third HospitalBeijingChina

Background:MRI segmentation offers crucial insights for automatic *** deep learningbased segmentation methods have attained cutting-edge performance,their efffcacy heavily relies on vast sets of meticulously annotated ***:In this study,we propose a novel semi-supervised MRI segmentation model that is able to explore unlabeled data in multiple aspects based on various semi-supervised learning ***:We compared the performance of our proposed method with other deep learning-based methods on 2 public datasets,and the results demonstrated that we have achieved Dice scores of 90.3%and 89.4%on the LA and ACDC datasets,***:We explored the synergy of various semi-supervised learning technologies for MRI segmentation,and our investigation will inspire research that focuses on designing MRI segmentation models.

关键词： analysis labeled designing

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Deep learning for the detection of colon polyps with malignant potential: ex vivo classification using feature-enhanced optical coherence tomography (OCT) images

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Biomedical Optics Express 2025年第6期16卷 2543-2554页

作者： Christos Photiou Andrew Thrapp Guillermo Tearney Costas Pitris KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Harvard Medical School Massachusetts General Hospital Wellman Center for Photomedicine Boston MA 02114 USA

Colorectal cancer (CRC) is the second leading cause of cancer-related morbidity and mortality in both men and women globally. CRC predominantly arises from dysplastic polyps that, over time, progressively evolve into malignancies. Population-wide screening through colonoscopy remains the cornerstone of CRC prevention. Optical coherence tomography (OCT) has the potential to increase the effectiveness and reduce the cost associated with colonoscopic screening. However, conclusive evidence that OCT can effectively detect pre-cancerous changes is still lacking. This study introduces a novel framework to address this challenge by extracting additional features, which can serve as biomarkers of disease, from ex vivo OCT images of colon polyps. These include first and second-order intensity and fractal statistics, as well as spectral characteristics and scatterer size, which depend on sub-cellular and biochemical tissue variations. Feature-enhanced images derived from these biomarkers were combined with intensity images and integrated into a deep-learning classification model decision-level fusion. This approach achieved 88.3% accuracy, 93.5% sensitivity, 77.9% specificity, and an AUC of 0.857 in distinguishing benign (normal and hyperplastic) polyps from cases with malignant potential (adenoma and sessile serrated adenoma), demonstrating the potential of this novel approach to enhance the role of OCT in improving CRC screening outcomes.

关键词： Clinical applications Deep learning Imaging systems Pattern recognition Refractive index Tissue optics

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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A Survey on Evolutionary Feature Selection in Multi-Label Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) involves assigning multiple labels to each instance from a predefined set of labels. With the increasing prevalence of multi-label datasets in real-world problems, MLC has become a popular area of research. These datasets frequently contain irrelevant, redundant, or noisy features, highlighting the importance of feature selection in MLC tasks. As a result, numerous multi-label feature selection (MLFS) approaches have been introduced in the literature. Given their effective search capabilities, evolutionary computation (EC) techniques have been adopted to develop MLFS approaches. However, there is a notable absence of a comprehensive survey dedicated to EC-based MLFS approaches to MLC tasks. Although few attempts have been made to address this gap, they do not provide detailed discussions on EC-based approaches. They also tend to overlook the strengths and weaknesses of existing approaches, especially regarding search, optimization, and evaluation processes. This paper aims to fill this gap by presenting a comprehensive survey of EC-based MLFS approaches, focusing on the latest advancements, current challenges, and future directions. To be specific, we categorize EC-based MLFS approaches based on different criteria and provide detailed descriptions for each category. © 1997-2012 IEEE.

关键词： Evolutionary Computation Multi-Label Classification Multi-Label Feature Selection Multi-Objective Optimization

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An Investigation into the Accuracy of Blood Oxygen Saturation Measurement using Smart Watch Devices Among Pediatric Users in Saudi Arabia

An Investigation into the Accuracy of Blood Oxygen Saturatio...

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Biomedical Instrumentation and Technology Conference (IBITeC), International

作者： Ahmad F. Turki Electrical and Computer Engineering Department Center of Excellence in Intelligent Engineering Systems King Abdul Aziz University Jeddah Saudi Arabia

Advancements in technology have led to the development and increased use of biomedical wearable devices equipped with various biosensors. This study aimed to evaluate the accuracy of blood oxygen saturation measurements in Saudi pediatric users using the Apple Watch Series 6 and Samsung Galaxy Watch 4. These smartwatches, widely available and relatively affordable fitness trackers, offer the capability to measure blood oxygen saturation and facilitate health monitoring. This paper presents preliminary findings from an investigation into the accuracy of the Apple Watch and Samsung Watch in measuring blood oxygen saturation. Data was collected from a sample of one hundred Saudi pediatric users, comprising 43 females and 57 males, with an average age of 13±3.3 years and a BMI of 21.07±6.3 kg/m 2 . Three measurements of blood oxygen saturation were simultaneously recorded using a hospital-grade pulse oximetry machine, as well as the Apple Watch and the Samsung Watch. Statistical comparisons were conducted using a T-test with a significance level of p=0.05, and the results were visualized using Bland-Altman plots. The T-test results indicated a statistically significant difference between the oximeter readings of the smartwatches and the medical-grade oximeter. Additionally, the calculation of errors revealed discrepancies between the measurements obtained from the smartwatches and the hospital-grade oximeter. It was observed that the errors were generally lower in measurements taken from male subjects compared to female subjects. The Bland-Altman plots, considering data from both the Apple Watch and the Samsung Watch, showed moderate agreement between the smartwatches and the hospital pulse oximetry. These findings suggest that while the Apple Watch and Samsung Watch demonstrate potential for measuring blood oxygen saturation, there are discrepancies compared to the accuracy of the hospital-grade oximeter. Further research and refinement may be necessary to improve the

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A Novel Method for Precipitation Nowcasting Based on ST-LSTM

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computers, Materials & Continua 2022年第9期72卷 4867-4877页

作者： Wei Fang Liang Shen Victor S.Sheng Qiongying Xue School of Computer&Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China Department of Computer Texas Tech UniversityLubbockTX79409USA

Precipitation nowcasting is of great significance for severe convective weather *** echo extrapolation is a commonly used precipitation nowcasting ***,the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation *** reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data *** order to solve the above problems,we select the spatiotemporal long short-term memory(ST-LSTM)as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model’s ability to capture short-term motion information which plays a vital role in the prediction of radar echo motion *** the purpose of enhancing the model’s ability to retain long-term important information,we also introduce the channel attention mechanism to achieve this *** the experiment,the training and testing datasets are constructed using radar data of Shanghai,we compare our model with three benchmark models under the reflectance thresholds of 15 and *** results demonstrate that the proposed model outperforms the three benchmark models in radar echo extrapolation task,which obtains a higher accuracy rate and improves the clarity of the extrapolated image.

关键词： Precipitation nowcasting radar echo extrapolation ST-LSTM attention mechanism

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A Secure and Reliable Supply chain management approach integrated with IoT and Blockchain 2

A Secure and Reliable Supply chain management approach integ...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Hanif, Maria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Islamabad Department of Computer Science and Software Engineering Islamabad Pakistan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah Saudi Arabia Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Nowadays, secure and reliable management of logistics is highly needed. Logistics is the delivery of goods from producers to legitimate consumers in accurate amounts and good conditions. The use of low-capable sensor nodes in smart logistics makes it vulnerable to many security threats. Smart logistics necessitated the delivery of suitable information to the authorized person at the appropriate time and place, which is only feasible with a stable infrastructure. This paper presents an authentication scheme together with blockchain technology to provide a secure supply chain management system. The presented authentication mechanism is based on standard KERBOROS scheme which is a ticket-based scheme. The scheme is evaluated by BAN logic which shows that it is an effective scheme in terms of improved response time and encryption/decryption time along with key generation time. © 2023 IEEE.

关键词： Supply chain management

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