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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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Enhancing Financial Sector Cybersecurity: A Multi-Component Framework for Supply Chain Risk Mitigation 6

Enhancing Financial Sector Cybersecurity: A Multi-Component ...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Kiran, Suryaa Praveen, Yomal Silva, Akila Bagya, Yasith Yapa, Kavinga Siriwardana, Deemantha Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Supply chains are being used at scale within new industries, mainly in the financial sector and the cyber security risks emerging in this area have been on the rise. Given recent attack scenarios the mitigation strategies taken within these areas are limited. This research paper focuses on the main areas where the risk can be reduced to provide an overall effective solution to reduce the risks. However, there are certain challenges in minimizing the risks associated, such as third-party vendor identification, classifying and securing data, and gathering relevant external threat information. This research utilizes a centralized risk assessment gateway to assess the security level of each third-party vendor. Further, there will be a secure transfer of text and image data once the vendors have been assessed and these will be done in correlation with a zero-trust policy engine where data classification occurs based on unique trust levels. In addition, it will also use machine learning to tailor the external cyber threat feeds to provide valuable insights regarding threat attacks, vulnerabilities, and patches within the sector to gain more granular intelligence and reduce analysis time. The overall solution will serve as a zero-trust approach to mitigate the attacks within the financial fields of the supply chain sector. © 2024 IEEE.

关键词： data classification machine learning risk assessment supply chains zero trust policy engine

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350350821

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure. © 2024 IEEE.

关键词： Global positioning system

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A Comparative Study of Convolutional Neural Networks for Oregano Diseases Classification in A Novel Dataset 31

A Comparative Study of Convolutional Neural Networks for Ore...

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31st IEEE International Conference on Electronics, Electrical engineering and Computing, INTERCON 2024

作者： Chaparro-Cruz, Israel N. Santana-Carbajal, Oliver I. Osco-Mamani, Erbert F. Universidad Nacional Jorge Basadre Grohmann Department of Computer Science and Systems Engineering Tacna Peru

ISBN: (纸本)9798350378344

Peru is South America's largest producer and ex-porter of oregano (Origanum vulgare L.), with the Tacna region being the most significant contributor, cultivating 2,499 hectares and producing 11,946 tons, accounting for 65% of the national production. To mitigate the impact of oregano diseases, which lead to substantial crop losses and negatively affect the economy, it is crucial to develop and upgrade technologies for disease management. Accurate classification of oregano diseases can prevent quality deterioration and yield reductions. However, the availability of data and the use of advanced technologies in oregano cultivation are still in their early stages. This study addresses these shortcomings by developing and publishing a novel public dataset for the classification of the most common oregano diseases-Alternaria, Powdery Mildew (Oidium), and Mosaic-using crops from Susapaya - Tarata in Tacna, Peru. To classify oregano diseases, we conducted a comprehensive comparative study, applying Transfer Learning and Fine-Tuning techniques to evaluate the performance of VGG16, MobileNet, MobileNetV2, and NasNetLarge architectures with modifications in the classifier. The experiments revealed that: (i) Fine-Tuning improved validation set accuracy by 4.47% compared to Transfer Learning, (ii) the VGG16 architecture achieved the highest accuracy at 0.993921, (iii) MobileNet and MobileNetV2 models performed similarly, (iv) the NASNetLarge model showed poor performance when only Transfer Learning was applied, and (v) there was a strong significant relationship (R2= 0.84) between the number of parameters and size (in MB) with the performance of the architectures on our dataset. We ensure the reproducibility of the results by making the novel dataset and the best model publicly available on GitHub. © 2024 IEEE.

关键词： Convolutional neural networks

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CART - Web-Based Institutional Placement Management System Using NLP 10

CART - Web-Based Institutional Placement Management System U...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Priyanka, G. Amritha, P. Gokul Sundar, U. Sanjai, M.B. Vasanth, R. Sri Eshwar College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The 'CART (Career at Right Time)', a pupil/ company information system, is a web- grounded programmed. The design is grounded on the' Placement Cell' that's presently employed in the University for storing and reacquiring information from scholars and companies who are registered in the placement cell. The Placement Cell maintains a large pupil database in which all pupil information, including particular records and academic performance in terms of marks and grades, is stored, as well as company information, similar as the company's capsule, sphere they work in, job descriptions, reclamation process, and so on. This data is recaptured by the software and shown when demanded by the users. © 2024 IEEE.

关键词： Electric grounding

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D2LFS2Net:Multi-class skin lesion diagnosis using deep learning and variance-controlled Marine Predator optimisation:An application for precision medicine

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 207-222页

作者： Veena Dillshad Muhammad Attique Khan Muhammad Nazir Oumaima Saidani Nazik Alturki Seifedine Kadry Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable *** imaging still faces a number of difficulties,including intra-class similarity,a scarcity of training data,and poor contrast skin lesions,notably in the case of skin *** optimisation-aided deep learningbased system is proposed for accurate multi-class skin lesion *** sequential procedures of the proposed system start with preprocessing and end with *** preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy *** of flipping and rotating data,the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next ***,two pre-trained deep learning models,MobileNetV2 and NasNet Mobile,are trained using deep transfer learning on the upgraded enriched ***,a dual-threshold serial approach is employed to obtain and combine the features of both *** next step was the variance-controlled Marine Predator methodology,which the authors proposed as a superior optimisation *** top features from the fused feature vector are classified using machine learning *** experimental strategy provided enhanced accuracy of 94.4%using the publicly available dataset ***,the proposed framework is evaluated compared to current approaches,with remarkable results.

关键词： contrast enhancement deep learning dermoscopic images features optimization fusion skin cancer

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A survey on RGB images classification using convolutional neural network (CNN) architectures: Applications and challenges

A survey on RGB images classification using convolutional ne...

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2024 International Conference on Circuit, systems and Communication, ICCSC 2024

作者： Khayya, El Kharrachi El Oirrak, Ahmed Datsi, Toufik Cadi Ayyad University Faculty of Sciences Semlalia Computer Systems Engineering Laboratory Marrakech Morocco

ISBN: (纸本)9798350365306

The process of classifying RGB images is a basic and noxious activity in computer vision with an array of applications in face recognition, traffic analysis, and security protocols. An important part of this mission encompasses Convolutional Neural Networks, deemed to be core technologies, as they are capable in many domains. This short survey paper aims at conducting an in-depth analysis of CNN-based RGB image classification approaches in a way that considers complex architectures, multiple uses, and the difficulties that are entailed in them. The motivation behind this survey is to talk about the ever-increasing importance of RGB image classification across different applications. It hopes to explore and attend to the challenges emerging in association with this technology while also discussing the latest architectures and techniques advancing the field. In this paper, we go through different methods used while building the CNN architectures, thereby explaining their way of handling RGB image classification tasks. Moreover, it gives a detailed discussion about various other areas where CNNs seem to be efficient, such as agriculture and veterinary. This short survey will also dwell on the main challenges faced when dealing with RGB image classification, such as dataset variability, model robustness, and computational complexity issues. We construct the comprehensive understanding of CNN-based RGB image classification by synthesizing ideas from well-known datasets, such as CIFAR-10 and others, that will provide the foundation for future research in this area © 2024 IEEE.

关键词： Convolutional neural networks

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Secure Virtual Desktop Infrastructure Solution Using Homomorphic Encryption and Machine Learning Models 3

Secure Virtual Desktop Infrastructure Solution Using Homomor...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Senuwan, W.M.D. Dissanayake, Nimantha Disanayaka, Malithi Hewage, Shashini Abeywardena, Kavinga Yapa Siriwardhana, Deemantha Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350354171

In the recent past, Virtual Desktop Infrastructure (VDI) technology has experienced rapid growth. Although many enterprise companies have implemented VDI systems, previous research has highlighted several critical issues, including data breaches and session hijacking. Security measures identified for such threats were suboptimal, especially for the firms with fewer resources. In this paper, authors combat existing threats to VDI systems with different technologies such as Homomorphic Encryption (HE) for the safe exchange of location data, and the Machine Learning (ML) model is used for real-time log analysis, thereby making it easy to detect some level of anomaly in the complex VDI environments. According to the result, this integrated posture introduces a new and positive dimension in VDI strengthening the security and privacy of data, while protecting against various threats. Additionally, the paper also addresses setting a new and more secure base for any virtualization technologies all while securely handling consumer data. © 2024 IEEE.

关键词： Adversarial machine learning

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FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harish, S. Somesh, K. Suntharavadivelan Venkatesan, M. Prabhavathy, P. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India Vellore Institute of Technology Department of Computer Science and Engineering and Information Systems Vellore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The global food supply heavily relies on fisheries, highlighting the crucial importance of ensuring the safety of fish products. However, the widespread application of antibiotics and the existence of compounds such as formaldehyde present significant hazards to both human consumers and the underwater ecosystem. These risks are compounded by processes like bio-magnification, further emphasizing the need for vigilant monitoring and regulation. This project presents a comprehensive approach to fisheries management and food safety monitoring through the integration of advanced machine learning, deep learning techniques and IoT sensor technology. The overall objective is to develop a model that can determine whether the given sample Fish Species is safe or unsafe for human consumption, by considering parameters: Fish Freshness Detection, using a HCHO Sensor to determine the Formaldehyde levels in the given Fish and finally using these parameters to determine whether the fish can be consumed or not. For the Fish Freshness Detection, utilizing Transfer Learning (pre-trained MobileNetV2 model), the system achieves an impressive accuracy of 99.02%. Thus, integrating all these models, determine whether the supplied fish is safe for consumption or not. By integrating these functionalities, the system contributes to proactive fisheries and food safety monitoring and protection. The holistic approach of this project, combining accurate positions the developed model and IoT application as valuable assets for sustainable aquaculture practices and the preservation of aquatic ecosystems. The project's outcomes contribute to the advancement of several interdisciplinary domains such as machine learning, and fisheries management, laying the groundwork for continued innovation in this interdisciplinary domain. © 2024 IEEE.

关键词： Fish

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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