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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lemei Da Yujue Wang Yong Ding Bo Qin Xiaochun Zhou Hai Liang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Pengcheng Laboratory Cyberspace Security Research Center Shenzhen China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China Shenzhen Olym Information Security Technology Co. Ltd. Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

关键词： Condition monitoring Privacy Data privacy Roads Vehicular ad hoc networks Forgery Sensors

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Chaotic dynamics, topological analysis and flat analog electronic control by physiological signals of a neurally-inspired system

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Chinese Journal of Physics 2025年 96卷 20-52页

作者： Tabekoueng Njitacke, Zeric Ngamsa Tegnitsap, Joakim Vianney Zhao, Manyu Barà, Chiara Fozin, Théophile Fonzin Awrejcewicz, Jan Yoshimura, Natsue Valdes-Sosa, Pedro A. Letellier, Christophe Minati, Ludovico School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China Department of Electrical and Electronic Engineering College of Technology (COT) University of Buea P.O. Box 63 Buea Cameroon Fotso-Victor University Institute of Technology University of Dschang P.O. Box 134 Dschang Cameroon Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang Cameroon Department of Engineering University of Palermo Palermo 90128 Italy Department of Electrical and Electronic Engineering Faculty of Engineering and Technology (FET) University of Buea P.O. Box 63 Buea Cameroon Department of Automation Biomechanics and Mechatronics Łódź University of Technology Stefanowskiego Str. 1/15 Lodz 90-924 Poland School of Computing Institute of Science Tokyo (formerly Tokyo Institute of Technology) Yokohama Japan ATR Brain Information Communication Research Laboratory Group Kyoto Japan Department of Neuroinformatics Cuban Neuroscience Center Havana 11600 Cuba CORIA Rouen Normandie University Saint-Etienne du Rouvray 76800 France Center for Mind/Brain Sciences (CIMeC) University of Trento Trento 38123 Italy

A novel abstract bicompartmental neural model based on a cubic nonlinearity, consisting of introducing a Josephson junction as a coupling element, is presented. Through numerical simulations and experimental measurements involving an analog electronic circuit, it is found that the system can generate a double-scroll chaotic attractor endowed with a remarkably complex topology. This is accompanied by diversification of the spectral signature across its four variables and multistability. An analysis of the global observability and controllability is conducted, leading to the synthesis of a flat controller that can steer the dynamics towards those of structurally different entities, as exemplified by a modified Lorenz system. The first analog realization of flat control is then introduced. The possibility of low-latency and high-accuracy manipulation of the dynamics is demonstrated by considering the challenging case of tracking extraneous external signals, namely, physiological recordings indexing autonomic and central nervous system activity. Besides showing the flexibility and adaptability of the approach, the controller enables the concomitant generation of multiple additional signals with spectrogram features compared to the individual inputs, offering a promising substrate for future applications in physical time series data augmentation. © 2025 The Physical Society of the Republic of China (Taiwan)

关键词： Chaotic circuits Flat control Josephson junction Multistability Neural models Physiological recordings Topological analysis

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The distance between the weights of the neural network is meaningful

arXiv

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arXiv 2021年

作者： Yang, Liqun Yang, Yijun Wang, Yao Yang, Zhenyu Zeng, Wei Florida International University School of Computing and Information Science United States Xi’an Jiaotong University School of Computer Science and Technology China

In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. To analyze the network’s capacity, quantifying the information learned by the network is necessary. This paper proves that the distance between the neural network weights in different training stages can be used to estimate the information accumulated by the network in the training process directly. The experiment results verify the utility of this method. An application of this method related to the label corruption is shown at the *** Codes 94-10 (Primary) 47N30(Secondary) © 2021, CC BY.

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Stacked neural nets for increased accuracy on classification on lung cancer

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Measurement: Sensors 2024年 32卷

作者： B R, Sampangi Rama Reddy Sen, Sumanta Bhatt, Rahul Dhanetwal, Murari Lal Sharma, Meenakshi Naaz, Rohaila Bangalore India Department of Pharmacy Arka Jain University Jharkhand Jamshedpur India School of Engineering and Computing Dev Bhoomi Uttarakhand University Uttarakhand Dehradun India Department of Pathology Jaipur National University Jaipur India Department of Education Sanskriti University Uttar Pradesh Mathura India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

Lung cancer is regarded as one of the most lethal diseases endangering human survival. It is difficult to detect lung cancer in its early stages, because of the ambiguity in the lung regions in the medical images. Healthcare business is automating itself with the use of image recognition and data analytics, much as the computing sector has completely automated. This article proposes a novel architecture, the Stacked Neural Network (SNN), for the detection and classification of lung cancer using CT scan data. The goal of the proposed technique is to investigate the accuracy levels of different Neural Networks (NN) and determine the early stage of lung cancer. The most effective technique for processing medical images, classifying and detecting lung nodules, extracting features, and predicting the stage of lung cancer is deep learning. First, lung areas are extracted using image processing techniques. SNN is used for the segmentation process. Various neural network techniques are utilised for the classification process once the features are retrieved from the segmented pictures. The suggested methods' performances are assessed using F1-Measure, accuracy, precision, and recall metrics. 96% classification accuracy is shown in the testing findings, which is comparatively greater than other methods currently in use. Proposed algorithm is clearly supported for real-world clinical practice. © 2024 The Authors

关键词： Deep learning

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Predicting circRNA-disease associations using similarity assessing graph convolution from multi-source information networks

Predicting circRNA-disease associations using similarity ass...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Li Xue-Gang Hu Pei-Pei Li Lei Wang Zhu-Hong You School of Computer Science and Information Engineering Hefei University of Technology Hefei China Big Data and Intelligent Computing Research Center Guangxi Academy of Sciences) Nanning China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Circular RNA (circRNA), a novel endogenous noncoding RNA molecule with a closed-loop structure, can be used as a biomarker for many complex human diseases. Determining the relationship between circRNAs and diseases helps us to understand the diagnosis, treatment, and pathogenesis of complex diseases, which plays a critical role in clinical research. Nevertheless, the discovery of new circRNA-disease associations by wet-lab methods is not only time-consuming and costly but also randomized and blinded, which is also limited to small-scale studies. Thus, there is an urgent need to establish efficient and reliable computational methods to infer potential circRNA-disease associations on a large scale to effectively reduce costs and save time, and avoid high false-positive rates. In this paper, we propose a novel computational method for predicting circRNA-disease association based on the Similarity Assessing Graph Convolution Network (SAGCN) algorithm, which combines the multi-source similarity network constructed by circRNA and disease. Firstly, we fuse the multi-source similarity information of circRNAs and diseases and construct the multi-source similarity network respectively. Then we use the SAGCN algorithm to extract the hidden feature representations of circRNAs and diseases efficiently and objectively in the way of measuring the similarity between different nodes in the network. Finally, the obtained high-level features of circRNAs and diseases are fed to the multilayer perceptron (MLP) classifier for accurate prediction. Using the 5-fold cross-validation method, the AUC scores of the four SAGCN algorithms, on the benchmark circR2Disease dataset are 93.30%, 92.98%, 92.22% and 91.94%, respectively. Furthermore, case studies further validated that the proposed model was supported by biological experiments, and 25 of the top 30 circRNA-disease associations with the highest scores were confirmed by recent literature. Based on these reliable results, it can be anticip

关键词： Convolution Computational modeling Biological system modeling RNA Predictive models Prediction algorithms Feature extraction

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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Survey on MTC Group Authentication and Key Management for MTC in LTE Networks 1st

Survey on MTC Group Authentication and Key Management for MT...

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1st International Conference on Data science, Machine Learning and Applications, 2019

作者： Jyothi, K. Krishna Chaudhari, Shilpa School of Computing and Information Technology REVA University Bangalore India Department of CSE Ramaiah Institute of Technology Bangalore India Department of CSE Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789811514197

Networking among machine terminals with interaction without human interventions is popularly known as Machine-to-Machine (M2M) application/service or Machine-Type Communications (MTC). The major consideration for successful deployment of next generation MTC to address this challenge involves the ready-to-use cellular infrastructure-based network technologies like Long term Evolution (LTE) and future 5G. The advantages of LTE-MTC include increase battery life, reduce device cost/complexity and enhance coverage. MTC device trying to attach to LTE network have to undergo the mutual authentication and key agreement (AKA) procedure resulting massive signaling overhead. The existing literature tries to reduce this signaling overhead by grouping the MTC devices based on various criteria. This paper elaborates and compares the AKA protocols specific to MTC on LTE network. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Dynamic Anonymous Hierarchical Edge-Assisted Batch Verifiable Authentication for VANETs

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IEEE Transactions on Vehicular technology 2025年

作者： Verma, Girraj Kumar Rana, Saurabh Tiwari, Asheesh Kumar, Neeraj Barnawi, Ahmed Amity University Madhya Pradesh Department of Mathematics Amity School of Engineering and Technology Gwalior India Bennett University Department of Mathematics Noida201310 India GLA University Department of Computer Engineering and Applications Mathura India Thapar University Department of Computer Science and Engineering Patiala India UEHS Poland Imam Abdulrahman Bin Faisal University Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia

The increasing density of vehicles on roads has raised concerns for transportation system safety and convenience. To mitigate accident and congestion risks, integrating advanced vehicular and communication technologies has facilitated the development of vehicular ad-hoc networks (VANETs). These networks enable vehicles to share real-time safety alerts with other vehicles and traffic authorities. However, open wireless links in VANETs are susceptible to security threats, making the authentication and integrity of transmitted safety alerts essential. Yan et al. addressed these needs with a hierarchical authentication scheme (IEEE Trans. on Veh. Tech., vol. 73, no. 1, pp. 1253–1262, 2023). Unfortunately, the devised study identifies vulnerabilities in Yan et al.'s design, specifically concerning forgery and anonymity exposure attacks. In response, an improved, secure hierarchical authentication scheme is proposed. Through both formal and informal security analyses, it is demonstrated to be resilient against various practical attacks. A thorough performance evaluation further supports the design's practical suitability for deployment within VANET infrastructure, offering enhanced security for safer transportation networks © 1967-2012 IEEE.

关键词： Ad hoc networks

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AI-based big data analytics model for medical applications

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Measurement: Sensors 2022年 24卷

作者： Lokesh, S. Chakraborty, Sudeshna Pulugu, Revathy Mittal, Sonam Pulugu, Dileep Muruganantham, R. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Engineering Narsimha Reddy Engineering College Kompally Telangana State Hyderabad500100 India Department of Information Technology B K Birla Institute of Engineering and Technology Rajasthan Pilani333031 India Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Kompally Telangana State Hyderabad500100 India Department of Information Technology TKR College of Engineering and Technology Meerpet Telangana Hyderabad500097 India

Today, people's daily lives depended on their mobile phones, facilitating many beneficial tasks. Mobile computing and cloud computing combine cloud-based mobile computing to improve their features and overcoming their disadvantages such as memory space, processor, and longer battery life. By combining the four "V's" of volume, variety, velocity and veracity, Big Data analytics tools allow the utility to be extracted through knowledge. This paper explores the development of interconnected healthcare systems and the contribution of mobile cloud applications and big data analysis. With the implementation of cloud computing in the healthcare system, inspiration and development of interconnected health - software and care services are given. Big Data services for the healthcare sector on a cloudlet-based mobile cloud computing platform. Big data analysis methods, equipment, and solutions are examined. We have concluded the development of interconnected medical systems using Big Data and mobile cloud computing. © 2022 The Authors

关键词： Mobile computing

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Visual Language Model based Cross-modal Semantic Communication Systems

arXiv

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arXiv 2024年

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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