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Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1007-1021页

作者： Yunjun Zheng Jinchuan Zheng Ke Shao Han Zhao Hao Xie Hai Wang the School of Mechanical Engineering Hefei University of TechnologyHefei 230009China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn VIC 3122Australia the School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China the Shanghai Aerospace Control Technology Institute Shanghai 201109 the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the College of Science HealthEngineering and EducationMurdoch UniversityPerth WA6150Australia

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external *** paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for *** with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances *** advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier *** benefit is that the overestimation of control gain can be eliminated,resulting in chattering ***,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the *** stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

关键词： Adaptive sliding mode barrier function nonholonomic wheeled mobile robot(NWMR) trajectory tracking control

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YOLOv4-Powered Traffic Intelligence: From Detection to Optimization

YOLOv4-Powered Traffic Intelligence: From Detection to Optim...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sanjiv, Sinha Tharunn, E. Baskar, M. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350382693

The Traffic Analysis System utilizes machine learning for real-time traffic insights at a junction. It provides instant stats like vehicle count, density, and FPS, while summarizing average vehicle crossings, densities, stop times, and identifying peak minutes. Machine learning techniques include object detection, tracking, and lane segmentation. The system offers a user-friendly interface with graphical representations, aiding traffic management decisions. It not only ensures efficient traffic control but also allows for predictive analytics and potential integration with traffic light systems for adaptive signal adjustments. Continuous model training and future enhancements promise improved accuracy and adaptability to evolving traffic scenarios. © 2024 IEEE.

关键词： Machine learning

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Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain 5

Secure Goods Storage and Anti-Theft Approach using Ethereum ...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Revolutionizing Lion Conservation with EfficientNet B7: Real-Time Detection and Health Classification 3

Revolutionizing Lion Conservation with EfficientNet B7: Real...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Koradaa, Sampath Kumar Potnuru, Syam Virat Gupta AshokKumar, C. Kumar, C. Jothi Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331532420

This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using unique facial features, gender classification, and health condition assessment, including malnutrition, injuries, and illnesses. By integrating GPS modules and IoT sensors, the system facilitates noninvasive and ethical monitoring of lions and their habitats. The study employs YOLO (You Only Look Once) for real-time detection of lions in video feeds and EfficientNet B7 for multitask classification, enabling precise identification of individual lions and their health status. DeepSORT is utilized for tracking lion movements over time, ensuring continuous observation and territorial mapping. These models are deployed on edge devices, such as drones and camera traps, to deliver instantaneous results while minimizing latency. The proposed system offers numerous advantages, including improved conservation efforts through real-time alerts and actionable insights. Its ability to monitor health conditions and movement patterns enables proactive interventions to safeguard lion populations. Moreover, the integration of IoT sensors ensures comprehensive environmental monitoring, supporting a holistic understanding of the ecosystem. By employing cloud platforms for data analytics and storage, the system provides accessible dashboards and detailed reports, empowering conservationists and wildlife authorities with valuable insights. This framework not only enhances the efficiency of lion conservation but also ensures ethical practices, setting a benchmark for integrating AI and IoT in wildlife preservation. The research underscores the potential of technology to address critical ecological challenges and promote sustainable conservation efforts. © 2024 IEEE.

关键词： Mammals

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Image Caption Generation with Python: A Deep Learning-Based Approach 5

Image Caption Generation with Python: A Deep Learning-Based ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Krishna, Siram Chaitanya Reddy, Painti Nagi Kirubanantham, P. School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331518097

Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and filtering them out becomes inevitable. This project proposes a sensitive image classification application using automatic captions in identifying and filtering inappropriate content. Thus, the information extracted from images by ResNet50 in the caption generation process is allowed to integrate such features with a standard model through Senti-Long Short-Term Memory networks (SLSTM) for better captioning. Deep learning models bring about drastically improved quality of captions along with contextual understanding. The captions are tested rigorously with training loss metrics, BLEU scores, and sentiment analysis through the VADER Sentiment Analyzer. This system efficiently categorizes images involving violence, negativity, or dangerous situations into the class of negative. A Flask-based web application was developed for implementing a scalable and user-friendly interface, ensuring efficient content moderation. This project has illustrated the possibility of integrating deep learning techniques with natural language processing to yield beneficial content moderation, further ensuring safe and controlled online environments. © 2024 IEEE.

关键词： Long short-term memory

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Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network 6th

Detection of Alzheimer’s Disease from Brain MRI Images Usin...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Santosh, Nomula Khan, Patan Imran Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819774227

Alzheimer's disease (AD) ranks as the predominant factor leading to the onset of dementia. AD leads to a steady decline in memory, reasoning, behavior, and social abilities. These changes affect a person's personality. Alzheimer's disease is most common in older adults. Conventional diagnostics methods for Alzheimer's disease face challenges like invasiveness, cost, imprecision, and patient discomfort. The aim is to develop an innovative AI model offering a non-invasive, cost-effective solution. In this suggested system, we use a convolutional neural network to classify AD into four classes: Normal Phase, First Phase, Second Phase, and Third Phase. The dataset size is 6000 MRI (Magnetic Resonance Imaging) images. AD causes the brain regions to shrink, and connections between the network of neurons may break down. Hence, accurate and timely Alzheimer's disease classification is crucial for effective treatment and better patient outcomes. The primary goal of the suggested model is to classify AD with greater accuracy using a deep learning model. The work follows several preprocessing steps for the input images, such as image resizing, image pixel resizing, and batching images to enhance the input features, and for disease classification, the Convolutional Neural Network model is used. The suggested model was evaluated using Kaggle dataset images, and a maximum accuracy of 97.25% was obtained using the Kaggle dataset. This research addresses the critical need for precise AD classification, potentially revolutionizing patient outcomes through early detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Universal and Efficient Adversarial Training Framework with Membership Inference Resistance

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IEEE Internet of Things Journal 2025年第12期12卷 18665-18677页

作者： Yan, Ran Du, Ruiying He, Kun Chen, Jing Li, Qiao Wu, Cong The Collaborative Innovation Center of Geospatial Technology School of Cyber Science and Engineering Wuhan University Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Shenzhen Institute of Wuhan University China Nanyang Technological University College of Computing and Data Science Singapore Singapore

Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this paper, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime. © 2014 IEEE.

关键词： Adversarial machine learning

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Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

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Intelligent Automation & Soft computing 2023年第1期35卷 491-505页

作者： S.Padmapriya R.Shankar R.Thiagarajan N.Partheeban A.Daniel S.Arun Department of Computing Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Electronics and Communication Engineering Teegala Krishna Reddy Engg.College500097TelanganaIndia School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Department of Electronics and Communication Engineering Prathyusha Engineering CollegeThiruvallur602025India

The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various *** most sticky topic in organizationalfields was MANET *** is helpless against various threats that affect its usability and *** dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious *** Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target ***-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening *** improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and *** recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network *** implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation ***,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.

关键词： Mobile ad-hoc network(MANET) wireless ad hoc network ADOV attacks denial of service

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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Computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks

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Space(science & Technology) 2024年第1期4卷 465-478页

作者： Chi Zhang Quan Chen Zuping Tang Jiaolong Wei Gaoyang Liu School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhanChina College of Aerospace Science and Engineering National University of Defense TechnologyChangshaChina School of Computing Science Simon Fraser UniversityBritish ColumbiaCanada

In order to achieve global multiple seamless coverage,space-based internet usually adopts low Earth orbit(LEO)mega-constellation networks structure,which has the characteristics of high network topology dynamics,limited on-board computing and storage capacity,and uneven distribution of ground *** features may cause problems such as high transmission delay,network congestion and link *** a stable,efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay,load balancing,and network *** advantage of the regularity of network topology,a pre-coded inter-satellite routing algorithm with load balancing is proposed,which includes 3 parts:(a)the routing sequence coding method and the concept of gateway satellite Service Region(GSSR)are proposed;(b)the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution;(c)aiming at the uneven distribution of traffic,the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite *** results show that,for the Starlink Group-4 constellation,the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.

关键词： mega constellation networks satellite communication link load balancing space based internet global coverage inter satellite communication pre coded routing low Earth orbit

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