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Recent technology Advancements in Smart City Management:A Review

Computers, Materials & Continua 2024年第12期81卷 3617-3663页

作者： Chiranjeevi Karri JoséJ.M.Machado Joao Manuel R.S.Tavares Deepak Kumar Jain Suresh Dannana Santosh Kumar Gottapu Amir H.Gandomi Department of Mechanical Engineering Faculty of EngineeringUniversity of PortoPorto4200-465Portugal Department of Electronics and Communication Engineering Andhra University College of EngineeringAndhra UniversityVisakhaptnam530003Andhra PradeshIndia Laboratory of lntelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamVizianagaram532127Andhra PradeshIndia Department of Civil Engineering Gayatri Vidya Parishad College of Engineering(GVPCE)Visakhapatnam530048Andhra PradeshIndia Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) óbuda UniversityBudapest1034Hungary

The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automation requirements in industries are the reasons for invention of *** cities aim to address these challenges through the integration of technology,data,and innovative *** a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living,improve resource efficiency,and create sustainable *** review presents five of the most critical technologies for smart and/or safe cities,addressing pertinent topics such as intelligent traffic management systems,information and communications technology,blockchain technology,re-identification,and the Internet of *** challenges,observations,and remarks of each technology in solving problems are discussed,and the dependency effects on the technologies’performance are also *** deep learning models for various applications are *** models performance,their dependency on dataset size,type,hyper-parameters,and the non-availability of labels or ground truth are discussed.

关键词： Smart cities intelligent traffic management systems internet of things deep learning blockchain technology

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A Time-Efficient Path Navigating Landmine Detection Robot

A Time-Efficient Path Navigating Landmine Detection Robot

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2024 IEEE International Conference on Intelligent Signal Processing and Effective communication Technologies, INSPECT 2024

作者： Bhattacharjee, Rituraj Mukherjee, Subhradip Hardik Gururaj Rajpurohit, Manish Manipal Institute of Technology Manipal Academy of Higher Education Instrumentation & Control Engineering Department Manipal India MVJ College of Engineering Electronics and communication Department Bangalore India

ISBN: (纸本)9798350379525

The goal of a landmine detection robot is to map the remaining area on a visual map with millimeter precision after covering as much ground as it can show landmines. The prototype land mine detection robot model described in this study offers a visual interface for mapping landmines, modifying PIDs, and aligning cameras. It is also reasonably powerful, accurate, and easy to operate with other sensors. The manual, semi-auto, and auto modes of controlling the differential drive robot are emphasized. Image processing determines the exact location of the robot and provides live reckoning feedback to its dead reckoning servo control. A beat metal detector is a kind of quiet sensor used to locate landmines. The overall objective of the system is to provide the user with something robust, affordable, and easily comprehensible for the user. © 2024 IEEE.

关键词： Landmine detection

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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A Repetitive Motion Obstacle Avoidance Planning Scheme for Omnidirectional Mobile Manipulators with Noise Suppression Capability

A Repetitive Motion Obstacle Avoidance Planning Scheme for O...

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2023 China Automation Congress, CAC 2023

作者： Shi-Jun, Tang Yun-Feng, Hu Zhong-Bo, Sun Chong, Zhang Yu-Zhe, Fei Xing-Tian, Xiao Jilin University College of Communication Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China Changchun University of Technology Intelligent Rehabilitation Engineering Technology Laboratory Department of Control Engineering China

ISBN: (纸本)9798350303759

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. To make OMRA accomplish repetitive motion, obstacle avoidance, and trajectory tracking tasks in complex dynamic environments with the presence of obstacles and external disturbances, a new noise-suppressed repetitive motion obstacle avoidance (NSRMOA) scheme is proposed. As established previously many OMRA motion planning assumes that the simulation is noise free. However, noise interference may prevent OMRA from accomplishing repetitive motion, obstacle avoidance, and trajectory tracking tasks, which may lead to task failure. The proposed the NSRMOA scheme with intrinsic noise suppression can solve external noise interference, so that the OMRA can accomplish the repetitive motion, obstacle avoidance and trajectory tracking tasks in the presence of external noise interference. Finally, the optimal solution of the NSRMOA scheme can be effectively obtained by using recurrent neural network (RNN). The effectiveness and accuracy of the scheme are verified by examples and comparisons. © 2023 IEEE.

关键词： Robotic arms

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A Fuzzy Logic controller for Path Navigation of Brain-controlled Mobile Robots in Complex Simulated Environments 1

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control Instrumentation System Conference, CISCON 2023

作者： Bhattacharjee, Rituraj Mukherjee, Subhradip Borah, Siddhanta Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Electronics and Instrumentation Engineering National Institute of Technology Nagaland Nagaland Chumukedima797103 India Department of Electronics and Communication Engineering MVJ College of Engineering Bengaluru India Department of Electronics and Communication Engineering Assam Engineering College Assam Guwahati India

ISBN: (数字)9789819758661

ISBN: (纸本)9789819758654

Electroencephalogram (EEG)-based mobile robots can be powerful everyday aids for persons with severe disabilities, especially if they need assistance in moving voluntarily. With the amalgamation of EEG signal processing, artificial intelligence algorithm and mobile robotics trajectory planning, it is possible to drive a mobile robot using noninvasively recorded brain activity. In this research work, with the help of MATLAB simulations, mobile robot path navigation has been shown where the target location was retrieved using EEG signal data processing. A fuzzy logic controller has been designed and presented to conduct the obstacle avoidance mechanism based on fuzzy logic. The fuzzy logic approach has been made functional with 25 sets of rules, dictating the movement positions. The autonomous navigation system uses this position to direct the mobile robot to the intended place while avoiding collisions with outside barriers. This approach could prove very useful in effective mobile robot path planning in brand new and unpredictable situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile robots

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A data-driven control method for vehicle lateral stability in snow and ice-rutted conditions under aperiodic sampling 7

A data-driven control method for vehicle lateral stability i...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Zhao, Xu Guo, Jingzheng Wang, Lianbing Guo, Hongyan Liu, Zhenze Zhang, Yiming College of Communication Engineering Jilin University The Department of Aircraft Control and Information Engineering Jilin Institute of Chemical Technology Changchun China College of Communication Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control The College of Communication Engineering Jilin University Changchun China SAIC Motor R&D Innovation Headquarter Jilin University Shanghai China

ISBN: (纸本)9798350340488

Designing a controller to ensure vehicle stability under snow and ice-rutted road conditions, in the absence of accurate model knowledge and with potential sampling data delay, poses a significant challenge. To address this, this paper presents a novel data-driven parametric approach that integrates linear matrix inequalities (LMIs) methods. This approach allows for the determination of an appropriate sampling time interval. By iteratively solving LMIs, a state-feedback controller is derived, which guarantees both system stability and robustness. The effectiveness of the proposed approach is demonstrated through numerical simulations conducted on the MATLAB/CarSim platform. The results highlight the good control performance and robustness achieved by the proposed method. © 2023 IEEE.

关键词： data-driven control linear matrix inequality sampling time interval vehicle stability control

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Home Monitoring System using Internet of Things 3

Home Monitoring System using Internet of Things

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Ramesh, A. Mohanraj, K. Venugopal, E. Sivakumar, P. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Chennai India Psg College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9781665462167

People are now keen to monitor home from anywhere in the world. Communicating the monitoring information immediately to the user has always been a difficult task. The emerging technological advances helps to tackle this challenge. The Internet serves as a platform to ease the monitoring process by continually communicating the status of household appliances to the users. This study has developed and implemented a novel prototype for kitchen monitoring system. The proposed prototype is composed of sensors, processing device, and an android application. Here, the monitoring process has been carried out virtually via a mobile application. In addition to, the software tools significantly accelerate the process of prototype development. © 2023 IEEE.

关键词： HTTP

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Fast Finite-Time Adaptive Neural control for Pure-Feedback Stochastic Nonlinear Systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Zhao, Ximei Zhang, Hanyu Shao, Lijun Huang, Lili Li, Jiao School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350316230

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality. © 2023 IEEE.

关键词： Feedback

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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