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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. This strategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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Artificial Intelligence Technology Promotes Sustainable Development of Agricultural Economy 14th

Artificial Intelligence Technology Promotes Sustainable Deve...

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14th International Conference on Frontier computing, FC 2024

作者： Lu, Xinyong Li, Changgui School of Economics and Trade Zhongkai College of Agricultural Engineering Guangdong Guangzhou510225 China Business School Guilin University of Technology Guangxi Guilin541004 China

ISBN: (纸本)9789819623907

With the progress of the times, traditional research methods cannot well meet the urgent needs of sustainable development of agricultural economy. Therefore, this article aims to study the application of AI technology to promote sustainable development of agricultural economy. Firstly, this article utilizes support vector machines in machine learning techniques to establish a prediction model and optimize the planting optimization techniques in the R area;secondly, it uses deep reinforcement learning technology to construct an intelligent scheduling system for R area, optimizing the scheduling of intelligent agricultural machinery operations;next, this article constructs a long-term and short-term memory network model to make intelligent market predictions based on market data in the R region. Finally, this article takes the irrigation system in R area as an example to conduct experimental evaluation of sustainable development indicators for agricultural economy. The results showed that the irrigation efficiency and water resource utilization rate of irrigation systems using AI (Artificial Intelligence) technology were 24.05% and 25.64% higher than traditional irrigation systems, respectively. It can be seen that the application of AI technology can effectively promote the sustainable development of agricultural economy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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MRI-Based Brain Tumour Detection and Classification Using Random Forest Algorithm 3rd

MRI-Based Brain Tumour Detection and Classification Using Ra...

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3rd International Conference on intelligent systems and Sustainable computing, ICISSC 2023

作者： Jareena Begum, D. Chokkalingam, S.P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783540

A brain tumour develops when cells in the brain multiply abnormally and out of control. The possibility of fatality makes this growth dangerous. The brain regulates everything from memory to vision to emotions. Recognizing these tumours is a challenging and complex task due to their location, size, and shape. There have been a number of successful attempts to enhance detection. However, the current level of precision is insufficient. This study details a method with a high likelihood of success in identifying brain cancers. The approach is implemented in Python LAB by use of an image segmentation technique and a classifier. One such classifier is the Random Forest Algorithm. The Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT) are also employed. The created method is put to the test using a dataset available on the Kaggle platform. The acquired results were approximately 97.68% accurate. A confusion matrix and a comparison of the developed method to other research in the literature are included in the investigation to facilitate the implementation of the proposed strategy in image testing. This Research shows that the offered system is superior to others in terms of detection rate, false positive rate, and recall. Results from this study showed that the developed method was effective at detecting brain tumours due to its high levels of accuracy, precision, and recall. According to the proposed strategy, it is essential to provide professionals who diagnose brain tumours with access to such technology. © The Author(s) 2025.

关键词： Image segmentation

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Revolutionizing Biomedical engineering with Microcontroller Applications: A Comprehensive Review 3rd

Revolutionizing Biomedical Engineering with Microcontroller ...

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3rd International Conference on intelligent computing Techniques for Smart Energy systems, ICTSES 2023

作者： Pawase, Chinmay D’Souza, Sandra Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9789819784288

This far-reaching study paper blends bits of knowledge from selected vital papers, featuring the instrumental job of microcontrollers in raising medical care diagnostics and checking. Microcontrollers, high level coordinated circuits with implanted handling capacities, are vital apparatuses for exact information procurement, top to bottom examination, and complex control frameworks, empowering momentous advancement in biomedical designing. The reconciliation of microcontrollers has become imperative for creating inventive clinical gadgets and analytic apparatuses as medical services progress. These conservative, superior stages are sent in different applications, from wearable wellbeing screens following imperative signs to implantable gadgets controlling insulin levels and supporting cardiovascular capabilities. They likewise assume a critical part in overseeing complex information streams, guaranteeing consistent transmission and capacity of patient data. The considered papers highlight microcontroller’s adaptability and effect in the biomedical field, upgrading analytic apparatus precision and unwavering quality, encouraging patient commitment and autonomy, and further developing medical care administrations. This paper has immensely encouraged in understanding the concepts gathered during the Microcontroller course and it gives a strong research base in the field of Microcontrollers. Microcontrollers have changed medical care by propelling diagnostics and observing accuracy, effectiveness, and availability. The cooperative energy among microcontrollers and biomedical applications has developed wonderful progressions for patients and medical care suppliers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Performance Analysis and Power Allocation for Massive MIMO ISAC systems

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IEEE Transactions on Signal Processing 2025年 73卷 1691-1707页

作者： Nguyen, Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu V. Ngo, Hien Quoc Swindlehurst, A. Lee Juntti, Markku University of Oulu Centre for Wireless Communications FI-90014 Finland VinUniversity Vinhomes Ocean Park College of Engineering and Computer Science Hanoi100000 Viet Nam University of Science and Technology the University of Danang Faculty of Electronics and Telecommunication Engineering Da Nang50000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Yongin-si Department of Electronic Engineering Gyeonggi-do17104 Korea Republic of University of California Center for Pervasive Communications and Computing IrvineCA92697 United States

Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform planar array with zero-forcing and maximum-ratio downlink transmission schemes combined with monostatic radar-type sensing. Our focus lies on deriving closed form expressions for the achievable communications rate and the Cramér-Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using a very large antenna array for each functionality. Further-more, we devise a power allocation strategy based on successive convex approximation to maximize the communications rate while guaranteeing the CRLB constraints and transmit power budget. Extensive numerical results are presented to validate our theoretical analyses and demonstrate the efficiency of the proposed power allocation approach. © 1991-2012 IEEE.

关键词： Budget control

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AI Enabled Swarm Application for Fire-Fighting Bots Over Web-Bluetooth APIs 3rd

AI Enabled Swarm Application for Fire-Fighting Bots Over Web...

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3rd International Conference on intelligent systems and Sustainable computing, ICISSC 2023

作者： Bhattacharya, Debasish Ali, Israj Custom Software Engineering Senior Analyst Accenture Solutions Pvt. Ltd PunePDC5 India School of Electronics KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9789819783540

According to estimates by NCRB from 2019–2020, fires and a lack of timely and adequate fire protection result in around 20,366 deaths annually in India ( G. Litta, R. Di Rienzo, R. Morello, R. Roncella, F. Baronti, and R. Saletti, "Flexible platform with wireless interface for DC-motor remote control," in 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy systems (IESES), 2018, vol. 2018-Janua, pp. 509–514.). Due to the intense heat, firefighters frequently pass away while putting out the flames. Therefore, a technological solution will make it easier to prevent the loss of life from such dangerous activities. Here, we have created a robot to put out the fire utilizing AI-enabled Bluetooth-based automated techniques hosted in the Cloud in order to decrease these incidents. We can design a model-based solution for this specific issue thanks to the extensive advancements in Bluetooth technology, Web-Bluetooth APIs, and IR sensors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fires

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Automatic Maintenance System Using Wireless Sensor Node for Productive Aquaculture 3rd

Automatic Maintenance System Using Wireless Sensor Node for ...

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3rd International Conference on intelligent computing Techniques for Smart Energy systems, ICTSES 2023

作者： Jegan, G. Masilamani Selvam, M. Rexiline Sheeba, I. Kavipriya, P. Varma, R. Rishav Selvam, Praveen Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai600119 India Department of Pharmaceutical Technology Paavai Engineering College Namakkal637018 India

ISBN: (纸本)9789819784639

Aquaculture is trending toward intense controlled environment production, which increases productivity but increases the danger of catastrophic loss from equipment or management failures. To optimize potential, intense production facility managers require reliable, real-time system status and performance information. This study designed and implemented low-cost short-range wireless sensor network modules for Productive Aquaculture to monitor and maintain environmental factors such as the toxic or harmful level of water used for aquaculture by using different types of sensors and collecting information through a wireless mess network. The results demonstrate that the proposed method optimizes the aquaculture system’s capacity for monitoring, control, and recording. Aquaculture environments with a high risk of fish mortality may be made safer via constant monitoring of the most important parameters. The outcome is a reduction in labor expenses and energy consumption with an increase in consumer safety, consumer trust, and economic benefit from aquaculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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intelligent IoT System for Irrigation and Water Management 5th

Intelligent IoT System for Irrigation and Water Management

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5th International Conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Chaudhari, Swapnil Warke, Rutuja Pungaonkar, Shreya Shivudkar, Atharvaraj Gurav, Uma Chanchal, Amit KIT’s College of Engineering Kolhapur India

ISBN: (纸本)9789819773701

Water is one of the most crucial aspects of one’s life and as the population is increasing drastically, the need for water is also increasing. However freshwater resources are scarce, and freshwater is the main component for irrigation. As the technology is growing rapidly the agriculture sector needs to use advanced irrigation systems. This study addresses the water control project by exploring the combination of Artificial Intelligence, Machine Learning, and the Internet of Things. Drip irrigation, a key generation in water conservation, ensures managed water release to plant roots. However, traditional methods fall short in optimizing water use, necessitating the utility of device studying for sensible irrigation. By the use of the Kaggle dataset Irrigation *** comprising soil moisture, temperature, altitude, etc. The observations of this paper employ four ML models—LR, Naive Bayes, SVM, and XGBoost. This model is evaluated based on accuracy scores;results exhibit the effectiveness of XGBoost which emerges as the top-performing model with a perfect accuracy of 0.9979. This comparative evaluation presents treasured insights, setting up a basis for the mixing of devices and gaining knowledge of enhancing irrigation structures. The research contributes to sustainable water useful resource control in agriculture, important for addressing the worldwide project of water shortage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Soil moisture

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Scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine Learning Model 3rd

Scalable Real-Time Anomaly Detection in Financial Markets Us...

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3rd International Conference on intelligent systems and Sustainable computing, ICISSC 2023

作者： Andi, Hari Krishnan Bommisetti, Ravi Kumar Subbarao, Buggavarapu V. S. S. Sonthi, Vijaya Krishna Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor Bahru81750 Malaysia Department of Business Administration and Dean-Research and Development Maris Stella College Vijayawada520008 India Department of MBA Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

Stakeholder objectives can be put at risk if anomalies are not detected sufficiently promptly in the constantly evolving financial systems. This paper recommends an innovative Machine Learning (ML) method, the Hybrid Temporal-Feature Embedding Model (HTEM), dedicated to real-time Anomaly Detection (AD). Fundamental to this framework is the use of a distributed system to store and process real-time data that has been collected through a strong Data Ingestion Layer. The HTEM framework is a vital component of this layout. The temporal flow of data collected from LSTM networks is precisely combined with the significance of individual features that are analyzed by autoencoders in this technique. By applying this dual embedding, a complete model of financial data sequences is accomplished. By applying a fully connected layer to provide this combined representation, the research affects an accurate anomaly value for each data point. If the overall rating is higher than a particular threshold, the data point is classified as an anomaly. Training this model frequently on known anomalies enables it to be fine-tuned to a higher level. The timely, accurate, and efficient finding of anomalies can be ensured through the incorporation of the HTEM within the model being proposed, protecting the interests of stakeholders in the financial market environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International Conference on intelligent systems and Sustainable computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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