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3rd international conference on Intelligent computing Techniques for smart Energy systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics Processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Enabling Technology for smart and Intelligent Transportation: Analyse the Uses and Impact of Blockchain Technology and Artificial Intelligent on Trucking Industry for Better Results 4th

Enabling Technology for Smart and Intelligent Transportation...

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4th international conference on smart IoT systems: Innovations and computing, SSIC 2023

作者： Parihar, Mahendra Akshaya Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS University Mumbai India Department of Information Technology Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS University Mumbai India

ISBN: (纸本)9789819751990

As a backbone of our economy, playing a very crucial role in the movement of our economy, the trucking industry still has not come out of the persistent problems it has been facing over the last few decades such as high cost of operation, low efficiency in operation, government issues, regulatory and legislative mechanism, information issues and many more. However, adopting these latest available technologies and their effective use can solve many of these problems significantly. One such is the adaptation of digitalization properly for Digital Governance which further results in Good Governance and another is the adoption of blockchain technology for data and information management to enhance operation efficiency of the trucking industry. Today in most cases especially for small truck operations, the operation is not viable due to poor technology adoption. Hence with this paper, an attempt is being made to initiate these improvements. The methodology adopted for the study is also relevant to the requirement of the work undertaken in the form of a structured approach. It consists of a relevant literature review, primary and secondary sources of information with available data, and a model framework about digitalization, digital governance, and possible application of blocking technology to bring improvement in operations of the trucking segment in our economy. The study found that the adoption of the latest technology in trucking operations in the form of digitization and blockchain reduces the problem of asymmetric information and brings good governance to enhance operation efficiency. So, the study concludes by stating that if the government supports the trucking industry, provides consultation, and coordinates with other stakeholders of the industry regarding technology adaptation and other aspects, the trucking industry can yield better results and solve many of its problems independently. © The Author(s), under exclusive license to Springer Nature Singapore Pte

关键词： Efficiency

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MARS-Net: Multi-Scale Attention Residual Spatiotemporal Network for Robust Left Ventricular Ejection Fraction Prediction in Echocardiography Videos

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international JOURNAL OF IMAGING systems AND TECHNOLOGY 2025年第2期35卷

作者： Cheng, Shun Guo, Fangqi Guo, Qihui Chen, Haobo Xu, Zhou Zhang, Bo Zhao, Jiaqi Zhang, Qi Shanghai Univ Sch Commun & Informat Engn SMART Smart Med & AI based Radiol Technol Lab Shanghai Peoples R China Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China Tongji Univ Shanghai Peoples Hosp 4 Dept Ultrasound Shanghai Peoples R China Tongji Univ East Hosp Dept Ultrasound Med Sch Med Shanghai Peoples R China

Left ventricular ejection fraction (LVEF) is a key measure of heart pumping performance, playing a pivotal role in the ongoing management and efficacy assessment of cardiovascular disease treatments. By quantifying the percentage of blood that is pumped out of the left ventricle with each heartbeat, LVEF provides invaluable insights into the overall efficiency of the heart's function, enabling clinical professionals to make informed decisions regarding point of care and therapeutic strategies. However, accurate LVEF measurement faces challenges such as large observer variability, poor image quality, and the complexity of cardiac motion. To address these issues, a residual spatiotemporal network with a multi-scale attention mechanism is proposed for robust LVEF prediction in transthoracic echocardiographic videos, named the Multi-scale Attention Residual Spatiotemporal Network (MARS-Net). The MARS-Net excels at extracting spatiotemporal features from echocardiographic videos, accurately capturing heart dynamics and morphology while demonstrating robust performance across multi-center data. The sub-video division block is first designed to partition echocardiographic videos into smaller sub-videos, capturing key cardiac motion. The input embedding block compresses these sub-videos for efficient processing. Then the multi-scale attention residual block enhances spatiotemporal feature extraction by combining multi-scale convolutions with attention mechanisms to improve focus on important details. Finally, the output convolutional block transforms the extracted features into the final LVEF prediction, ensuring accurate measurement. Through extensive evaluations, our MARS-Net outperforms comparative deep learning models in LVEF prediction, offering exceptional promise for diagnosing heart dysfunction. Notably, it has achieved commendable results in three medical centers, underscoring its generalizability and reliability across varied clinical environments.

关键词： deep learningechocardiogram left ventricular ejection fraction (LVEF) multi-center multi-scale attention

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Chemiresistive Gas Sensors: Bibliometric Trends and Recent research Progress 3

Chemiresistive Gas Sensors: Bibliometric Trends and Recent R...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Ramesh, N. Venkata Reddy, G. Pradeep Bonam, Satish Singh, Shiv G. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Amaravati Campus Andhra Pradesh 522503 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Karnataka Manipal576104 India University College Cork Tyndall National Institute CorkT12 R5CP Ireland Indian Institute of Technology Hyderabad Department of Electrical Engineering Telangana Sangareddy502285 India

ISBN: (纸本)9798331509675

The rapid increase in industrialization and technological progress in recent years has led to the emission of various toxic and harmful gases, which contribute to the deterioration of air quality;Therefore, air quality monitoring has become of paramount importance. Among the various sensor technologies available, chemiresistive gas sensors (CGSs) have attracted considerable attention due to their simplicity in construction, cost-effectiveness, and ability to detect a wide range of gases. This review provides a comprehensive overview of the importance of CGSs in diverse applications, including environmental monitoring, industrial safety, and healthcare. The present review discusses bibliometric analysis highlighting publication trends, citation metrics, collaboration networks, and keyword usage, providing insights into the research field by analyzing Scopus data from 2013 to 2023. It also summarizes recent advances in CGS technology, including new sensor materials, fabrication techniques, and integration of CGS into smart devices. Despite significant progress, challenges such as excellent selectivity and sensitivity, and long-term stability still remain uncertain. We also discuss future research directions aimed at addressing these challenges, including development of novel materials, use of machine learning for improved selectivity, and miniaturization of sensors for various applications, including flexible and wearable applications. © 2025 IEEE.

关键词： Air quality

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The Innovation Imagined for the Window: international Patents in Comparison 11th

The Innovation Imagined for the Window: International Patent...

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11th international conference of Ar.Tec.

作者： Cernaro, Alessandra Minutoli, Fabio Univ Messina Dept Engn I-98158 Messina Italy

ISBN: (纸本)9783031718694;9783031718670;9783031718663

The window is an element of the building system that has always been a field of stylistic and technological experimentation. By virtue of belonging to the envelope, it assumes a non-secondary role in the formal composition of facades and affects levels of thermal, luminous, and acoustic comfort. Recently, the aim of environmental sustainability has encouraged the offer of more efficient solutions, also responding to the circular economy, for example by providing for the use of recycled materials. To intercept both the direction taken by the research sector and the direction that industrial production may take, the purpose of the study was to understand what the innovative features of the window will be in the near future;this was done by analysing published patent applications. The methodological approach required prior investigation of patent types and of the territorial extension of their protection. In order to detect current trends on a global scale, interest was directed to international patent applications (PCT - Patent Cooperation Treaty), which allow for the possible protection of the invention in the 157 Treaty countries and which are published in the PATENTSCOPE database of WIPO. In 2023, the chosen year of observation, 195 patent applications were published in the "window" category and 132 were included in the study, selected according to previously defined inclusion and exclusion criteria. The classification was based on the sub-systems identified within the window element (Attachment to the structure;Frame, Sash;Glazing;Screen;Movement and maneuvering;Gasket) and categorised into three innovation areas (A1. Design, A2. Performance, A3. Sustainability), and related sub-areas. The study showed that the area of Sustainability is still lacking;while greater interest on the part of the researchers concerned the area of Performance, and in particular thermal/luminous comfort, with a significant focus on the integration of the smart component in windows.

关键词： Window Patents Design Innovation Performance Innovation Sustainable Innovation

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A MEMS Pressure Sensor Array for Sleep Apnea Recognition and Monitoring Based on The Principle of Traditional Chinese Medicine 38

A MEMS Pressure Sensor Array for Sleep Apnea Recognition and...

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38th IEEE international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Qin, Lin Cheng, Long Zeng, Xianzhang Xia, Yuanlin Wang, Zhuqing Sichuan University School of Mechanical Engineering China Sichuan University Med + X Center for Manufacturing West China Hospital Chengdu China

ISBN: (纸本)9798331508890

This paper reports a novel MEMS-based pressure sensor array, inspired by traditional Chinese medicine, for detecting and identifying pulse signals. A multi-dimensional pulse acquisition array was developed by emulating traditional Chinese pulse diagnosis techniques to capture pulse signals at the cun, guan, and chi positions along the radial artery. A database was created with pulse signals recorded under normal breathing and breath-holding conditions. These features were used to train a support vector machine (SVM) classification model. The experimental results show that the model achieved high accuracy (99.33%) and sensitivity (98%) in detecting sleep apnea. Overall, this system could offer innovative technical support and promising applications for modernizing traditional Chinese diagnostic methods and developing smart healthcare devices. © 2025 IEEE.

关键词： Sleep research

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The Critical Cone and Second-Order Optimality Conditions for a State-Constrained Optimal Control Problem 4th

The Critical Cone and Second-Order Optimality Conditions fo...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Karamzin, Dmitry Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031812408

A state-constrained optimal control problem is considered in which the starting position is fixed and the terminal position is free. The endpoints are strictly included within the set shaped by the given state constraint which is considered scalar. For this problem formulation, the second-order necessary optimality conditions are derived including the Legendre condition over the critical cone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimal control systems

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Hybrid Intelligent System for Improved Decision Support in Customer Churn Prediction for a Telecommunication Company 8th

Hybrid Intelligent System for Improved Decision Support in C...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Jaya, R. Soms, Nisha Isaac, Lydia D. Priya, S. Sathiya Great Learning Karnataka Bangalore India KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Sona College of Technology Tamil Nadu Salem India Kumarasamy College of Engineering Tamil Nadu Karur India

ISBN: (纸本)9789819784219

This research paper explores the integration of artificial neural networks (ANNs) and data mining to create hybrid intelligent systems that offer enhanced decision support. The paper emphasizes this approach's significance in overcoming traditional AI methods’ limitations. Through a comprehensive literature review, the study highlights the strengths of ANNs and data mining while identifying gaps in existing research on hybrid intelligent systems. The methodology section details the construction of the hybrid system, including the selection of ANN architectures, data mining algorithms, and integration strategies. Experimental validation using diverse datasets demonstrates the system's superiority over traditional AI methods in decision support applications. The paper also examines the real-world applications of the hybrid intelligent system in various industries, such as healthcare, finance, and manufacturing, while addressing ethical considerations for responsible deployment. In conclusion, the research highlights the immense potential of hybrid intelligent systems to significantly improve decision-making processes significantly, showcasing the value of combining ANNs and data mining for practical and impactful outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： smart manufacturing

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Design and Implementation of a Wireless Monitoring Application for Pregnant Women's Fetal Movement

Design and Implementation of a Wireless Monitoring Applicati...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Qin, Manping Xu, Yong Chan, Choon Kit Chen, Zhiling Zhu, Caitong School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

The fetal movement signal represents the most intuitive indicator of the health of the fetus and the mother. In order to effectively monitor the health status of the fetus and the pregnant woman in the third trimester, an application was designed based on the Android operating system for real-time monitoring of fetal movement in pregnant women. Using Android Studio as the development platform, the SQLite database was utilized to facilitate user registration and login. The low-power Bluetooth API is utilized to establish the connection between the mobile terminal and the wireless fetal movement monitor for pregnant women, enabling the collection of raw fetal movement data. Real-time transmission of data to the smartphone via low-power Bluetooth is achieved, followed by the processing and recognition of fetal movement signals on the app, culminating in the visualization of the recognition results. Through rigorous testing and application, the system facilitates the visualization of fetal health data, addressing family concerns about the health of pregnant women and fetuses, and is applicable in the field of fetal health monitoring. © 2025 IEEE.

关键词： Fetal monitoring

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