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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47413 条记录，以下是11-20 订阅

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Leveraging Quantum Communication with 6G Network: Optimize the Model for Secured Communications 1

Leveraging Quantum Communication with 6G Network: Optimize t...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar Gautam, Anupam Kumar Rajendran, Naveen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka India

ISBN: (纸本)9798331518578

A broad-ranging consciousness-raising movement of mathematical ideas is dawning accompanying the onset of 6G science, that promises ubiquitous and flowing relations. Many visualize the inclusion of quantum computing (QC) into specific networks as a potential responsible for the complex computational and safety needs concerning this revolution. In light of 6G science, this paper delves further into the likely functions and results of QC. Totally classification of some (AP15) fault-finding QC uses in 6G science into four main classifications established in the literature review. Speaking accompanying experts engaged rooted these results even further;they uncovered best practices guide individual of the emphasize fault-finding applications. Researchers in addition to manufacturing experts alike grant permission to benefit from the research's judgments, which determine a firm basis for including the detracting uses of QC within 6G Wi-Fi electronics and profitable arrangements for implementing it. © 2025 IEEE.

关键词： Wireless local area networks (WLAN)

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CALFNet: A Light Field Reconstruction Method Based On Channel Attention Mechanism 16

CALFNet: A Light Field Reconstruction Method Based On Channe...

引用

2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Gao, Yuxuan Chen, Zhihong Zhang, Haiwei School of Integrated Circuit Science and Engineering Tianjin University of Technology China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Light field reconstruction, a crucial domain in computer vision, generates 3D models of real-world scenes by combining multi-view images. Multi-view stereo technology is essential in this process, leveraging images from various angles to reconstruct 3D structures. It is widely used in applications such as cultural heritage preservation, robotics, and virtual reality. However, current methods often depend on rigid feature extraction and fusion techniques, restricting their capacity to fully exploit the rich multi-view data. This frequently results in blurred reconstructions, particularly in complex scenes with intricate edges and contours. To address these limitations, we propose CALFNet, a novel light field reconstruction method based on a channel attention mechanism. CALFNet combines this attention mechanism with a detail loss module, enabling the network to prioritize crucial channel information and edge features. This significantly enhances the accuracy and completeness of 3D reconstructions from multi-view images. Experiments on the DTU 3D reconstruction dataset demonstrate that CALFNet improves overall accuracy by 2.4% compared to existing methods. Additionally, tests on the Tanks and Temples dataset demonstrate the model's strong generalization capabilities, effectively handling diverse reconstruction tasks across various scenarios. This advancement paves the way for new applications in fields such as cultural heritage, robotics, and virtual reality. © 2025 SPIE.

关键词： Stereo image processing

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Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Drivers Through Haptic Feedback 4

Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Dr...

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4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Chowdhury, Shah Fahim Rabbi, Abid Mahamud Saif, Mushfiq Qayyum Khan, Nafisa Anjum Hasan, Khandaker Tabin Deparment of Computer Science and Engineering Dhaka Bangladesh Faculty of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9798331530938

This research explores the use of haptic technology in heavy vehicle driving safety, focusing on its potential to enhance driver assistance systems and improve driving experience. The study addresses driver-induced causes of traffic accidents, including drivers' inability to see around the vehicle due to its length and height. The proposed system provides 360-degree cognitive awareness through haptic feedback, which can improve road safety and mitigate challenges in diverse driving conditions. By using haptic feedback, the driver will be able to get a tangible sense of the presence of objects surrounding the vehicle. Through a comprehensive review of existing literature and experimental studies, the research demonstrates the potential of haptic feedback in revolutionizing the driving experience. The findings highlight the optimum mapping between sensors and vibration motors positions on the human body © 2025 IEEE.

关键词： Vehicle safety

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China Is the Leading Country in the World in the Implementation of Robotic technology as the Basic technology of Industry 4.0 12th

China Is the Leading Country in the World in the Implementat...

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12th international conference of Machine and Industrial Design in Mechanical engineering

作者： Karabegovic, Isak Husak, Ermin Karabegovic, Edina Mahmic, Mehmed Acad Sci & Arts Bosnia & Herzegovina Sarajevo Bosnia & Herceg Univ Bihac Tech Fac Bihac Bihac Bosnia & Herceg

ISBN: (纸本)9783031805141;9783031805127;9783031805110

It is well-known that the research and development of new technologies, and their implementation in production processes and human environment lead to great changes and the shaping of a new world. Technological innovations continuously develop and change over time, however implementation is delayed and the reason is rigid infrastructure. In order to ensure an optimal future we must adapt to the changes that are coming. Many developed and developing countries have realized this and adopted their strategies for implementing Industry 4.0 and carry out its implementation. Change their production processes in such a way that they implement the basic technologies of Industry 4.0 (there are currently over forty in the world), such as: Internet of Things (IoT), Big Data, robotics, Cloud Computing, Smart Sensors, Radio Frequency Identification (RFID), 3D Printing, Advanced Security Systems, Virtual and Augmented Reality, Artificial Intelligence (AI), etc. By introducing the aforementioned basic technologies into production processes, they become more automated and flexible with the tendency to become smart production processes. The paper provides an analysis of the implementation of robotic technology as the basic technology of Industry 4.0 in the country of China as the leading country in the world in research, development and implementation of this technology. The paper attempts to find an answer as to why China is the world leader in the implementation of robotic technology.

关键词： robotics China Industry 4.0 Automation Smart Processes Advanced Technologies

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Combining Convolutional Neural Networks with Reinforcement Learning for Autonomous robotics

Combining Convolutional Neural Networks with Reinforcement L...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Padhiary, Mrutyunjay Powar, Swati Asokan, Anju Sundar Rajan, M. Al-Said, Khaleel Al Said, Nidal Assam University Triguna Sen School of Technology Department of Agricultural Engineering Assam Silchar788011 India Mit Adt University Computer Science Engineering Maharashtra Pune India Cse Nehru College of Engineering and Research Centre Kerala Thrissur India Arbaminch University Ethiopia Faculty of Electrical and Computer Engineering Arbaminch Institute of Technology India Middle East University Faculty of Arts and Educational Sciences Department of Educational Technology Amman Jordan Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

The use of artificial intelligence (AI) in robotics, particularly in the advancement of gaming robotics, has drawn a lot of interest as technological and scientific developments continue to advance. This innovation opens up new possibilities for creating smart autonomous robots for gaming. Nevertheless, there is more work to be done to achieve great accuracy and consistency in motion management. The challenges of the robotics' level of smart and the motion-capturing structure's capacity to prevent interruption still require resolution, although numerous methods have been put out to improve monitoring accuracy by merging various data. This study proposes an adaptable reinforcement learning (RL)-oriented multifaceted data combination (AdRL-MDC) system for training a robotic hand to play games alongside humans. It incorporates an adaptable training process for updating the ensemble classification algorithm, an RL paradigm that provides the robots with smart knowledge, and a multifaceted data combination framework that is resistant to interruption. The following studies demonstrate the AdRL-MDC system's previously indicated functionality. The ensemble framework, which combines a convolutional neural network (CNN), performs well in terms of accuracy and computing duration. Furthermore, the depth vision-oriented CNN classification algorithm achieves one hundred percent recognition accuracy, implying that the forecasted motions are the real value. © 2025 IEEE.

关键词： Autonomous robots Convolutional neural network Gaming Motion management Reinforcement Learning

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Deep Neural Network Based Relocalization of Mobile Robot in Visual SLAM 6th

Deep Neural Network Based Relocalization of Mobile Robot in ...

引用

6th IFIP TC 12 international conference on Intelligence science, ICIS 2024

作者： Hamza, Azhar Muhammad Shi, Chaoxia Wang, Yanqing Nanjing University of Science and Technology Nanjing210094 China Nanjing Xiaozhuang University Nanjing211171 China

ISBN: (纸本)9783031712524

Relocalization after track loss in Visual Simultaneous Localization and Mapping (Visual SLAM) is a critical challenge in robotics, especially for autonomous mobile robots navigating dynamic environments. This paper introduces a deep learning approach that employs Deep Neural Networks (DNNs), particularly VGG16 and ResNet34, to reorient and relocalize robots effectively. Trained on a vast repository of indoor images from the Multimodal Indoor Simulator (MINOS) and Matterport3D dataset, the DNN models discern the most viable direction for movement be it translation or rotation based on the robot’s current visual input in relation to its last known position within the ORB-SLAM2 generated map. The methodology involves real-time data exchange between MINOS and the ORB-SLAM2 system via a dedicated ROS node, facilitating the recovery process. Extensive testing shows that our proposed model successfully predicts the appropriate recovery action in over 90% of track loss instances, substantiating its efficacy and potential for deployment in real-world applications. This research contributes to the advancement of robust relocalization strategies in Visual SLAM, enhancing the autonomous capabilities of mobile robotics. © IFIP international Federation for Information Processing 2025.

关键词： SLAM robotics

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Optimizing of 6G Network Performance Through Machine Learning by Allocating Resources 1

Optimizing of 6G Network Performance Through Machine Learnin...

引用

1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Agarwal, Ankita Thulasiram, Ramachandran Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

The impediments with 6G network construction have encouraged the activity of Machine Learning Resource Allocation Algorithms (MLRA). In terms of flexibility and ability utilisation., MLRA outperforms more normal algorithms. Using data on how the network is functioning, the algorithms can resolve how to deliver resources most efficiently. To confirm that properties are delivered accurately and capably, MLRA will be decisive as 6G networks are employed to a greater extent. Quickly reallocating unaccustomed resources to more creative consumers or apps is entity the algorithms are fit achievement. The expected GB speeds and a 1000x increase in illiteracy from 6G networks are intense. Smart talent allocation algorithms that can exactly and expeditiously readjust to changeful consumer demands and behaviours are essential for this. The change to 6G networks and the facilities they supply will not be likely without MLRA. © 2025 IEEE.

关键词： Queueing networks

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Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System 4

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

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4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Rongon, Rabab Khan Akhtaruzzaman, M. Das, Krishna Sharif, Shekh Tanjil Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331530938

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection. © 2025 IEEE.

关键词： Differential privacy

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Wind energy production using novel HCS algorithm to reach MPPT for small-scale wind turbines under rapid change wind speed

Wind energy production using novel HCS algorithm to reach MP...

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international conference on Innovation and Technological advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

In this paper, a novel algorithm had been proposed to improve the efficiency performance of the wind energy conversion system (WECS) to reach the maximum possible captured power under fast varying wind speed. The permanent magnet synchronous generator (PMSG) had been selected because of its reliability and efficient performance, especially in low-speed conditions environment. The novel algorithm applied the power prediction mode to make optimization for the reached decision to the maximum peak point. The proposed algorithm shows better efficiency performance for different wind speed conditions. The novel algorithm accelerates convergence to detect the maximum peak point without sacrificing efficiency. The novel algorithm’s simplicity reduces the overall time required by 33% when compared to the three-mode algorithm. © 2025 The Author(s).

关键词： Windmill

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SAE-EDA: Sparse AutoEncoder-Based Feature Extraction and Exponential Discriminant Analysis for Robust Face Recognition 16th

SAE-EDA: Sparse AutoEncoder-Based Feature Extraction and Ex...

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16th international conference on Social robotics, ICSR + AI 2024

作者： Maafiri, Ayyad Himeur, Yassine Chougdali, Khalid Atalla, Shadi Mansoor, Wathiq Ziti, Soumia Ibn Tofail University Kenitra Morocco College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Intelligent Processing and Security of Systems Research Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9789819635245

Despite its advances, face recognition (FR) technology confronts significant challenges, especially in adverse conditions such as varying lighting, facial expressions, poses, and aging effects, which necessitate the development of more robust and accurate recognition algorithms. The importance of face recognition for social robotics lies in its ability to facilitate personalized and secure interactions, enhancing the robot’s social engagement and functionality. This research introduces a novel FR methodology integrating statistical learning and shallow neural networks to refine facial feature representation and recognition. Our approach, which combines a Sparse Auto-Encoder with Exponential Discriminant Analysis, is evaluated across renowned face databases (GT, AR, LFW), demonstrating superior recognition rates and outperforming existing state-of-the-art techniques. Specifically, our system achieved recognition accuracy of 91.94% on the AR dataset under sunglass occlusion, 90.04% under scarf occlusion, and an impressive 99.74% on the LFW database, highlighting its efficacy and resilience against challenging conditions. This work not only advances the field of facial recognition but also underscores the potential of integrated machine learning approaches in addressing complex real-world challenges in digital authentication and security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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