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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications XI"

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Research on Robot Path Planning and Localization Based on ROS 5

Research on Robot Path Planning and Localization Based on RO...

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5th International conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Ji, Wenhan Wu, Shuai Wang, Minze Duan, Chengge Yang, JunYang Lu, Jun Cheng, Peng Xu, Kainan Suzhou Hanyun Keling Network Information Service Co. Ltd. Suzhou China China Mobile Communications Group Jiangsu Co. Ltd. Suzhou Branch Suzhou China Xi'an Jiaotong - Liverpool University School of Advanced Technology Suzhou China Southeast University Nanjing China Relight Studio IQIYI Inc. Shanghai China Suzhou PinYi Film and Television Co. Ltd. Suzhou China Henan University School of Software Kaifeng China

ISBN: (纸本)9798331533991

Recently, the rapid development of robotics technology has made enhancing autonomous navigation capabilities in complex environments increasingly essential. Path planning is key to improving the autonomy of robots, while localization is the process of determining the robot's accurate position within the environment. Given this, this research delves into path planning and localization technologies based on the Robot Operating System (ROS), comparing and analyzing path planning algorithms such as Dijkstra, A*, RRT and its variants, as well as particle filter localization techniques. By discussing the performance of these technologies in practical applications, we highlight their strengths and weaknesses in high-dimensional spaces and complex terrains and proposes corresponding optimization strategies to enhance robots' navigation capabilities in complex environments. The research indicates that the appropriate combination of various path planning and localization technologies in different application scenarios can effectively improve the performance of robotic systems, providing a theoretical basis and practical guidance for the optimization of robotic navigation systems. © 2024 IEEE.

关键词： Robot programming

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Study on the effect of aperture on laser scintillation characteristics 6

Study on the effect of aperture on laser scintillation chara...

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6th conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical systems

作者： Zhang, Ying Qiao, Chunhong Feng, xiaoxing Li, Nan Fang, Yuan Li, Lin'ge Zhu, Liming Cai, Yihua Liu, Guangwei Fan, Chengyu Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences No.10 Science Academy Road Anhui Hefei230031 China Science Island Branch of Graduate School University of Science and Technology of China No.96 Jinzhai Road Anhui Hefei230026 China Advanced Laser Technology Laboratory of Anhui Province No.10 Science Academy Road Anhui Hefei230031 China Institute of Physical Science and Information Technology Anhui University No. 3 Fei Xi Road Anhui Hefei230601 China

ISBN: (数字)9781510679672

ISBN: (纸本)9781510679665

Scintillation is a highly discussed problem in laser atmospheric transmission, which plays an important role in improving the reliability and performance of laser communication systems. In this study, numerical simulations are conducted using a computational model to investigate the variation of the on-axis scintillation index of the focused platform beam during horizontal atmospheric propagation, considering different transmitter aperture sizes. The simulation results indicate that the aperture size significantly affects the scintillation index. Under the same Rytov variance conditions, larger aperture sizes lead to stronger scintillation effects. For smaller aperture sizes, the scintillation index maintains a relatively linear relationship with the Rytov variance. The findings of this study provide further insights into the factors influencing the scintillation characteristics in laser atmospheric transmission and offer valuable references for the design and engineering applications of laser communication systems. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Scintillation

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Diagnostic Analysis Approach to Correlation Maps Through Large Language Models 17

Diagnostic Analysis Approach to Correlation Maps Through Lar...

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17th IEEE International Scientific conference on Informatics, INFORMATICS 2024

作者： Dudas, Adam Vagac, Michal Matej Bel University Faculty of Natural Sciences Department of Computer Science Banská Bystrica Slovakia

ISBN: (纸本)9798350387674

Diagnostic data analysis is a methodological process aimed at the identification of underlying causes of events, the effect of which is the presence of trends and patterns in a multidimensional dataset. Since in most cases this cause originates in the area described by the data itself, knowledge from the field of data analysis, computer science or statistics is rarely enough for the analyst to explain the patterns, and it is necessary to examine the field of data expertise itself. In this work, we focus on creating a model for diagnostic data analysis, which automatically describes naturally correlated pairs of attributes using a large language model and provides an output in the form of a Diagnostic Sheet Visualization. The proposed concept is then experimentally verified in case studies on two multidimensional datasets focused on environmental variable sensor measurements and cubic graph properties. © 2024 IEEE.

关键词： Artificial Intelligence applications Correlation Analysis Diagnostic Analysis Large Language Models

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DL-Based Secure Wearable Scheme for Remote Patient Data Monitoring in Healthcare 4.0 4

DL-Based Secure Wearable Scheme for Remote Patient Data Moni...

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4th IEEE International conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Ramoliya, Fenil Darji, Krisha Gupta, Rajesh Kakkar, Riya Tanwar, Sudeep Kumari, Aparna Inst. of Technology Nirma University Dept. of Computer Sci. and Engg. Ahmedabad382481 India

ISBN: (纸本)9798350306118

The paper articulates an exploration into the integration of wearable devices for remote patient data monitoring, which marks a transformative shift in healthcare practices. This transition is underscored by user-centric interfaces and applications, enabling convenient remote monitoring. However, the escalating concern of patient data vulnerability to network breaches presents a substantial impediment. In response, we introduce the proposed scheme that harnesses Machine Learning (ML) and Deep Learning (DL) techniques. The main objective of the proposed scheme is to meet the exacting requirements of healthcare systems, aiming to fortify data integrity and confidentiality. Within this context, the proposed scheme unveils a sophisticated DL-driven detection model, adeptly tailored for discerning and countering data manipulation endeavors through a nuanced multi-label classification paradigm. Notably, the emphasis lies on safeguarding patient data privacy and security, which is paramount in today's information-rich landscape. The narrative seamlessly interweaves elements of wearable technology, sensor networks, advanced DL capabilities, and robust security measures, resulting in an innovative scheme. Finally, the proposed scheme is evaluated against various metrics such as accuracy and loss curve and F1, recall, and precision comparison. © 2023 IEEE.

关键词： sensor networks

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Feasibility assessment of an optical sensor for long-term electrocardiogram monitoring 11

Feasibility assessment of an optical sensor for long-term el...

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conference on advanced sensor systems and applications xi

作者： Zhao, Jing Li, Mingwei Wang, Qi Zhang, Hongxia Liu, Tiegen Jia, Dagong Tianjin Univ Coll Precis Instrument & Optoelect Engn Key Lab Optoelect Informat Technol Minist Educ Tianjin 300072 Peoples R China

ISBN: (数字)9781510646520

ISBN: (纸本)9781510646520;9781510646513

Cardiovascular disease is a major risk to human health, which needs long-term monitoring for prevention and early diagnosis. Optical sensors present the advantage of immunity to electromagnetic field and high sensitivity, and have been growing in a variety of emerging medical applications to monitor human cardiac parameters. Most of the current optical sensors can only measure limited cardiovascular information such as the heart rate, therefore, the optics-based approach for cardiac electrophysiology has attracted the attention of more researchers. In this paper, we developed a method to evaluate the availability of our proposed anti-EMI optical sensor. The sensitivity of optical sensor based on electro-optic modulation can achieve 266.4 mu W/V and detect the electrocardiogram (ECG) by attached to the chest and edge of clavicle. A series of ECG signals over 1 hour were analyzed using proposed method, which is driven by the optimization of R-peak location, Lorenz plot and statistical correlation. ECG monitoring results of the optical sensors are in accordance with a standard clinical device (SOMNOtouch (TM) RESP) among different subjects. Moreover, both the sensors are tested in daily electromagnetic conditions, and it causes some obvious signal artifacts to the SOMNO system, but almost no effect on the optical sensors during the long-term test. We provide further grounds for such clinical applications by demonstrating, for the first time to our knowledge, optics-based device used in long-term ECG monitoring, an essential tool in modern cardiac monitoring applications.

关键词： Optical sensors Electro-optic modulation Long-term ECG Monitoring

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A Robust Framework to Secure IoT sensor Data using Elliptic Curve Cryptography based CP-ABE and Secret Sharing Scheme 2

A Robust Framework to Secure IoT Sensor Data using Elliptic ...

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2nd International conference on Information and Communication Technology, ICICT 2024

作者： Bushra, Anika Paul, Mahit Kumar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9798331508227

The growth of the Internet of Things (IoT) sector has ushered in an unprecedented era of connectedness, enabling a profusion of applications ranging from smart homes to industrial automation. Researchers predict that by 2030, there will be about 50 billion IoT devices and gadgets globally, completely changing how we interact with our environment. However, because these interconnected systems are vulnerable to many cyber attacks, the rise of IoT devices has also raised worries about data security and privacy. This paper investigates the potential of cryptographic approaches to mitigate IoT data security issues. It proposes an all-encompassing framework to protect IoT data from various threats and vulnerabilities. The framework leverages the robustness of the advanced Encryption Standard (AES) as the primary data encryption technology. Additionally, it integrates ciphertext-policy attribute-based encryption (CP-ABE) in conjunction with Elliptic Curve Cryptography to enable fine-grained access control, ensuring that only authorized entities can access specific IoT data. Furthermore, to fortify the encryption process, the framework implements a secret sharing method for the encryption key, distributing the key across multiple entities, reducing the risk of a single point of failure. The suggested framework seeks to improve the security posture of IoT systems by integrating these cryptographic algorithms and guaranteeing the confidentiality, integrity, and accessibility of data in IoT contexts. ©2024 IEEE.

关键词： Anonymity

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Multi-Vehicle System Localization by Distributed Moving Horizon Estimation over a sensor Network with Sporadic Measurements

Multi-Vehicle System Localization by Distributed Moving Hori...

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IEEE conference on Control Technology and applications (CCTA)

作者： Venturino, Antonello Bertrand, Sylvain Maniu, Cristina Stoica Alamo, Teodoro Camacho, Eduardo F. Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Univ Paris Saclay ONERA Traitement Informat & Syst F-91123 Palaiseau France Univ Seville Dept Ingn Sistemas & Automat Camino Descubrimientos Seville 41092 Spain

ISBN: (数字)9781665473385

ISBN: (纸本)9781665473385

This paper proposes a Distributed Moving Horizon Estimator (DMHE) for the Multi-Vehicle system localization problem using sensor Networks with sporadic measurements. Due to its capability to efficiently exploit environmental information via constraints, the proposed DHME technique is well-suited to better estimate the system state when measurements are available at time instants a priori unknown. Indeed, the use of output constraints can contribute to locally improve estimation accuracy, especially when dealing with sporadic measurements and biased sensors data. A realistic case study is proposed within the Robot Operating System framework and Gazebo to localize a Multi-Vehicle System using an inexpensive sensor Network with low-computation capabilities. A comparative campaign simulation is performed to confirm the effectiveness of the proposed DMHE algorithm in terms of accuracy, computation time, and constraints handling with respect to existing results.

关键词： Location awareness Constraint handling Operating systems Computational modeling Estimation Robot sensing systems Time measurement

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From Vision to Precision: The Dynamic Transformation of Object Detection in Autonomous systems 6

From Vision to Precision: The Dynamic Transformation of Obje...

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6th International conference on Computing and Informatics, ICCI 2024

作者： Alabyad, Nesma Hany, Ziad Mostafa, Abdelrahman Eldaby, Reem Tagen, Ibrahim Ayman Mehanna, Amal Future University In Egypt Cairo Egypt

ISBN: (纸本)9798350373875

This paper explores the evolution of self-driving cars, from early radio-controlled prototypes to today's advanced autonomous vehicles, highlighting key milestones such as Houdina Radio Control's 1920s efforts and Carnegie Mellon University's Navlab project in the 1980s. Recent advancements driven by industry leaders like Google, Tesla, and Uber incorporate advanced technologies, including sensors, cameras, radar, and lidar, empowering self-driving cars for autonomous navigation. The literature review in Frontiers in Future Transportation focuses on performance metrics for automated driving systems, discussing methodologies for quantifying obstacle threats and enhancing human-like perception and response mechanisms, with a significant emphasis on deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Object detection algorithms' dynamic evolution, influenced by CNNs, is explored, and the integration of hardware accelerators like GPUs, FPGAs, and specialized AI accelerators is highlighted for real-time responsiveness. Deep learning applications in self-driving cars, including obstacle detection, lane recognition, and traffic sign and light recognition, are outlined. Vision sensor-based obstacle detection using monocular and binocular approaches is discussed, emphasizing the importance of binocular vision technology. Lane recognition's significance for deviation warning, collision prevention, and path planning is highlighted, along with deep learning approaches such as John et al.'s lane detection algorithm. Traffic sign and light recognition are explored for safety and regulatory compliance, utilizing CNNs and Deep Neural Networks (DNNs). The discussion on sensors covers simultaneous localization and mapping (SLAM) using both passive (digital cameras) and active sensors (LIDAR, RADAR), emphasizing the integration of sensors and pre-existing maps to enhance the SLAM process and contribute to the overall perception of a

关键词： Autonomous vehicles

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Bridging Hardware and Software: A Holistic Approach to Home Automation via IOT

Bridging Hardware and Software: A Holistic Approach to Home ...

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2024 International conference on Computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Manoj, P. Karthik Reddy, K.V. Raghu, Kavya Rudresh, M.D. M S Ramaiah Institute of Technology Dept. of ECE Bangalore India M S Ramaiah Institute of Technology Dept. of TCE Bangalore India Kalpataru Institute of Technology Dept. of ECE Tiptur India

ISBN: (纸本)9798350388534

The Internet of Things (IoT) has emerged as a transformative paradigm, revolutionizing various aspects of modern life, including residential living. As the world becomes increasingly interconnected, the integration of IoT technologies into home automation systems holds immense relevance in optimizing comfort, efficiency, and security within households. This research paper explores the significance of IoT in the context of a home automation project leveraging a Node MCU-Flask-React architecture. In today's fast-paced world, the demand for smart solutions that streamline daily tasks and enhance quality of life is ever-growing. IoT-based home automation offers a compelling solution by enabling remote control and monitoring of household devices through interconnected systems. The project described herein exemplifies the practical application of IoT principles, demonstrating how hardware and software can seamlessly collaborate to create intelligent environments tailored to individual preferences. The relevance of IoT in home automation extends beyond mere convenience;it encompasses broader implications for energy conservation, safety, and resource optimization. By integrating sensors, actuators, and data analytics capabilities, IoT-enabled systems can intelligently adapt to user behavior, optimize energy consumption, and enhance home security protocols. Such advancements are crucial in addressing contemporary challenges related to energy sustainability and urban living. Moreover, the proposed project underscores the utilization of smart technologies, making advanced automation capabilities accessible to a broader audience. By leveraging open-source frameworks like Node MCU, Flask, and React, the barriers to entry for developing and deploying IoT solutions are significantly reduced, fostering innovation and collaboration in the field. This research paper highlights the profound relevance of IoT in reshaping residential living experiences. it elucidates how IoT technologie

关键词： sensor nodes

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Adaptive Gain Fuzzy-projection of Permanent Magnet Linear Synchronous Motor with Dead-zone Compensation 26

Adaptive Gain Fuzzy-projection of Permanent Magnet Linear Sy...

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26th International conference on Electrical Machines and systems, ICEMS 2023

作者： Zhang, Hengrui Sun, Peng Zhang, Tianru Xue, Zeyang Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei China Changzhou Institute of Advanced Manufacturing Technology Changzhou China

ISBN: (纸本)9798350317589

Adaptive robust control (ARC) is widely used in linear motor systems because of its robustness and ability to deal with the uncertain parameters. However, in industrial applications, undesirable operating trajectories and uncertain nonlinear external disturbances of permanent magnet linear synchronous motors (PMLSM) can adversely affect the parameter adaptive part of the ARC controller, which can lead to undesirable performance. Therefore, this paper introduces an adaptive gain fuzzy-projection with dead-zone compensation (DAFP) strategy based on ARC, which uses the dead-zone shutdown mechanism and adaptive regulation of the parameter gains to achieve fast estimation of uncertain parameters and reduce the impact of parameter estimation errors on the system. Furthermore, in this paper, by conducting two sets of comparative experiments under different expectation trajectories, the results consistently validate that the proposed DAFP method effectively enhances accuracy and speed of parameter identification while mitigating the impact of nonlinear disturbances on the system. © 2023 IEEE.

关键词： Uncertainty analysis

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