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

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Recent advances in SAR Image Analysis Using Deep Learning Approaches: Examples of Speckle Denoising and Change Detection 4

Recent Advances in SAR Image Analysis Using Deep Learning Ap...

引用

4th international conference on Innovative Research in Applied science, engineering and technology, IRASET 2024

作者： Imad, Hamdi Sara, Zada Hajji, Morad Yassine, Tounsi Abdelkrim, Nassim Superior School Of Technology Moulay Ismail University Image Laboratory Meknes Morocco Chouaib Doukkali University Instrumentation Measurement And Control Team Faculty Of Science El Jadida Morocco

ISBN: (纸本)9798350309508

Deep learning is a machine learning technique that has significantly improved results in many areas such as computer vision, speech recognition, machine translation, and biomedical imaging analysis and understanding. Recently in the field of synthetic aperture radar (SAR) images analysis, deep learning approaches have become a powerful tool making information extraction from SAR images very accurate and giving good interpretations related to environment and earth surface observation. This work examines the recent development of scientific productions on the applications of deep learning approaches in the SAR imaging field. These applications concern the speckle noise reduction from SAR images and the algorithms-based method for change detection and classification of these remote sensing images. Moreover, an analysis of scientific production in this field is discussed by exploiting the Scopus database. © 2024 IEEE.

关键词： Change detection

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Design of ARM-Based CMGs Control System 8

Design of ARM-Based CMGs Control System

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Wang, Shufeng Chen, Hongxuan Tang, Guoyuan Hu, Qi Sun, Jianglong School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology CO Wuhan430074 China China Special Vehicle Research Institute Key Laboratory of High-Speed Hydrodynamic Aviation Science and Technology CO Jingmen448035 China

ISBN: (纸本)9798350300178

Aiming at the attitude control of unmanned underwater vehicle (UUV), a control moment gyros (CMGs) control system is designed based on ARM stm32f407, which owns many advantages such as large output torque and does not depend on fluid motion. Firstly, this paper briefly describes the system attitude control mechanism. Then the hardware design scheme is proposed with detailed design and type selection. Considering the requirements of the control task, the lower computer control program and the upper computer control interface are designed. Finally, the system is tested and verified by experiment. The results show that the designed system can effectively control the speed of execution motors, so as to output the required torque and meet the attitude control demand of UUV. © 2023 IEEE.

关键词： Attitude control

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Retinal Blood Vessel Segmentation using The U-NetModel with the XAI Technique

Retinal Blood Vessel Segmentation using The U-NetModel with ...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Indikar, Muskan Baligar, Vishwanath P. Kle Technological University Department of Computer Science Karnataka Hubballi580031 India

ISBN: (纸本)9798350388602

Segmenting retinal blood vessels is important because the retinal microvasculature is a vital biomarker for severalclinical disorders. On the other hand, the conventional approach is thought to be a laborious and time-consuming process. The potential of deep learning and artificial intelligence in particular has created new opportunities for effectively segmenting retinal arteries. In our research, we provide a strategy to extract retinal blood vessels from fundus visuals using the U-Net model, whichis commonly used in semantic segmentation. A dataset for the study is obtained from the Kaggle resource. We performed pre-processing before feeding the images into the models based onthe need for deep-learning models. Moreover, we applied a U-Netmodel to segment the stereophotogrammetric converted images. Concerning the quality of the approach, the Intersection of Union (IoU), accuracy, and F1-Score measurements were employed. Furthermore, to enhance the grain of the segmentation results and to build confidence in the method we discuss the incorporation of eXplainable AI known as Grad-CAM. Therefore, this work shows that deep learning can be highly effective for retinal vessel segmentation and at the same time underlines the need for interpretability issues in medical image analysis. © 2024 IEEE.

关键词： Semantic Segmentation

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IoT-Driven Revolution: Transforming Elderly Healthcare Through Dynamic Heart Health Monitoring and Analysis

IoT-Driven Revolution: Transforming Elderly Healthcare Throu...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Mani, Kamlesh Singh, Kamlesh Kumar Litoriya, Ratnesh Amity University Department of Computer Science and Engineering Lucknow India Amity University Department of Electronics and Communication Engineering Lucknow India Medi-Caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

This research works to analyze the practical implementation of IoT technology in revamping the elderly health care system, with cardio care and heart health monitoring and analysis in mind. The study goes to great lengths not just to depict but also to seek solutions for the increasing problems of an aging population. As a result, new strategies for the provision of healthcare to the elderly are among the priorities. At its core, the research team will exploit Internet of Things (IoT) devices, sensors, and advanced data analytics to come up with a way of dynamic, continuous, and real-time monitoring of heart health. The paper starts by outlining whether changing demographics related to elderly people should be a top priority in healthcare services and, mainly, how much can be crucial in tackling heart health issues. After setting goals, the study will proceed by formulating, testing, and assessing an efficient Internet of Things-based healthcare approach for monitoring the heart health of elderly people. Regarding details of a method that involves picking up IoT gadgets and sensors, data collection procedures and strategies for participant recruitment will be provided. This article is focused on such technologies as architecture, heart disease monitoring, and algorithms for data processing. Findings unveiled some basic facts about the data collected from the elderly, for instance, their cardiac-related research. The implementation of the research findings in practice is mentioned, stressing patient-oriented projects such as personalized care plans and the interventions done at the right time by the Internet of Things-driven healthy lifestyle system. The last paragraph outlines the future directions for the technology-proficient healthcare assistants of tomorrow, including their refinement and mass use, ultimately ending up with the affirmation of the revolutionized elderly care practices of the future brought forth by IoT technologies. Firstly, the study exemplifies

关键词： Electronic health record

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Design and Implementation of Light Weight Cryptography Using Spartan-6 FPGA 2

Design and Implementation of Light Weight Cryptography Using...

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2nd IEEE international conference on Recent advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Poojari, Asmita Kiran Kumar, V.G. NMAM Institute of Technology Department of Computer Science and Engineerng Karnataka Nitte574110 India A J Institute of Engineering and Technology Department of Electronics and Communication Engineering Kottara Mangaluru575006 India

ISBN: (纸本)9798350354461

In the current scenario where our lives are totally obsessed with the internet, data security (confidentiality, authenticity, integrity, non-repudiation) plays a cardinal role. Therefore, there's constant demand of stronger encryption methods. Because of these reasons, information systems need to be secured. The foundation of these systems are cryptographic algorithms, which guard data and resources against unauthorized access. In recent years, the Internet of Things and the cyber-physical systems have expanded rapidly and significantly. This expansion has fuelled the development and broad adoption of low-cost devices across widespread IoT *** of the initial lack of security features in resource-constrained devices, many are being used to meet the broad application needs of the IoT and the cyber physical systems, where privacy and security have become key concerns and thus need for lightweight cryptography for these resource constrained devices. This study presents a lightweight cipher that combines the BRIGHT and SIMON structures into a single Feistel framework using ARX (Addition-Modulo, Rotation, and XOR) operations. The cipher passes the Avalanche test, the key sensitivity test, correlation coefficient, entropy, and histogram requirements, encrypting 32-bit plaintext with a 64-bit key. The software implementation is done with the Xilinx toole or Abstract. © 2024 IEEE.

关键词： Ciphertext

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MAAS-Net: Efficient Multi-Scale Attention Aggregation Supervision Network for Tire Tread Segmentation 4

MAAS-Net: Efficient Multi-Scale Attention Aggregation Superv...

引用

4th international conference on Industrial Automation, robotics and Control engineering, IARCE 2024

作者： Ji, Xu Tian, Yongqin Xu, Yibo Liu, Dehua Zhao, Wenyi Zhao, Liang Yang, Mu Yang, Huihua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information Engineering Henan Institute of Science and Technology Xinxiang China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Industrial Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350380323

Overlap detection plays a critical role in ensuring tire safety performance, as defects like overlapping anomalies due to mold aging and wear elevate the risk of tire blowouts. However, traditional edge detection algorithms entail a cumbersome manual parameter adjustment process and are limited to fixed hardware equipment scenarios, thereby compromising the flexibility and robustness of surface defect detection. Moreover, conventional image segmentation methods often fall short in achieving high-precision segmentation of the tread area. In this paper, we introduce MAAS-Net, a network employing a U-shaped encoder-decoder architecture. Integrated within are Convolutional Block Attention Modules (CBAM) after each decoding stage to generate multi-resolution attention feature maps. These maps are progressively fused into Multi-scale Aggregated Supervision (MAS) to enhance the model's capacity to discern features across various levels and scales. Additionally, to enhance the model's accuracy near the tread edge area, we propose a post-processing method named ContourFix. Leveraging data augmentation and multi-task learning, ContourFix aims to replace unstable prediction values near the edges with high-confidence prediction values inside the target area. The efficacy of these techniques is evaluated using a dataset of 3D tire tread. Experimental results demonstrate that MAAS-Net significantly outperforms conventional semantic segmentation models in segmenting tire treads, and ContourFix effectively enhances the accuracy of the segmentation model near the regional boundary. © 2024 IEEE.

关键词： Semantic Segmentation

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Programmable Pressure Pneumatic System for Soft Robots

Programmable Pressure Pneumatic System for Soft Robots

引用

2023 IEEE international conference on robotics and Biomimetics, ROBIO 2023

作者： Xiao, Xinyu Huang, Kanghe Zhu, Renjie Xue, Yahui Wang, Wenjin Wang, Hongqiang Dai, Jian S. Southern University of Science and Technology Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Shenzhen518055 China Southern University of Science and Technology Institute of Robotics Shenzhen518055 China

ISBN: (纸本)9798350325706

In the realm of soft robotics, the pneumatic system is a crucial component that plays an essential role in achieving optimal motion performance since all motion performance is ultimately linked to precise pressure control within the air chambers. To efficiently provide programmable pressure states for driving soft robots, this paper proposes a programmable pressure pneumatic system consisting of air pumps and valves with eight independent air channels. This system can provide precise hybrid negative and positive pressure for soft pneumatic robots. Closed-loop pressure control is achieved by sensing the pressure states in each air channel through sensors. Furthermore, two types of soft robots based on single-chamber soft pneumatic actuators are designed to verify the effectiveness and practicality of the pneumatic system. The proposed pneumatic system is a universal platform that can meet the pressure control requirements of various soft robots driven by both positive and negative pressure and holds significant implications for actuation and control in soft robotics. © 2023 IEEE.

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Architecture and Implementation of High-Fidelity Digital Twins for Industrial Robots

Architecture and Implementation of High-Fidelity Digital Twi...

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2023 international conference on Frontiers of robotics and Software engineering, FRSE 2023

作者： Liu, Yongkui Wang, Xinyu Yang, Kang Pan, Yaduo Wang, Qianji Xidian University School of Mechano-Electronic Engineering Xian China Lanzhou Jiaotong University School of Mechanical Engineering Lanzhou China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China

ISBN: (纸本)9798350301113

Digital twin is an important technology emerging in recent years. Industrial robots are an important type of equipment for the manufacturing industry. Construction of digital twins for industrial robots is of great significance. Fidelity of digital twins of robots is important as it determines their usability in manufacturing. In order to enable transparent management and intelligent applications of industrial robots, this paper proposes an architecture and a method of building high-fidelity digital twins for robots. First, a high-fidelity model of industrial robots is proposed. Then, logical architecture and functional architecture of the high-fidelity digital twins of industrial robots are proposed. The logical architecture comprises physical robot, virtual robot as well as communication and interaction between them. The functional architecture consists of five layers, including equipment layer, transmission layer, digital twin layer, application layer, and industry layer. Finally, based on the model and architecture, and taking the SD3/500 industrial robot as an example, a digital twin system is developed, which implements functions of virtual-real synchronization, real-time monitoring, kinematic simulation, data visualization, and real-time finite element analysis. The digital twin system verifies the feasibility of the proposed model and architecture. © 2023 IEEE.

关键词： Kinematics

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Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke 2

Utilizing Gradient Boosting Models to Identify Risk Factors ...

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Tamilselvi, P. Harini, C. Rao, Guduri Padma Narasimha Raju, P.V. Subbulakshmi, R. Channabasava, Ugranada Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Maisammaguda Secunderabad India SRKR Engineering College Andhra Pradesh Bhimavaram India Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore India Department of Information Science and Engineering Don Bosco Institute of Technology VTU Karnataka Belagavi India

ISBN: (纸本)9798350383867

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals. © 2024 IEEE.

关键词： Risk assessment

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Visual SLAM with a Multi-Modal Semantic Framework for the Visually Impaired Navigation-Aided Device 8

Visual SLAM with a Multi-Modal Semantic Framework for the Vi...

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Li, Shaohu Gu, Jason Feng, Ying Institute of Advanced Technology University of Science and Technology of China Hefei230031 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350300178

Visual Simultaneous Localization and Mapping (VSLAM) based on image feature points often contains feature points on non-stationary objects in dynamic scenes, resulting in poor localization performance. Recent research has improved VSLAM performance by removing dynamic feature points utilizing optical flow, deep learning, or multi-view geometry, however, the volume of data makes real-time operation challenging. In this study, we propose a multimodal framework for semantic VSLAM. To eliminate dynamic feature points, the system effectively blends object detection, instance segmentation, and geometry modules. To ensure high-precision pose estimation and quick back-end optimization, we exclusively use object detection in the front-end of VSLAM to mask dynamic feature points. In the keyframes of the local map, semantic segmentation is utilized to remove dynamic feature points, and an effective geometric module is employed to assess the dynamic consistency of objects that are challenging to categorize. Therefore, more feature points are retained, and the running speed is also ensured. Evaluations of the system on public datasets and in the real world show that it improves the accuracy and stability of VSLAM in dynamic environments. © 2023 IEEE.

关键词： Object detection

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