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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

共 2094 条记录，以下是141-150 订阅

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Combining Deep Learning Techniques for Enhanced Human Activity Recognition: A Hybrid CNN-LSTM fusion Approach 2

Combining Deep Learning Techniques for Enhanced Human Activi...

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2nd IEEE International conference on Contemporary Computing and Communications, InC4 2024

作者： Prabu, M. Rupesh Naidu, M. Asif, P. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383652

Over recent years conventional pattern recognition pattern recognition techniques have advanced significantly. Nonetheless, these methods heavily depend on manual feature extraction, potentially limiting the overall performance and generalizability of the model. Given the rising prominence and efficiency of deep learning approaches, there has been a growing interest in leveraging these methods for human action recognition in mobile and wearable computing contexts. Despite this enthusiasm, existing deep learning architectures may not fully capitalize on the complex spatio-temprial patterns inherent in human actions. Moreover, deploying deep learning models in real-world scenarios often demands robustness, efficiency, and interpretability, which remain significant challenges. To address these issues, this paper proposes a novel approach: a deep neural network that integrates convolutional layers with long short-term memory(LSTM) units. By fusing the strengths of CNNs in spatial feature extraction with the temporal modelling capabilities of LSTMs, our hybrid architecture aims to enhance the accuracy and robustness of human activity recognition systems. This fusion enables the model to automatically learn hierarchical representations from data, eliminating the need for manual feature engineering. The approach holds, eliminating the need for manual feature engineering. The approach holds promise for applications in healthcare, sports analysis and assistive technology, where accurate and reliable HAR is essential. Overall, The proposed model achieves accuracy of 80% and 92% on the UCF50 dataset, a comprehensive video dataset, utilizing the CNN and LSTM models individually, respectively. These findings indicate that our hybrid model exhibits superior robustness and enhanced activity detection capabilities compared to certain previously reported results of only sensor data, surpassing an 90% accuracy threshold when trained on video-based data. Notably, the model demonstrate

关键词： Convolutional neural networks

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sensor fusion Techniques for Accurate Indoor Tracking in IoT-Based Smart Environments 3

Sensor Fusion Techniques for Accurate Indoor Tracking in IoT...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Krishnakumar, S. Acharjya, Kalyan Khurana, Shivam Swain, Rakesh Ranjan Rampal, Sourav Manjunath, C. Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

sensor fusion integrates data from multiple sensors to provide a more accurate, reliable, and contextual sense of the surrounding environment. So, it became a key method for correct monitoring in battery-dependent Internet of Things clever environments, especially Indoor tracking. There is only one dedicated sensor. To benefit the most from it and surround these weaknesses in complex indoor environments with wrongness. The sensor fusion methods for accurate indoor tracking in IoT-based Smart Environments are proposed to solve the problems above by integrating data from multiple sources, including Wi-Fi APs and BLE beacons, RFID readers, and IMUs. This results in a more complete and detailed view of the environment crucial for location-based applications such as asset tracking, localization, or navigation within smart buildings. There are many algorithms to fuse sensor data. The algorithms process the sensor data and give a fused result that outperforms all of these individual components. sensor fusion in IoT-based smart environments improves indoor tracking accuracy, robustness, and scalability. It also permits real-time tracking localization of objects and people;it is the key to optimally managing smart environments. The sensor fusion method generally improves indoor tracking properties in the rapidly growing IoT-based smart environments. © 2025 IEEE.

关键词： Accurate applications Environment Integrates Properties

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Application of Inventive Design Methodology for a sensor fusion Approach in Structure-from-Motion (SfM) applications 1

Application of Inventive Design Methodology for a Sensor Fus...

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1st International conference on Information System and Information Technology, ICISIT 2022

作者： Schmidt, Antonius Aufderheide, Dominik Faculty of Electrical Engineering Soest Germany

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Generating three-dimensional models from real world objects based on images from a single camera is an important application in computer vision and numerous possible applications. For this Structure-from-Motion (SfM) algorithms provide an approach based on the successful tracking of 2D feature points throughout an image sequence and the subsequent camera egomotion estimation and a global 3D structure recovery. However, the performance of those approaches is still limited according their precision, denseness and completeness of the reconstructed scene model. This paper introduces an approach based on Inventive Design Methodology (IDM) for the systematic analysis of existing SfM approaches and a subsequent derivation of a novel sensor fusion approach in order to overcome current application-related restrictions. © 2022 IEEE.

关键词： Three dimensional computer graphics

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An Adaptive Robust Student's t-Based Kalman Filter Based on Multi-sensor fusion 15th

An Adaptive Robust Student's t-Based Kalman Filter Based on ...

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15th International conference on Intelligent Robotics and applications (ICIRA ) - Smart Robotics for Society

作者： Wang, Dapeng Zhang, Hai Huang, Hongliang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9783031138225;9783031138218

In practical applications, Kalman filter and its variants such as UKF may suffer from the time-varying measurement noise and process-error. Especially, when the process-error is heavy-tailed probability distribution, the Gaussian assumption would be no longer as accurately as expected Aiming at the time-varying measurement noise and the situation with heavy-tailed process-error problems, in this paper, a new algorithm is proposed based on Student's t-distribution and multi-sensor information fusion. The robustness of the proposed algorithm is guaranteed by the timely estimation of the measurement noise, and the adaptiveness is realized by replacing the Gaussian by the Student's t-distribution. The Kullback-Leible Divergence (KLD) is used as the criterion for distinguishing the Gaussian distribution from the Student's t-distribution. Finally, a challenging target tracking example is presented and the simulation results show that the proposed algorithm achieves a higher accuracy than the other algorithms.

关键词： Adaptive Kalman filter Nonlinear filtering Robust estimation Target tracking Student's t-distribution

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Robust Adaptive Cubature Kalman Filter for Attitude Determination in Wearable Inertial sensor Networks 17th

Robust Adaptive Cubature Kalman Filter for Attitude Determin...

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17th International conference on Wireless algorithms, Systems, and applications (WASA)

作者： Zhao, Hongkai Wang, Huihui Wang, Zhelong Liu, Long Qiu, Sen Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian 116024 Peoples R China Dalian Neusoft Univ Informat Dalian 116024 Peoples R China

ISBN: (纸本)9783031192135;9783031192142

Attitude analysis and recognition can be applied in wearable computing for medical assistance, motor-function assessment and dexterous human-robot interaction. The main problems, however, are serious drift and instability during traditional motion measurement fusion methods due to the high dynamic complexity of limb movements. To the best of our knowledge, it is the first attempt to employ an adaptive robust cubature Kalman filtering algorithm in the human attitude analysis based on wearable inertial sensors with time-varying state-process noise. Experiment results show that the adaptive robust CKF algorithm based on quaternion and gyroscope error modeling proposed in this paper can solve motion attitude solution. Lastly, we compare our method with CKF and EKF algorithm, the proposed algorithm can effectively improve the precision of attitude analysis.

关键词： Cubature Kalman filter Body sensor network sensor fusion Adaptive factor Robust filter

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Edge Computing in Wireless Sensing applications 11

Edge Computing in Wireless Sensing Applications

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + Systems and Internet of Things (CPS and IoT)

作者： Santejudean, Tudor Voicu Mois, George Dan Sanislav, Teodora Folea, Silviu Corneliu Tech Univ Cluj Napoca Dept Automat Cluj Napoca Romania

ISBN: (纸本)9781665468282

Edge computing has emerged as the solution to the continuously increasing numbers of Internet-connected devices and to the ever increasing amounts of data transmitted by them. Through the use of advanced algorithms and hardware components it allows sensor data processing at the source and reduces the volumes of data transmitted within the network. This paper focuses on the idea of edge computing, looks at some of the most important achievements in the field, and presents a use case system that shows how edge intelligence is becoming more common. The developed use case system is an environmental wireless sensor that is able to run for long periods of time on a single coin-cell battery while measuring parameters like equivalent CO2 (carbon dioxide) and VOC (Volatile Organic Compounds). This was made possible through the device's design, components, and advanced firmware, that uses high-level signal processing and fusion algorithms.

关键词： Edge computing Internet-of-Things wireless sensor

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Research on the Application of Multimodal Sensing Technology in Surveying and Mapping Data Collection

Research on the Application of Multimodal Sensing Technology...

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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Liu Bo Weng Jianying School of Architectural Engineering Shandong University of Engineering and Vocational Technology Jinan Shandong China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes an innovative CNN and RNN fusion algorithm to optimize the application of multimodal sensing technology in surveying and mapping data collection. The algorithm combines the data of multiple sensors such as lidar, RGB camera and infrared sensor, uses CNN to extract the spatial features of the image, and uses RNN to analyze the temporal relationship between different modal data, so as to achieve efficient fusion of multimodal data. Experimental results show that the proposed algorithm improves the data accuracy by about 18% in multiple surveying and mapping scenarios compared with the traditional single-modal sensor method, and the processing efficiency is improved by 15%. In addition, the algorithm shows significant robustness in complex environments, can effectively cope with the challenges brought by environmental changes, and improves the accuracy and stability of data. The research in this paper provides technical support for the development of intelligent surveying and mapping systems and promotes the popularization of multimodal data fusion algorithms in practical applications.

关键词： Deep learning Solid modeling Accuracy Multimodal sensors Data integration Data collection sensor fusion Feature extraction Stability analysis Thermal stability

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Short Course: Architecture and Design Approaches to ML Hardware Acceleration: Edge and Mobile Environments

Short Course: Architecture and Design Approaches to ML Hardw...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Marian Verhelst KU Leuven Leuven Belgium

ISBN: (数字)9798350306200

ISBN: (纸本)9798350306217

Various applications demand more and more powerful machine inference in resource-scarce distributed devices, such as phones, watches, glasses, robots or drones. To allow intelligent applications at ultra-low energy and low latency, one needs customized processor architectures optimized for extreme edge applications. This need has resulted in the creation of a wide variety of novel hardware architectures, supported by HW-algorithm co-optimization methods. This talk will zoom into ML processor architectures for the edge, as well as tools for efficient mapping of ML algorithms onto such architectures.

关键词： Tutorials Radio frequency Wireless communication Transceivers Solid state circuits Quantum computing Optical transmitters Computer architecture Wireless sensor networks Training

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Graph Neural Network-Enhanced Multivariate Time Series Forecasting with Series-Core fusion

Graph Neural Network-Enhanced Multivariate Time Series Forec...

引用

Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Yuntian Hou Di Zhang Department of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

This study introduces Starformer, a hybrid model combining Graph Neural Networks (GNNs) with a novel Series-Core fusion (SC-fusion) mechanism for urban traffic prediction. By leveraging GNNs for spatial modeling and SC-fusion for efficient temporal dependency capture, the model effectively addresses complex spatio-temporal dynamics in traffic systems. Evaluated on six widely used traffic datasets-METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, and PEMS08-Starformer demonstrates consistent and robust performance across diverse traffic conditions and regions. The results highlight its ability to model both short-term and long-term dependencies, making it well-suited for real-world applications. These findings emphasize the potential of integrating advanced neural network architectures for intelligent traffic management, contributing to smarter, more sustainable urban transportation systems.

关键词： Control engineering Accuracy Scalability Time series analysis Transportation Predictive models Prediction algorithms Graph neural networks Forecasting

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Weighted fusion of Multiple Classifiers for Human Activity Recognition 26

Weighted Fusion of Multiple Classifiers for Human Activity R...

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26th International conference on Information fusion, fusion 2023

作者： Zuo, Kezhu Li, Xinde Dezert, Jean Dong, Yilin Southeast University School of Cyber Science and Engineering Nanjing China Southeast University Key Laboratory of Measurement and Control of Cse School of Automation Nanjing China The French Aerospace Lab Onera DTIS/MIDL Palaiseau France Shanghai Maritime University College of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798890344854

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster's rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods. © 2023 International Society of Information fusion.

关键词： Frequency domain analysis

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