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

共 2092 条记录，以下是61-70 订阅

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DBIF: Dual-Branch Feature Extraction Network for Infrared and Visible Image fusion 7th

DBIF: Dual-Branch Feature Extraction Network for Infrared an...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Zhang, Haozhe Cui, Rongpu Zheng, Zhuohang Gao, Shaobing Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9789819786848;9789819786855

In image fusion, combining infrared and visible images from different sensors is crucial to create a complete representation that merges complementary information. However, current deep learning approaches, mainly using Convolutional Neural Networks (CNNs) or Transformer architectures, do not fully capitalize on the distinct features of infrared and visible images. To overcome this limitation, we introduce a novel Dual-Branch feature extraction network for infrared and visible image fusion (DBIF). DBIF optimally leverages the advantages of CNN and Transformer for feature extraction from different types of images. Specifically, the Transformer's proficiency in extracting global features renders it more suitable for extracting target information from infrared images, while the CNN's superior sensitivity to capturing local information makes it more adept at extracting background texture information from visible images. Consequently, our DBIF architecture incorporates two distinct branches, content and detail, for feature extraction from infrared and visible images, respectively. Additionally, we introduce a Detailed Feature Enhancement Module (DFEM) to consolidate and amplify the prominent features extracted by the detailed branch. Through extensive experimentation across multiple datasets, we validate the effectiveness of our proposed approach, showcasing its superiority over existing fusion algorithms. Furthermore, our method shows substantial performance improvements, especially in object detection tasks. This underscores its practical relevance in various real-world applications that require accurate and efficient fusion of diverse image data types.

关键词： Image fusion CNN Transformer Object Detection

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Enhancing Stress Detection Accuracy through Multimodal fusion by Exploiting Biological sensors and Audio Signals 1

Enhancing Stress Detection Accuracy through Multimodal Fusio...

引用

1st IEEE International conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Nithyasri, P. Thanka, M. Roshni Bijolin Edwin, E. Ebenezer, V. Kirubakaran, Stewart Mathu, T. Karunya Institute of Technology and Sciences Department of Dscs Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Cse Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372748

Accurate stress detection is vital for various applications, including health monitoring and well-being assessment. This research study addresses the challenge of stress detection through a multimodal fusion approach, leveraging data from biological sensors and audio signals. The biological data recorded by the sensor is preprocessed, and techniques like feature scaling and categorical variable encoding are employed. For the audio data, features such as Zero Crossing Rate (ZCR) and Fast Fourier Transform (FFT) are extracted to capture emotional cues indicative of stress. Several machine learning algorithms are trained and compared on the respective datasets, including Artificial Neural Networks (ANN), Random Forest, Gradient Boosting Machine (GBM), and XG-Boost. The analysis reveals the superior performance and accuracy of GBM (97%) and XG-Boost (94%) in stress detection compared to ANN and Random Forest, highlighting the efficacy of ensemble learning and boosting techniques. The fusion of sensor and audio data enables a comprehensive understanding of stress states, enhancing detection accuracy and robustness. This research work contributes to advancing stress detection systems with potential applications in personal health monitoring, mental health support, and stress management interventions. © 2024 IEEE.

关键词： Bioinformatics

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Unilateral Lower Extremity Exoskeleton Utilizing sensor fusion algorithms 6

Unilateral Lower Extremity Exoskeleton Utilizing Sensor Fusi...

引用

6th Annual IEEE Research and applications of Photonics in Defense conference, RAPID 2023

作者： Dizor, R. Raj, A. Stewart, T. Gonzalez, B. Smith, G. Carter, Z. Domingues, B. Newton, J. University of West Florida Department Intelligent Systems and Robotics PensacolaFL United States Florida Institute of Human and Machine Cognition PensacolaFL United States Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9781665423526

Electromyography (EMG) and Inertial Measurement (IMU) sensors are increasingly associated with exoskeletons and prosthetics. Coordination and synchronization of these sensors with a sensor fusion algorithm modifying an array of multiple armband sensors allows sensing the user's intended movement and controlling a unilateral lower extremity exoskeleton. © 2023 IEEE.

关键词： Machine learning

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FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous sensors 34

FieldHAR: A Fully Integrated End-to-end RTL Framework for Hu...

引用

34th IEEE International conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Liu, Mengxi Zhou, Bo Zhao, Zimin Hong, Hyeonseok Kim, Hyun Suh, Sungho Rey, Vitor Fortes Lukowicz, Paul German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci Kaiserslautern Germany Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul South Korea

ISBN: (纸本)9798350346855

In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration. FieldHAR aims to address the lack of apparatus to transform complex HAR methodologies often limited to offline evaluation to efficient run-time edge applications. The framework uses parallel sensor interfaces and integer-based multi-branch convolutional neural networks (CNNs) to support flexible modality extensions with synchronous sampling at the maximum rate of each sensor. To validate the framework, we used a sensor-rich kitchen scenario HAR application which was demonstrated in a previous offline study. Through resource-aware optimizations, with FieldHAR the entire RTL solution was created from data acquisition to ANN inference taking as low as 25% logic elements and 2% memory bits of a low-end Cyclone IV FPGA and less than 1% accuracy loss from the original FP32 precision offline study. The RTL implementation also shows advantages over MCU-based solutions, including superior data acquisition performance and virtually eliminating ANN inference bottleneck.

关键词： FPGA sensor fusion Human Activity Recognition Neural Networks

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Human Activity Recognition Using sensor fusion and Deep Learning for Ergonomics in Logistics applications

Human Activity Recognition Using Sensor Fusion and Deep Lear...

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2023 IEEE International conference on Internet of Things and Intelligence Systems, IoTaIS 2023

作者： Arab, Ahmad Schmidt, Antonius Aufderheide, Dominik South Westphalia University of Applied Sciences Faculty of Electrical Engineering Soest Germany

ISBN: (纸本)9798350319040

The rapid growth within the e-commerce industry and the necessity of hiring more inexperienced labor in logistics made the warehouse safety more critical than ever. Monitoring the workers' activity during warehouse operations is a fundamental task to create an ergonomic workplace and to reduce operational costs. Therefore, an advanced Human Activity Recognition (HAR) solution is needed to monitor such industrial environments. In this paper, the development of an accurate and reliable HAR classifier using sensor fusion methods is investigated. Three Inertial Measurement Units (IMUs) are used to capture body movement while performing typical warehouse tasks. Deep Learning (DL) algorithms are employed to model the raw time series data and two sensor fusion techniques are applied to combine the data from the different sensors. Overall, the optimal HAR classifier was found to be a stacked model, being more practical and less computationally expensive for industrial applications. © 2023 IEEE.

关键词： Ergonomics

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A Literature Survey on Image fusion methods for Remote Sensing applications 4

A Literature Survey on Image Fusion methods for Remote Sensi...

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4th International conference on Communication, Computing and Industry 6.0, C216 2023

作者： Harish, L. Viswanath, K. Dept. of TCE SIT Karnataka Tumakuru India

ISBN: (纸本)9798350327328

The abundance of available image capturing technologies is driving up demand for fusing images in the domain of image processing. Image fusion creates a single composite image by merging pertinent input from several sensors using mathematical algorithms. This method preserves important aspects of the image while enhancing its quality by integrating complementary multi-sensor data into a one image. In applications including robot vision, satellite and aerial imaging, imaging for medical purposes, and robotic or vehicle guiding, it is a crucial pre-processing step. This research discusses the benefits and drawbacks of many state-of-the-art image fusion methodologies at different levels. Along with quality metrics, it also examines spatial and transform-based methodologies and their applicability across several fields. The paper concludes by outlining possible future developments for image fusion in diverse applications. © 2023 IEEE.

关键词： Image fusion

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sensor fusion for Non-Intrusive Adaptive Distancing of Visually Impaired Users 20

Sensor Fusion for Non-Intrusive Adaptive Distancing of Visua...

引用

20th International conference on Body sensor Networks

作者： Liu, Zheng Xu, Xueyicheng Mamishev, Maxwell Mamishev, Alexander V. Univ Washington Seattle WA 98195 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9798331530143

There are over 300 million severely visually impaired people worldwide, and in their constant struggle for independence, there is a pressing need for advanced Electronic Travel Aid (ETA) products. The FireFly wearable ETA device presented here enhances mobility and safety for visually impaired individuals. The design fuses an 8x8 pixel Grid-EYE thermal imaging sensor to differentiate different objects based on their thermal signatures and a Time-of-Flight (ToF) LiDAR sensor for detecting distances. FireFly provides real-time feedback through acoustic, vibration, and light alerts, which vary in frequencies and amplitudes depending on the proximity and type of detected object. Its open architecture allows for easy integration into various wearable devices, ensuring application versatility. Experimental validation quantified the system's ability for object detection and human identification. Future improvements will focus on increasing system speed and thermal sensitivity and implementing machine learning algorithms for better object classification. FireFly aims to offer a reliable, low-power solution that promotes independence and safety for visually impaired individuals.

关键词： infrared sensor LiDAR sensor Wearable sensor Electronic Travel Aids I2C Arduino Obstacle Avoidance Object recognition Visually Impaired assistive Smart devices

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LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading 56

LIPSFUS: A neuromorphic dataset for audio-visual sensory fus...

引用

56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Rios-Navarro, A. Pinero-Fuentes, E. Canas-Moreno, S. Javed, A. Harkin, J. Linares-Barranco, A. Univ Seville SCORE I3US Robot & Technol Comp Lab Seville Spain Sch Comp Engn & Intel Syst Magee Campus Derry Londonderry North Ireland

ISBN: (纸本)9781665451093

This paper presents a sensory fusion neuromorphic dataset collected with precise temporal synchronization using a set of Address-Event-Representation sensors and tools. The target application is the lip reading of several keywords for different machine learning applications, such as digits, robotic commands, and auxiliary rich phonetic short words. The dataset is enlarged with a spiking version of an audio-visual lip reading dataset collected with frame-based cameras. LIPSFUS is publicly available and it has been validated with a deep learning architecture for audio and visual classification. It is intended for sensory fusion architectures based on both artificial and spiking neural network algorithms.

关键词： Neuromorphic dataset sensory fusion dynamic vision sensor neuromorphic auditory sensor

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Research on the Standard System and Key Technologies of Digital Transmission Technology

Research on the Standard System and Key Technologies of Digi...

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2024 International conference on Innovative Design and Intelligent Manufacturing, IDIM 2024

作者： Liu, Zhuoya Zhang, Xun Fan, Qiang Huang, Junkai Chen, Xiangli Electrical Science Institute of Guizhou Power Grid Co. Ltd. CSG Guiyang550000 China Electric Power Research Institute Beijing100000 China

ISBN: (纸本)9781643685427

This paper proposes a digital substation standard system to enhance management efficiency. It introduces a model for comprehensive digital information perception and Bayesian algorithms for monitoring. Wireless sensor identification collects subsystem data, analyzed by decision tree clustering. Key meta-data scheduling and fusion methods integrate digital information across the substation. Digital twin technology enables dynamic data fusion and sharing, optimizing substation management. © 2024 The Authors.

关键词： Data fusion

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Advancements in Object Detection for Unmanned Aerial Vehicles: applications, Challenges, and Future Perspectives 12

Advancements in Object Detection for Unmanned Aerial Vehicle...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Sezgin, Anil Boyaci, Aytug Siemens Corporate Technology Istanbul Turkey National Defence University Air Force Academy Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350330366

Unmanned Aerial Vehicles (UAVs) are used in different fields ranging from recreational vehicles to agriculture, environmental monitoring, infrastructure inspection, disaster management, security, surveillance and logistics. This paper provides an overview of UAV applications and highlights the importance of object detection in improving drone autonomy. Object detection facilitates tasks such as precision agriculture, disaster response, environmental protection, infrastructure inspection and autonomous navigation. The paper reviews recent advances in UAV-specific object detection algorithms and methodologies and highlights challenges such as varying altitudes, motion blur, real-time processing constraints and limited computational resources. Solutions and adaptations are discussed to overcome these challenges, including lightweight neural network architectures, transfer learning, data augmentation, edge computing, multi-sensor fusion, attention mechanisms and adaptive algorithms. The study highlights the importance of integrating object detection systems into UAVs to improve their use in various sectors and contribute to improvements in surveillance missions. © 2024 IEEE.

关键词： Object detection

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