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19th ieee Sensors applications symposium (SAS)

作者： Falaschetti, Laura Campanella, Sara Curina, Sofia Martini, Giorgia Pierleoni, Paola Belli, Alberto Turchetti, Claudio Palma, Lorenzo Polytech Univ Marche Dept Informat Engn Ancona Italy Polytech Univ Marche Ancona Italy Univ Udine Dept Med Udine Italy

ISBN: (纸本)9798350369267;9798350369250

The skin can exhibit symptoms of internal pathologic conditions. Cutaneous autoimmunity is one such instance, serving as a sign of a systemic lack of immune tolerance. It is quite challenging to appropriately identify skin conditions because of their seemingly identical clinical presentations. Thus, this work suggests an embedded vision system based on a CNN model that is put into practice on a low-cost, low-power platform and intended for the real-time monitoring and identification of autoimmune skin conditions. A custom dataset for scleroderma and healthy skin images was created. To enhance diversity, images of vitiligo, psoriasis, and lupus from DermNet website were added due to the small size of the original dataset. To increase accuracy, transfer learning and fine-tuning methods were applied and six state-of-the-art networks were tested: EfficientNet, Xception, MobileNetV2, VGG19, InceptionV3, and ResNet50. All architectures obtained promising results, also thanks to transfer learning, with MobileNetV2 leading the way (with a testing accuracy of 95.18% and 75.90% for binary and multi-class classification respectively, and a memory occupancy of 2.5 MB), making it suitable for implementation on a low-cost embedded device, the OpenMV Cam H7 Plus, that may be used to realize an early warning system prototype for autoimmune skin diseases detection.

关键词： Convolutional neural network (CNN) disease classification embedded systems multi-class classification skin diseases transfer learning

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embedded Piezoresistive Sensor Network for Multi-Failure Mode Monitoring in CFRP Bolted Joints

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ieee SENSORS JOURNAL 2025年第2期25卷 3490-3502页

作者： Liu, Qijian Sun, Maozhi Qin, Haoxuan Wang, Kai Sun, Hu Wang, Yishou Qing, Xinlin Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

Carbon fiber-reinforced polymer (CFRP) bolted joint structures are susceptible to multiple failure modes during service, including net-tension, shear-out, and bearing failures, which present risks to structural integrity and operational performance. While piezoresistive sensing technology has the potential for monitoring CFRP structures, existing approaches still need to be improved in their ability to detect these diverse failure modes simultaneously. This study introduces an embedded piezoresistive sensor network based on carbon nanotube/graphene nanoplatelet (CNT/GNP) nanocomposites for the in situ monitoring of multiple failure modes within CFRP bolted joints. The proposed sensor network integrates CNT and GNP fillers into a polyvinylpyrrolidone (PVP) matrix, utilizing sodium dodecylbenzene sulfonate (SDBS) as a dispersant. The experimental results for the embedded integration method indicated that the proposed nanocomposite piezoresistive sensor network effectively detected the initiation and propagation of bearing, net-tension, and shear-out failures within CFRP bolted joints. In addition, the embedded integration demonstrated reliability and a significant increase in monitoring sensitivity compared to surface integration methods. This research advances the methodology for real-time structural health monitoring (SHM) of composite materials by proposing a methodology with potential applications improving the safety and maintenance strategies of high-performance engineering systems.

关键词： Sensors Monitoring Piezoresistance Nanocomposites Stress Sensor phenomena and characterization Sensitivity Resistance Electromechanical sensors Mechanical sensors Composite bolted joints embedded piezoresistive sensor multiple failure modes structural health monitoring (SHM)

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Advancing Towards a Lower-Power Wearable Ultrasound Sensor for real-time Bladder Volume Monitoring using CNN Optimization

Advancing Towards a Lower-Power Wearable Ultrasound Sensor f...

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2024 ieee Ultrasonics, Ferroelectrics, and Frequency Control Joint symposium

作者： Imenabadi, Rouzbeh Molaei Avanji, Mojtaba Hasanpour Brown, Katherine Bhatia, Dinesh Univ Texas Dallas Dept Bioengn Richardson TX 75080 USA Sahand Univ Technol Elect Engn Dept Sahand Iran Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9798350371918;9798350371901

Recent advancements in sensor technologies have enabled the development of wearable ultrasound devices for continuous bladder volume monitoring. However, challenges related to power consumption, form factor, cost, and technical limitations associated with depth of signal penetration remain to be fully addressed. To tackle these challenges, this paper introduces a novel 11-channel wearable ultrasound sensor that leverages an efficient innovative convolutional neural network (CNN) framework and FPGA-based deep learning processing unit (DPU) accelerator to triple pitch the proposed sensor from 11-channel to 32-channel in real-time. This framework infers 32-channel full-array plane-wave channel datasets from a periodically sparse 11-channel array operating with as few as one-third of the elements in the full aperture. This device leverages a lightweight Conv-LSTM (LConv-LSTM) CNN model optimized for deployment on a single core of a DPUCZDX8G with B3136 architecture and has a balance between a high degree of parallelism and power efficiency. This device paves the way for transformative advancements in real-time bladder monitoring and management.

关键词： ultrasonic wearable ultrasound bladder monitoring low-power design embedded system

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Soft Gripper With Movable Variable Stiffness Mechanism and embedded Soft Sensors for Adaptive Grasping Strategies

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ieee ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2710-2717页

作者： Ki, Beom Geun Park, Yong-Jai Kangwon Natl Univ Dept Smart Hlth Sci & Technol Chunchon Gangwon South Korea Kangwon Natl Univ Coll Engn Dept Mechatron Engn Chunchon Gangwon South Korea

Soft grippers have gained significant attention in robotics for their ability to adapt to diverse object geometries, comply with variable surfaces, and handle fragile items safely. Incorporating variable stiffness mechanisms has enhanced their performance by enabling adjustments in gripping force and structural rigidity. However, many existing designs primarily focus on stiffness modulation from the perspective of gripping force, limiting their ability to achieve localized control or handle objects with varying sizes, shapes, and weights effectively. This letter introduces a novel soft gripper integrating a movable variable stiffness mechanism and embedded soft sensors to overcome these challenges. The gripper locally modulates stiffness, allowing control over gripping force, contact area, and load distribution. embedded soft sensors provide real-time feedback on gripping status, enabling stiffness adjustment and secure object handling. Fabricated as a monolithic structure through multi-material 3D printing, the gripper integrates functional components into a single manufacturing process with minimal post-processing. Experimental results validate the gripper's adaptability and versatility in handling delicate, irregular, and heavy objects, highlighting its potential for advanced robotic applications.

关键词： Grippers Force Bending Three-dimensional printing Soft sensors Shape Robots Pressure sensors Tendons Position measurement Soft Gripper variable stiffness adaptive grasping grasping strategies embedded sensors multi-material 3D printing

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Tracking a Hand in real time with Accelerometers for VR applications

Tracking a Hand in Real Time with Accelerometers for VR Appl...

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2024 ieee Opportunity Research Scholars symposium, ORSS 2024

作者： Mcelroy, Baylor Diviney, Alex Chiu, Steve C. Ashour, Ahmed F. Idaho State University Department of Electrical and Computer Engineering PocatelloID83209 United States

ISBN: (纸本)9798350390698

Virtual reality (VR) technology presents a paradigm shift in human-computer interaction, offering unprecedented mobility, immersion, and customization. However, current input devices for VR applications lack the portability, speed, and computational feasibility required for mainstream adoption. This paper proposes a novel solution using wearable sensors and an embedded processor to address these challenges. Specifically, we explore the feasibility of tracking hand movements in real-time using five accelerometers mounted on the fingers, coupled with a cost-effective processor. Our methodology involves signal smoothing, hand and finger vector separation, and tracking one-dimensional tapping gestures. Experimental results demonstrate the effectiveness of a moving average filter in reducing signal noise, and the challenges in accurately isolating finger motion vectors. Despite remaining challenges, such as gravity-induced acceleration deflection, our findings underscore the potential of our approach in enabling intuitive and responsive hand tracking for VR interactions. This research lays the groundwork for developing improved input devices tailored to the unique requirements of VR environments, contributing to the advancement and widespread adoption of VR technology. © 2024 ieee.

关键词： Accelerometers

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real-time Acoustic Monitoring of Foraging Behavior of Grazing Cattle Using Low-Power embedded Devices 18

Real-Time Acoustic Monitoring of Foraging Behavior of Grazin...

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18th ieee Sensors applications symposium (SAS)

作者： Martinez-Rau, Luciano Sebastian Adin, Veysi Luis Giovanini, Leonardo Oelmann, Bengt Bader, Sebastian Mid Sweden Univ Dept Comp & Elect Engn Sundsvall Sweden FICH UNL CONICET Inst Invest Senales Sistemas & Inteligencia Compu Santa Fe Argentina

ISBN: (纸本)9798350323078

Precision livestock farming allows farmers to optimize herd management while significantly reducing labor needs. Individualized monitoring of cattle feeding behavior offers valuable data to assess animal performance and provides valuable insights into animal welfare. Current acoustic foraging activity recognizers achieve high recognition rates operating on computers. However, their implementations on portable embedded systems (for use on farms) need further investigation. This work presents two embedded deployments of a state-of-the-art foraging activity recognizer on a low-power ARM Cortex-M0+ microcontroller. The parameters of the algorithm were optimized to reduce power consumption. The embedded algorithm processes masticatory sounds in real-time and uses machine-learning techniques to identify grazing, rumination and other activities. The overall classification performance of the two embedded deployments achieves an 84% and 89% balanced accuracy with a mean power consumption of 1.8 mW and 12.7 mW, respectively. These results will allow this deployment to be integrated into a self-powered acoustic sensor with wireless communication to operate autonomously on cattle.

关键词： embedded system real-time acoustic processing precision livestock farming foraging behavior low-power micro-controller

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Improved Lightweight YOLOv8 With DSConv and Reparameterization for Continuous Casting Slab Detection on embedded Device

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Ju, Hao Fang, Yiming Yang, Hongliang Si, Fengfei Kang, Kesong Yanshan Univ Key Lab Ind Comp Control Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Educ Minist Intelligent Control Syst & Intelligent Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Res Ctr Educ Minist Intelligent Control Syst & Intelligent Qinhuangdao 066004 Hebei Peoples R China Huitang IoT Technol Co Ltd Tangshan 063000 Hebei Peoples R China

The molten steel is transformed into slabs in the process of continuous casting. These slabs are subsequently conveyed to the hot rolling area for further processing or stored in the slab storage area for future utilization. An improved lightweight object detection algorithm called You Only Look Once version 8 (YOLOv8) with DSConv, light Efficient RepGFPN, and CARAFE (DSCLERC-YOLOv8) was proposed in this article for real-time detection of slabs transmitted on the roller table, to be applied in embedded devices. DSCLERC-YOLOv8 utilizes an enhanced C2f with distribution shifting convolution (C2f_DSConv) as the feature extraction module, which enhances the processing speed of the object detection algorithm while maintaining accuracy. Moreover, the algorithm incorporates an improved Lightweight Efficient-reparameterization generalized feature pyramid network (RepGFPN) and content-aware reassembly of features (CARAFE) for feature map upsampling, enhancing the accuracy and efficiency of image prediction. In addition, the article investigates the impact of reducing network stages on the algorithm. For practical applications, a P4 network structure is employed, resulting in a slab detection accuracy rate of 86.91% for mAP50:95. Although this accuracy is slightly lower than YOLOv8n (87.78%) by 0.87%, the significant improvement of this algorithm lies in a 14.7% reduction in network latency, a 77.22% reduction in parameter count, a 66.29% reduction in computational complexity, and a 62.90% reduction in model size. Finally, by implementing model pruning, quantization, and deploying with the TensorRT engine, the algorithm successfully achieves a real-time speed of 76 FPS on Jeston Orin NX. This algorithm offers a dependable solution for the real-time detection of steel slabs during continuous casting and rolling processes in the steel industry.

关键词： Slabs Feature extraction Convolution Accuracy Object detection real-time systems Computational modeling Steel Transformers Head Distribution shifting convolution (DSConv) embedded devices lightweight You Only Look Once version (YOLOv8) object detection reparameterization slab detection

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Digital Twin-Based Cloud-Native Vehicular Networks Architecture for Intelligent Driving

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ieee NETWORK 2024年第1期38卷 69-76页

作者： Tan, Xiaobin Meng, Qiushi Wang, Mingyang Zheng, Quan Wu, Jun Yang, Jian Univ Sci & Technol China USTC Dept Automat Hefei 230000 Peoples R China Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230039 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Tongji Univ Key Lab Minist Educ Embedded Syst & Serv Comp Shanghai 201804 Peoples R China

As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability, and high efficiency requirements of task execution of intelligent driving applications. Meanwhile, the deployment and combination of some key technologies supporting this system are not well-established. To address these issues, this article proposes a Digital Twin-based Cloud-native Vehicular Networks (DT-CVN) architecture to enhance the efficiency of virtual-reality integration in real-world vehicle traffic scenarios. In DT-CVN, the digital twins, which can bridge the physical space and cyberspace gaps in real-time, are implemented and deployed in a distributed manner by leveraging the distributed features of microservices based on cloud-native technology. DT-CVN employs the cybertwin as a smart communication agent in cloud-native vehicular networks, enabling efficient communication between cyberspace and physical spaces. Moreover, a case study is presented to demonstrate the effectiveness of DT-CVN. Simulation result shows the potential to address the challenges of integrating resources in vehicular networks with our proposed DT-CVN.

关键词： Cloud computing Task analysis Digital twins Cyberspace real-time systems Servers Vehicle dynamics

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NetReach: Guaranteed Network Availability and Reachability to enable Resilient Networks for embedded Systems 9

NetReach: Guaranteed Network Availability and Reachability t...

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9th ieee European symposium on Security and Privacy (EuroS and P)

作者： Van Eyck, Tom Michiels, Sam Du, Xiaojiang Hughes, Danny Katholieke Univ Leuven DistriNet Leuven Belgium Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9798350367324;9798350367294

Networked embedded devices are increasingly deployed in safety critical environments such as robotics, smart manufacturing and autonomous vehicles. Availability is an essential prerequisite of safety critical systems, which depend upon timely access to sensed data to inform the real-time control of actuators. Recent work has demonstrated that trusted computing features can be used to guarantee the availability of local resources to the safety-critical applications. However, prior work fails to guarantee the availability of a network connection, which is essential for correct system operation. To address this issue, we contribute NetReach, which uses Arm TrustZone to guarantee network availability to, and the reachability of, critical applications via a secure backup channel. Evaluation of NetReach shows that it can preserve the network connectivity of critical applications while under attack, with a worst case overhead of 18.66% for networked software running in the Normal World. Furthermore, NetReach introduces minimal additional code in the Secure World (only 418 lines of code). The presented features of NetReach enable future work toward resilient networks.

关键词： Arm TrustZone Network Availability Network Reachability

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Mini-YOLOX: A Lightweight Network for real-time embedded applications 66

Mini-YOLOX: A Lightweight Network for Real-Time Embedded App...

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2023 ieee 66th International Midwest symposium on Circuits and Systems, MWSCAS 2023

作者： El-Zeiny, Ahmed N. Hassan, Adham Mostafa, Hassan Khalil, Ahmed H. Cairo University Electronics and Communications Engineering Department Egypt University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza12578 Egypt

ISBN: (纸本)9798350302103

In this paper, we present an efficient real-time object detector designed for embedded system applications. Although increased memory and power consumption pose significant challenges, our network adopts techniques to overcome them. Depthwise and pointwise separable convolutions are used across the backbone, neck, and head of the network to reduce the number of parameters. To compensate for accuracy loss, we employ several techniques including starting with a larger baseline, using depthwise separable convolution mixed with blueprint separable convolution in the backbone, replacing the SiLU activation function with the Mish activation function, and switching from Adam to SGD optimization algorithms. These techniques help to achieve a state-of-the-art object detection network called Mini-YOLOX, which optimizes YOLOX-S from the YOLOX series and reaches only ∽57% of its parameter size and ∽52% of its floating-point operations with better accuracy. Mini-YOLOX achieves impressive results with only 5.08M parameters and 13.95 GFLOPs, resulting in a 40.9 AP score in just 5.71 ms. © 2023 ieee.

关键词： convolutional neural network (CNN) embedded applications real-time object detector YOLOX

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