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

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Model Based Navigation for a Twin Thruster Autonomous Surface Catamaran 15

Model Based Navigation for a Twin Thruster Autonomous Surfac...

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15th IFAC conference on Control applications in Marine systems, Robotics and Vehicles (CAMS)

作者： Wanderlingh, Francesco Simetti, Enrico Indiveri, Giovanni Univ Genoa DIBRIS Via AllOpera Pia 13 I-16145 Genoa Italy

The paper describes the design, implementation, and performance assessment of a navigation system for a small-size robotic twin thruster autonomous surface vessel (ASV). The navigation solution is designed building on a first principles maneuvering model that includes sources of nonlinearity and asymmetry of this class of catamaran robots. The paper illustrates how the proposed model is exploited in the navigation filter design and implementation and it discusses its impact on the overall navigation performance. Experimental results relative to the ULISSE ASV are included and illustrated. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Navigation of autonomous surface vehicles sensor integration in autonomous robots Architectures for autonomous vehicles System identification for marine systems

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Intelligent Mixed-Signal Embedded systems for Electromagnetic Tracking applications

Intelligent Mixed-Signal Embedded Systems for Electromagneti...

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IEEE lnternational conference on advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Holyaka, Roman Marusenkova, Tetyana Shpur, Olha Lviv Polytechnic National University S. Bandery Str. 12 Lviv79013 Ukraine

ISBN: (纸本)9783031612206

Real-time analytics is the main advantage of Edge Computing (EC) in the concept of Artificial Intelligence (AI). AI EC brings high-performance computing capabilities to the edge, where sensors of Internet of Things (IoT) devices are located and data is processed locally, which significantly improves processing speed as compared to cloud computing. The core of AI EC is based on real-time embedded systems. This work presents intelligent embedded systems for electromagnetic tracking (EMT) applications. EMT is a novel development trend in sensor electronics and object navigation for IoT, Virtual, and Augmented Reality. The EMT technology relies on calculating the position of an object being tracked based on dynamic measurement of reference electromagnetic field vectors. In contrast to optical tracking systems, EMT does not require any clear line of sight (LOS) and therefore is not susceptible to occlusions. Inertial sensor tracking (IST) systems, which are based on inertial measurement units (IMUs), suffer from measurement errors intrinsic for accelerometers and gyroscopes, primarily from bias drift and random walk. EMT systems are free of these errors and overcome inertial tracking systems in their accuracy. The former provides the opportunity to obtain high-precision measurements of the sensor coordinates in 3D EM field space formed by the actuator array. Nevertheless, EMT systems have a limited application area. Such limitations are caused by significant signal changes and strict requirements set to the environment where reference EM fields are supposed to be formed. We have shown that one faces two main issues when designing an EMT system. Firstly, one should ensure a wide measurement range (up to 120 dB). Secondly, highly noise-immune measurements are required under specific operating conditions. The novelty of the presented solution is the intelligent algorithm of noise-immune conversion in conditions when the signal chain parameters are switched dynamically. This

关键词： Signal detection

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Fiber Bragg grating temperature sensor for marine application 12

Fiber Bragg grating temperature sensor for marine applicatio...

引用

conference on advanced sensor systems and applications XII

作者： Wang, Guangyao Sun, Jianping Li, Ting Wang, Hongjun Li, Jiahao Natl Inst Metrol Beijing 100029 Peoples R China

ISBN: (纸本)9781510657090;9781510657083

Fiber Bragg grating (FBG) sensor for marine exploration has gradually attracted people's attention because of its natural characteristics of low investment and high expansion capability. However, it is well known that FBG is not only sensitive to temperature, but also affected by strain, pressure, vibration, refractive index etc. The cross-sensitivity of FBG results in large measurement error for fiber temperature measurement. As an important error source of optical fiber temperature sensing, strain has attracted much attention of researchers. In order to eliminate the interference of strain on FBG temperature measurement, in this paper, we propose a FBG sensor for ocean applications. Two fiber Bragg gratings with different central wavelengths are cascaded to realize the decoupling of temperature and strain sensing. Experimental results show that the temperature sensitivity of the proposed sensor can reach to 9.49 pm/degrees C in the range from -5 degrees C to 35 degrees C.

关键词： fiber Bragg grating temperature sensor ocean applications

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Towards a Smart Multi-sensor Ionizing Radiation Monitoring System 26

Towards a Smart Multi-Sensor Ionizing Radiation Monitoring S...

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26th Euromicro conference on Digital System Design (DSD) / 49th Euromicro conference on Software Engineering and advanced applications (SEAA)

作者： Andjelkovic, Marko Chen, Junchao Syed, Rizwan Tariq Vargas, Fabian Ulbricht, Markus Krstic, Milos Ilic, Stefan Marjanovic, Milos Veljkovic, Sandra Mitrovic, Nikola Dankovic, Danijel Ristic, Goran Duane, Russell Vasovic, Nikola Jaksic, Aleksandar Palma, Alberto J. Lallena, Antonio M. Carvajal, Miguel A. IHP Leibniz lnst Innovat Mikroelekt Franfurt Germany Univ Potsdam Potsdam Germany Univ Nis Fac Elect Engn Nish Serbia Tyndall Cork Ireland Varadis Cork Ireland Sievert Consultancy Cork Ireland Univ Granada Granada Spain

ISBN: (纸本)9798350344196

Detection and measurement of ionizing radiation is required in a wide range of terrestrial applications, as well as in space missions. For this purpose, special instruments composed of radiation sensors and readout electronics are utilized. As ionizing radiation may affect the operation of electronic systems, radiation hardness is one of the main design requirements for radiation monitoring systems. In this work, we present a concept of a smart multi-sensor radiation monitoring system. The proposed design is based on the results achieved within the framework of EU-funded ELICSIR project. Our solution provides a new perspective on smart radiation monitoring by combining the concepts of self-awareness, self-adaptivity and artificial intelligence. This solution is suitable for applications where long-term autonomous radiation monitoring is required, such as environmental monitoring at terrestrial level or radiation monitoring in space missions.

关键词： smart radiation monitoring artificial intelligence self-awareness self-adaptivity reliability monitors

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Wearable strain sensor for motion capture 2

Wearable strain sensor for motion capture

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2nd International conference on Mechatronics and Smart systems, CONF-MSS 2024

作者： Mo, Zhenhua School of Advanced Technology Xi'An Jiaotong-liverpool University Suzhou China

Strain sensor is a type of sensor that is used to measure the strain or deformation applied to an object. These sensors are widely used in engineering, scientific and industrial applications to monitor and record the deformation of an object to provide information about the forces applied. Motion Capture is a technology used to capture and record human movements, postures and actions and convert them into digital data. This technology plays a key role in industries such as gaming, movies and robotics. In recent years with the growth of these industries, there has been an increasing demand for lightweight and accurate motion capture devices, which have made wearable strain sensors for motion, capture a popular research area. This review explores wearable strain sensors for motion capture by delving into their classification and the underlying working principles. Furthermore, it will offer comprehensive insights into the primary sensing materials employed in motion capture strain sensors, coupled with a thorough analysis of the structural design aspects. Finally, a reasonable conclusion is achieved: While this type of sensors has gained significant momentum in recent years, several critical shortcomings prevent their practical application and commercialization, which are expected to be overcome soon. © Published under licence by IOP Publishing Ltd.

关键词： Wearable sensors

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HotWire- Decreasing Latency Between Wake-Up Calls in Semi-Active WUR-Scenarios 12

HotWire- Decreasing Latency Between Wake-Up Calls in Semi-Ac...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Ziesmann, Maurice-Christian Fuhner, Claus Busching, Felix Ostfalia - University of Applied Sciences Wolfenbüttel38302 Germany

ISBN: (纸本)9798350358056

Wireless sensor Networks using Wake-up Radios (WURs) in dense deployments benefit from addressing at WUR-level. Many semi-active WURs rely on a simple frontend that is able to wake-up the back-end circuits in a relatively short time. However, once woken-up the design of these frontends prevents a quick consecutive receiving of Wake-up Calls (WUCs). So, whenever a non-matching address is received by the address decoder, it takes quite a while until a new address can be decoded. In this paper, we demonstrate how a significant increase in WUC frequency can be achieved with relatively simple adaptations to the WUR front-end. The dimensioning of the individual components is also considered and derived. In the respective evaluation, we show that by means of our adaptation, consecutive WUCs can be handled approximately 1000 times faster than without this adaptation. © 2023 IEEE.

关键词： addressing internet of things latency wake-up calls wake-up radios wireless sensor networks

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Enhanced In-Home Human Activity Recognition Using Multimodal Sensing and Spatiotemporal Machine Learning Architecture 46

Enhanced In-Home Human Activity Recognition Using Multimodal...

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Torabi, Seyyed Mahdi Narimani, Mohammad Park, Edward J. Simon Fraser University School of Mechatronic Systems Engineering SurreyBC Canada Simon Fraser University WearTech Labs SurreyBC Canada

ISBN: (纸本)9798350371499

In this research, we present an enhanced human activity recognition (HAR) framework using advanced machine learning models incorporating temporal dynamics, leveraging multimodal sensor data. Data from wearable wristbands and real-time location systems (RTLS) were used to detect human activities within a home environment. A key advancement is the development of a spatiotemporal machine learning model combining convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and neural structured learning (NSL), which significantly surpasses traditional machine learning baseline models like RF, SVM. We also highlight the efficacy of sensor fusion achieving an accuracy of 86.21% and an F1 score of 87.40% for routine daily activities by combining IMU and RTLS sensors, compared to using each sensor modality independently. The proposed model paves the way for developing smart environments that can intelligently adapt to the varying routines and behaviors of daily life. Our investigation shows potential applications across diverse domains, including elderly care, smart home technologies, and healthcare monitoring, suggesting the broad applicability and benefits of the developed HAR system. © 2024 IEEE.

关键词： Adversarial machine learning

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Electronic Prognostics Innovations for applications to Aerospace systems

Electronic Prognostics Innovations for Applications to Aeros...

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IEEE Aerospace conference

作者： Gerdes, Matthew Gross, Kenny Wang, Guang Chao Oracle Corp Austin TX 78741 USA

ISBN: (纸本)9781665490320

Oracle has an anomaly detection solution for monitoring time-series telemetry signals for dense-sensor IoT prognostic applications. It integrates an advanced prognostic pattern recognition technique called Multivariate State Estimation Technique (MSET) for high-sensitivity prognostic fault monitoring applications in commercial nuclear power and aerospace applications. MSET has since been spun off and met with commercial success for prognostic Machine Learning (ML) applications in a broad range of safety critical applications, including NASA space shuttles, oil-and-gas asset prognostics, and commercial aviation streaming prognostics. MSET proves to possess significant advantages over conventional ML solutions including neural networks, autoassociative kernel regression, and support vector machines. The main advantages include earlier warning of incipient anomalies in complex time-series signatures, and much lower overhead compute cost due to the deterministic mathematical structure of MSET. Both are crucial for dense-sensor avionic IoT prognostics. In addition, Oracle has developed an extensive portfolio of data preprocessing innovations around MSET to solve the common big-data challenges that cause conventional ML algorithms to perform poorly regarding prognostic accuracy (i.e, false/missed alarm probabilities). Oracle's MSET-based prognostic solution helps increase avionic reliability margins and system availability objectives while reducing costly sources of "no fault found" events that have become a significant sparing-logistics issue for many industries including aerospace and avionics. Moreover, by utilizing and correlating information from all on-board telemetry sensors (e.g., distributed pressure, voltage, temperature, current, airflow and hydraulic flow), MSET is able to provide the best possible prediction of failure precursors and onset of small degradation for the electronic components used on aircrafts, benefiting the aviation Prognostics and Health Mana

关键词： NASA

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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...

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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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Flexible Piezoresistive Pressure sensor Based on Graphene Nano Plateletes 1

Flexible Piezoresistive Pressure Sensor Based on Graphene Na...

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IEEE Biosensors conference (Biosensors)

作者： Chowdhury, Azmal Huda Jafarizadeh, Borzooye Sozal, Md Shariful Islam Cheng, Zhe Pala, Nezih Wang, Chunlei Florida Int Univ Dept Mech & Mat Engn Miami FL 33174 USA Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

ISBN: (纸本)9798350346046

Flexible pressure sensors have gained significant attention within the research community due to their exceptional capabilities across numerous applications. Among them, piezoresistive pressure sensors have demonstrated excellent performance, thanks to their simple working mechanism, low-cost sensor fabrication methods, and the extensive range of materials available. In this study, we demonstrate a flexible piezoresistive pressure sensor by following a low-cost fabrication approach that allows us to realize both the sensing layer and substrate. We utilized a sandpaper template-assisted method to achieve a micropatterned sensing substrate, while an efficient liquid-based transfer technique was used to fabricate the sensing layer. By repeating the transfer deposition method, we were able to achieve stable conductivity of the sensing layer, resulting in pressure sensitivity of 0.0002 kPa(-1) in the low-pressure range. The moderate sensitivity of the sensor was capable of detecting tactile pressure, as demonstrated by generating Morse code.

关键词： piezoresistive pressure sensor graphene-nanoplatelets (GNP) sandpaper mold liquid-based transfer method

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