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

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Embedded Real-Time Human Activity Recognition on an ESP32-S3 Microcontroller Using Ambient Audio Data 12

Embedded Real-Time Human Activity Recognition on an ESP32-S3...

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

作者： Pfitzinger, Thomas Wohrle, Hendrik Dortmund University of Applied Sciences and Arts Dortmund Germany

ISBN: (纸本)9798350358056

The task of Human Activity Recognition (HAR) finds application in smart homes and assisted living and is mostly approached with machine learning. sensor data for HAR is often collected and processed by separate devices. Performing the recognition task directly on the sensor device can decrease network traffic and reduce the related energy consumption and privacy concerns. In this work, an intelligent sensor device is developed that performs real-time HAR on an ESP32-S3 microcontroller using ambient audio data. A convolutional neural network to classify human activities is designed and subsequently reduced in size and computation cost in order to enable its inference on the microcontroller. This is achieved with neural architecture search, resulting in multiple simplified model variations. These are tested on the ESP32-S3 and a feasible version is identified. © 2023 IEEE.

关键词： Embedded systems Human Activity Recognition Machine Learning Microcontrollers Neural Architecture Search

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Higher Reconstruction Performance with Dual Deep Neural Networks for Wireless sensor Networks Compressed Temporal Signals 12

Higher Reconstruction Performance with Dual Deep Neural Netw...

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

作者： Rousseau, Sebastien Negrier, Romain Courreges, Fabien Engoti, Frank Itoua Quere, Raymond University of Limoges Xlim Institute Brive-la-Gaillarde19100 France NAIoBEE Company Brive-la-Gaillarde19100 France

ISBN: (纸本)9798350358056

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. The method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. This method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. Through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. Thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals. © 2023 IEEE.

关键词： compressed sensing deep learning recurrent neural networks signal processing algorithms wireless sensor networks

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A Self-Assembled Flexible Strain and Temperature sensor based on the Microstructure of Pyramids with High Elasticity, Temperature Sensitivity and Wide Range 38

A Self-Assembled Flexible Strain and Temperature Sensor base...

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38th IEEE International conference on Micro Electro Mechanical systems, MEMS 2025

作者： Yu, Yangtao Yan, Bo Cui, Wenbo Li, Mengqiu Li, Chenyuan Zang, Faheng Yang, Zhuoqing Shanghai Jiao Tong University National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Department of Micro/Nano Electronics Shanghai China Harbin Institute of Technology School of Electronics and Information Engineering Harbin China

ISBN: (纸本)9798331508890

Polymers have been extensively studied in the field of flexible sensors for wearable electronics. However, polymer-based flexible sensors still face great challenges in terms of sensitivity, sensing range and stretchable properties. This paper reports a novel self-assembled flexible strain and temperature sensor, constructed from NiO molecular spheres assembled between MXene flakes. This sensor exhibits exceptional temperature sensitivity, with a sensitivity up to -15.11%°C-1. It operates a broad temperature range(-20~60°C) and boasts a high measurement gauge factor(GF) of 6.57, along with a significant elongation at break exceeding 572%. Additionally, the sensor demonstrates an ultrahigh tensile strength of over 45 MPa. The microstructure of the sensor, featuring pyramidal formations, facilitates rapid testing capabilities. Furtherly, breathing cycle test under 0°C was performed, showing robust reliability. Furthermore, an array sensing circuit has been developed and designed to precisely measure spatial temperature distributions, enhancing utility of the sensor in various applications. © 2025 IEEE.

关键词： Tensile testing

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Passive Cylindrical Resonator Temperature sensor based on Commercial Low-Loss Dielectric Material 14

Passive Cylindrical Resonator Temperature Sensor based on Co...

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14th IEEE International conference on RFID Technology and applications, RFID-TA 2024

作者： Shivakumar, Jayaprakash Reed, Nicholas Clark, Cory Govindarajan, Rishikesh Srinivasaraghavan Kim, Daewon Rojas, Eduardo Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach United States Department of Aerospace Engineering Embry-Riddle Aeronautical University Daytona Beach United States WavePro Palmyra United States

ISBN: (纸本)9798331540227

This research focuses on the development of a passive X-band cylindrical resonator sensor antenna for temperature sensing applications. The resonator sensor antenna design aims to enable precise and real-time monitoring of temperature while embedded inside various materials and structures like polymers and composites. By integrating the sensing capabilities and a slot antenna directly into the cylindrical resonator structure, the research seeks to minimize additional components and streamline the sensing process. The cylindrical resonator sensor antenna operates at a frequency of 10.6 GHz to minimize the size of the resonator and enable high sensitivity and accuracy in detecting subtle changes in temperature. The cylindrical resonator is made from high-permittivity ceramic-loaded PTFE material by WavePro to ensure minimized dielectric losses and allow for sensor miniaturization. The sensor is interrogated through a high bandwidth probing antenna or a Frequency Modulated Continuous Wave (FMCW) radar and the reflected signals are analyzed to extract resonance data. This study proposes a design for the resonator and antenna through high-fidelity simulations and prototypes ensuring reliable and efficient sensing across a range of industrial and structural applications. The outcomes of this research hold the potential to significantly advance the capabilities of temperature monitoring systems. The novelty of this work resides on (a) using a commercial low-loss dielectric material for sensor implementation, (b) the use of advanced manufacturing techniques including 5-axis femtosecond laser machining, and (c) a novel embedded wireless sensor concept. A linear sensor temperature response is measured in the range of 25-210°C, at a sensing distance of 0.5 cm. © 2024 IEEE.

关键词： High temperature applications

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Soft Normal and Shear Force sensor for Underwater Robotic applications 5

Soft Normal and Shear Force Sensor for Underwater Robotic Ap...

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5th IEEE International conference on Flexible and Printable sensors and systems, FLEPS 2023

作者： Kamp, David Subad, Rafsan Al Shafatul Islam Park, Kihan University of Massachusetts Dartmouth Department of Mechanical Engineering DartmouthMA02747 United States

ISBN: (纸本)9781665457330

Tactile perception is a fundamental requirement to perceive and control the manipulation of an object via physical interaction. However, contemporary sensing technologies have some shortcomings in terms of directional force recognition such as inflexibility, intricate design, expensive fabrication methodology, and most importantly incompatibility with underwater applications. In this work, we report a facile and cost-effective methodology to build a soft and multi-directional force sensor especially focusing on its applicability in the underwater environment. The sensor consists of a silicone cover, a 3D printed pyramidal shape, piezo-resistive sensor nodes, and a silicone base. Whenever there is any force applied normal to the sensor, the pyramid shape would distribute the force equally to all the nodes and provide similar outputs from them whereas any other directional force would result in putting differential forces on the nodes and generate distinctive output patterns. The developed sensor was characterized experimentally and tested successfully for underwater demonstrations. © 2023 IEEE.

关键词： Silicones

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Interfacing and Testing High-Resolution, Video-Based Avionics and Mission systems that Use ARINC 818-3 42

Interfacing and Testing High-Resolution, Video-Based Avionic...

引用

IEEE/AIAA 42nd Digital Avionics systems conference (DASC)

作者： Keller, Tim Great River Technol Albuquerque NM 87113 USA

ISBN: (纸本)9798350333572

The complexity of modern mission and sensor systems continues to increase as the applications for video-based sensors become more demanding. Real-time, man-in-the-loop missions require higher levels of precision than previous class systems. This higher precision is achieved via higher-resolution, low-latency stereoscopic sensors and displays for better 3D performance. The new class of systems also sets the stage for removing the man-in-the-loop in favor of autonomous systems, for: aerial refueling;automatic targeting and tracking;and EO/IR ISR applications. The requirements of this new class of mission and sensor systems line up well with inherent features of the ARINC 818-3 (Avionics Digital Video Bus) protocol. Although some of these features have been defined in the specification for more than a decade, newer systems are just being developed that tap into some of the more advanced ARINC 818-3 features for the first time. For example: center dot 64B/66B encoding using new control characters and ensuring interoperability between independently developed systems center dot Video concentration and de-concentration of multiple sensors onto a single link, while maintaining frame synchronous timing center dot Support for 4K+ stereo displays, including robustness testing capabilities and considerations for channel skew center dot Support for new pixel modes, including YCbCr and Bayer encoding. center dot Stringent Stereo Display timing This paper provides lessons learned which highlight the tools and new test capabilities needed to develop and validate complex mission and video systems using multiple, high-speed EO/IR sensors and displays.

关键词： ARINC 818 avionics mission systems sensors displays data transfer switching video concentration video deconcentration

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Quantum-Enhanced AI Robotics for Sustainable Agriculture: Pioneering Autonomous systems in Precision Farming

Quantum-Enhanced AI Robotics for Sustainable Agriculture: Pi...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Khobragade, Prashant Dhankar, Praveen Kumar Titarmare, Abhijit Dhone, Mrunali Thakur, Shruti Saraf, Pranay G H Raisoni College of Engineering Dept of Computer Science and Engineering Nagpur India Pune India G H Raisoni College of Engineering Dept of Electronic and Telecommunication Engineering Nagpur India G H Raisoni College of Engineering Dept of DIC Nagpur India

ISBN: (纸本)9798331517953

Quantum computing, artificial intelligence (AI), and robots are all coming together to make sustainable agriculture possible by making it possible to build advanced self-driving systems for precision farming. This paper looks at how quantum-enhanced AI might be able to make farming much more productive, use resources more efficiently, and protect the environment. In traditional farming, old techniques are often still used, which can waste resources and hurt the environment. By combining quantum computing with AI and robotics, we can use the power of quantum algorithms to process huge amounts of agricultural data faster than with traditional systems. This makes it possible to create smart decision-making tools that increase crop yields while lowering costs and damage to the environment. Quantum computing has unmatched computer power, which makes it possible to analyze large files from a variety of sources, such as earth monitors, weather forecasts, and satellite images. Because of this feature, complex prediction models can be made that can accurately predict how crops will do in different situations. The paper study used suggested model along with AI to handle important farming jobs like planting, watering, and gathering by using robots with advanced sensors and machine learning algorithms. With the use of self-driving robots in farming not only makes operations more efficient, but it also cuts down on the need for physical labor, which helps the agricultural sector deal with its lack of workers. © 2024 IEEE.

关键词： Smart agriculture

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Energy Efficient Cognitive Wireless sensor Network for Loud Noise Detection 17

Energy Efficient Cognitive Wireless Sensor Network for Loud ...

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17th IEEE International conference on advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Yatsenko, Anastasiia Parkhomenko, Anzhelika Tulenkov, Artem Wolff, Carsten Parkhomenko, Illia Pozdnyakov, Oleg National University «Zaporizhzhia Polytechnic» Software Tools Department Zaporizhzhia Ukraine Institute for the Digital Transformation of Application and Living Domains University of Applied Sciences and Arts Dortmund Germany

ISBN: (纸本)9798331520564

The development of small, autonomous sensor devices and their integration into wireless sensor networks has resulted in robust, easy-to-deploy and easy-to-operate systems that are used in a wide range of applications. However, despite the obvious advantages, a number of problems need to be solved to ensure their long-term and uninterrupted operation. One of them is energy efficiency and uniform distribution of energy between the network nodes. Studies have shown that cognitive wireless sensor networks are able to adapt to the state and conditions of networks, take into account aspects such as nodes energy levels, and act accordingly. Among the adaptive protocols, LEACH is considered to be quite effective as it helps to reduce the energy consumption of the network, however, the cluster head selection is based on random approach, which may not be the most efficient way. This paper proposes a modification of the cluster head selection method that allows taking into account the energy of each network node. This approach will help to equalize the energy consumption of nodes and optimize the network workflow as a whole, increasing its reliability and service life. © 2024 IEEE.

关键词： sensor nodes

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Innovating Smart Buildings and Cities Through Agent-Based Modeling, Discrete Event Simulation and sensor-Based Data Analytics 12

Innovating Smart Buildings and Cities Through Agent-Based Mo...

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

作者： Lopinski, Juliane Sachweh, Sabine Muller, Claudia IDiAL Institute University of Applied Sciences and Arts Dortmund Dortmund Germany University Siegen Siegen Germany

ISBN: (纸本)9798350358056

The paper emphasizes the significance of sensor-based data analytics (SBDA) for modeling and simulation in a smart city, using the methods discrete event simulation (DES) and agent-based modeling (ABM). Moreover, the project reviewing paper presents several selected project examples, such as CitySim, AIRE, Smart Grid, and the Gotland project that demonstrate the potential of SBDA in smart buildings and urban systems and analyse possible advantages of the application of the SBDA method. Furthermore, the result table demonstrates the possibilities of using SBDA in ABM and DES in other sectors, thus obtaining accurate results in a modeling system in the future. These findings have significant implications for policymakers, urban planners, and stakeholders in smart city development who seek to enhance the quality of life for urban citizens. The paper contributes to a better understanding of advantages of using sensor-based data analysis in ABM and DES and the method's potential benefit for other sectors. It provides a basis for analysis on some methodologies in the field of smart cities and buildings. It is intended to serve as an overview for future researchers and to contribute to the understanding of general concepts of research institutions. © 2023 IEEE.

关键词： efficiency modelling simulation smart city systems

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Deep Learning-Based Strategies for Integrated Autonomous Navigation: A Review

Deep Learning-Based Strategies for Integrated Autonomous Nav...

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2024 International Telecommunications conference, ITC-Egypt 2024

作者： Fekry, Alaa Kamel, Ahmed M. Elhalwagy, Yehia Abosekeen, Ashraf Military Technical College Department of Electrical and Computer Engineering Cairo Egypt October University for Modern Sciences and Arts Department of Mechatronics Systems Engineering Giza Egypt

ISBN: (纸本)9798350351408

Multi-sensor integrated navigation/positioning systems (MSINPS) play a crucial role in achieving precise positioning and navigation in challenging environments. By combining data from various sensors such as Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cameras, and Light Detection and Ranging (LiDar), MSINPS enhance accuracy and robustness, ensuring uninterrupted operation even in the event of sensor malfunction. This paper explores autonomous vehicle guidance for safe and efficient navigation, focusing on two paths of data fusion: sensor-based fusion and learning-based fusion. The effectiveness of MSINPS hinges on meticulous sensor selection and optimization to meet the requirements of autonomous navigation applications. Addressing noise, biases, and inconsistencies through data pre-processing is essential for robust data fusion. advanced fusion algorithms leveraging machine learning and deep learning techniques exhibit superior accuracy and adaptability. Furthermore, integrating MSINPS with artificial intelligence (AI) opens new avenues for navigation and positioning enhancement. This integration can lead to improvements in sensor technology and solution outcomes. Striking a balance between computational complexity and available resources promotes efficient operation while implementing fault tolerance strategies ensures system robustness across diverse environments. © 2024 IEEE.

关键词： Global positioning system

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