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Dual-Band Flexible Terahertz sensor Based on Rotated Metamaterial Structure

Dual-Band Flexible Terahertz Sensor Based on Rotated Metamat...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Wang, Dongxu Xu, Kai-Da School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China Science and Technology on Electromechanical Dynamic Control Laboratory Xi'an China East China Normal University State Key Laboratory of Precision Spectroscopy Shanghai200241 China

ISBN: (纸本)9798350336535

In this article, a terahertz flexible metamaterial sensor with dual-band operating characteristics is introduced. The gold metamaterial array is arranged on the top surface of a polyimide film, while the structure is made up of a rotated structure with spiral-shaped strips. Two resonances occur at 0.37 and 1.13 THz with outstanding polarization insensitivity. The metamaterial sensor also has sensitive refractive index sensing properties, with sensitivities of 0.1 THz/RIU and 0.3 THz/RIU, respectively, at the two operating bands. This work shows potential applications in refractive index and nesticide sensing. © 2023 IEEE.

关键词： Metamaterials

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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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Integrating Traditional Machine Learning Approaches with Explainable Anomaly Detection for Multimodal sensor Data 1

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16th International conference on Ubiquitous Computing and Ambient Intelligence

作者： Imad, Muhammad Cleland, Ian Nugent, Chris McAllister, Patrick Ulster Univ Sch Comp Belfast North Ireland

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Anomaly detection has recently become essential in the context of human activity recognition, especially with emerging technologies like the Internet of Things and smart environments. These technologies are key contributors to data streams, generating vast quantities of continuous data across various applications. Nevertheless, the ever-changing characteristics of these data streams present ongoing challenges that remain to be addressed to understand and interpret the predictions. Providing clear explanations for detected anomalies is crucial for trust and usability, especially in critical applications like healthcare. In an effort to address this challenge, we have employed various traditional machine-learning approaches such as SVM, KNN, DT, and RF alongside XAI techniques like LIME. This combination will be used to show the efficacy of traditional ML methods and to explain the prediction process of anomaly detection, offering insights into how these technologies can be effectively applied to multimodal sensor data. The results demonstrated that SVM, RF, and DT achieved an accuracy of 0.95, while KNN achieved an accuracy of 0.94. In the future, we aim to explore the integration of deep learning techniques with XAI to enhance the interpretability and transparency of human activity recognition models. By incorporating XAI methods, we seek to provide insights into the decision-making process of deep learning models, enabling users to understand the reasoning behind the detected anomalies and increasing trust in the system's outputs.

关键词： Explainable AI Anomaly Detection Machine Learning Multimodal sensors

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MEMS Design Process Based Simulation and Analysis of a Hypothetical sensor Design with Electromechanics Interface 2nd

MEMS Design Process Based Simulation and Analysis of a Hypot...

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2nd International conference on Data Security and Privacy Protection, DSPP 2024

作者： Sathish, K. Ravikumar, C.V. Su, Chunhua Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Deemed University Chennai602105 India School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Division of Computer Science University of Aizu Aizuwakamatsu City Fukushima965-8580 Japan

ISBN: (纸本)9789819785452

This study simulates and examinesthe MEMS design process for a hypothetical sensor with an electromechanical interface. Conceptualizing the sensor entails establishing its purpose, operational environment, and capacitive sensing mechanism for sensitivity and precision. Geometric and physical models are developed using advanced COMSOL Multiphysics software to create a moveable proof mass supported by micro-fabricated beams. Static and dynamic finite element analysis (FEA) simulations can be used to evaluate the sensor’s mechanical reaction to input signals and electrode electrostatic forces. This guarantees that mechanical signals are properly transformed into electrical signals. These data are evaluated to determine the best performance criteria such as sensitivity, linearity, and thermal stability, resulting in iterative design improvements. Robustness analysis, including tolerance and FMEA, ensures dependability. Additionally, typical MEMS manufacturing technologies are being examined for fabrication. This simulation-driven strategy prioritises electro-mechanical interface issues throughout the design process, resulting in high-performance, and dependable MEMS sensors for a wide range of applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microchannels

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SK-EEGNet: A Novel Multiscale EEGNet Improved by SKNet for RSVP-Based Target Detection 1

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17th International conference on Intelligent Robotics and applications

作者： Wang, Shun Chen, Kun Liu, Quan Ma, Li Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

In recent years, the development of Brain-Computer Interface (BCI) technology has garnered widespread attention and research interest. The core of BCI systems lies in their ability to accurately interpret brain intentions and translate these intentions into specific control commands to drive external devices or software systems. Among various BCI systems, Rapid Serial Visual Presentation (RSVP) stands out as an effective method for target detection and recognition, widely applied in scenarios such as visual attention tracking and cognitive load assessment. However, RSVP faces several challenges in Electroencephalogram (EEG) signal detection and recognition, including signal noise interference, significant individual differences, and insufficient model generality and robustness. To address these issues, this study proposes a novel end-to-end model architecture that references the Selective Kernel Networks (SKNet) to enhance EEGNet, aiming to improve the accuracy of EEG-based RSVP signal detection and the generalizability of the model. We adopt SKNet's selective kernel mechanism to enhance the network structure of EEGNet, designing a specialized network for extracting spatio-temporal features from EEG signals. The selective kernel mechanism of SKNet dynamically adjusts the weights of different-sized convolutional kernels, enabling the network to flexibly capture multi-scale feature information. Through this approach, our model can more accurately identify Event-Related Potentials (ERPs) in RSVP tasks, along with other task-related EEG signal patterns. Experimental results on an RSVP-based EEG dataset demonstrate that our proposed method outperforms three other advanced analysis methods in terms of accuracy, Area Under the Curve (AUC), and F1-score evaluation metrics. These results not only validate the effectiveness of our proposed method but also provide new insights and methods for constructing BCI systems based on RSVP with broader applicability and higher recognit

关键词： brain computer interface rapid serial visual presentation EEGNet SKNet

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Autonomous Crop Pollination Using a Double-Flap UAV Integrated with Real-Time SLAM Technology: Design, Algorithms, and Experimental Validation 11th

Autonomous Crop Pollination Using a Double-Flap UAV Integrat...

引用

11th International conference on Robot Intelligence Technology and applications, RiTA 2023

作者： Li, Yangyifan Zhang, Haoyi Teng, xiaoming Li, Muyang Chen, Yi School of Advanced Technology Xi’an Jiao Tong-Liverpool University Suzhou China School of AI and Advanced Computing Xi’an Jiao Tong-Liverpool University Suzhou China School of Intelligent Manufacturing Ecosystem Xi’an Jiao Tong-Liverpool University Suzhou China

ISBN: (纸本)9783031706837

This paper introduces a Double-Flap Unmanned Aerial Vehicle（UAV）, utilizing Simultaneous Localization and Mapping (SLAM) technology, for an autonomous pollination system in crop farming. The UAV, with flapping wings generating thrust, adapts to 3D maneuvering by adjusting thrust size. advanced perception technologies, including vision cameras and computer assistance, handle attitude control, target recognition, and path planning. By integrating SLAM algorithms with autonomous sensing, navigation, and control, the system reduces human intervention. It can identify obstacles, plan safe paths, and generate optimal trajectories and flight instructions in real-time. The paper details the vehicle's principle, airframe design, visual recognition, system design, key components, and algorithms. Experimental results show high maneuverability and performance in complex farmland environments, indicating potential for promoting agricultural modernization and sustainable development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Ambient Cardiovascular Monitoring with Metamaterial Textile sensors

Ambient Cardiovascular Monitoring with Metamaterial Textile ...

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45th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Nguyen, Dat T. Zeng, Qihang Tian, xi Ho, John S. Natl Univ Singapore Integrat Sci & Engn Program Grad Sch Singapore Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Natl Univ Singapore SIA NUS Digital Aviat Corp Lab Singapore Singapore Natl Univ Singapore Inst Hlth Innovat & Technol Singapore Singapore Natl Univ Singapore Dept Elect & Comp Engn Inst Hlth Innovat & Technol Singapore Singapore Natl Univ Singapore Inst Hlth 1 Singapore Singapore

ISBN: (纸本)9798350324471

Contactless sensors embedded in the ambient environment have broad applications in unobtrusive, long-term health monitoring for preventative and personalized healthcare. Microwave radar sensors are an attractive candidate for ambient sensing due to their high sensitivity to physiological motions, ability to penetrate through obstacles and privacy-preserving properties, but practical applications in complex real-world environments have been limited because of challenges associated with background clutter and interference. In this work, we propose a thin and soft textile sensor based on microwave metamaterials that can be easily integrated into ordinary furniture for contactless ambient monitoring of multiple cardiovascular signals in a localized manner. Evaluations of our sensor's performance in human subjects show high accuracy of heartbeat and arterial pulse detection, with >= 96.5% sensitivity and < 5% mean absolute relative error (MARE) across all subjects. We demonstrate our sensor's utility for cuffless blood pressure monitoring on a human subject over a continuous 10-minute period. Our results highlight the potential of metamaterial textile sensors in ambient health and wellness monitoring applications.

关键词： Metamaterials

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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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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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