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IEEE sensors LETTERS 2024年第8期8卷

作者： Singh, Neha Adhikari, Mainak Indian Inst Informat Technol Lucknow Dept Comp Sci Lucknow 226002 Uttar Pradesh India Indian Inst Sci Educ & Res Thiruvananthapuram Sch Data Sci Thiruvananthapuram 695551 Kerala India

This letter explores the integration of federated learning (FL) techniques into edge networks to address the pressing issue of inefficient irrigation practices in paddy fields by proposing a prototype, equipped with various agriculture sensors and advanced data analytics. Deploying sensor networks directly in soil enables continuous data collection, creating a dynamic and responsive irrigation system. Leveraging this wealth of data, we employ advanced analytics techniques, such as synchronous FL and predictive modeling, to analyze historical trends and predict future irrigation requirements. FL, a decentralized machine learning paradigm, offers collaborative learning that can enhance the prototype by enabling inference at the edge. The experimental results of laboratory and field trials demonstrate the effectiveness of the proposed prototype in significantly improving irrigation management and enhancing overall crop productivity.

关键词： sensors Irrigation Data models Prototypes Servers Real-time systems Predictive models sensor applications agriculture sensors and sensor data processing edge networks federated learning (FL) irrigation management

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3-D Magnetic Angular sensor Enabled by Spin-Orbit Torque

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IEEE TRANSACTIONS ON MAGNETICS 2025年第4期61卷

作者： Li, Xin Zhan, Wei Guo, Zhe Fan, Xiaoyu Zhou, Chenxi Wang, Yihan Zhang, Shuai You, Long Huazhong Univ Sci & Technol Sch Integrated Circuits Wuhan 430074 Peoples R China Hubei Univ Sch Microelect Wuhan 430062 Peoples R China Hubei Univ Key Lab Intelligent Sensing Syst & Secur Minist Educ Wuhan 430062 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Data Storage Syst & Technol Key Lab Informat Storage Syst Minist Educ China Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci Technol Res Inst Shenzhen 518000 Peoples R China

The application scenarios of magnetic angular position sensors are extensive, spanning diverse fields, such as navigation, medical devices, robotics, automotive industries, and aerospace. However, the fabrication of 3-D angular sensors presents significant challenges, as it necessitates the integration of sensors with varying orientations and precise orientation correction. In this work, we propose a novel 3-D angular position sensor based on the current-induced spin-orbit torque (SOT) effect, which features a simple and compact heavy metal (HM)/ferromagnetic (FM) heterostructure configuration. Spatial angular detection is accomplished by determining two projection angles, which represent the projection of a magnetic field onto three orthogonal planes within a spatial coordinate system. Compared to both commercially available and recently proposed angular position sensors, our design distinguishes itself by enabling 3-D magnetic field sensing with a single device, while maintaining a straightforward and scalable manufacturing process. Experimental results demonstrate that the angular error of the proposed sensor remains below 1 degrees under magnetic fields of less than 15 Oe.

关键词： Magnetic fields Robot sensing systems Three-dimensional displays Magnetic field measurement Magnetization sensors Saturation magnetization Magnetic devices Magnetic domains IP networks Magnetic angular position sensor scalability spin-orbit torque (SOT) vectoral magnetic field sensing

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Application of Artificial Intelligence and sensor Fusion for Soil Organic Matter Prediction

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sensors 2024年第7期24卷 2357页

作者： Uddin, Md Jasim Sherrell, Jordan Emami, Anahita Khaleghian, Meysam Texas State Univ Coll Sci & Engn San Marcos TX 78666 USA

Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil;aerial photos taken by UAVs display the vegetative index (NDVI);and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.

关键词： Artificial Intelligence sensor fusion soil organic matter

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A Planar Compact Absorber for Microwave Sensing Based on Transmission-Line Metamaterials

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IEEE sensors JOURNAL 2024年第24期24卷 41864-41874页

作者： Kazemi, Nazli Abdolrazzaghi, Mohammad Musilek, Petr Baladi, Elham Polytech Montreal Elect Engn & Polygrames Res Ctr Montreal PQ H3T1J4 Canada Univ Toronto Elect & Comp Engn Toronto ON M5S3G4 Canada Univ Alberta Elect & Comp Engn Edmonton AB T6G2R3 Canada Univ Hradec Kralove Dept Appl Cybernet Hradec Kralove 50003 Czech Republic

This work introduces a novel microwave sensor leveraging a perfect metamaterial absorber (PMA) based on transmission-line metamaterials, marking a significant step toward enhancing sensing capabilities. Unique design choices, including the integration of lumped inductors and series capacitors within the unit cell to synthesize a negative and near-zero permittivity, elevate the sensor's resolution and lower the absorption frequency. These modifications enhance sensitivity and precision for detecting small material quantities, with the sensor achieving an absorption efficiency exceeding 98%. The design also ensures robust performance against changes in incident angles and polarization due to its compact profile, rendering it versatile for diverse sensing applications. Experimental validation confirms the sensor's performance, highlighting its efficacy in material detection with a notable frequency shift sensitivity of 500 MHz for permittivity changes from 3 to 18, establishing it as a transformative structure for metamaterial-based microwave sensing technologies. Furthermore, the sensor demonstrates a resolution of 5.01 MHz per percentage increase in soil moisture content, offering a new benchmark in the precision of environmental sensing. This breakthrough in design and functionality establishes the sensor as a pivotal tool in the advancement of metamaterial-based microwave sensing technologies, promising widespread applicability, and impact.

关键词： sensors Metamaterials Absorption Permittivity Magnetic materials Impedance Resonant frequency Inductors Resonance Mathematical models LC-resonator metamaterial absorbers microwave sensing precision sensing applications sensitivity

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Empowerment of AI algorithms in biochemical sensors

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TRAC-TRENDS IN ANALYTICAL CHEMISTRY 2024年 173卷

作者： Zhou, Zhongzeng Xu, Tailin Zhang, Xueji Shenzhen Univ Inst Adv Study IAS Coll Chem & Environm Engn Shenzhen 518060 Guangdong Peoples R China

Biochemical sensors have become indispensable tools for real-time, on -site monitoring and analysis in diverse domains such as healthcare, environmental protection, and food safety. The rapid evolution of artificial intelligence (AI) has opened new frontiers for enhancing the capabilities of these sensors across a spectrum of detection modalities. This paper delves into the recent integration of AI algorithms into biochemical sensors, examining this advancement from a functional standpoint and focusing on the empowerments it brings to electrochemical, electrochemiluminescence, colorimetric, and Raman sensors. AI techniques aim to enhance the capabilities of biochemical sensors beyond traditional techniques and have enabled improved selectivity, drift correction, efficiency, resolution, assisted diagnosis, and biomarker screening from complex multidimensional data. In the end, we provide a personal perspective on future development and address the remaining challenges in the commercialization of AI -based biochemical sensors.

关键词： Artificial intelligence Machine learning Analytical chemistry Biochemical sensor

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Rational integration of SnMOF/SnO2 Hybrid on TiO2 Nanotube Arrays: An Effective Strategy for Accelerating Formaldehyde Sensing Performance at Room Temperature

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ACS sensors 2023年第11期8卷 4189-4197页

作者： He, Zhen-Kun Zhao, Jiahui Li, Keke Zhao, Junjian He, Haoxuan Gao, Zhida Song, Yan-Yan Northeastern Univ Coll Sci Shenyang 110819 Peoples R China

Formaldehyde is ubiquitously found in the environment, meaning that real-time monitoring of formaldehyde, particularly indoors, can have a significant impact on human health. However, the performance of commercially available interdigital electrode-based sensors is a compromise between active material loading and steric hindrance. In this work, a spaced TiO2 nanotube array (NTA) was exploited as a scaffold and electron collector in a formaldehyde sensor for the first time. A Sn-based metal-organic framework was successfully decorated on the inside and outside of TiO2 nanotube walls by a facile solvothermal decoration strategy. This was followed by regulated calcination, which successfully integrated the preconcentration effect of a porous Sn-based metal-organic framework (SnMOF) structure and highly active SnO2 nanocrystals into the spaced TiO2 NTA to form a Schottky heterojunction-type gas sensor. This SnMOF/SnO2@TiO2 NTA sensor achieved a high room-temperature formaldehyde response (1.7 at 6 ppm) with a fast response (4.0 s) and recovery (2.5 s) times. This work provides a new platform for preparing alternatives to interdigital electrode-based sensors and offers an effective strategy for achieving target preconcentrations for gas sensing processes. The as-prepared SnMOF/SnO2@TiO2 NTA sensor demonstrated excellent sensitivity, stability, reproducibility, flexibility, and convenience, showing excellent potential as a miniaturized device for medical diagnosis, environmental monitoring, and other intelligent sensing systems.

关键词： SnMOF/SnO2 hybrid spaced TiO2 NTA preconcentration room temperature formaldehyde sensor

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Prototype Alarm Integrating Pulse-Driven Nitrogen Dioxide sensor Based on Holey Graphene Oxide/In2O3

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ACS sensors 2024年第10期9卷 5425-5435页

作者： Han, Jiayin Gu, Guoxuan Gao, Yuan Yu, Ning Zhou, Weirong Wang, Yong Kong, Dehao Gao, Yubing Lu, Geyu Jilin Univ Coll Elect Sci & Engn State Key Lab Integrated Optoelect Key Lab Gas Sensors Changchun 130012 Jilin Peoples R China Jilin Univ Int Ctr Future Sci Changchun 130012 Jilin Peoples R China

NO2 seriously threatens human health and the ecological environment. However, the fabrication of highly sensitive NO2 sensors with rapid response/recovery rates, low detection limits, and ease of integration remains a challenge. Herein, benefiting from the fast carrier transfer and rich active sites, holey graphene oxide (HGO) was adopted to functionalize the In2O3 nanosheet to construct NO2 gas sensors. Characterization and theoretical calculations established the merits of HGO decoration in the NO2 sensing. The optimal sample, 0.5 wt % HGO/In2O3-sheet, exhibited superior sensing properties, resulting in a 1.37-fold improvement in response to 1 ppm of NO2 compared to the GO/In2O3 counterpart. Gas-sensing kinetics analysis revealed its lower activation energy and higher kinetic rate constants. Importantly, pulsed-temperature modulation was employed to decouple the gas adsorption from surface activation processes, achieving an ultrahigh response of 2776 to 1 ppm of NO2 for the 0.5 wt % HGO/In2O3-sheet sensor. Compared to the isothermal mode, this strategy enhanced the response value by 1.6 times, reduced the response/recovery time by 33%/70%, and enabled the detection of NO2 concentrations as low as 1 ppb. Finally, an NO2 monitoring alarm system based on the 0.5 wt % HGO/In2O3-sheet sensor with pulsed-temperature modulation was demonstrated for hazard warnings.

关键词： NO2 sensor trace detection pulsed-temperaturemodulation holey graphene oxide In2O3 nanosheets

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Ultra-high sensitivity integrated photonic biosensors based on a feedback-coupled microring resonator

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OPTICS EXPRESS 2025年第5期33卷 10355-10363页

作者： Chen, Zhituo Wang, Han Zhang, A. ping Dai, Daoxin Shi, Yaocheng Zhejiang Univ Coll Opt Sci & Engn Int Res Ctr Adv Photon Ctr Opt & Electromagnet Res Hangzhou 310058 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Hong Kong Peoples R China

A high-performance sensor is crucial for the integration of optical biosensors, enabling the precise and rapid identification of target analytes. We present the correlation between feedback-coupled microring resonator (FBCMR) and variations in Mach-Zehnder interferometer (MZI) and microring resonator (MRR) phases. By introducing an asymmetric MZI into FBCMR, we have successfully achieved ultra-high sensitivity integrated photonic sensor whose refractive index sensitivity and limit of detection are 5752.5 nm/RIU and 1.6514 x 10-5, respectively. The photonic sensor is packaged with a PDMS microfluidic layer, forming an integrated optofluidic chip, which is applied to detect human alpha-fetoprotein (AFP). Such an integrated photonic sensor has no suspended or subwavelength grating (SWG) structure so that no need to challenge manufacturing processes which paves the way for application in high-resolution biochemical sensing and environmental monitoring.

关键词： Biosensors Effective refractive index Image sensors Refractive index sensor performance Waveguide sensors

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In-sensor Computing-Based Smart Sensing Architecture Implemented Using a Dual-Gate Metal-Oxide Thin-Film Transistor Technology

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ADVANCED ELECTRONIC MATERIALS 2024年

作者： Lei, Tengteng Hu, Yushen Xie, Xinying Shi, Runxiao Wong, Man Hong Kong Univ Sci & Technol HKUST Dept Elect & Comp Engn State Key Lab Adv Displays & Optoelect Technol Hong Kong Peoples R China Guangzhou HKUST Fok Ying Tung Grad Sch Guangzhou 511458 Peoples R China

An array of sensors generating a collection of correlated signals can benefit from integration with a "smart" system for autonomous inferencing. Mimicking their biological counterparts, smart sensor systems shall possess the capabilities of sensing, memory, and neuromorphic computation. However, state-of-the-art biomimetic systems either do not employ a full set of devices to cover the complete range of capabilities or incorporate devices that are capable of all but appropriate only for a limited range of sensing applications. Presently proposed is a smart sensor architecture that combines an array of sensing elements with an overlapping array of computing and memory elements, thus emulating an innervated peripheral sensing system (IPSS) capable of local and autonomous neuromorphic in-sensor data pre-processing. Compatibility of the proposed architecture with functionally distinct elements for sensing, memory, and computing removes the restrictive demand for a single element simultaneously capable of all, thus making this architecture more generally applicable to a wider range of sensors and usage scenarios. An artificial synapse as a computing element is implemented using dual-gate (DG) thin-film transistors (TFTs) and the low-leakage current of transistors based on metal-oxide semiconductors allows the deployment of capacitors as memory elements. The outputs of the IPSS are passed on to an adjacent artificial neural network (ANN) for near-sensor inferencing. Monolithic integration of the IPSS and the ANN is made possible by the deployment of the same memory and computing elements in their construction. A smart tactile sensing system based on the proposed architecture is constructed and characterized. The functionality of the system is demonstrated by its application to the classification of a set of tactile images of 3-dimensionally printed alphabet stamps.

关键词： artificial synapse dual-gate in-sensor computing metal-oxide thin-film transistor tactile sensor array

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Recent advancements in physical and chemical MEMS sensors

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ANALYST 2024年第13期149卷 3498-3512页

作者： Tanaka, Yo Samsung Japan Corp Samsung Dev Solut R&D Japan DSRJ 2-7 Sugasawa ChoTsurumi Ku Yokohama Kanagawa 2300027 Japan

Microelectromechanical systems (MEMSs) are microdevices fabricated using semiconductor-fabrication technology, especially those with moving components. This technology has become more widely used in daily life, e.g., in mobile phones, printers, and cars. In this review, MEMS sensors are largely classified as physical or chemical ones. Physical sensors include pressure, inertial force, acoustic, flow, temperature, optical, and magnetic ones. Chemical sensors include gas, odorant, ion, and biological ones. The fundamental principle of sensing is reading out either the movement or electrical-property change of microstructures caused by external stimuli. Here, sensing mechanisms of the sensors are explained using diagrams with equivalent circuits to show the similarity. Examples of multiple parameter measurement with single sensors (e.g. quantum sensors or resonant pressure and temperature sensors) and parallel sensor integration are also introduced. This review classifies MEMS sensors (both physical and chemical) in terms of their targets and explains their fundamental principles and trends. It also introduces the parallelization of different types of sensors or sensing functions.

关键词： Equivalent circuits

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