Pub Date : 2024-10-02DOI: 10.1109/JSEN.2024.3468329
Yufan Li;Keqing Duan;Zizhou Qiu;Yongliang Wang
Space-based early warning radar (SBEWR) offers advantages such as extended detection distances and more flexible deployment options compared to airborne early warning radar (AEWR). However, range dependence or nonstationarity of clutter becomes more complex in SBEWR. Theoretically, the traditional 3-D space-time adaptive processing (3D-STAP) method can effectively suppress nonstationary clutter. Nonetheless, the substantial computational demands and extensive requirements of training samples make real-time processing of the full-dimension 3D-STAP method impractical. In this article, we analyze the complex coupling relationship of clutter in SBEWR and further develop a reduced-dimension 3D-STAP method. The proposed method combines beamforming and subarray synthesis, where the former is employed to mitigate clutter densely distributed in the azimuth dimension, and the latter is utilized to suppress clutter continuously varied in the elevation-Doppler domain. This tailor-made reduction structure can effectively decouple the clutter of SBEWR in the azimuth-elevation–Doppler domain, demonstrating superior performance compared to other reduced-dimension 3D-STAP methods. In comparison to the full-dimension 3D-STAP method, the proposed method significantly reduces computational complexity and sample requirements. Furthermore, extensive experimental results demonstrate the superiority of the proposed method regarding signal-to-clutter-plus-noise ratio loss, minimum detectable velocity (MDV), and target detection performance.
{"title":"Reduced-Dimension STAP Method via Beamforming Joint Subarray Synthesis for Space-Based Early Warning Radar","authors":"Yufan Li;Keqing Duan;Zizhou Qiu;Yongliang Wang","doi":"10.1109/JSEN.2024.3468329","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468329","url":null,"abstract":"Space-based early warning radar (SBEWR) offers advantages such as extended detection distances and more flexible deployment options compared to airborne early warning radar (AEWR). However, range dependence or nonstationarity of clutter becomes more complex in SBEWR. Theoretically, the traditional 3-D space-time adaptive processing (3D-STAP) method can effectively suppress nonstationary clutter. Nonetheless, the substantial computational demands and extensive requirements of training samples make real-time processing of the full-dimension 3D-STAP method impractical. In this article, we analyze the complex coupling relationship of clutter in SBEWR and further develop a reduced-dimension 3D-STAP method. The proposed method combines beamforming and subarray synthesis, where the former is employed to mitigate clutter densely distributed in the azimuth dimension, and the latter is utilized to suppress clutter continuously varied in the elevation-Doppler domain. This tailor-made reduction structure can effectively decouple the clutter of SBEWR in the azimuth-elevation–Doppler domain, demonstrating superior performance compared to other reduced-dimension 3D-STAP methods. In comparison to the full-dimension 3D-STAP method, the proposed method significantly reduces computational complexity and sample requirements. Furthermore, extensive experimental results demonstrate the superiority of the proposed method regarding signal-to-clutter-plus-noise ratio loss, minimum detectable velocity (MDV), and target detection performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37404-37419"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flexible flow sensors show potential applications in aerospace, wearable devices, biomedicine, and other fields. In this article, a flexible microelectromechanical system (MEMS) calorimetric flow sensor with high sensitivity is designed and implemented. In the sensor, polydimethylsiloxane (PDMS) is used as the substrate in order to suppress the heat conduction loss in the sensor. The adoption of PDMS substrate can simplify the fabrication process because the technology of etching isolation trench is no longer needed. Additionally, four thermistors are symmetrically placed on both sides of the heater to form the Wheatstone double bridges, resulting in highly sensitive detection in both low- and high-speed ranges. The sensitivity and the range of the flow sensor are significantly improved. The results show that the measurable speed of the sensor can be as high as 50 m/s in a 100 K constant temperature difference (CTD) mode. The sensitivity is 22 mV/(m/s) with the flow rate in the range of 1–50 m/s and up to 3.308 V/(m/s) with the flow rate in the range of 0–0.1 m/s. Compared with the traditional flow sensor in silicon substrate, the sensitivity and the range of the designed sensor are significantly improved. The influences of specific flexible characters on the designed MEMS flow sensor, including the different curvatures and various overheat temperature values, are simulated and analyzed.
柔性流量传感器在航空航天、可穿戴设备、生物医学和其他领域有着潜在的应用前景。本文设计并实现了一种具有高灵敏度的柔性微机电系统（MEMS）热量流量传感器。该传感器采用聚二甲基硅氧烷（PDMS）作为基底，以抑制传感器的热传导损耗。采用 PDMS 衬底可以简化制造工艺，因为不再需要蚀刻隔离沟槽的技术。此外，四个热敏电阻对称置于加热器两侧，形成惠斯通双桥，从而实现了低速和高速范围内的高灵敏度检测。流量传感器的灵敏度和量程都得到了显著提高。结果表明，在 100 K 恒定温差（CTD）模式下，传感器的可测量速度可高达 50 m/s。流量在 1-50 m/s 范围内时，灵敏度为 22 mV/(m/s)；流量在 0-0.1 m/s 范围内时，灵敏度高达 3.308 V/(m/s)。与传统的硅衬底流量传感器相比，所设计传感器的灵敏度和量程都有显著提高。模拟和分析了特定柔性特征对所设计的 MEMS 流量传感器的影响，包括不同的曲率和各种过热温度值。
{"title":"A PDMS-Based Flexible Calorimetric Flow Sensor With Double-Bridge Technology","authors":"Junkai Zhang;Xingyu Guan;Xinyuan Hu;Mengye Cai;Yanfeng Jiang","doi":"10.1109/JSEN.2024.3468375","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468375","url":null,"abstract":"Flexible flow sensors show potential applications in aerospace, wearable devices, biomedicine, and other fields. In this article, a flexible microelectromechanical system (MEMS) calorimetric flow sensor with high sensitivity is designed and implemented. In the sensor, polydimethylsiloxane (PDMS) is used as the substrate in order to suppress the heat conduction loss in the sensor. The adoption of PDMS substrate can simplify the fabrication process because the technology of etching isolation trench is no longer needed. Additionally, four thermistors are symmetrically placed on both sides of the heater to form the Wheatstone double bridges, resulting in highly sensitive detection in both low- and high-speed ranges. The sensitivity and the range of the flow sensor are significantly improved. The results show that the measurable speed of the sensor can be as high as 50 m/s in a 100 K constant temperature difference (CTD) mode. The sensitivity is 22 mV/(m/s) with the flow rate in the range of 1–50 m/s and up to 3.308 V/(m/s) with the flow rate in the range of 0–0.1 m/s. Compared with the traditional flow sensor in silicon substrate, the sensitivity and the range of the designed sensor are significantly improved. The influences of specific flexible characters on the designed MEMS flow sensor, including the different curvatures and various overheat temperature values, are simulated and analyzed.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36530-36538"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/JSEN.2024.3468878
Changyan Ran;Peijun Xiao;Zhihui Luo;Xiaoan Chen
Aiming at the problem of low signal identification accuracy due to various noises in pipeline intrusion signals collected by the ultraweak fiber grating distributed acoustic sensors (uwDAS) system, we propose an identification method on top of a new denoising approach for pipeline intrusion signals in this article. The denoising approach uses improved complete ensemble empirical mode decomposition with adaptive noise, fuzzy entropy, and adaptive interval thresholding (ICEEMDAN-FE-AIT). The fisher score feature selection and extreme learning machine (F-ELM) are combined to identify the intrusion signals. We build a data acquisition platform in the laboratory to collect the pipeline intrusion signals, including chainsaw, mechanical vibration, excavator digging, artificial digging, and no-intrusion. Experiments show that the ratio of noise signal to noise reduction ( ${R}_{text {DNSN}}$ ) of ICEEMDAN-FE-AIT is better than those of four other denoising methods, namely, variational mode decomposition and permutation entropy (VMD-PE) method; the ICEEMDAN-PE-AIT method; the ICEEMDAN, energy density and average periodicity, and AIT (ICEEMDAN-ET-AIT) method; and the ICEEMDAN-FE and wavelet soft threshold denoising (ICEEMDAN-FE-WSTD) method. The values of ${R}_{text {DNSN}}$ for the five signals are 15.2043, 16.7654, 14.9815, 15.5541, and 13.5428 dB, respectively. The average identification accuracy is 93.27%, in subsequent identification experiments using F-ELM.
{"title":"Identification of Pipeline Intrusion Signals Based on ICEEMDAN-FE-AIT and F-ELM in the uwDAS System","authors":"Changyan Ran;Peijun Xiao;Zhihui Luo;Xiaoan Chen","doi":"10.1109/JSEN.2024.3468878","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468878","url":null,"abstract":"Aiming at the problem of low signal identification accuracy due to various noises in pipeline intrusion signals collected by the ultraweak fiber grating distributed acoustic sensors (uwDAS) system, we propose an identification method on top of a new denoising approach for pipeline intrusion signals in this article. The denoising approach uses improved complete ensemble empirical mode decomposition with adaptive noise, fuzzy entropy, and adaptive interval thresholding (ICEEMDAN-FE-AIT). The fisher score feature selection and extreme learning machine (F-ELM) are combined to identify the intrusion signals. We build a data acquisition platform in the laboratory to collect the pipeline intrusion signals, including chainsaw, mechanical vibration, excavator digging, artificial digging, and no-intrusion. Experiments show that the ratio of noise signal to noise reduction (\u0000<inline-formula> <tex-math>${R}_{text {DNSN}}$ </tex-math></inline-formula>\u0000) of ICEEMDAN-FE-AIT is better than those of four other denoising methods, namely, variational mode decomposition and permutation entropy (VMD-PE) method; the ICEEMDAN-PE-AIT method; the ICEEMDAN, energy density and average periodicity, and AIT (ICEEMDAN-ET-AIT) method; and the ICEEMDAN-FE and wavelet soft threshold denoising (ICEEMDAN-FE-WSTD) method. The values of \u0000<inline-formula> <tex-math>${R}_{text {DNSN}}$ </tex-math></inline-formula>\u0000 for the five signals are 15.2043, 16.7654, 14.9815, 15.5541, and 13.5428 dB, respectively. The average identification accuracy is 93.27%, in subsequent identification experiments using F-ELM.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36874-36881"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
LiDAR-based 3-D object detection is an important perceptual task in various fields such as intelligent transportation, autonomous driving, and robotics. Existing two-stage point-voxel methods contribute to the boost of accuracy on 3-D object detection by utilizing precise pointwise features to refine 3-D proposals. Although obtaining promising results, these methods are not suitable for real-time applications. First, the inference speed of existing point-voxel hybrid frameworks is slow because the acquisition of point features from voxel features consumes a lot of time. Second, existing point-voxel methods rely on 3-D convolution for voxel feature learning, which increases the difficulty of deployment on embedded computing platforms. To address these issues, we propose a real-time two-stage detection network, named HybridPillars. We first propose a novel hybrid framework by integrating a point feature encoder into a point-pillar pipeline efficiently. By combining point-based and pillar-based networks, our method can discard 3-D convolution to reduce computational complexity. Furthermore, we propose a novel pillar feature aggregation network to efficiently extract bird’s eye view (BEV) features from pointwise features, thereby significantly enhancing the performance of our network. Extensive experiments demonstrate that our proposed HybridPillars not only boosts the inference speed, but also achieves competitive detection performance compared to other methods. The code will be available at https://github.com/huangzhicong3/HybridPillars .
{"title":"HybridPillars: Hybrid Point-Pillar Network for Real-Time Two-Stage 3-D Object Detection","authors":"Zhicong Huang;Yuxiao Huang;Zhijie Zheng;Haifeng Hu;Dihu Chen","doi":"10.1109/JSEN.2024.3468646","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468646","url":null,"abstract":"LiDAR-based 3-D object detection is an important perceptual task in various fields such as intelligent transportation, autonomous driving, and robotics. Existing two-stage point-voxel methods contribute to the boost of accuracy on 3-D object detection by utilizing precise pointwise features to refine 3-D proposals. Although obtaining promising results, these methods are not suitable for real-time applications. First, the inference speed of existing point-voxel hybrid frameworks is slow because the acquisition of point features from voxel features consumes a lot of time. Second, existing point-voxel methods rely on 3-D convolution for voxel feature learning, which increases the difficulty of deployment on embedded computing platforms. To address these issues, we propose a real-time two-stage detection network, named HybridPillars. We first propose a novel hybrid framework by integrating a point feature encoder into a point-pillar pipeline efficiently. By combining point-based and pillar-based networks, our method can discard 3-D convolution to reduce computational complexity. Furthermore, we propose a novel pillar feature aggregation network to efficiently extract bird’s eye view (BEV) features from pointwise features, thereby significantly enhancing the performance of our network. Extensive experiments demonstrate that our proposed HybridPillars not only boosts the inference speed, but also achieves competitive detection performance compared to other methods. The code will be available at \u0000<uri>https://github.com/huangzhicong3/HybridPillars</uri>\u0000.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38318-38328"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/JSEN.2024.3447730
D. Rajeswari;Srinivasan Rajendran;A. Arivarasi;Alagiri Govindasamy;A. Ahilan
In high-speed railways, train collisions with obstructions on the trackside are prevented using automated railroad security systems. Rail safety is being improved, and accident rates are reduced through continuous research. The rapid advancement of deep learning (DL) has created new possibilities for research. In this article, a novel track object detection using smart sensor (TOSS) approach has been proposed for tracking the objects in railway track (RT) using DL networks. A TOSS approach uses a camera and light detection and ranging (LiDAR) as primary sensors for detecting objects and faults in RT to prevent accidents. Preprocessing methods include data cleaning, min–max normalization, and calibration to ensure data quality by removing unwanted data from datasets. Then, clustering the preprocessed data to determine objects that are initial sizes and positions. In visual data processing, the camera images are denoised using a bilateral filter (BF) to remove noise. In order to prevent accidents on the RT, the YOLOv8 network is utilized to accurately localize and detect objects on the track. The visual and digital data from the camera and LiDAR sensor are given as an input to the fuzzy system. This data will be used to generate the system alert message that is sent to the loco-pilot and nearby control rooms. In the experimental analysis, the proposed TOSS approach achieved an overall accuracy of 98.91% and an mean average precision (mAP) of 97.1% for detecting objects and faults efficiently. The TOSS approach demonstrates significant progress in the overall accuracy range by 13.86%, 10.22%, 5.46%, 8.8%, and 1.50% better than 2-D singular spectrum analysis (SSA) + Deep network, YOLOv8, YOLOv5s-VF, FR-CNN, and YOLO-GD, respectively.
{"title":"TOSS: Deep Learning-Based Track Object Detection Using Smart Sensor","authors":"D. Rajeswari;Srinivasan Rajendran;A. Arivarasi;Alagiri Govindasamy;A. Ahilan","doi":"10.1109/JSEN.2024.3447730","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3447730","url":null,"abstract":"In high-speed railways, train collisions with obstructions on the trackside are prevented using automated railroad security systems. Rail safety is being improved, and accident rates are reduced through continuous research. The rapid advancement of deep learning (DL) has created new possibilities for research. In this article, a novel track object detection using smart sensor (TOSS) approach has been proposed for tracking the objects in railway track (RT) using DL networks. A TOSS approach uses a camera and light detection and ranging (LiDAR) as primary sensors for detecting objects and faults in RT to prevent accidents. Preprocessing methods include data cleaning, min–max normalization, and calibration to ensure data quality by removing unwanted data from datasets. Then, clustering the preprocessed data to determine objects that are initial sizes and positions. In visual data processing, the camera images are denoised using a bilateral filter (BF) to remove noise. In order to prevent accidents on the RT, the YOLOv8 network is utilized to accurately localize and detect objects on the track. The visual and digital data from the camera and LiDAR sensor are given as an input to the fuzzy system. This data will be used to generate the system alert message that is sent to the loco-pilot and nearby control rooms. In the experimental analysis, the proposed TOSS approach achieved an overall accuracy of 98.91% and an mean average precision (mAP) of 97.1% for detecting objects and faults efficiently. The TOSS approach demonstrates significant progress in the overall accuracy range by 13.86%, 10.22%, 5.46%, 8.8%, and 1.50% better than 2-D singular spectrum analysis (SSA) + Deep network, YOLOv8, YOLOv5s-VF, FR-CNN, and YOLO-GD, respectively.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37678-37686"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drinking water that contains microbiological contamination can lead to the spread of dangerous waterborne diseases, posing a significant risk to human health. It is important to detect and identify microbial pathogens (such as bacteria, fungi, viruses, and parasites) in water accurately to prevent these negative situations. In this work, we have proposed an optical-fiber (OF) surface plasmon resonance (SPR)-based sensor for the detection of pathogenic bacteria that are Bacillus anthracis, Vibrio cholera, Enterococcus faecalis, and Escherichia coli in the drinking water. The finite element method (FEM) is implemented to determine the wavelength sensitivity (WS). The sensor shows excellent performance and can identify the samples externally. The sensor has gold (Au) and gallium nitride (GaN) as the plasmonic sensing layer in nano-grating structures over the surface of the multimode fiber (MMF). It can detect all four bacteria from the drinking water with the highest sensitivity achieved is 21276.6 nm/RIU for E. coli. The performance parameters: detection accuracy (DA), signal-to-noise ratio (SNR), resolution (R), detection limit (DL), quality factor (QF), and figure of merit (FOM), are also evaluated. The results produced by the sensor are superior in comparison to the previously reported biosensors.
{"title":"High-Sensitivity SPR Fiber-Optic Biosensor With Nano-Grating Structure for Pathogenic Bacteria Detection in Drinking Water","authors":"Ananya Banerjee;Rahul Rahul;Jaisingh Thangaraj;Santosh Kumar","doi":"10.1109/JSEN.2024.3469028","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3469028","url":null,"abstract":"Drinking water that contains microbiological contamination can lead to the spread of dangerous waterborne diseases, posing a significant risk to human health. It is important to detect and identify microbial pathogens (such as bacteria, fungi, viruses, and parasites) in water accurately to prevent these negative situations. In this work, we have proposed an optical-fiber (OF) surface plasmon resonance (SPR)-based sensor for the detection of pathogenic bacteria that are Bacillus anthracis, Vibrio cholera, Enterococcus faecalis, and Escherichia coli in the drinking water. The finite element method (FEM) is implemented to determine the wavelength sensitivity (WS). The sensor shows excellent performance and can identify the samples externally. The sensor has gold (Au) and gallium nitride (GaN) as the plasmonic sensing layer in nano-grating structures over the surface of the multimode fiber (MMF). It can detect all four bacteria from the drinking water with the highest sensitivity achieved is 21276.6 nm/RIU for E. coli. The performance parameters: detection accuracy (DA), signal-to-noise ratio (SNR), resolution (R), detection limit (DL), quality factor (QF), and figure of merit (FOM), are also evaluated. The results produced by the sensor are superior in comparison to the previously reported biosensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36882-36890"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/JSEN.2024.3468333
Cheong-Weng Ooi;Waldo Udos;Kok-Sing Lim;Heming Wei;Hangzhou Yang;Harith Ahmad
This article reviews the characteristics and properties of various modes in tilted fiber Bragg gratings (TFBGs). It explores the fundamental theory and optical characteristics of radiation modes, cutoff modes, guided cladding modes, ghost modes, and leaky mode in TFBGs. The unique behaviors of these modes are associated with distinctive fabrication techniques, surface modifications, and characterization methods for optimal performance in diverse applications. In addition, the excitation of surface plasmon resonance (SPR) in TFBGs is reviewed. The excitation involves the coupling between incident light and collective electron oscillations on a metallic surface, within the context of TFBGs. Recent advancements in high refractive index (RI) coatings, femtosecond laser inscription, and graphene integration are further explored for their impact on mode excitation and sensing capabilities. This review offers insights into preserving and enhancing leaky mode resonances (LMRs) and exploring ultrahigh-order cladding modes. The discussion provides valuable perspectives on future research directions and practical applications in optical fiber sensing and photonics.
{"title":"Insights and Perspectives on Modal Characteristics in Tilted Fiber Bragg Gratings: A Review","authors":"Cheong-Weng Ooi;Waldo Udos;Kok-Sing Lim;Heming Wei;Hangzhou Yang;Harith Ahmad","doi":"10.1109/JSEN.2024.3468333","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468333","url":null,"abstract":"This article reviews the characteristics and properties of various modes in tilted fiber Bragg gratings (TFBGs). It explores the fundamental theory and optical characteristics of radiation modes, cutoff modes, guided cladding modes, ghost modes, and leaky mode in TFBGs. The unique behaviors of these modes are associated with distinctive fabrication techniques, surface modifications, and characterization methods for optimal performance in diverse applications. In addition, the excitation of surface plasmon resonance (SPR) in TFBGs is reviewed. The excitation involves the coupling between incident light and collective electron oscillations on a metallic surface, within the context of TFBGs. Recent advancements in high refractive index (RI) coatings, femtosecond laser inscription, and graphene integration are further explored for their impact on mode excitation and sensing capabilities. This review offers insights into preserving and enhancing leaky mode resonances (LMRs) and exploring ultrahigh-order cladding modes. The discussion provides valuable perspectives on future research directions and practical applications in optical fiber sensing and photonics.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36247-36260"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes the design and fabrication of near-zero radio frequency (RF) power sensors for GaAs monolithic microwave integrated circuit (MMIC) applications, with the principle of RF power-heat-electricity conversion. These power sensors are designed to be broadband (0.1–30 GHz), modest sensitivity ( $sim 53.71~mu $ V/mW), and low-cost manufacturing (no substrate membrane structure required). The detailed design of the near-zero RF power sensors is investigated, and the effects of the number of thermocouples and the overlap size between the resistors and the thermopile on RF and sensing performances are revealed in this article. Moreover, the fabrication is completely compatible with the GaAs MMIC technology. In addition, the measured reflection losses of the power sensors are lower than −16.3 dB up to 30 GHz. The measured sensitivities for the sensors B1, B2, C1, and C2 are 55.30, 91.00, 29.70, and $60.29~mu $ V/mW at 10 GHz, and 32.02, 53.71, 18.01, and $36.63~mu $ V/mW at 30 GHz, respectively. And the good linearity of the output responses is obtained. Experiments show that the increase of the thermocouples’ number and the overlap distance contributes to improving the sensitivities of the RF power sensors.
{"title":"Design and Measurement of Near-Zero Thermopile RF Power Sensors for GaAs MMIC Applications","authors":"Zhiqiang Zhang;Runqi Gu;Yuhao Xie;Zijie Yuan;Meng Tang;Sixu Lv;Jianqiu Huang","doi":"10.1109/JSEN.2024.3468402","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468402","url":null,"abstract":"This article proposes the design and fabrication of near-zero radio frequency (RF) power sensors for GaAs monolithic microwave integrated circuit (MMIC) applications, with the principle of RF power-heat-electricity conversion. These power sensors are designed to be broadband (0.1–30 GHz), modest sensitivity (\u0000<inline-formula> <tex-math>$sim 53.71~mu $ </tex-math></inline-formula>\u0000 V/mW), and low-cost manufacturing (no substrate membrane structure required). The detailed design of the near-zero RF power sensors is investigated, and the effects of the number of thermocouples and the overlap size between the resistors and the thermopile on RF and sensing performances are revealed in this article. Moreover, the fabrication is completely compatible with the GaAs MMIC technology. In addition, the measured reflection losses of the power sensors are lower than −16.3 dB up to 30 GHz. The measured sensitivities for the sensors B1, B2, C1, and C2 are 55.30, 91.00, 29.70, and \u0000<inline-formula> <tex-math>$60.29~mu $ </tex-math></inline-formula>\u0000 V/mW at 10 GHz, and 32.02, 53.71, 18.01, and \u0000<inline-formula> <tex-math>$36.63~mu $ </tex-math></inline-formula>\u0000 V/mW at 30 GHz, respectively. And the good linearity of the output responses is obtained. Experiments show that the increase of the thermocouples’ number and the overlap distance contributes to improving the sensitivities of the RF power sensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36412-36418"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/JSEN.2024.3469112
Hamed Mohebbi-Kalkhoran;Nicholas C. Makris;Purnima Ratilal
Developing automatic algorithms for real-time monitoring of underwater acoustic events is essential in ocean acoustic applications. Most previous ocean acoustic ecosystem monitoring studies are non-real-time, focusing on data received on a single hydrophone or a specific analysis, such as bearing estimation or detection, without considering the full end-to-end analysis system. Here, we develop a unified framework for real-time ocean acoustic data analysis including beamforming, detection, bearing estimation, and classification of transient underwater acoustic events. To detect sound sources, thresholding on computed mel-scale per-channel energy normalization (PCEN) is applied, followed by morphological image opening to extract pixels with significant intensities. Next, connected component analysis is applied for grouping pixel detections. The bearing of signal detections is next estimated via nonmaximum suppression (NMS) of 3-D stacked beamformed spectrogram imageries. To classify a variety of whale species from their calls, time-frequency features are extracted from each detected signal’s beamformed power spectrogram. These features are next applied to train three classifiers, including support vector machine (SVM), neural networks, and random forest (RF), to classify six whale vocalization categories: Fin, Sei, Unidentified Baleen, Minke, Humpback, and general Odontocetes. Best results are obtained with the RF classifier, which achieved 96.7% accuracy and 87.5% F1 score. A variety of accelerating approaches and fast algorithms are implemented to run on GPU. During an experiment in the U.S. Northeast coast in September 2021, the software and hardware advances developed here were used for near real-time analysis of underwater acoustic data received by Northeastern University’s in-house fabricated 160-element coherent hydrophone array system.
{"title":"Real-Time Detection, Bearing Estimation, and Whale Species Vocalization Classification From Passive Underwater Acoustic Array Data","authors":"Hamed Mohebbi-Kalkhoran;Nicholas C. Makris;Purnima Ratilal","doi":"10.1109/JSEN.2024.3469112","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3469112","url":null,"abstract":"Developing automatic algorithms for real-time monitoring of underwater acoustic events is essential in ocean acoustic applications. Most previous ocean acoustic ecosystem monitoring studies are non-real-time, focusing on data received on a single hydrophone or a specific analysis, such as bearing estimation or detection, without considering the full end-to-end analysis system. Here, we develop a unified framework for real-time ocean acoustic data analysis including beamforming, detection, bearing estimation, and classification of transient underwater acoustic events. To detect sound sources, thresholding on computed mel-scale per-channel energy normalization (PCEN) is applied, followed by morphological image opening to extract pixels with significant intensities. Next, connected component analysis is applied for grouping pixel detections. The bearing of signal detections is next estimated via nonmaximum suppression (NMS) of 3-D stacked beamformed spectrogram imageries. To classify a variety of whale species from their calls, time-frequency features are extracted from each detected signal’s beamformed power spectrogram. These features are next applied to train three classifiers, including support vector machine (SVM), neural networks, and random forest (RF), to classify six whale vocalization categories: Fin, Sei, Unidentified Baleen, Minke, Humpback, and general Odontocetes. Best results are obtained with the RF classifier, which achieved 96.7% accuracy and 87.5% F1 score. A variety of accelerating approaches and fast algorithms are implemented to run on GPU. During an experiment in the U.S. Northeast coast in September 2021, the software and hardware advances developed here were used for near real-time analysis of underwater acoustic data received by Northeastern University’s in-house fabricated 160-element coherent hydrophone array system.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37432-37444"},"PeriodicalIF":4.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
2-D materials are promising candidates for gas sensing applications due to their high surface to volume ratio. However, graphene and MoS2, two prominent members of these materials, show little sensitivity toward gas molecules such as NH3, CO2, and H2O. In this work, the gas sensing properties of graphene and MoS2 lateral heterostructures are investigated theoretically using density functional theory (DFT) in combination with a non-equilibrium Green’s function (NEGF) formalism. The heterostructure consists of a MoS2 part, which is sandwiched between two graphene sides. There are distinct interfaces between MoS2 and graphene, whereby C-Mo and C-S bonds connect the two materials. The results reveal that CO2 and H2O are weakly adsorbed on different parts of the heterostructure, while NH3 molecules are strongly adsorbed on the C-Mo interface with an energy equal to −1.233 eV. Further analyses reveal that only the adsorbed NH3 at the C-Mo surface leads to significant changes in the electronic structure, even in an atmospheric environment, where O2 molecules are pre-adsorbed at the interface. The planar average of electrostatic potential and the calculated currents at ±0.5 V applied voltages reveal that the Schottky barrier at C−Mo graphene/MoS2 interface is very sensitive to the adsorption of NH3 gas molecule.
{"title":"Gas Sensing Properties of Graphene/MoS₂/Graphene Lateral Heterostructure: A First Principles Investigation","authors":"Forough Ghayyem;Ali Kiakojouri;Irmgard Frank;Ebrahim Nadimi","doi":"10.1109/JSEN.2024.3468168","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3468168","url":null,"abstract":"2-D materials are promising candidates for gas sensing applications due to their high surface to volume ratio. However, graphene and MoS2, two prominent members of these materials, show little sensitivity toward gas molecules such as NH3, CO2, and H2O. In this work, the gas sensing properties of graphene and MoS2 lateral heterostructures are investigated theoretically using density functional theory (DFT) in combination with a non-equilibrium Green’s function (NEGF) formalism. The heterostructure consists of a MoS2 part, which is sandwiched between two graphene sides. There are distinct interfaces between MoS2 and graphene, whereby C-Mo and C-S bonds connect the two materials. The results reveal that CO2 and H2O are weakly adsorbed on different parts of the heterostructure, while NH3 molecules are strongly adsorbed on the C-Mo interface with an energy equal to −1.233 eV. Further analyses reveal that only the adsorbed NH3 at the C-Mo surface leads to significant changes in the electronic structure, even in an atmospheric environment, where O2 molecules are pre-adsorbed at the interface. The planar average of electrostatic potential and the calculated currents at ±0.5 V applied voltages reveal that the Schottky barrier at C−Mo graphene/MoS2 interface is very sensitive to the adsorption of NH3 gas molecule.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"36334-36341"},"PeriodicalIF":4.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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