This paper presents simulation models developed for investigating singlemode-multimode-singlemode (SMS) structures, with the intention of utilising precise nano-scale 3D printing to print the center-section of the structures modelled. The models allow the design of structures for use in SMS fiber sensors, replacing the silica fiber center-section structures commonly used, with 3D printed elements. The paper discusses the process of evaluating and validating COMSOL propagation models by comparison to existing reported results and proposes the use of SMS self-imaging length and the transmission spectra as useful metrics for model comparisons. Both 2D and 3D COMSOL models are developed, and both show good agreement in calculating self-imaging length with the other referenced models. In particular, the 3D model not only simulates the self-imaging length with high accuracy, but also shows good spectral agreement with the referenced models and an analytical calculation. In addition, results from previously published work by the group are used for comparison with the 3D COMSOL model.
{"title":"An evaluation of modelling of propagation using COMSOL for a singlemode-multimode-singlemode structure","authors":"Thomas Freir , Arun Kumar Mallik , Yuliya Semenova , Gerald Farrell","doi":"10.1016/j.measen.2026.101992","DOIUrl":"10.1016/j.measen.2026.101992","url":null,"abstract":"<div><div>This paper presents simulation models developed for investigating singlemode-multimode-singlemode (SMS) structures, with the intention of utilising precise nano-scale 3D printing to print the center-section of the structures modelled. The models allow the design of structures for use in SMS fiber sensors, replacing the silica fiber center-section structures commonly used, with 3D printed elements. The paper discusses the process of evaluating and validating COMSOL propagation models by comparison to existing reported results and proposes the use of SMS self-imaging length and the transmission spectra as useful metrics for model comparisons. Both 2D and 3D COMSOL models are developed, and both show good agreement in calculating self-imaging length with the other referenced models. In particular, the 3D model not only simulates the self-imaging length with high accuracy, but also shows good spectral agreement with the referenced models and an analytical calculation. In addition, results from previously published work by the group are used for comparison with the 3D COMSOL model.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"44 ","pages":"Article 101992"},"PeriodicalIF":0.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1016/j.measen.2026.101991
Changdong Yin , Jun Yao , Jianfei Xu , Zhou Xu , Luanfei Wan , Qiang Liu , Dongdong Ye
The advancement of agricultural automation intensifies demands for highly reliable visual recognition systems in orchard harvesting robots. However, it is difficult to achieve robust fruit detection for balancing high accuracy and real-time performance under complex orchard conditions with variable illumination, occlusions, and phenotypic diversity. This study proposes a collaborative framework integrating adaptive image processing with heterogeneous model inference. The methodology begins with Lab color space conversion to ensure illumination invariance. It further utilizes dual-threshold HSV segmentation for handling both red and green apples, alongside morphological optimization with elliptical structuring elements to address occlusion. A novel architecture allocates real-time screening to an embedded Random Forest (RF) classifier and precise localization to a host-based lightweight YOLOv5 model through fused color-morphological features. Experimental results demonstrate that morphological feature enhancement consistently outperforms color-based approaches across both models. The models with dual-feature input achieves optimal performance that the RF classifier attains accuracy of 83.35 %, while lightweight YOLOv5 reaches 98.90 % accuracy. Quantitative analysis reveals dual-feature fusion with color and morphology improving all metrics by over 3 % compared to non-enhanced baselines. Notably, the observed accuracy improvement exceeded the sum of gains from individual features, confirming a synergistic effect and proving the necessity of feature fusion. This work provides a computationally viable solution for reliable apple recognition in unstructured environments.
{"title":"Heterogeneous models with synergistic feature fusion for real-time apple recognition in robust apple harvesting robotics","authors":"Changdong Yin , Jun Yao , Jianfei Xu , Zhou Xu , Luanfei Wan , Qiang Liu , Dongdong Ye","doi":"10.1016/j.measen.2026.101991","DOIUrl":"10.1016/j.measen.2026.101991","url":null,"abstract":"<div><div>The advancement of agricultural automation intensifies demands for highly reliable visual recognition systems in orchard harvesting robots. However, it is difficult to achieve robust fruit detection for balancing high accuracy and real-time performance under complex orchard conditions with variable illumination, occlusions, and phenotypic diversity. This study proposes a collaborative framework integrating adaptive image processing with heterogeneous model inference. The methodology begins with Lab color space conversion to ensure illumination invariance. It further utilizes dual-threshold HSV segmentation for handling both red and green apples, alongside morphological optimization with elliptical structuring elements to address occlusion. A novel architecture allocates real-time screening to an embedded Random Forest (RF) classifier and precise localization to a host-based lightweight YOLOv5 model through fused color-morphological features. Experimental results demonstrate that morphological feature enhancement consistently outperforms color-based approaches across both models. The models with dual-feature input achieves optimal performance that the RF classifier attains accuracy of 83.35 %, while lightweight YOLOv5 reaches 98.90 % accuracy. Quantitative analysis reveals dual-feature fusion with color and morphology improving all metrics by over 3 % compared to non-enhanced baselines. Notably, the observed accuracy improvement exceeded the sum of gains from individual features, confirming a synergistic effect and proving the necessity of feature fusion. This work provides a computationally viable solution for reliable apple recognition in unstructured environments.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"44 ","pages":"Article 101991"},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.measen.2026.101990
V.B. Murali Krishna , Hossein Fotouhi , Rajesh Cheruku , Abdul Wahid , Tien Anh Tran
The transition to truly smart cities demands more than layered technologies; it requires convergent intelligence that unifies physical sensing, adaptive control, and ethical security. This editorial paper brief about the special issue entitled “Measurement, Control and Security of Systems for Smart Cities”. As smart cities are among the most active research areas, the call for papers for this special issue, “VSI: Systems for Smart Cities” attracted a wide range of manuscripts spanning multiple disciplines, including Computer Science, Electrical and Electronics Engineering, Communication Engineering, Civil Engineering, Mechanical Engineering, Urban Construction, Artificial Intelligence and Machine Learning, Cybersecurity, and Renewable Energy etc. Notably, authors from 16 different countries, including Algeria, Chile, China, Egypt, England, Ethiopia, India, Iran, Jordan, Kosovo, Nigeria, Portugal, Saudi Arabia and United Arab Emirates have contributed their research articles. In this editorial, we synthesize insights from all 30 published articles to present a holistic vision of next-generation urban ecosystems. Together, these works span renewable energy, structural health, healthcare, transportation, noise pollution, public space, and data security, these works collectively redefine what it means to build intelligent infrastructure that is sustainable, equitable, resilient, and trustworthy. We organize these advances around three interwoven pillars: (1) Measurement for Awareness, (2) Control for Adaptation, and (3) Security and Ethics for Trust-and demonstrate how fractional-order controllers, lightweight crack detectors, edge-based triage systems, multimodal transport models, and secure data-mining frameworks all contribute to a human-centered urban future. This synthesis serves as both a technical roadmap and a philosophical compass for the responsible evolution of smart cities.
向真正的智慧城市过渡需要的不仅仅是分层技术；它需要融合智能，将物理感知、自适应控制和道德安全统一起来。这篇社论简要介绍了题为“智能城市系统的测量、控制和安全”的特刊。由于智慧城市是最活跃的研究领域之一，本期《VSI: Systems for smart cities》特刊的论文征稿吸引了广泛的稿件，涉及多个学科，包括计算机科学、电气与电子工程、通信工程、土木工程、机械工程、城市建设、人工智能与机器学习、网络安全、可再生能源等。值得注意的是，来自阿尔及利亚、智利、中国、埃及、英国、埃塞俄比亚、印度、伊朗、约旦、科索沃、尼日利亚、葡萄牙、沙特阿拉伯和阿拉伯联合酋长国等16个不同国家的作者贡献了他们的研究文章。在这篇社论中，我们综合了所有30篇已发表文章的见解，以呈现下一代城市生态系统的整体愿景。这些作品涵盖了可再生能源、结构健康、医疗保健、交通运输、噪音污染、公共空间和数据安全，这些作品共同重新定义了建设可持续、公平、有弹性和值得信赖的智能基础设施的意义。我们围绕三个相互交织的支柱来组织这些进展：(1)意识测量，(2)适应控制，(3)信任安全和道德，并展示分数阶控制器、轻量级裂缝检测器、基于边缘的分类系统、多式联运模式和安全数据挖掘框架如何为以人为中心的城市未来做出贡献。这种综合既可以作为技术路线图，也可以作为智慧城市负责任发展的哲学指南针。
{"title":"Editorial article (Special Issue): Measurement, control and security of systems for smart cities","authors":"V.B. Murali Krishna , Hossein Fotouhi , Rajesh Cheruku , Abdul Wahid , Tien Anh Tran","doi":"10.1016/j.measen.2026.101990","DOIUrl":"10.1016/j.measen.2026.101990","url":null,"abstract":"<div><div>The transition to truly smart cities demands more than layered technologies; it requires convergent intelligence that unifies physical sensing, adaptive control, and ethical security. This editorial paper brief about the special issue entitled “<em>Measurement, Control and Security of Systems for Smart Cities</em>”. As smart cities are among the most active research areas, the call for papers for this special issue, “VSI: Systems for Smart Cities” attracted a wide range of manuscripts spanning multiple disciplines, including Computer Science, Electrical and Electronics Engineering, Communication Engineering, Civil Engineering, Mechanical Engineering, Urban Construction, Artificial Intelligence and Machine Learning, Cybersecurity, and Renewable Energy etc. Notably, authors from 16 different countries, including Algeria, Chile, China, Egypt, England, Ethiopia, India, Iran, Jordan, Kosovo, Nigeria, Portugal, Saudi Arabia and United Arab Emirates have contributed their research articles. In this editorial, we synthesize insights from all 30 published articles to present a holistic vision of next-generation urban ecosystems. Together, these works span renewable energy, structural health, healthcare, transportation, noise pollution, public space, and data security, these works collectively redefine what it means to build intelligent infrastructure that is sustainable, equitable, resilient, and trustworthy. We organize these advances around three interwoven pillars: (1) Measurement for Awareness, (2) Control for Adaptation, and (3) Security and Ethics for Trust-and demonstrate how fractional-order controllers, lightweight crack detectors, edge-based triage systems, multimodal transport models, and secure data-mining frameworks all contribute to a human-centered urban future. This synthesis serves as both a technical roadmap and a philosophical compass for the responsible evolution of smart cities.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"44 ","pages":"Article 101990"},"PeriodicalIF":0.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145963430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1016/j.measen.2026.101983
Yanyan Zheng , Zhibo Yang , Xiaoshui Zhang , Wei Li , Ruiqin Gu , Hongjun Ren
Lead selenide (PbSe) thin films are promising materials for mid-infrared photodetection owing to their narrow bandgap (0.2–0.4 eV), high carrier mobility, and tunable optoelectronic properties. However, current fabrication processes for PbSe detectors still face challenges in producing large-area, high-performance photosensitive films, which has hindered the commercialization of large-format, high-performance PbSe infrared imaging systems. In the chemical bath deposition (CBD) of PbSe thin films, deposition time is a critical parameter influencing film thickness, crystallinity, grain size, compactness, surface morphology, and stoichiometry. This study systematically investigates the role of deposition time in tailoring the morphology, crystallinity, and photoresponse of CBD-prepared PbSe films. Experimentally, the PbSe film deposited for 4.5 h exhibited a dark resistance of 3.078 MΩ and a photoresponse ratio of 20.4 %. In comparison, films deposited for 3 h and 5.5 h showed higher dark resistances of 7.396 MΩ and 6.84 MΩ, respectively. XRD and SEM characterization revealed a U-shaped relationship between deposition time and the optoelectronic properties of the PbSe films. Therefore, systematic optimization of deposition time is a key step toward obtaining high-performance PbSe films. This work provides valuable insights for the application of PbSe materials in infrared detection.
{"title":"Effect of deposition time on microstructure and optoelectronic properties of lead selenide thin films","authors":"Yanyan Zheng , Zhibo Yang , Xiaoshui Zhang , Wei Li , Ruiqin Gu , Hongjun Ren","doi":"10.1016/j.measen.2026.101983","DOIUrl":"10.1016/j.measen.2026.101983","url":null,"abstract":"<div><div>Lead selenide (PbSe) thin films are promising materials for mid-infrared photodetection owing to their narrow bandgap (0.2–0.4 eV), high carrier mobility, and tunable optoelectronic properties. However, current fabrication processes for PbSe detectors still face challenges in producing large-area, high-performance photosensitive films, which has hindered the commercialization of large-format, high-performance PbSe infrared imaging systems. In the chemical bath deposition (CBD) of PbSe thin films, deposition time is a critical parameter influencing film thickness, crystallinity, grain size, compactness, surface morphology, and stoichiometry. This study systematically investigates the role of deposition time in tailoring the morphology, crystallinity, and photoresponse of CBD-prepared PbSe films. Experimentally, the PbSe film deposited for 4.5 h exhibited a dark resistance of 3.078 MΩ and a photoresponse ratio of 20.4 %. In comparison, films deposited for 3 h and 5.5 h showed higher dark resistances of 7.396 MΩ and 6.84 MΩ, respectively. XRD and SEM characterization revealed a U-shaped relationship between deposition time and the optoelectronic properties of the PbSe films. Therefore, systematic optimization of deposition time is a key step toward obtaining high-performance PbSe films. This work provides valuable insights for the application of PbSe materials in infrared detection.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"43 ","pages":"Article 101983"},"PeriodicalIF":0.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial vehicles (UAVs) have become indispensable in both civilian and military domains, enabling applications such as smart surveillance, environmental monitoring, and search-and-rescue operations. However, effective object detection in UAV imagery remains challenging due to the small size of targets, high object density, frequent occlusions, and complex backgrounds resulting from varying altitudes and viewpoints. Existing algorithms, such as You Only Look Once (YOLO) v5, exhibit limited accuracy in detecting targets in UAV images. To address these challenges, this study proposes an enhanced YOLOv5-based detection model. The model incorporates an optimized detection module with three prediction heads for multi-scale bounding box predictions. Additionally, self-attention mechanisms and a Convolutional Block Attention Module (CBAM) are integrated to focus on salient regions and mitigate the impact of occlusions. Furthermore, we introduce a ConvELU layer, which replaces the default SiLU activation with the Exponential Linear Unit (ELU). This modified ConvELU layer is applied to the backbone, neck, and head components, effectively improving the model's feature extraction capabilities. Experimental results of the VisDrone dataset demonstrate that the proposed model achieves a precision of 95.1 %, a recall of 86.3 %, and a mean Average Precision (mAP) of 91.6 %, outperforming the standard YOLOv5 and other state-of-the-art detectors.
无人驾驶飞行器（uav）在民用和军事领域都是不可或缺的，可以实现智能监视、环境监测和搜救行动等应用。然而，由于目标尺寸小、目标密度高、频繁遮挡和不同高度和视点导致的复杂背景，在无人机图像中有效的目标检测仍然具有挑战性。现有的算法，例如You Only Look Once (YOLO) v5，在UAV图像中探测目标表现出有限的精度。为了应对这些挑战，本研究提出了一种增强的基于yolov5的检测模型。该模型结合了一个优化的检测模块，具有三个预测头，用于多尺度边界框预测。此外，自我注意机制和卷积块注意模块（CBAM）相结合，专注于突出区域，减轻闭塞的影响。此外，我们引入了一个ConvELU层，它用指数线性单元（ELU）取代了默认的SiLU激活。将改进后的ConvELU层应用于脊柱、颈部和头部部件，有效提高了模型的特征提取能力。VisDrone数据集的实验结果表明，该模型的准确率为95.1%，召回率为86.3%，平均平均精度（mAP）为91.6%，优于标准的YOLOv5和其他最先进的检测器。
{"title":"UAV-based object detection model for smart surveillance using deep neural network","authors":"Gyanendra Kumar , Sur Singh Rawat , Jyoti Gautam , Ayodeji Olalekan Salau","doi":"10.1016/j.measen.2025.101982","DOIUrl":"10.1016/j.measen.2025.101982","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) have become indispensable in both civilian and military domains, enabling applications such as smart surveillance, environmental monitoring, and search-and-rescue operations. However, effective object detection in UAV imagery remains challenging due to the small size of targets, high object density, frequent occlusions, and complex backgrounds resulting from varying altitudes and viewpoints. Existing algorithms, such as You Only Look Once (YOLO) v5, exhibit limited accuracy in detecting targets in UAV images. To address these challenges, this study proposes an enhanced YOLOv5-based detection model. The model incorporates an optimized detection module with three prediction heads for multi-scale bounding box predictions. Additionally, self-attention mechanisms and a Convolutional Block Attention Module (CBAM) are integrated to focus on salient regions and mitigate the impact of occlusions. Furthermore, we introduce a ConvELU layer, which replaces the default SiLU activation with the Exponential Linear Unit (ELU). This modified ConvELU layer is applied to the backbone, neck, and head components, effectively improving the model's feature extraction capabilities. Experimental results of the VisDrone dataset demonstrate that the proposed model achieves a precision of <strong>95.1 %,</strong> a recall of <strong>86.3 %,</strong> and a mean Average Precision (mAP) of <strong>91.6 %,</strong> outperforming the standard YOLOv5 and other state-of-the-art detectors.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"43 ","pages":"Article 101982"},"PeriodicalIF":0.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1016/j.measen.2025.101981
Wafa Ben Hassen, Mariem Slimani, Fabrice Auzanneau
Electrical reflectometry is widely utilized for cable diagnosis to detect and locate both hard faults (such as open or short circuits) and soft faults (such as chafing, bending radius issues, pinching, etc.). It offers the advantage of using only one end of the cable under test to inject an electromagnetic wave and simultaneously record reflected echoes at each impedance discontinuity. Then, the analysis of these echoes makes it possible to obtain information about that discontinuity. Reflectometry is valued for its simplicity of deployment, sensitivity to electrical parameter variations in the propagation medium, and accuracy in fault location. These attributes make it a promising method in various domains such as Structural Health Monitoring (SHM), complex load monitoring, environmental monitoring, etc., especially considering that the cable used for reflectometry signal propagation can also function as a sensor. In this context, this paper aims to provide an in-depth review of potential applications of electrical reflectometry beyond cable diagnostics and compare it with other established methods (e.g., acoustic, fiber optics, etc.). A bibliometric analysis is presented in this paper, which, to the best of our knowledge, is the first of its kind in the reflectometry-related literature.
{"title":"A review of electrical reflectometry applications: State of the art, positioning, bibliometric analysis and future directions","authors":"Wafa Ben Hassen, Mariem Slimani, Fabrice Auzanneau","doi":"10.1016/j.measen.2025.101981","DOIUrl":"10.1016/j.measen.2025.101981","url":null,"abstract":"<div><div>Electrical reflectometry is widely utilized for cable diagnosis to detect and locate both hard faults (such as open or short circuits) and soft faults (such as chafing, bending radius issues, pinching, etc.). It offers the advantage of using only one end of the cable under test to inject an electromagnetic wave and simultaneously record reflected echoes at each impedance discontinuity. Then, the analysis of these echoes makes it possible to obtain information about that discontinuity. Reflectometry is valued for its simplicity of deployment, sensitivity to electrical parameter variations in the propagation medium, and accuracy in fault location. These attributes make it a promising method in various domains such as Structural Health Monitoring (SHM), complex load monitoring, environmental monitoring, etc., especially considering that the cable used for reflectometry signal propagation can also function as a sensor. In this context, this paper aims to provide an in-depth review of potential applications of electrical reflectometry beyond cable diagnostics and compare it with other established methods (e.g., acoustic, fiber optics, etc.). A bibliometric analysis is presented in this paper, which, to the best of our knowledge, is the first of its kind in the reflectometry-related literature.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"43 ","pages":"Article 101981"},"PeriodicalIF":0.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.measen.2025.101980
Talia Tene , Cristina Estefanía Ramos Araujo , Natalia Alexandra Pérez Londo , Lorenzo S. Caputi , Salvatore Straface , Cristian Vacacela Gomez
Surface plasmon resonance (SPR) sensors enable label-free readout of refractive-index (RI) changes at metal–dielectric interfaces and are promising for on-site water monitoring, whereas ICP-MS/AAS lack portability. We model prism-coupled SPR using a CaF2 prism and a Cu/Si3N4 stack overlaid with graphene-family films at 633 nm. Four overlayers—graphene, semiconducting single-wall carbon nanotubes (s-SWCNTs), graphene oxide (GO), and reduced graphene oxide (rGO)—are compared via a transfer-matrix approach. Metrics include resonance-angle shift (Δθ), angular sensitivity (S), detection accuracy (DA), quality factor (QF), figure of merit (FoM), limit of detection (LoD), and a combined sensitivity factor (CSF) in deionized water and heavy-metal solutions. Simulations reveal a dual-regime design: rGO maximizes raw sensitivity (318.21° RIU−1 for Pb2+) with LoD ≈ 1.57 × 10−5 RIU, whereas GO provides the sharpest resonances (QF ≈ 195 RIU−1; DA ≈ 0.64); graphene and s-SWCNTs are intermediate. Electric-field profiles yield a penetration depth of ≈50–52 nm with the 2D layer at the field maximum. We justify the CaF2/Cu choice, map trade-offs across 2D films, and outline functionalization, scalable fabrication, stability, and miniaturization, alongside microfluidic, multi-wavelength, and AI-assisted validation toward meeting WHO/EPA guidelines.
{"title":"SPR sensors enhanced by nanomaterials for monitoring heavy metal ions in water: A mathematical modeling","authors":"Talia Tene , Cristina Estefanía Ramos Araujo , Natalia Alexandra Pérez Londo , Lorenzo S. Caputi , Salvatore Straface , Cristian Vacacela Gomez","doi":"10.1016/j.measen.2025.101980","DOIUrl":"10.1016/j.measen.2025.101980","url":null,"abstract":"<div><div>Surface plasmon resonance (SPR) sensors enable label-free readout of refractive-index (RI) changes at metal–dielectric interfaces and are promising for on-site water monitoring, whereas ICP-MS/AAS lack portability. We model prism-coupled SPR using a CaF<sub>2</sub> prism and a Cu/Si<sub>3</sub>N<sub>4</sub> stack overlaid with graphene-family films at 633 nm. Four overlayers—graphene, semiconducting single-wall carbon nanotubes (s-SWCNTs), graphene oxide (GO), and reduced graphene oxide (rGO)—are compared via a transfer-matrix approach. Metrics include resonance-angle shift (Δθ), angular sensitivity (S), detection accuracy (DA), quality factor (QF), figure of merit (FoM), limit of detection (LoD), and a combined sensitivity factor (CSF) in deionized water and heavy-metal solutions. Simulations reveal a dual-regime design: rGO maximizes raw sensitivity (318.21° RIU<sup>−1</sup> for Pb<sup>2+</sup>) with LoD ≈ 1.57 × 10<sup>−5</sup> RIU, whereas GO provides the sharpest resonances (QF ≈ 195 RIU<sup>−1</sup>; DA ≈ 0.64); graphene and s-SWCNTs are intermediate. Electric-field profiles yield a penetration depth of ≈50–52 nm with the 2D layer at the field maximum. We justify the CaF<sub>2</sub>/Cu choice, map trade-offs across 2D films, and outline functionalization, scalable fabrication, stability, and miniaturization, alongside microfluidic, multi-wavelength, and AI-assisted validation toward meeting WHO/EPA guidelines.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"43 ","pages":"Article 101980"},"PeriodicalIF":0.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.measen.2025.101979
S. Amosedinakaran , P. Anitha , R. Kannan , K. Karthikeyan , S. Suresh , A. Bhuvanesh
The economy of any country is significantly dependent on agricultural yield. A major concern for all countries is facing leaf diseases in agriculture due to bacteria or infections that decrease the yield. To prevent the spreading of leaf diseases, early detection and diagnosis are essential. The plant disease detection technique has been utilized to avoid a reduction in yield percentage. Image processing-based solutions have been quite essential in practice and must be quick, automated, affordable, and precise. The multi-class support vector machine (multi-class SVM) technique has been adopted for this study. This technique extracts information from given samples and provides exceptional results that would help identify and classify the diseases in the plants. Paddy leaf has been adopted for study. This study is focuses clearly on four key components: (i) the research problem early and accurate detection of paddy leaf diseases using image processing; (ii) the methodological approach multi-class SVM classification combined with K-means++ segmentation and complementary descriptors such as color statistics, HOG, and GLCM; (iii) the results achieving an average recognition accuracy of 93 % and peak performance of 96 % with 17 % faster execution compared to traditional methods; and (iv) the practical implications demonstrating the method's potential for efficient, low-cost disease diagnosis in agricultural environments. This strategy has a lot of promise to help with early plant disease diagnosis and enhance crop management techniques. Its implementation has the potential to address a significant gap in agricultural disease management and contribute meaningfully to improving global food security.
任何国家的经济都严重依赖农业产量。所有国家关注的一个主要问题是，由于细菌或感染导致的农业叶片病害会降低产量。为了防止叶病的蔓延，早期发现和诊断是必不可少的。利用植物病害检测技术，避免了产量百分比的下降。基于图像处理的解决方案在实践中非常重要，并且必须快速、自动化、经济实惠和精确。本研究采用多类支持向量机（multi-class support vector machine，简称multi-class SVM）技术。该技术从给定的样品中提取信息，并提供有助于识别和分类植物疾病的特殊结果。以水稻叶片为研究对象。本研究明确了四个关键组成部分：(1)利用图像处理技术早期准确检测水稻叶片病害的研究问题；（ii）结合k -means++分割和互补描述符（如颜色统计、HOG和GLCM）的多类SVM分类方法；（iii）与传统方法相比，平均识别准确率达到93%，峰值性能达到96%，执行速度提高17%；（iv）证明该方法在农业环境中具有高效、低成本疾病诊断潜力的实际意义。这一策略在帮助早期植物病害诊断和提高作物管理技术方面具有很大的前景。它的实施有可能解决农业疾病管理方面的重大差距，并为改善全球粮食安全作出有意义的贡献。
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Pub Date : 2025-11-07DOI: 10.1016/j.measen.2025.101978
Alban Petitjean, Olivier Musset
The Analysis of Variance (Anova) method, alongside the range method, is widely used to conduct Gauge Repeatability and Reproducibility (Gauge R&R) studies for measurement systems. While Anova provides more precise and robust statistical insights compared to the range-based approach, it is often perceived as more complex due to the necessity of calculating sum of squares, mean squares, and performing F-tests. Furthermore, this method can lead to misleading conclusions if the results—especially residuals—are not thoroughly and correctly analyzed. Despite its analytical complexity, the Anova method can be effectively implemented using spreadsheet tools such as Excel®. Excel® offers a flexible and accessible environment to perform rapid recalculations when input data are modified. In this paper, we demonstrate how to conduct a Gauge R&R study using the Anova approach within Excel®. The dual objective of this work is (i) to provide a user-friendly implementation within Excel®, and (ii) to enable rigorous and accurate interpretation of the statistical results produced.
{"title":"Can Excel® be used for gauge R&R study based on the analysis of variance (Anova)?","authors":"Alban Petitjean, Olivier Musset","doi":"10.1016/j.measen.2025.101978","DOIUrl":"10.1016/j.measen.2025.101978","url":null,"abstract":"<div><div>The Analysis of Variance (Anova) method, alongside the range method, is widely used to conduct Gauge Repeatability and Reproducibility (Gauge R&R) studies for measurement systems. While Anova provides more precise and robust statistical insights compared to the range-based approach, it is often perceived as more complex due to the necessity of calculating sum of squares, mean squares, and performing F-tests. Furthermore, this method can lead to misleading conclusions if the results—especially residuals—are not thoroughly and correctly analyzed. Despite its analytical complexity, the Anova method can be effectively implemented using spreadsheet tools such as Excel®. Excel® offers a flexible and accessible environment to perform rapid recalculations when input data are modified. In this paper, we demonstrate how to conduct a Gauge R&R study using the Anova approach within Excel®. The dual objective of this work is (i) to provide a user-friendly implementation within Excel®, and (ii) to enable rigorous and accurate interpretation of the statistical results produced.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"42 ","pages":"Article 101978"},"PeriodicalIF":0.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1016/j.measen.2025.101977
V. Esposito, E. Sciurti, A. Calogiuri, D. Bellisario, L. Velardi, F. Casino, L. Blasi, L. Francioso
Traditional methods of blood glucose monitoring are invasive and can cause anxiety, pain and infection, resulting in poor patient compliance. Sweat-based glucose sensing has emerged as a promising non-invasive alternative, but the significantly lower glucose concentrations (10–100 times lower than in blood) pose a challenge for sensor sensitivity and operation. Here, we present different measurement protocols for enzymatic electrochemical glucose sensors with enhanced sensitivity and sub-second response calibration algorithm. The resulting amperometric response accurately reflects glucose concentration, demonstrating the sensor's potential for non-invasive monitoring of glucose in sweat. To enhance the reliability of the measurements and mitigate the variability among sensors arising from differences in sweat composition and secretion, a post-measurement protocol was developed. This protocol exploits a Response Correction Factor (RCF) calculated from the specific sensitivity of each sensor. This approach compensates for variability among different sensors reducing the standard deviation, thereby improving calibration accuracy (R2 = 0.995 vs. R2 = 0.822 without correction) allowing the prevention of phenomena related to enzyme inactivation or allogeneic reactions that may affect individual sensors in Continuous Glucose Monitoring (CGM) systems. An in-depth analysis was also conducted using sample microvolumes (20 μL), the typical amount of sweat available in wearable devices, to study thin-layer chronoamperometry response. To enhance the linearity of the sensor response, a differential compensation algorithm based on the slope of the response curve was adopted, employing a sensor without enzyme as a reference. This measurement method enhanced the dynamic range of slope values from 0.0085 μA/s to 0.0125 μA/s. The experimental results identified in a reliable way three operational regions: physiological (60–110 μM), warning values (110–160 μM) and alert/risk (>160 μM). The proposed strategies increase the robustness and applicability of sweat-based glucose monitoring for real-world applications.
{"title":"Sub-second response algorithm for wearable glucose sensors: normalized slope-based calibration and microvolumes differential compensation measurements","authors":"V. Esposito, E. Sciurti, A. Calogiuri, D. Bellisario, L. Velardi, F. Casino, L. Blasi, L. Francioso","doi":"10.1016/j.measen.2025.101977","DOIUrl":"10.1016/j.measen.2025.101977","url":null,"abstract":"<div><div>Traditional methods of blood glucose monitoring are invasive and can cause anxiety, pain and infection, resulting in poor patient compliance. Sweat-based glucose sensing has emerged as a promising non-invasive alternative, but the significantly lower glucose concentrations (10–100 times lower than in blood) pose a challenge for sensor sensitivity and operation. Here, we present different measurement protocols for enzymatic electrochemical glucose sensors with enhanced sensitivity and sub-second response calibration algorithm. The resulting amperometric response accurately reflects glucose concentration, demonstrating the sensor's potential for non-invasive monitoring of glucose in sweat. To enhance the reliability of the measurements and mitigate the variability among sensors arising from differences in sweat composition and secretion, a post-measurement protocol was developed. This protocol exploits a Response Correction Factor (RCF) calculated from the specific sensitivity of each sensor. This approach compensates for variability among different sensors reducing the standard deviation, thereby improving calibration accuracy (R<sup>2</sup> = 0.995 vs. R<sup>2</sup> = 0.822 without correction) allowing the prevention of phenomena related to enzyme inactivation or allogeneic reactions that may affect individual sensors in Continuous Glucose Monitoring (CGM) systems. An in-depth analysis was also conducted using sample microvolumes (20 μL), the typical amount of sweat available in wearable devices, to study thin-layer chronoamperometry response. To enhance the linearity of the sensor response, a differential compensation algorithm based on the slope of the response curve was adopted, employing a sensor without enzyme as a reference. This measurement method enhanced the dynamic range of slope values from 0.0085 μA/s to 0.0125 μA/s. The experimental results identified in a reliable way three operational regions: physiological (60–110 μM), warning values (110–160 μM) and alert/risk (>160 μM). The proposed strategies increase the robustness and applicability of sweat-based glucose monitoring for real-world applications.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"42 ","pages":"Article 101977"},"PeriodicalIF":0.0,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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