Pub Date : 2026-02-03DOI: 10.1016/j.measurement.2026.120580
Piotr Knutel , Łukasz Ciura , Jacek Boguski , Jarosław Wróbel , Andrzej Kolek
In this article, we develop a model and method that enable the evaluation of bulk and contact components of 1/f noise in Transfer Length Method (TLM) structures. A key advantage of the model, compared to existing approaches, is its generic formulation: it does not assume dominance of either bulk or contact contributions to the TLM resistance or noise. Consequently, both the state-of-the-art bulk Hooge parameter and the contact noise parameter can be extracted by fitting the model to experimental data — specifically, to the normalized noise as a function of sample length. The model was experimentally verified using n-type and p-type InAs layers with different doping concentrations. The results show that dominance in total resistance does not necessarily correspond to dominance in total noise. In particular, contacts are the primary noise source for all p-type samples, even when their resistances are minor contributions for sufficiently long TLM sections. Contact noise is higher in p-type than in n-type structures, but it decreases with increasing doping density. For moderately doped n-type InAs layers, we obtained a reliable estimate of the Hooge parameter, , describing the 1/f noise intensity.
{"title":"Evaluation of bulk and contact noise components with the Transfer Length Method: Model and experiment","authors":"Piotr Knutel , Łukasz Ciura , Jacek Boguski , Jarosław Wróbel , Andrzej Kolek","doi":"10.1016/j.measurement.2026.120580","DOIUrl":"10.1016/j.measurement.2026.120580","url":null,"abstract":"<div><div>In this article, we develop a model and method that enable the evaluation of bulk and contact components of 1/f noise in Transfer Length Method (TLM) structures. A key advantage of the model, compared to existing approaches, is its generic formulation: it does not assume dominance of either bulk or contact contributions to the TLM resistance or noise. Consequently, both the state-of-the-art bulk Hooge parameter <span><math><msub><mrow><mi>α</mi></mrow><mrow><mi>H</mi></mrow></msub></math></span> and the contact noise parameter <span><math><mi>K</mi></math></span> can be extracted by fitting the model to experimental data — specifically, to the normalized noise as a function of sample length. The model was experimentally verified using <em>n</em>-type and <em>p</em>-type InAs layers with different doping concentrations. The results show that dominance in total resistance does not necessarily correspond to dominance in total noise. In particular, contacts are the primary noise source for all <em>p</em>-type samples, even when their resistances are minor contributions for sufficiently long TLM sections. Contact noise is higher in <em>p</em>-type than in <em>n</em>-type structures, but it decreases with increasing doping density. For moderately doped <em>n</em>-type InAs layers, we obtained a reliable estimate of the Hooge parameter, <span><math><mrow><msub><mrow><mi>α</mi></mrow><mrow><mi>H</mi></mrow></msub><mo>≅</mo><mn>2</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math></span>, describing the 1/f noise intensity.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"269 ","pages":"Article 120580"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1016/j.measurement.2026.120698
Faraz Javaid , Amir Hamza , Muhammad Osama Ali , Muhammad Mubasher Saleem
This paper presents the design and analysis of the first robust neuromorphic microelectromechanical system (MEMS) tuning fork gyroscope (TFG) integrating inertial rate-sensing with tunable physical reservoir computing (PRC) within a single device for real-time wearable fall detection. The TFG inertial mass is electrostatically coupled to eight oscillating beams with a gap. Coriolis-induced motion modulates the electrostatic field, driving the beams into a nonlinear regime, enabling on-chip neuromorphic computing via delayed feedback. The design features electrostatic frequency tuning in the sense mode enabling dynamic reservoir tuning, alongside an optimized antiphase drive lever and diamond-shaped sense structure to enhance robustness under high-g vibrations and shocks. Designed using the MIDIS process, the TFG exhibits a natural frequency of and a sensitivity of . Shock analysis confirms resistance to shock signal with safe displacement in drive and sense modes without structural damage. Electrostatic tuning lowers the critical voltage from to , as the tuning voltage increases to . PRC performance is validated by using nonlinear autoregressive moving average (NARMA-10) as a benchmark task, demonstrating acceptable prediction accuracy. Leveraging this framework, the neuromorphic MEMS TFG is evaluated for real-time fall detection in wearable systems, achieving accuracy, sensitivity, and specificity, while maintaining minimal false-alarm rates. By integrating mechanical robustness, dynamic tunability, and embedded intelligence within a single MEMS device, this work paves the way for ultra-low-latency, on-edge health monitoring in next-generation wearable systems.
{"title":"Robust neuromorphic MEMS tuning fork gyroscope with integrated sensing and tunable physical reservoir computing for fall detection in wearable systems","authors":"Faraz Javaid , Amir Hamza , Muhammad Osama Ali , Muhammad Mubasher Saleem","doi":"10.1016/j.measurement.2026.120698","DOIUrl":"10.1016/j.measurement.2026.120698","url":null,"abstract":"<div><div>This paper presents the design and analysis of the first robust neuromorphic microelectromechanical system (MEMS) tuning fork gyroscope (TFG) integrating inertial rate-sensing with tunable physical reservoir computing (PRC) within a single device for real-time wearable fall detection. The TFG inertial mass is electrostatically coupled to eight oscillating beams with a <span><math><mrow><mn>2.2</mn><mi>μ</mi><mi>m</mi></mrow></math></span> gap. Coriolis-induced motion modulates the electrostatic field, driving the beams into a nonlinear regime, enabling on-chip neuromorphic computing via delayed feedback. The design features electrostatic frequency tuning in the sense mode enabling dynamic reservoir tuning, alongside an optimized antiphase drive lever and diamond-shaped sense structure to enhance robustness under high-g vibrations and shocks. Designed using the MIDIS process, the TFG exhibits a natural frequency of <span><math><mrow><mn>40</mn><mi>k</mi><mi>H</mi><mi>z</mi></mrow></math></span> and a sensitivity of <span><math><mrow><mn>0.3</mn><mi>m</mi><mi>V</mi><mo>/</mo><mo>(</mo><msup><mrow><mspace></mspace></mrow><mo>°</mo></msup><mo>/</mo><mi>s</mi><mo>)</mo></mrow></math></span>. Shock analysis confirms resistance to <span><math><mrow><mn>1000</mn><mi>g</mi><mo>,</mo><mn>25</mn><mi>μ</mi><mi>s</mi></mrow></math></span> shock signal with safe displacement in drive and sense modes without structural damage. Electrostatic tuning lowers the critical voltage from <span><math><mrow><mn>120</mn><mi>V</mi></mrow></math></span> to <span><math><mrow><mn>70</mn><mi>V</mi></mrow></math></span>, as the tuning voltage increases to <span><math><mrow><mn>13.5</mn><mi>V</mi></mrow></math></span>. PRC performance is validated by using nonlinear autoregressive moving average (NARMA-10) as a benchmark task, demonstrating acceptable prediction accuracy. Leveraging this framework, the neuromorphic MEMS TFG is evaluated for real-time fall detection in wearable systems, achieving <span><math><mrow><mn>97.37</mn><mo>%</mo></mrow></math></span> accuracy, <span><math><mrow><mn>96.29</mn><mo>%</mo></mrow></math></span> sensitivity, and <span><math><mrow><mn>98.33</mn><mo>%</mo></mrow></math></span> specificity, while maintaining minimal false-alarm rates. By integrating mechanical robustness, dynamic tunability, and embedded intelligence within a single MEMS device, this work paves the way for ultra-low-latency, on-edge health monitoring in next-generation wearable systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120698"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1016/j.measurement.2026.120699
Fei Li , Hongshou Li , Shunren Wang , Zhengmo Zhang , Xiaowei Wang
Understanding the relationship between earth-air activity and atmospheric pressure (AP) is therefore essential for the prevention of earth-air activity in the caves. This study uses Cave 61 in the Mogao Grottoes as its research object and uses continuous wavelet transform and wavelet coherence (WTC) analysis to explore the correlation between earth-air flow (EF) and AP variation on different timescales. The results indicate that the outflow of earth-air at the points monitored in the cave fluctuated significantly with AP variation. Moreover, the magnitudes of the fluctuations at different monitoring points were clearly different, suggesting the earth-air is unevenly distributed in the cave. From the perspective of periodic characteristics, the AP and EF both exhibited specific main oscillation periods in different time series. However, their patterns of dynamic change were fundamentally consistent in time. On a timescale corresponding to a year or so, the first main AP period scale is a 18-month one, with a 12-month period obtained under this main period scale; the first main EF period scale is a 28-month one, with a 18-month period obtained under this main period scale. On a daily timescale, the first main period scale for both is 21-hour, with a 14-hour period obtained under this main period scale. However, the peaks and troughs in the EF data clearly led those in the AP data. The WTC analysis revealed a negative correlation between AP and EF, with EF leading AP in phase.
{"title":"Wavelet analysis of the correlation between earth-air flow and atmospheric pressure variation in Cave 61 of the Mogao Grottoes in China","authors":"Fei Li , Hongshou Li , Shunren Wang , Zhengmo Zhang , Xiaowei Wang","doi":"10.1016/j.measurement.2026.120699","DOIUrl":"10.1016/j.measurement.2026.120699","url":null,"abstract":"<div><div>Understanding the relationship between earth-air activity and atmospheric pressure (AP) is therefore essential for the prevention of earth-air activity in the caves. This study uses Cave 61 in the Mogao Grottoes as its research object and uses continuous wavelet transform and wavelet coherence (WTC) analysis to explore the correlation between earth-air flow (EF) and AP variation on different timescales. The results indicate that the outflow of earth-air at the points monitored in the cave fluctuated significantly with AP variation. Moreover, the magnitudes of the fluctuations at different monitoring points were clearly different, suggesting the earth-air is unevenly distributed in the cave. From the perspective of periodic characteristics, the AP and EF both exhibited specific main oscillation periods in different time series. However, their patterns of dynamic change were fundamentally consistent in time. On a timescale corresponding to a year or so, the first main AP period scale is a 18-month one, with a 12-month period obtained under this main period scale; the first main EF period scale is a 28-month one, with a 18-month period obtained under this main period scale. On a daily timescale, the first main period scale for both is 21-hour, with a 14-hour period obtained under this main period scale. However, the peaks and troughs in the EF data clearly led those in the AP data. The WTC analysis revealed a negative correlation between AP and EF, with EF leading AP in phase.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120699"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1016/j.measurement.2026.120687
Shengjian Hu , Weining Fang , Haifeng Bao
Accurate 3D human pose estimation has important application value in fields such as human–computer interaction, motion analysis, and medical rehabilitation. Traditional single-modal methods have significant limitations in complex environments. This paper proposes a dynamic multi-modal human pose estimation method that fuses visual sensors and millimeter-wave radar. First, we construct a radar point cloud processing framework based on graph neural networks. This framework maintains spatial topological relationships through a k-nearest neighbor graph structure and fuses five-dimensional feature information using a reflection intensity-weighted message passing mechanism. Second, we design a dynamic fusion strategy that combines basic quality assessment, learnable quality assessment, and modal prior weights to achieve quality-aware adaptive fusion. Systematic experiments on two datasets demonstrate the effectiveness of our approach. On the standard environment mRI dataset, our method achieves an MPJPE of 91.82 41.81 mm. On the complex environment mmBody dataset, the average MPJPE is 62.47 22.39 mm. Statistical analysis indicates that all improvements are significant (). This method demonstrates excellent robustness in complex environments.
{"title":"R-DMRF-HPE: Robust Dynamic Multi-modal Radar-vision Fusion for Human Pose Estimation","authors":"Shengjian Hu , Weining Fang , Haifeng Bao","doi":"10.1016/j.measurement.2026.120687","DOIUrl":"10.1016/j.measurement.2026.120687","url":null,"abstract":"<div><div>Accurate 3D human pose estimation has important application value in fields such as human–computer interaction, motion analysis, and medical rehabilitation. Traditional single-modal methods have significant limitations in complex environments. This paper proposes a dynamic multi-modal human pose estimation method that fuses visual sensors and millimeter-wave radar. First, we construct a radar point cloud processing framework based on graph neural networks. This framework maintains spatial topological relationships through a k-nearest neighbor graph structure and fuses five-dimensional feature information using a reflection intensity-weighted message passing mechanism. Second, we design a dynamic fusion strategy that combines basic quality assessment, learnable quality assessment, and modal prior weights to achieve quality-aware adaptive fusion. Systematic experiments on two datasets demonstrate the effectiveness of our approach. On the standard environment mRI dataset, our method achieves an MPJPE of 91.82 <span><math><mo>±</mo></math></span> 41.81 mm. On the complex environment mmBody dataset, the average MPJPE is 62.47 <span><math><mo>±</mo></math></span> 22.39 mm. Statistical analysis indicates that all improvements are significant (<span><math><mrow><mi>p</mi><mo><</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>). This method demonstrates excellent robustness in complex environments.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120687"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate assessment of damage in concrete structures requires monitoring techniques that can capture both global stiffness degradation and local cracking processes. Existing structural health monitoring approaches typically rely on separate sensors for vibration measurements and acoustic emission (AE) monitoring, while conventional surface-mounted devices often suffer from poor and variable coupling. This study presents an embedded piezoelectric (PZT) sensor developed for dual mode vibroacoustic monitoring in concrete structures. The sensor is cast within the concrete matrix to improve mechanical coupling and enable robust measurement of structural response during damage evolution. Dual-mode monitoring is achieved through sequential operation of the same embedded sensor in two distinct modes passive acoustic emission (AE) monitoring during fracture loading and impulse-excited vibration testing conducted before and after fracture test. Benchmarking experiments include comparison with commercial accelerometers and AE sensors, confirming that the embedded configuration enhances high-frequency sensitivity and coupling performance. The fracture process is interpreted by correlating AE activity with Digital Image Correlation (DIC)-based crack kinematics, enabling zone-wise understanding of crack development. The vibration response is interpreted using a stiffness-reduction framework consistent with hinge-type crack formation, explaining the observed modal-frequency reduction and increase in damping. Electromechanical impedance measurements quantify sensor–matrix interaction, highlighting the role of epoxy-mediated impedance matching. Overall, the results demonstrate that the proposed embedded sensor provides a unified platform for validated AE-vibration sensing, offering a promising approach for integrated structural health monitoring of concrete infrastructure.
{"title":"Embedded PZT sensors for combined viboacoustic sensing of concrete structures","authors":"Murali Duddi , Amarteja kocherla , Kolluru V.L. Subramaniam","doi":"10.1016/j.measurement.2026.120690","DOIUrl":"10.1016/j.measurement.2026.120690","url":null,"abstract":"<div><div>Accurate assessment of damage in concrete structures requires monitoring techniques that can capture both global stiffness degradation and local cracking processes. Existing structural health monitoring approaches typically rely on separate sensors for vibration measurements and acoustic emission (AE) monitoring, while conventional surface-mounted devices often suffer from poor and variable coupling. This study presents an embedded piezoelectric (PZT) sensor developed for dual mode vibroacoustic monitoring in concrete structures. The sensor is cast within the concrete matrix to improve mechanical coupling and enable robust measurement of structural response during damage evolution. Dual-mode monitoring is achieved through sequential operation of the same embedded sensor in two distinct modes passive acoustic emission (AE) monitoring during fracture loading and impulse-excited vibration testing conducted before and after fracture test. Benchmarking experiments include comparison with commercial accelerometers and AE sensors, confirming that the embedded configuration enhances high-frequency sensitivity and coupling performance. The fracture process is interpreted by correlating AE activity with Digital Image Correlation (DIC)-based crack kinematics, enabling zone-wise understanding of crack development. The vibration response is interpreted using a stiffness-reduction framework consistent with hinge-type crack formation, explaining the observed modal-frequency reduction and increase in damping. Electromechanical impedance measurements quantify sensor–matrix interaction, highlighting the role of epoxy-mediated impedance matching. Overall, the results demonstrate that the proposed embedded sensor provides a unified platform for validated AE-vibration sensing, offering a promising approach for integrated structural health monitoring of concrete infrastructure.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120690"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tissue density is a critical parameter influencing various biological functions, including protein folding and cellular signaling, making accurate measurements essential for advancing our understanding of tissue mechanics and pathology. This work provides a standard operating procedure (SOP) for assessing the bulk density of soft materials using a liquid pycnometer and an analytical balance, along with its validation to ensure precision and reliability. This methodology guarantees precise density evaluations and a rigorous step-by-step procedure for measurement uncertainty estimation. Moreover, the SOP aligns with ASTM F2150-19 standard for biomaterials characterization, which endorses the use of ASTM D792-20 for bulk density measurements, based on the principle of Archimedes. By employing this approach, the study aims to enhance the development of in vitro models for biomedical research, drug testing, and tissue engineering. The SOP allows for bulk density estimation with a relative expanded uncertainty of 3%. Furthermore, it was demonstrated that the material employed for the validation, fibrin gel, is comparable, with a 95% level of confidence, to breast fat, placenta, breast gland, eye (choroid), kidney, liver, heart muscle, pancreas, spleen, diaphragm, eye (ciliary body), muscle, and tongue. Shaping biomimicry: a standardized protocol for soft materials bulk density measurement supporting tissue-like performance.
{"title":"Shaping biomimicry: A standardized protocol for soft materials bulk density measurement supporting tissue-like performance","authors":"Sabrina Caria , Laura Revel , Jessica Petiti , Federico Picollo , Carla Divieto","doi":"10.1016/j.measurement.2026.120590","DOIUrl":"10.1016/j.measurement.2026.120590","url":null,"abstract":"<div><div>Tissue density is a critical parameter influencing various biological functions, including protein folding and cellular signaling, making accurate measurements essential for advancing our understanding of tissue mechanics and pathology. This work provides a standard operating procedure (SOP) for assessing the bulk density of soft materials using a liquid pycnometer and an analytical balance, along with its validation to ensure precision and reliability. This methodology guarantees precise density evaluations and a rigorous step-by-step procedure for measurement uncertainty estimation. Moreover, the SOP aligns with ASTM <span><span>F2150</span><svg><path></path></svg></span>-19 standard for biomaterials characterization, which endorses the use of ASTM <span><span>D792</span><svg><path></path></svg></span>-20 for bulk density measurements, based on the principle of Archimedes. By employing this approach, the study aims to enhance the development of in vitro models for biomedical research, drug testing, and tissue engineering. The SOP allows for bulk density estimation with a relative expanded uncertainty of 3%. Furthermore, it was demonstrated that the material employed for the validation, fibrin gel, is comparable, with a 95% level of confidence, to breast fat, placenta, breast gland, eye (choroid), kidney, liver, heart muscle, pancreas, spleen, diaphragm, eye (ciliary body), muscle, and tongue. Shaping biomimicry: a standardized protocol for soft materials bulk density measurement supporting tissue-like performance.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120590"},"PeriodicalIF":5.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.measurement.2026.120634
Faming Wang , Shujin Guo , Wenbin Tian , Ling Wang , Jie Zhou , Du Chen
Online monitoring of the throughput of corn combine harvester is crucial for low-loss and high-efficiency harvesting. Currently, most monitoring methods in this field rely on single sensor signals or simple feature fusion. However, given the heterogeneous structure and complex, variable physical properties of maize ears, these methods generally suffer from large monitoring fluctuations and insufficient accuracy. Therefore, this study focuses on constructing an online throughput monitoring system driven by multi-sensor data, aiming to improve the accuracy and stability of online monitoring through deep fusion of multi-source information. Firstly, a multi-source sensor and data communication system was developed to realize online monitoring of engine parameters, travel speed, header height, threshing drum rotating speed, grain moisture content and grain flow rate. Secondly, a throughput monitoring model was proposed, using cascaded autoencoders (AE), support vector regression (SVR), and hippopotamus optimization algorithm (HO). AE was used to achieve multi-source data dimensionality reduction, SVR was used for regression modeling, and HO was used for hyperparameter optimization. Finally, field tests were carried out to verify the performance of developed system. The results showed that the mean absolute error (MAE) of the throughput monitoring system was 1.07 kg/s, the mean relative error (MRE) was 7.00%, and the monitoring fluctuation range was [−0.50, 0.50] kg/s, which had high monitoring accuracy and stability. The effectiveness of each cascade module in the multi-sensor fusion model of the input quantity was verified ablation study. Compared with the SVR model, the R2 of AE-SVR-HO on the test set increased by 17%, and the MSE, RMSE, MAPE, MAE, and SE decreased by 2.25, 0.91, 9.27%, 0.80, and 0.27, respectively. The sensitivity of different cascade modules to monitoring accuracy is discussed, and the results show that the regression model has the most significant impact on the overall performance in the cascade structure. This provides technical support for the intelligent low-loss control of combine harvester.
{"title":"Design and experiment of online throughput monitoring system for corn combine harvester driven by multi-sensor data","authors":"Faming Wang , Shujin Guo , Wenbin Tian , Ling Wang , Jie Zhou , Du Chen","doi":"10.1016/j.measurement.2026.120634","DOIUrl":"10.1016/j.measurement.2026.120634","url":null,"abstract":"<div><div>Online monitoring of the throughput of corn combine harvester is crucial for low-loss and high-efficiency harvesting. Currently, most monitoring methods in this field rely on single sensor signals or simple feature fusion. However, given the heterogeneous structure and complex, variable physical properties of maize ears, these methods generally suffer from large monitoring fluctuations and insufficient accuracy. Therefore, this study focuses on constructing an online throughput monitoring system driven by multi-sensor data, aiming to improve the accuracy and stability of online monitoring through deep fusion of multi-source information. Firstly, a multi-source sensor and data communication system was developed to realize online monitoring of engine parameters, travel speed, header height, threshing drum rotating speed, grain moisture content and grain flow rate. Secondly, a throughput monitoring model was proposed, using cascaded autoencoders (AE), support vector regression (SVR), and hippopotamus optimization algorithm (HO). AE was used to achieve multi-source data dimensionality reduction, SVR was used for regression modeling, and HO was used for hyperparameter optimization. Finally, field tests were carried out to verify the performance of developed system. The results showed that the mean absolute error (MAE) of the throughput monitoring system was 1.07 kg/s, the mean relative error (MRE) was 7.00%, and the monitoring fluctuation range was [−0.50, 0.50] kg/s, which had high monitoring accuracy and stability. The effectiveness of each cascade module in the multi-sensor fusion model of the input quantity was verified ablation study. Compared with the SVR model, the R<sup>2</sup> of AE-SVR-HO on the test set increased by 17%, and the MSE, RMSE, MAPE, MAE, and SE decreased by 2.25, 0.91, 9.27%, 0.80, and 0.27, respectively. The sensitivity of different cascade modules to monitoring accuracy is discussed, and the results show that the regression model has the most significant impact on the overall performance in the cascade structure. This provides technical support for the intelligent low-loss control of combine harvester.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120634"},"PeriodicalIF":5.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.measurement.2026.120688
Yinnan Sun, Cong Du, Yumeng Sun, Zhenkun Guo, Yanxue Wang
Generating stable ultrasound is foundational to reliable structure health monitoring (SHM), yet conventional piezoelectric transmitters (PZT) often fail to demonstrate stable performance in harsh environments such as hydrothermal space and electromagnetic fields. This work presents a miniature photoacoustic (PA) transmitter based on gold nanocomposites. The composites are synthesized via a one-pot method. The 4.01 wt% concentration with a higher optical density at 532 nm is selected as the PA material by the absorption spectrum. The transmitter is fabricated by dip coating the composites on the endface of the multimode fiber (MMF). The PA signal generation experiments are conducted underwater. To reduce distortion metrics and enhance SNR, a novel multi-order discrete wavelet transform (DWT) is explored to process the acquired PA signals. The amplitude of the processed PA signal is increased from 1.726 10-3 V to 1.866 10-3 V, and the corresponding sound pressure reaches 37.32 kPa. Meanwhile, the -6 dB bandwidth is broadened to 6.24 MHz. Overall, this study demonstrates a compact PA transmitter and a practical post-acquisition denoising strategy that together enhance signal reliability for SHM applications.
{"title":"A miniature photoacoustic transmitter based on gold nanocomposites and post-acquisition denoising performance assessment","authors":"Yinnan Sun, Cong Du, Yumeng Sun, Zhenkun Guo, Yanxue Wang","doi":"10.1016/j.measurement.2026.120688","DOIUrl":"10.1016/j.measurement.2026.120688","url":null,"abstract":"<div><div>Generating stable ultrasound is foundational to reliable structure health monitoring (SHM), yet conventional piezoelectric transmitters (PZT) often fail to demonstrate stable performance in harsh environments such as hydrothermal space and electromagnetic fields. This work presents a miniature photoacoustic (PA) transmitter based on gold nanocomposites. The composites are synthesized via a one-pot method. The 4.01 wt% concentration with a higher optical density at 532<!--> <!-->nm is selected as the PA material by the absorption spectrum. The transmitter is fabricated by dip coating the composites on the endface of the multimode fiber (MMF). The PA signal generation experiments are conducted underwater. To reduce distortion metrics and enhance SNR, a novel multi-order discrete wavelet transform (DWT) is explored to process the acquired PA signals. The amplitude of the processed PA signal is increased from 1.726 <span><math><mo>×</mo></math></span> 10<sup>-3</sup> <!-->V to 1.866 <span><math><mo>×</mo></math></span> 10<sup>-3</sup> <!-->V, and the corresponding sound pressure reaches 37.32<!--> <!-->kPa. Meanwhile, the -6<!--> <!-->dB bandwidth is broadened to 6.24<!--> <!-->MHz. Overall, this study demonstrates a compact PA transmitter and a practical post-acquisition denoising strategy that together enhance signal reliability for SHM applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120688"},"PeriodicalIF":5.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fiber positioning systems in multi-object spectroscopic telescopes require highly precise fiducial fiber coordinates. While suitable for on-site measurement, laser trackers exhibit inherent angular errors that limit their accuracy. This paper presents an on-site compensation method using scale bar length constraints and a polynomial model. By utilizing only one-dimensional length data without known 3D coordinates, we construct a spatial error field for coordinate correction. The polynomial parameters are efficiently solved via least-squares fitting. Simulations show the mean coordinate error in a 4mx4mx4m space reduces from to Laboratory tests verify the mean residual improvement from to . Applied to LAMOST’s 1.75-meter focal plane, the method achieves fiducial fiber length errors below and final calibration accuracy of , meeting telescope requirements. This provides a cost-effective solution for large-scale astronomical and industrial metrology.
{"title":"Joint adjustment measurement method for multi-laser trackers based on scale bar","authors":"Yingfu Wang, Jiahao Zhou, Kai Yun, Wenfei Liu, Chen Yang, Zhigang Liu, Jiaru Chu, Zengxiang Zhou","doi":"10.1016/j.measurement.2026.120679","DOIUrl":"10.1016/j.measurement.2026.120679","url":null,"abstract":"<div><div>The fiber positioning systems in multi-object spectroscopic telescopes require highly precise fiducial fiber coordinates. While suitable for on-site measurement, laser trackers exhibit inherent angular errors that limit their accuracy. This paper presents an on-site compensation method using scale bar length constraints and a polynomial model. By utilizing only one-dimensional length data without known 3D coordinates, we construct a spatial error field for coordinate correction. The polynomial parameters are efficiently solved via least-squares fitting. Simulations show the mean coordinate error in a 4mx4mx4m space reduces from <span><math><mrow><mn>35</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> to <span><math><mrow><mn>10</mn><mo>.</mo><mn>1</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> Laboratory tests verify the mean residual improvement from <span><math><mrow><mn>19</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> to <span><math><mrow><mn>9</mn><mo>.</mo><mn>6</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>. Applied to LAMOST’s 1.75-meter focal plane, the method achieves fiducial fiber length errors below <span><math><mrow><mo><</mo><mn>20</mn><mi>μ</mi><mi>m</mi></mrow></math></span> and final calibration accuracy of <span><math><mrow><mn>10</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>, meeting telescope requirements. This provides a cost-effective solution for large-scale astronomical and industrial metrology.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120679"},"PeriodicalIF":5.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.measurement.2026.120695
Md. Musfiqur Rahman , Jubayer Al Mahmud , Md. Firoj Ali , Md. Abdur Rahim , Subrata K. Sarker , Bing Yan
Reliable indoor navigation remains a major challenge for autonomous robots, particularly in perceptually degraded environments where vision-based methods fail under low or variable lighting. Existing solutions such as stereo vision, RGB-D, and event-camera-based Simultaneous Localization and Mapping (SLAM) partially mitigate these issues but suffer from scale ambiguity, sensitivity to texture loss, or high hardware costs. To address these limitations, this paper presents a tightly coupled multi-sensor fusion framework integrating a monocular camera, 2D LiDAR, inertial measurement unit (IMU), and wheel encoders within the Robot Operating System 2 (ROS 2). The framework extends ORB-SLAM3 with LiDAR-based depth correction and encoder-constrained motion estimation, enabling adaptive re-weighting of sensor contributions across varying illumination. Experimental validation in both Gazebo simulations and real-world testbeds demonstrates consistent high-precision navigation, achieving 100% success with mean Absolute Pose Error (APE) of 2.8–2.9 cm under daylight and artificial lighting, 90% success with 4.5 cm APE in low light, and 80% success with 6.2 cm APE in complete darkness. Comparative analysis confirms graceful performance degradation, with mapping time increasing by 75% (68 s to 119 s), occupancy grid accuracy reducing moderately from 97.5% to 88.2%, and initial localization error remaining bounded below 0.09 m. These results demonstrate that the proposed framework outperforms existing methods in robustness, scalability, and cost-effectiveness, offering a practical solution for autonomous navigation in real-world environments such as warehouses, hospitals, and service facilities where lighting conditions cannot be guaranteed.
{"title":"ROS2-based real-time autonomous mapping and navigation: Integrating visual SLAM and sensor fusion with performance analysis under varying light","authors":"Md. Musfiqur Rahman , Jubayer Al Mahmud , Md. Firoj Ali , Md. Abdur Rahim , Subrata K. Sarker , Bing Yan","doi":"10.1016/j.measurement.2026.120695","DOIUrl":"10.1016/j.measurement.2026.120695","url":null,"abstract":"<div><div>Reliable indoor navigation remains a major challenge for autonomous robots, particularly in perceptually degraded environments where vision-based methods fail under low or variable lighting. Existing solutions such as stereo vision, RGB-D, and event-camera-based Simultaneous Localization and Mapping (SLAM) partially mitigate these issues but suffer from scale ambiguity, sensitivity to texture loss, or high hardware costs. To address these limitations, this paper presents a tightly coupled multi-sensor fusion framework integrating a monocular camera, 2D LiDAR, inertial measurement unit (IMU), and wheel encoders within the Robot Operating System 2 (ROS 2). The framework extends ORB-SLAM3 with LiDAR-based depth correction and encoder-constrained motion estimation, enabling adaptive re-weighting of sensor contributions across varying illumination. Experimental validation in both Gazebo simulations and real-world testbeds demonstrates consistent high-precision navigation, achieving 100% success with mean Absolute Pose Error (APE) of 2.8–2.9 cm under daylight and artificial lighting, 90% success with 4.5 cm APE in low light, and 80% success with 6.2 cm APE in complete darkness. Comparative analysis confirms graceful performance degradation, with mapping time increasing by 75% (68 s to 119 s), occupancy grid accuracy reducing moderately from 97.5% to 88.2%, and initial localization error remaining bounded below 0.09 m. These results demonstrate that the proposed framework outperforms existing methods in robustness, scalability, and cost-effectiveness, offering a practical solution for autonomous navigation in real-world environments such as warehouses, hospitals, and service facilities where lighting conditions cannot be guaranteed.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120695"},"PeriodicalIF":5.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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