Sensor Measurements Research Articles

Simultaneous Measurement of Gas Pressure and Temperature Sensor Based on F-P Interference Using Hollow Core Bragg Fiber

Simultaneous Measurement of Gas Pressure and Temperature Sensor Based on F-P Interference Using Hollow Core Bragg Fiber

Preparation and application of copper nanoclusters as a fluorescent sensor for sensitive detection of tartrazine

As an important way to detect tartrazine, a probe with excellent fluorescence properties is of great significance in fluorescence detection method. Copper nanoclusters (Cu NCs) was prepared by chemical reduction method, in which copper sulfate as metal body, folic acid as capping reagent and ascorbic acid as reducing agent. In the light of inner filter effect and static quenching mechanism, a fluorescence sensor for tartrazine measurement was designed based on Cu NCs with excellent selectivity. The spherical Cu NCs with uniform size emitted blue fluorescence at 443 nm when the excitation wavelength was located at 353 nm. Under the optimum detecting condition, the corresponding linear range and LOD were 0.5–50 μM and 0.071 μM, respectively. Coupled with pleasing recovery experiments in real samples, this as-proposed platform was very promising for tartrazine detection.

Probe-type optical fiber sensors for electric field distribution measurement

This paper reports a compact fiber optical electric field (E-field) sensor aiming for the precise detection of transient E-field distributions. Here, a reflective polarization-reciprocal optical path is proposed, which inherently mitigates the temperature-induced birefringence interference of the electro-optical crystal without the need for additional optical elements, thereby facilitating a reduced-size sensing probe. Furthermore, an adaptive particle swarm optimization (A-PSO) algorithm has been utilized for the first time to optimize the insulation structure of the optical E-field sensor, which significantly suppresses field distortion within the sensing region by 50%. This method addresses the technical gap in optical E-field sensor structure optimization, providing an effective means to improve its spatial resolution. The experimental results demonstrate that the proposed sensor exhibits a broad response frequency range from 50 Hz to 15 MHz, with a sensitivity of 0.675 V/kV cm−1. The proposed sensor successfully monitors the spatial distribution of electric fields in the lightning interception region of a lightning rod, thereby validating its effectiveness.

A tracker pose optimization method for robotic measuring system based on spatial distance constraints

In large-scale metrology (LSM), the transformation of the tracker base frame (TBF) is a predominant method to enlarge the field of view (FOV) of the tracking sensor for full-field 3D measurements. Nevertheless, such a process will introduce cumulative errors and significantly diminish the global point cloud alignment accuracy. To address this problem, we propose a novel tracker pose optimization method for TBF transformation. A pose graph optimization (PGO) model based on spatial distance constraints is implemented to improve the tracker pose accuracy. We also adopt a robust coefficient and a damping factor to simplify the experimental process and stabilize the convergence results. Simulations and experiments on high-speed train surfaces are conducted to validate our method’s accuracy and effectiveness. The results indicate that our optimization method outperforms two existing methods in spatial positioning accuracy and point cloud alignment accuracy, which showcases its practical applicability and superiority in manufacturing scenarios.

Fast measurement of group index variation with optimum precision using Hong–Ou–Mandel interferometry

Hong–Ou–Mandel (HOM) interferometry has emerged as a valuable means for quantum sensing applications, particularly in measuring physical parameters that influence the relative optical delay between photon pairs. Unlike classical techniques, HOM-based quantum sensors offer higher resolution due to the intrinsic dispersion cancellation property of correlated photon pairs. Due to the use of single photons, HOM-based quantum sensors typically involve a large integration time to acquire the signal and subsequent post-processing for high-resolution measurements, restricting their use for real-time operations. Based on our understanding of the relationship between measurement resolution and the gain medium length that produces photon pairs, we report here on the development of an HOM-based quantum sensor for high-precision group index measurements. Using a 1 mm long periodically poled KTP (PPKTP) crystal for photon-pair generation, we have measured the group index with a precision of ∼6.75×10−6 per centimeter of sample length at an integration time of 100 ms, surpassing the previous reports by 400%. Typically, the measurement range reduces with the increase in the resolution. However, using a novel scheme compensating photon delay due to group index changes stepwise with an optical delay stage, we have measured the group index variation of PPKTP crystal over a range of 3.5 × 10−3 for a temperature change from 25 to 200 °C, corresponding to an optical delay adjustment of ∼200 μm while maintaining the same precision (∼6.75×10−6 per centimeter of sample length). The current results establish the usefulness of HOM-interferometer-based quantum sensors for fast, precise, and long-range measurements in various applications, including quantum optical coherence tomography.

An IoT-based Intelligent Irrigation and Weather Forecasting System

Introduction:: The most crucial ingredient in agriculture is water. The amount of water that plants require must be provided to them. However, growers alternate between giving their plants more water than they truly need and giving them less and partly because they become overwatered due to meteorological circumstances like unexpected rainfall. Methods:: We employ an IoT-based intelligent irrigation system to get around this problem. It includes a centrifugal pump, a motor driver board, and a soil moisture sensor with YL69 probes. When the soil moisture level drops, the pump automatically delivers water to the plants with minimal human involvement. The electrical conductivity theory is how the sensor for soil moisture functions. A DHT11 sensor and a barometer, which provide information on the local temperature, humidity, and atmospheric pressure, are both parts of the weather monitoring system with the help of this, farmers can forecast the local weather and plan their irrigation accordingly. Results:: In this patent study, the thing speak API enables us to continually monitor information from a computer or mobile device, and the ESP8266 module links the complete system to the internet. Through this approach, water waste is reduced, and irrigation efficiency is increased while crop health and quality are preserved. Conclusion:: Overall, this research demonstrated how the Internet of Things-based intelligent irrigation systems may enhance agricultural water management. By combining soil moisture monitoring, weather monitoring, and autonomous management, we may develop irrigation techniques that are more precise, effective and patent leading to higher crop yields and sustainable agricultural practices.

Dual-mode AT-cut quartz resonant pressure sensor for wide pressure and temperature measurement ranges

In this paper, a dual-harmonic-mode excited resonant pressure sensor with an AT-cut all-quartz sensing element was developed to synergistically achieve a wide measurement range for both temperature and pressure, along with a fast transient response. In this device, the excited third overtone mode indicated the applied pressure, while the beat frequency generated by the overtone and fundamental modes was used for self-temperature compensation. At the same time, the sandwiched all-quartz sensitive structure design effectively addressed the limitations associated with narrow pressure and temperature ranges. Especially, the dual-mode self-temperature sensing mechanism has been validated in the subzero temperature range for the first time. Quartz crystal processing techniques were employed to manufacture the sensor, and key performance metrics were characterized. The experimental results demonstrated that the sensor achieved an accuracy of ± 0.015 % full scale (FS) under a pressure range of 0–120 MPa and a temperature range of −40 to 150 °C. The pressure sensitivity of the overtone frequency was quantified as 365 Hz/MPa, and the beat frequency provided a temperature accuracy of 0.4 °C. Additionally, the developed sensor exhibited fast transient response under dynamic pressure and temperature conditions attributed to its self-temperature compensation capability. Thus, this study provides a practical solution for high-pressure measurements across a wide temperature range, combined with excellent transient performance.

Casual inference-enabled graph neural networks for generalized fault diagnosis in industrial IoT system

Data-driven fault diagnosis plays a crucial role in diagnosing the operational status within the Industrial Internet of Things (IIoT) systems. Although Graph Neural Networks (GNNs) have recently gained traction in fault diagnosis by adeptly modeling complex dependencies present in high-dimensional sensor measurements, they still grapple with the challenges presented by varying working conditions and pervasive environmental noise, which can significantly hinder their generalization capabilities. Hence, we propose Causal Inference-Enabled Graph Neural Networks (CIE-GNN) for generalized fault diagnosis in large-scale IIoT systems. Specifically, we establish a structural causal model for the GNN-based fault diagnosis model, revealing that the non-causal factors lead to spurious correlations and act as the confounders, thus impairing the generalization performance of fault diagnosis models. To rectify this issue, we design the disentangled transformation module and the causal disentanglement regularization based on mutual information minimization strategy, facilitating the effective decoupling between the fault-causal factors and the non-causal factors in the representation level. Additionally, we propose the random pairing-based backdoor adjustment regularization to mitigate the negative effects of non-causal factors. Extensive experiments and rigorous theoretical analysis validate the generalization capabilities of CIE-GNN across diverse working environments.

StressFit: a hybrid wearable physicochemical sensor suite for simultaneously measuring electromyogram and sweat cortisol

This study introduces StressFit, a novel hybrid wearable sensor system designed to simultaneously monitor electromyogram (EMG) signals and sweat cortisol levels. Our approach involves the development of a noninvasive skin patch capable of monitoring skin temperature, sweat pH, cortisol levels, and corresponding EMG signals using a combination of physical and electrochemical sensors integrated with EMG electrodes. StressFit was optimized by enhancing sensor output and mechanical resilience for practical application on curved body surfaces, ensuring accurate acquisition of cortisol, pH, body temperature, and EMG data without sensor interference. In addition, we integrated an onboard data processing unit with Internet of Things (IoT) capabilities for real-time acquisition, processing, and wireless transmission of sensor measurements. Sweat cortisol and EMG signals were measured during cycling exercises to evaluate the sensor suite’s performance. Our results demonstrate an increase in sweat cortisol levels and decrease in the EMG signal’s power spectral density following exercise. These findings suggest that combining sweat cortisol levels with EMG signals in real-time could serve as valuable indicators for stress assessment and early detection of abnormal physiological changes.

Worst-Case Optimal Fault-Tolerant, Attack-Resilient Fusion Functions

From practical considerations, crisp sensor measurements are associated with tolerance value. Thus, the sensor measurements lead to intervals. Researchers proposed M, F, Ω, and N functions for the fusion of interval-valued sensor measurements. We proved that the F-function provides the worst-case optimal interval estimate given the number of faulty sensors. We generalize the F-function to multi-dimensional intervals. We also propose new results on the attack resilience of fusion functions. Thus, this research paper provides new insights into robust sensor fusion.

Measurement of field emission array current distributions by metal-coated CMOS image sensors

A CMOS image sensor is utilized to determine the time- and spatially resolved distribution of the total electron emission current of a silicon field emission array. The sensor measures electron emission without the need for phosphorus screens or scintillators as converters. However, in initial experiments, rather low field emission currents of several hundreds of nanoamperes per emitter already damaged the sensor surface, which altered the systems’ signal response over the measurement time. In consequence, we coated the CMOS sensor surface with a Cu layer for surface protection. In contrast to the original insulating surface, Cu is an excellent current- and heat-conductor, which avoids lens charging by providing a conductive path for incident electrons and has an improved heat dissipation capability. Measurements using a segmented field emission cathode with four individually addressable tips demonstrate a consistent correlation between the emission current and the sensor signal of the metal-coated image sensor. Furthermore, the characterization of a field emission array showed that single tip emission currents of up to 12 μA per tip are measurable without discernible damage effects of the sensor’s surface.

Flight testing of laser-optical techniques for future optical air data sensors

AbstractOptical air data sensors, which can serve as alternatives or supplements to traditional air data systems, have been a subject of ongoing research. These remote sensing optical sensors offer the advantage of measuring primary air data parameters in undisturbed airflow. As active sensors, they can self-diagnose, associating each measurement with an uncertainty and accounting for signal loss due to factors such as icing or light blocking, thus avoiding silent false measurements. In this paper, we discuss a recent flight test campaign of optical measurement techniques for potential development into air data sensors for future aircraft. We used laser Doppler anemometry, which relies on aerosol scattering of laser light, to observe the Doppler shift of scattered light from multiple directions. This enables the reconstruction of the full wind vector and the retrieval of true air speed, angle of attack, and angle of sideslip. A second instrument used tunable diode laser absorption spectroscopy, which measures an individual transition of molecular oxygen in the A band, in order to extract the static pressure from the observed collisional broadening. The techniques were implemented as airborne research instruments on the DLR’s Dassault Falcon 20 aircraft and tested in two campaigns in 2022 under different atmospheric conditions and dynamic maneuvers. We present results from these campaigns, focusing on general sensor performance, measurement rates, accuracy, and experimental challenges that need to be addressed for future applications.

A curvature and temperature simultaneous measurement sensor based on micro-taper Formed MZI and an Embedded FBG

Abstract A compact fiber Bragg grating (FBG) and Mach–Zehnder interferometer (MZI) compound for simultaneous measurement of bending and temperature is described. The FBG and MZI are fabricated in fiber forming a compact device, which is different from the previously developed axial cascade of different structures. Experimental results show that the FBG and MZI have different sensitivities for bending and temperature, allowing this device to measure bending and temperature simultaneously. The device has the advantages of small size, simple preparation steps, low cost, short preparation cycle, etc. Due to its excellent performance, it has the potential to become an excellent universal bending and temperature sensor.

Characterization and Optimization of a Locally Boron-Doped Diamond Tool for Self-sensing of Cutting Temperature

High-sensitivity and rapid-response measurements of micro zone cutting temperature are crucial for characterizing and optimizing machining states in the ultra-precision cutting process. This innovative method uses a locally boron-doped diamond (LBDD) tool itself as the sensor for in-process measurements of cutting temperature. However, the heterogeneity of boron doping and the resulting lattice distortions considerably affect the mechanical properties and temperature-sensing performances of the tool. In this work, optimized synthesis processes and structural designs for LBDD tools that function as self-sensing cutting temperature devices were proposed. Annealing treatments under high-pressure conditions were conducted to promote the diffusion and ionization of boron multimers in the boron-doped diamond zone, thereby enhancing the crystal quality and semiconductor electrical properties of the LBDD. Various LBDDs with thin-layer temperature-sensing structures of different doping concentrations and thicknesses were synthesized. The optimal components and structures were identified as the temperature-sensing cutting tool through comparative analyses of temperature measurement capabilities and semiconductor properties. The selected tool was employed for in-process cutting temperature measurement during single-point diamond turning of copper and carbon fiber. Results indicate that the LBDD tool can accurately monitor cutting temperature during steady cutting processes and identify the micromorphological features of the machined surfaces based on cutting temperature characteristics. These insights are pivotal for controlling cutting temperature and refining the ultra-precision cutting process.

Decreased prefrontal glutamatergic function is associated with a reduced astrocyte-related gene expression in treatment-resistant depression

Glutamatergic dysfunction is involved in the pathophysiology of treatment-resistant depression (TRD). However, few physiological studies have evaluated its pathophysiology in vivo in individuals with TRD. Transcranial magnetic stimulation-electroencephalography (TMS-EEG) techniques can assess intracortical facilitation (ICF), which reflects glutamatergic neurophysiological function in specific cortical regions. The objectives of this study were (1) to compare glutamatergic receptor-mediated function as indexed with ICF TMS-EEG in the dorsolateral prefrontal cortex (DLPFC) between participants with TRD and healthy controls (HCs) and (2) to explore the relationships between cell-specific gene expression levels and the group difference in glutamatergic neural propagation using virtual histology approach. Sixty participants with TRD and thirty HCs were examined with ICF TMS-EEG measure (80 single-pulse TMS and paired-pulse ICF) in the left DLPFC. Both sensor and source-level ICF measures were computed to compare them between the TRD and HC groups. Furthermore, we conducted spatial correlation analyses interregionally between ICF glutamatergic activity and cell-specific gene expression levels employing the Allen Human Brain Atlas dataset. DLPFC-ICF at the sensor level was not significantly different between the two groups, whereas DLPFC-ICF at the source level was reduced in the TRD group compared with the HC group (p = 0.026). Moreover, the reduced ICF signal propagation of TRD correlated with astrocyte-specific gene expression level (p < 0.0001). The glutamatergic neural activities indexed by ICF in the left DLPFC were decreased in participants with TRD. Additionally, a relative reduction in glutamatergic signal propagation originating from the DLPFC in TRD may be associated with astrocytic abnormality.

Development of Textile-Based Strain Sensors for Compression Measurements in Sportswear (Sports Bra)

Women sports wearer’s comfort and health are greatly impacted by the breast movements and resultant sports bra compression to prevent excessive movement. However, as sports bras are only made in universal sizes, they do not offer the right kind of support that is required for a certain activity. To prevent this issue, textile-based strain sensors may be utilized to track compression throughout various activities to create activity-specific designed sports bras. Textile-based strain sensors are prepared in this study using various conductive yarns, including steel, Ag-coated polyamide, and polypropylene/steel-blended threads. Various embroidery designs, including straight, zigzag, and square-wave embroidery patterns, etc., were created on knitted fabric and characterized for strain sensing efficiencies. The experiments concluded that strain sensors prepared from polypropylene/steel thread using a 2-thread square-wave design were best performed in terms of linear conductivity, sensitivity of mechanical impact, and wide working range. This best-performed sample was also tested by integrating it into the sportswear for proposed compression measurements in different body movements.

A novel high-dimensional sensor calibration framework integrating thermodynamic laws in complex HVAC systems

Accurate calibration of sensors is critical for ensuring energy efficient operation of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. Due to the high dimensionality of sensor data and the complexity of multiple-fault scenarios, calibrating sensors in large and complex HVAC systems presents significant challenges. To address this issue, this study introduces a novel sensor calibration framework that integrates thermodynamic laws for high-dimensional sensor calibration in complex HVAC systems. The traditional calibration method heavily relies on accurate data, making it difficult to apply in practical engineering projects. The innovative aspect of our method lies in its integration of thermodynamic laws, such as mass balance and energy conservation, with sensor calibration framework. This approach enables the framework to handle high-dimensional sensor measurements effectively without any training data. We compared five optimization algorithms and applied them to a central cooling system in Hong Kong. The results demonstrated that the simulated annealing (SA) is the most robust for solving the calibration problem, even in scenarios with up to 21 faulty sensors, with the calibrated sensor accuracy meeting the standards for conventional chiller plant operations. This novel framework provides a robust and reliable solution for high-dimensional sensor calibration in large and complex HVAC systems, addressing the growing need for precise sensor calibration as the number of installed sensors increases.

New fluorogenic triacylglycerols as sensors for dynamic measurement of lipid oxidation.

Lipids are major constituents of food but are also highly relevant substructures of drugs and are increasingly applied for the development of lipid-based drug delivery systems. Lipids are prone to oxidative degradation, thus affecting the quality of food or medicines. Therefore, analytical methods or tools that enable the degree of lipid oxidation to be assessedare of utmost importance to guarantee food and drug safety. Herein, we report the design, synthesis and application of the first-in-class fluorogenic triacylglycerols that enable dynamic monitoring of lipid oxidation via straightforward fluorescence readout. Our fluorogenic triacylglycerols can be used in both aqueous and lipid-based environments. Furthermore, we showed that the sensitivity of our fluorescent tracers towards oxidation could be tuned by incorporating either saturated or unsaturated acyl chains in their triacylglycerol core structure. With this, we provide a first proofofprinciple for the applicability of fluorescently labelled triacylglycerols as tracers to monitor the dynamics of lipid oxidation, thus paving the way for novel discoveries in the area of lipid analytics.

Characterizing indoor-outdoor PM2.5 concentrations using low-cost sensor measurements in residential homes in Dhaka, Bangladesh

We collected simultaneous indoor and outdoor PM2.5 measurements from 17 homes in Dhaka, Bangladesh, to characterize spatio-temporal variations and identify factors influencing indoor and outdoor PM2.5 levels. A pair of PurpleAir PM2.5 sensors were deployed at each home, one indoors and the other outdoors, during the wet and dry seasons, and the locally calibrated data were used for analysis. Indoor and outdoor PM2.5 levels were three times higher during the dry season (indoor 146 ± 22 μg/m³, outdoor 153 ± 23 μg/m³) than during the wet season (indoor 52 ± 12 μg/m³, outdoor 50 ± 11 μg/m³). Indoor to outdoor (I/O) ratios were close to 1 in both seasons (dry: 0.97 ± 0.14, wet: 1.05 ± 0.19). This suggests that regional background pollution levels significantly influence indoor levels observed in different households. Infiltration factors closer to 1 (dry: 0.83 ± 0.12; wet: 0.87 ± 0.14), determined through mixed-effects regression of indoor and outdoor time series data, further highlight the substantial impact of outdoor pollution on indoor levels. Data from individual households exhibited strong temporal correlation between indoor and outdoor levels in both seasons (Pearson R: 0.82 ± 0.12 during the dry season and 0.83 ± 0.14 during the wet season), whereas indoor-outdoor spatial correlations across measured households were moderate (R: 0.49 and 0.62 during dry and wet seasons, respectively). These spatial correlations and empirical regression modeling suggest that while the spatial variation of outdoor PM2.5 levels significantly influences indoor levels' spatial variation, other factors such as indoor source activities and ventilation-related features play crucial roles in explaining variabilities in indoor PM2.5 across homes. Overall, our study suggests that indoor environments in Dhaka city are nearly as polluted as outdoor settings, and this locally derived scientific evidence can be valuable for enhancing public awareness and developing mitigation measures to reduce PM2.5 exposures in Bangladesh.

Real-time fatigue assessment of Floating Offshore Wind Turbine Mooring employing sequence-to-sequence-based deep learning on indirect fatigue response

Mooring lines in Floating Offshore Wind Turbines (FOWT) are subjected to significant environmental loading, leading to fatigue degradation over prolonged usage which necessitates real-time monitoring to ensure operational safety. Traditional fatigue assessment measures stress fluctuations under operational loads with expensive underwater sensors, which are costly and complex to install, especially for monitoring mooring at higher depths. This eventually increases the levelized cost of energy and maintenance expenses. This study proposes a cost-efficient fatigue monitoring method for catenary mooring using a novel sequence-to-sequence deep-learning (DL) approach that leverages platform motions as indirect fatigue response to infer fatigue assessment. By correlating FOWT platform motions with their impact on the fatigue life of mooring lines, the proposed method offers an alternative to costly underwater sensor measurements. In the absence of real data, the DL model is trained using simulated datasets for an OC4 semi-submersible wind turbine model in the OpenFAST open-source simulation package, capturing a wide range of tension fluctuations under diverse metocean conditions. Accordingly, this synthetic data has been treated as “real data” throughout this study. The trained network effectively captures the nonlinear relationship between platform motion and mooring fatigue response in real-time, enabling efficient assessment of fatigue-induced damage on mooring lines.