Sensor Calibration Research Articles

Evaluating Indoor Air Quality in Residential Environments: A Study of PM2.5 and CO2 Dynamics Using Low-Cost Sensors

Indoor air quality (IAQ) poses a significant public health concern, and exposures to high levels of fine particulate matter (PM2.5) and carbon dioxide (CO2) could have detrimental health impacts. This study focused on assessing the indoor air pollutants in a residential house located in the town of Mission, Hidalgo County, South Texas, USA. The PM2.5 and CO2 were monitored indoors: the kitchen and the bedroom. This investigation also aimed to elucidate the effects of household activities such as cooking and human occupancy on these pollutants. Low-cost sensors (LCSs) from TSI AirAssure™ were used in this study. They were deployed within the breathing zone at approximately 1.5 m above the ground. Calibration of the low-cost sensors against Federal Equivalent Method (FEM) instruments was undertaken using a multiple linear regression method (MLR) model to improve the data accuracy. The indoor PM2.5 levels were significantly influenced by cooking activities, with the peak PM2.5 concentrations reaching up to 118.45 μg/m3. The CO2 levels in the bedroom increased during the occupant’s sleeping period, reaching as high as 1149.73 ppm. The health risk assessment was assessed through toxicity potential (TP) calculations for the PM2.5 concentrations. TP values of 0.21 and 0.20 were obtained in the kitchen and bedroom, respectively. The TP values were below the health hazard threshold (i.e., TP < 1). These low TP values could be attributed to the use of electric stoves and efficient ventilation systems. This research highlights the effectiveness of low-cost sensors for continuous IAQ monitoring and helps promote better awareness of and necessary interventions for salubrious indoor microenvironments.

Neural Approach to Coordinate Transformation for LiDAR–Camera Data Fusion in Coastal Observation

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

Laser Scan Compression for Rail Inspection

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans.

Real‐Time Force Monitoring of Electrically Stimulated 3D‐Bioengineered Muscle Bioactuators Using Organic Sensors with Tunable Sensitivity

The contractile nature of skeletal muscle tissue makes it especially attractive for powering biohybrid actuators. Significant efforts have been dedicated to the improvement and control of contraction force, going one step forward toward the automation of these biohybrid platforms. Herein, 3D‐bioengineered skeletal muscle tissues are integrated with organic transistor‐based sensors to define a soft bioactuator with real‐time force monitoring capabilities. The muscle tissue is electrically stimulated while the organic sensor ensures transduction of the exerted force into an electrical signal that allows direct monitoring of the bioactuator performance. Sensor calibration is carried out to define its sensitivity at different biasing conditions: as opposed to standard, two‐terminal piezoresistive devices, transistor‐based strain sensors show tunable sensitivity by acting on the voltage applied to a third terminal—the gate. A complete evaluation of sensing performances is provided, demonstrating that real‐time monitoring is effective under different conditions, including stimulation signal frequency and chemical modulation of the bioactuator contraction, demonstrating its potential use as a drug testing platform. In the reported results, the way is paved for a complete exploitation of organic devices in soft robotic applications and to the development of novel biohybrid machines in bioengineering and biomedicine.

Assessing the Reliability and Validity of Inertial Measurement Units to Measure Three-Dimensional Spine and Hip Kinematics During Clinical Movement Tasks

Inertial measurement units (IMUs) provide benefits over the traditional optoelectronic motion capture (OMC) systems in measuring kinematics for the low back pain population. The reliability and validity of IMUs to quantify three-dimensional motion for multiple hip/spine segments have not been systematically evaluated. The purpose of this study was to determine the repeated-measures reliability and concurrent validity of an IMU system for measuring the three-dimensional spine/hip kinematics in six common movement assessments. Seventeen participants (32.3 (14.7) years; 11 female) performed two trials each of six range-of-motion assessments while fitted with four IMUs (T1/T2, T12/L1, L5/S1, and femur). The IMUs showed good–excellent reliability for most of the movements in the primary plane and poor–moderate reliability in the non-primary planes. The IMU and OMC systems showed generally good–excellent agreement in the primary plane and RMSE values between 3.03° and 15.75°. The removal of outliers based on the Bland–Altman analysis resulted in RMSE values between 2.44° and 10.30°. The system agreement in the non-primary planes was generally poor–moderate, and the RMSE values ranged from 2.19° to 45.88°. Anomalies in the proprietary sensor fusion algorithm or calibration may have contributed to the large RMSE values, highlighting the importance of assessing data for physiological relevance. The results suggest that these IMUs may be best suited for population-based studies measuring movement in the primary plane and point toward the need for the development of more robust approaches for broader implementation.

Estimation of Dry Matter Yield in Mediterranean Pastures: Comparative Study between Rising Plate Meter and Grassmaster II Probe

This study evaluates two expedient electronic sensors, a rising plate meter (RPM) and a “Grassmaster II” capacitance probe (GMII), to estimate pasture dry matter (DM, in kg ha−1). The sampling process consisted of sensor measurements, followed by pasture collection and a laboratory reference analysis. In this comparative study, carried out throughout the 2023/2024 pasture growing season, a total of 288 pasture samples were collected in two phases (calibration and validation). The calibration phase (n = 144) consisted of measurements on three dates (6 December 2023, 29 February and 10 May 2024) in 48 georeferenced sampling areas of the experimental field “Eco-SPAA” (“MG” field), located at Mitra farm (Évora, Portugal). This pasture is a permanent mixture of various botanical species (grasses, legumes, and others) grazed by sheep, and is representative of biodiverse dryland pastures. The validation phase (n = 144) was carried out between December 2023 and April 2024 in 18 field tests (each with eight pasture samples), in three types of representative pastures: the same mixture for grazing (“MG” field), a commercial and annual mixture for cutting (mowing) and conservation (“MM” field), and legumes for grazing (“LG” field). The best estimation model for DM was obtained based on measurements carried out in February in the case of the GMII probe (R2 = 0.61) and December 2023 and February 2024 in the case of RPM (R2 = 0.76). The estimation decreased very significantly for both sensors based on measurements carried out in May (spring). The validation phase showed greater accuracy (less RMSE) in “MG” field tests (RMSE of 735.4 kg ha−1 with GMII and 512.3 kg ha−1 with the RPM). The results open perspectives for other works that would allow the testing, calibration, and validation of these electronic sensors in a wider range of pasture production conditions, in order to improve their accuracy as decision-making support tools in pasture management.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Studies on a Mobile Acoustic Emission Sensor Verification Device

Despite the fact that there are two ASTM standards, ASTM 1106 for primary calibration and ASTM E 1781 for secondary calibration of acoustic emission sensors, there is no company or institution currently offering calibration services based on these standards due to lack of availability or practical aspects. Another option is CEN ISO/TR 13115, which needs a large volumetric propagation medium leading to similar practical limitations as for the ASTM standards. Many acoustic emission users try to fill the gap of calibration services by using simplified verification standards. However, there is currently no verification standard suitable for using in the field. In that sense, one ambition of the project CalibrAEte is to establish a new standard for primary and secondary calibration that can be implemented in laboratories without a huge invest. Another objective of the project is to establish a verification procedure using a device to verify acoustic emission sensors by a wave-based approach in the field. The proposed approach uses a durable propagation medium that carries the elastic waves from a repeatable source to the sensor under test. This research focuses on such a mobile verification device in the sense that an optimal configuration is proposed. This contribution will present the major aspects and details of an acoustic emission sensor verification setup that can be used not just in the laboratory environment but also in harsh field conditions. It will be shown the performance of the reference sensor developed for this purpose along with the setup itself, which was partially developed by aid of numerical methods. Also, experimental results in different temperature conditions, will be presented to assess the robustness of the setup. It also be presented a sensitivity analysis of the setup using different sensors in different stages of degradation.

Development and Calibration of a Low-Cost LIDAR Sensor for Water Level Measurements

Development and Calibration of a Low-Cost LIDAR Sensor for Water Level Measurements

Novel multi-function setup comprising ultra-bright uniform source for spectral and radiometric calibrations of digital imaging sensors and multiple spectrometric applications

Abstract A new setup is being developed for calibrating CCD/CMOS sensors and cameras, and measuring spectrophotometric quantities in both point-wise and imaging modes. The setup includes a laser-driven light source (LDLS), double-grating monochromator, and an integrating sphere with specific design for two-dimensional (2D) spectrophotometric measurements of complex samples and spectral responsivity calibration of digital imaging sensors and cameras in the visible to near infrared (VIS-NIR) region. The setup can cover a spectral range from 250 nm to 2000 nm with relatively high radiance. The study presents optimization and validation work in the visible through NIR spectral range, up to 1000 nm. Preliminary experiments suggest improved accuracy and lower uncertainties for better metrological performance and sophisticated applications. The developed instrument promises a variety of novel applications while suppressing systematic errors and inaccuracies. The presented measurements results include the validation of 2D spectral reflectance/transmittance measurements in the 360-1000 nm spectral region using selected homogenous standards. Additionally provided are the findings of demonstration measurements for 2D spectral transmittance and the CCD camera’s spectral responsivity within the same spectral range.

Implementation of An Application-Based Motor Vehicle Exhaust Emission Level Detection System

Currently, air conditions on earth are getting worse over time due to the impact of air pollution. One example of air pollution is motor vehicle exhaust emissions. Exhaust gas emissions are the result of combustion residue in motor vehicle engines that use fuel. Motor vehicle exhaust emissions contain carbon monoxide (CO), hydrocarbons (HC), carbon dioxide gas (CO2) which have a negative impact on the environment and living creatures. This research will create a motor vehicle exhaust emission detection device. In this design, an Arduino microcontroller was used and the manufacture of this tool used an MQ-7 gas sensor to detect carbon monoxide (CO) gas, an MQ-2 sensor to detect hydrocarbon gas (HC), and an MQ-135 to detect carbon dioxide (CO2) gas. The emission test results will be sent to the application, this data includes plate number, vehicle type, vehicle brand, vehicle year and emission test results. The results of the carbon monoxide (CO) gas sensor calibration test taken from 5 data showed an error of 5.51%. The results of the hydrocarbon (HC) gas sensor calibration test taken from 5 data showed an error of 4.23%. The results of the carbon dioxide (CO2) gas sensor calibration test taken from 5 data showed an error of 1.06%. In the implementation of the tool and application, the test results were obtained for 15 vehicles. Where the highest hydrocarbon (HC) gas content value was 412 ppm, the highest carbon monoxide (CO) gas content value was 1.48%, and the highest carbon dioxide (CO2) gas content value was 21.4%.

Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine.

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon 'texture' has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm-1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm-1. For O2 in the range of 20-70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4-80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor's curvature. Further studies are needed to confirm result's reliability and the correctness of the interpretation.

Employing a multi-resonance microwave sensor for in-line moisture monitoring of fluidized bed agglomeration

Amorphous substances are relatively common in food industry and their characteristics depend highly on the moisture content. Process parameters are adjusted based on online measurements to control the product quality of the agglomerates. This leads to two major challenges: precise moisture content measurements of amorphous particles at (i) different temperatures and for (ii) a wide moisture content range.In this study two workflows (inline fluidized bed and conditioned fixed bed) calibration of microwave sensors are compared and it is shown that for fluidized beds inline calibration is necessary to achieve accurate moisture measurements. The application of the novel microwave sensor MW 4200 working at resonance frequencies of 2.3 and 5.6 GHz is described. The temperature influence on the measurement is investigated with measurements of a fixed particle bed. Rapid, reliable in-process moisture control at a wide range of moisture contents and temperatures for agglomeration in fluidized beds, with a mean relative accuracy of 82 %, is achieved. This technology improves productivity and precision in industrial operations, minimizes waste and optimizes energy usage.

Novel static decoupling algorithm for the multi-axis wheel force sensor based on the Informer network

Ensuring the stability of heavy multi-axle vehicles necessitates the accurate calibration and decoupling of Multi-axis Wheel Force Sensors (MWFS). Traditional methods often neglect the temporal coupling present in MWFS output data, leading to reduced accuracy. This paper introduces an Improved Decoupling Algorithm (IDI) based on the Informer network, designed to temporally decouple MWFS and enhance precision. The Decoupling embedding layer (DE) performs linear decoupling of the MWFS, while the Token embedding layer (TE) and Informer encoder extract timing coupling features. The highway network and linear fully-connected layer then provide nonlinear decoupling compensation. Experimental results demonstrate that the IDI algorithm significantly outperforms traditional methods like Extreme Learning Machine (ELM) and Back Propagation Network (BPNN), achieving at least a 41.12% improvement in accuracy in the highly coupled Mz channel. In conclusion, the IDI algorithm not only achieves high-precision decoupling of MWFS but also presents a robust framework for modeling various sensor types.

Numerical Investigation on the Influence of Turbine Rotor Parameters on the Eddy Current Sensor for the Dynamic Blade Tip Clearance Measurement.

Eddy current sensors are increasingly being used to measure the dynamic blade tip clearance in turbines due to their robust anti-interference capabilities and non-contact measurement advantages. However, the current research primarily focuses on enhancing the performance of eddy current sensors themselves, with few studies investigating the influence of turbine rotor parameters on the measurements taken by these sensors for dynamic blade tip clearance. Hence, this paper addresses this gap by using COMSOL Multiphysics 6.2 software to establish a finite model with circuit interfaces. Additionally, the model's validity was verified through experiments. This model is used to simulate the voltage output of the sensor and the measurement of dynamic blade tip clearance under various rotor parameters. The results indicate that the length and number of blades, as well as the hub radius, significantly affect the sensor voltage output in comparison to rotation speed. Furthermore, we show that traditional static calibration methods are inadequate for measuring dynamic blade tip clearance using eddy current sensors. Instead, it is demonstrated that incorporating rotor parameters into the calibration of eddy current sensors can enhance the accuracy of dynamic blade tip clearance measurements.

Calibration of Low-Cost Moisture Sensors in a Biochar-Amended Sandy Loam Soil with Different Salinity Levels.

With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm-3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions.

Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation.

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.

A novel approach on calibration of a laser line sensor’s position in the machine coordinate system

Abstract On-machine measurement facilitates real-time monitoring and feedback of the machining status of parts, thereby preventing secondary positioning errors in the offline measurement of parts. Calibrating spatial position of the measuring sensor is crucial for ensuring the accuracy of on-machine measurements. This paper introduces a novel approach to on-machine calibration of line laser displacement sensors, based on the fitted sphere-center method and iterative position in both height and width directions along the line laser. By constructing a transformation matrix and measuring multiple sets of data to determine the unknowns in the matrix, the spatial position of the laser displacement sensor can be calibrated. The results showed that the polar deviation of the standard ball radius was 0.0067 mm, confirming the feasibility and accuracy of the method.

Effect of sensor noise characteristics and calibration errors on the choice of IMU-sensor fusion algorithms

This paper focuses on accurate and precise orientation estimation with consumer-grade MEMS-IMUs for ‘slow’ orientation change and ‘short’-time applications. A simulation platform is developed to predict a suitable algorithm for a MEMS-IMU of known noise specifications, improving similar works. Experimentally measured noise characteristics of two commercial grade IMUs (MPU9250 and BNO055) are used in the simulation platform to generate simulated data and evaluate some popular orientation estimation algorithms along with two new Kalman filter-based algorithms. Real experiments are conducted with the same IMUs using an electromagnetic tracker as reference sensor. The output orientation results for two new improved algorithms are compared with other algorithms in simulations and real experiments. We show that the choice of the ‘best’ algorithm varies with the noise characteristics of individual sensors within the sensor module. The two new best-performing algorithms tested achieve<1˚ RMS angle error for the two low-cost consumer-grade IMUs.

In chamber calibration and performance evaluation of air quality low-cost sensors

Assessing individual exposure to PM2.5 (particulate matter of aerodynamic diameter lesser than 2.5 μm) requires precise monitoring of PM2.5 concentrations at specific geographical and temporal scales. This demand is met globally by low-cost particulate matter sensors, although calibrating them is difficult. In this study, four low-cost PM sensors, Sharp GP2Y1010AU0F, Honeywell HPMA115S0-XXX, Plantower PMSA003-A, and Sensirion SPS30, were calibrated and tested using various aerosols. The calibration method has three steps: individual (considering each sensor independently to a single aerosol type; n = 1), combined (all sensors for a specific model together for a specific aerosol type; n = 4), and generic (all sensors for a given model together to all aerosols; n = 16). Sensor responses are processed using linear, quadratic, power-law, and artificial neural network (ANN) algorithms in each calibration stage. Performance metrics, including coefficient of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage coefficient of variation (% CV), were utilized for assessment. Amongst all the four tested sensors, the Sensirion SPS30 sensors gave the best performance with a minimum R2 value of 0.911 when calibrated with a generic ANN calibration algorithm. Also, the MAPE was less than 10 %, and the RMSE was less than 7 % when exposed to different particles. Sensirion SPS30 showed the lowest inter-sensor variability with % CV less than 6 %. Sensors identified monodisperse polystyrene latex (PSL) particle size in the investigation. Regardless of exposure to 0.3, 0.46, 0.60, or 1.0 μm PSL, the reported number size distribution for the PMSA003 sensor remained consistent and did not align with the results from Grimm. As the PSL size rose, the SPS30 size distribution changed towards larger particle sizes, although it did not always match Grimm data. As the PSL size increased, the sensor's PM1, PM2.5, and PM10 mass proportions altered.