Conventional Calibration Methods Research Articles

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration. This approach was used to acquire 3DMPCs of perilla, tomato, and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using partial least squares regression with the generated 3DMPCs. Collectively, our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits, and thus will facilitate plant biology and genetic studies as well as crop breeding.

A 62.5 kHz-BW 92 dB-SNDR noise-shaping SAR ADC with NS-CAL method

DAC mismatch is a significant error in NS-SAR ADCs, as it introduces essentially nonlinear behavior and limits the number of bits in the DAC array. In this paper, we propose a novel foreground digital calibration method for NS-SAR ADCs. This method, combined with noise shaping technique, improves calibration accuracy and eliminates the impact of error accumulation on high-bit weights. Thus, it increases the number of bits in the DAC array in NS-SAR ADCs, and decreases the noise floor caused by quantization error. We implemented this design using a 110-nm CMOS process. As a result, post-layout simulation shows 92 dB SNDR and 108 dB SFDR under × 16 OSR and 1.5 V supply. Compared with conventional foreground calibration method, the SNDR increases from 81 dB to 92 dB and the SFDR increases from 84 dB to 108 dB with a power consumption of 40 μW, resulting in a FoMs of 182 dB and a FoMw of 15.3 fJ/conversion-step, respectively.

Facile barometric calibration of photoluminescence-based oxygen sensors on a standard rotary evaporator system with a digital vacuum pump

A simple setup and methodology for barometric calibration of quenched phosphorescence oxygen sensors are described, which utilize a standard rotary evaporator (Rotavap) system equipped with a digital vacuum pump. They were demonstrated with a panel of different sensing materials based on PtBP dye, including solid-state sensor coatings on different substrates (membrane inserts, stickers or inner wall of the vacuum flask), as well as liquid sensor formulations. The sensors inside the flask were exposed to different conditions created by the Rotavap system (dry or humid air, water, temperatures ranging 2°C-40°C, residual total pressure), and their intensity, lifetime or phase shift signals were measured with handheld sensor readers Optech (Mocon) and Firesting (PyroScience) in a contact-less manner to generate multi-point calibration curves. Several critical points of the method, which require careful consideration, have been revealed, including sensor mounting inside the Rotavap, its temperature control during calibration, choice of the reader. Finally, a detailed characterization of several sensor types was carried out and their key operational parameters (τ0, KSV, heterogeneity, T-coefficients), and calibration equations for the different conditions were determined and compared to each other and the results of conventional O2 calibration. Overall, barometric calibration method using Rotavap provides a useful alternative to conventional O2 calibration methods, being simple, accurate, convenient and versatile.

Technical note: Determining chemical composition of atmospheric single particles by a standard-free mass calibration algorithm

Abstract. The chemical composition of individual particles can be revealed by single-particle mass spectrometers (SPMSs). With higher accuracy in the ratio of mass to charge (m/z), more detailed chemical information could be obtained. In SPMSs, the conventional standard-based calibration methods (internal/external) are constrained by the inhomogeneity of ionization lasers and the finite focusing ability of the inlet system, etc.; therefore, the mass accuracy is restricted. In this study, we obtained the detailed and reliable chemical composition of single particles utilizing a standard-free mass calibration algorithm. In the algorithm, the characteristic distributions of hundreds of ions were concluded and collected in a database denoted as prototype. Each single-particle mass spectrum was initially calibrated by a function with specific coefficients. The range of coefficients was constrained by the magnitude of mass deviation to a finite vector space. To find the optimal coefficient vector, the conformity of each initially calibrated spectrum to the prototype dataset was assessed. The optimum calibrated spectrum was obtained with maximum conformity. For more than 98 % ambient particles, a 20-fold improvement in mass accuracy, from ∼ 10 000 ppm (integer) to ∼ 500 ppm (two decimal places), was achieved. The improved mass accuracy validated the determination of adjacent ions with a m/z difference ∼ 0.05 Th. Furthermore, atmospheric particulate trace elements that were poorly studied before are specified. The obtained detailed single-particle-level chemical information could help explain the source apportionment, reaction mechanism, and mixing state of atmospheric particles.

Research on system spectral response functions’ calibration and data processing methods for computational imaging spectrometers based on filter modulation

To enhance the performance of the optical payload, a novel computational imaging spectrometer using a board-bandpass film-filter line array (IS-BFLA) was developed. Since there is an obvious difference between the IS-BFLA and the one utilizing dispersion, conventional calibration and evaluation methods are not suitable. In this paper, we present a calibration method for the IS-BFLA. The calibration objects are determined by the mathematical model. We develop an automated calibration system and design a set of data processing procedures. Two evaluation methods: self-evaluation of multi-measurement based on Euclidean distance and multi-pixel inter-evaluation based on spectral angle, are developed to evaluate the precision and accuracy of the calibration. Furthermore, comparing the calibration value with the design value and spectral reconstruction are also utilized. The accuracy of spectral reconstruction: R2 is greater than 0.99, which determines the feasibility of the calibrated data. We hope that the work in this paper will assist in subsequent related studies.

A geometric calibration method for thermal cameras using a ChArUco board

Accurate and efficient geometric calibration is important to precisely acquire geometric information in thermal cameras. Typically, the calibration procedure involves obtaining defined control points from the images of a calibration target and utilizing them to estimate the geometric calibration parameters based on a camera model. Calibration targets include checkerboards, circle grids, Hermann grids, and specially designed patterns. Thermal cameras typically have lower resolution than visible cameras and the control points are localized by creating contrast through heating. These differences make the calibration process of thermal cameras more complex. Conventional thermal camera calibration methods require that the entire calibration target is captured and that all the control points from each image are identified. Due to these limitations, geometric calibration of a thermal camera traditionally requires advanced image processing or substantial manual intervention.This work presents a calibration method using a ChArUco board as the calibration target. The proposed method allows the calibration to be performed with partial-view images and does not require that all the control points from each image are extracted, thereby allowing the calibration to be performed without requiring complex image processing. The proposed calibration method was successfully implemented to estimate the calibration parameters of two different thermal cameras; in both cases achieving an overall mean reprojection error below 0.4 pixels. The effectiveness of the approach was further demonstrated by performing the calibration with a lower mean reprojection error than the MATLAB camera calibrator app. Furthermore, the effectiveness of using different heat sources to create contrast was evaluated.

Automatic calibration and association for roadside radar and camera based on fluctuating traffic volume

Accurate perception of the movement and appearance of vehicles depends on the robustness and reliability of the extrinsic parameters calibration in a multi-sensor fusion scenario. However, conventional calibration methods require manual acquisition of prior information, leading to high labor costs and low calibration accuracy. Therefore, we proposed an automatic coarse-to-fine calibration method for roadside radar and camera sensors to lower costs and improve accuracy. Next, an association strategy based on fluctuating traffic volumes was also developed to assist in robust target matching during the coarse-to-fine calibration process. Finally, extrinsic parameters between the radar coordinate system and camera coordinate system were calibrated through double rotations of the position vectors obtained from each system. To verify the proposed method, an experiment was conducted on a pedestrian bridge using an uncalibrated 4D millimeter-wave radar and a traffic monocular camera. The results showed that our proposed method reduced the interquartile range of the roll angle by 41.5% compared to a state-of-the-art neural network method. It also outperformed the manual calibration method by 2.47% in terms of the average reprojection error.

Extended eigenvalue calibration method for overdetermined Mueller matrix polarimeters.

The eigenvalue calibration method is a versatile approach that can be applied to any type of the Mueller matrix polarimetic setup because a precise knowledge of the optical response of the setup components is not required. The method has usually been employed in its original form to calibrate non-overdetermined polarimeters dealing with intensity data arranged in 4 × 4 matrices, but it can also be applied to calibrate overdetermined polarimeters with intensity data matrices of higher dimension. The main drawback with the original formulation of the method is its sensitivity to noise in the input data, especially if applied as it is to overdetermined intensity matrices. In the present work, we present a rigorous extension of the conventional eigenvalue calibration method to treat overdetermined data. We experimentally show that the proposed method does not enhance noise propagation, and therefore it does not degrade the quality of Mueller matrices measured with overdetermined polarimeters.

Calibrating nonlinearity coefficients of a nano-g accelerometer by dual-frequency excitation on a shaker

Nano-g accelerometers are utilized extensively in high precision applications, such as gravity measurements on moving bases, which often face strong motion disturbances. Accurately-calibrated coefficients are crucial for suppressing their high-order error effects, but challenging to measure using conventional calibration methods. This paper presents an investigation into calibration of the nonlinearity coefficient of nano-g accelerometers on a horizontal shaker driven by a dual frequency signal. A nonlinear output component occurring at the low difference frequency, with a high signal-to-noise ratio, is utilized. The calibration principle and strategy are investigated, along with an analysis of the potential experimental error sources. Two critical accompanying systematical errors, arising from shaker’s movement distortion along the primary vibration direction and gravity coupling of input acceleration caused by the guide rail deflection, are identified and eliminated through a proposed procedure. Experimental tests demonstrate an uncertainty level two orders of magnitude better than the traditional method based on the dividing head.

Non-invasive calibration for volumetric flow measurements in confined spaces

The calibration of a multi-camera system is a crucial step of volumetric flow measurements with photogrammetric methods. Conventional calibration methods are based on recording hardware targets, which are placed in the cameras fields of view. Calibrating in confined spaces with those methods is associated with an increased technical or mechanical effort. This work presents a calibration method without the use of a hardware target. Instead, crossing laser beams are introduced into the volume for creating unique calibration points. The underlying algorithms discussed in this paper for detecting the laser beams are: Ransac algorithm, Template Matching (via cross correlation) and Probabilistic Hough Transformation. The algorithms are tested with experimental data and synthetic data.

A novel calibration method for continuous airborne metal measurements: Implications for aerosol source apportionment

Continuous metal monitors have been widely used in environmental monitoring due to the high temporal resolution, high detection limit, and necessity for near real-time source apportionment. However, the reliability of the conventional calibration method, the deviation caused by uncalibrated monitoring data, and the subsequent impact on source identification results are rarely discussed. In this study, a reliable multi-point calibration approach by Primary Standard Aerosol Mass Concentration Calibration System (PAMAS) for the Xact625i Ambient Metals Monitor was developed and applied. The measured data was almost meaningless in the low-concentration range with bias even exceeding 100 % by using the conventional single-point calibration method based on thin-film standards. PAMAS was utilized to generate aerosols with known concentrations of the 20 metal elements including Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Tl, Pb, and Bi, in two concentration ranges of 150–1200 ng m−3 and 2.5–30 ng m−3 to validate the Xact625i Monitor. The results showed that the elemental concentrations were underestimated, especially in the low-concentration range, only for Cr, As, and Sr with slopes close to unity (1.00 ± 0.03). After calibration by PAMAS, the slopes of the linear relationships between measured and standard concentrations were all unity for the 19 elements in the high-concentration range, and close to unity for the 15 elements in the low-concentration range, and the accuracy of the remaining elements was also improved. After considering the calibration of aerosol metal data, it was found the number of source factors and their contributions to metals and PM2.5 in Chongming Dongtan, China, based on the PMF model significantly changed. This study highlighted the need of developing reliable calibration methods for online aerosol monitoring instruments and implied that the source apportionment results could be biased without careful data calibration.

De‐embedding calibration for an electromagnetic dual probe using GCPW

AbstractAn ultrawideband de‐embedding calibration method (DCM) for a dual probe is proposed in this paper. Different from the conventional calibration method (CCM), in which a microstrip calibrator and the dual probe form a three‐port calibration network, the proposed calibration system is a four‐port network. By using two different lengths of grounded coplanar waveguide (GCPW) calibrators, namely line‐kit and through‐kit, the de‐embedded ‐parameter model of the calibration network is obtained. Measured results show that the ripple of the DCM with GCPW reduced by 59% of the CCM with the microstrip calibrator, and the phase delay has been almost compensated. In addition, the proposed method reduces the attenuation by approximately 0.7 dB at 10 GHz and 1.15 dB at 20 GHz. The calibration results indicate that the working frequency of the DCM is up to 22 GHz.

Comparison of temperature-dependent calibration methods of an instrument to measure OH and HO2 radicals using laser-induced fluorescence spectroscopy

Abstract. Laser-induced fluorescence (LIF) spectroscopy has been widely applied to fieldwork measurements of OH radicals and HO2, following conversion to OH, over a wide variety of conditions, on different platforms and in simulation chambers. Conventional calibration of HOx (OH + HO2) instruments has mainly relied on a single method, generating known concentrations of HOx from H2O vapour photolysis in a flow of zero air impinging just outside the sample inlet (SHOx=CHOx. [HOx], where SHOx is the observed signal and CHOx is the calibration factor). The fluorescence assay by gaseous expansion (FAGE) apparatus designed for HOx measurements in the Highly Instrumented Reactor for Atmospheric Chemistry (HIRAC) at the University of Leeds has been used to examine the sensitivity of FAGE to external gas temperatures (266–348 K). The conventional calibration methods give the temperature dependence of COH (relative to the value at 293 K) of (0.0059±0.0015) K−1 and CHO2 of (0.014±0.013) K−1. Errors are 2σ. COH was also determined by observing the decay of hydrocarbons (typically cyclohexane) caused by OH reactions giving COH (again, relative to the value at 293 K) of (0.0038±0.0007) K−1. Additionally, CHO2 was determined based on the second-order kinetics of HO2 recombination with the temperature dependence of CHO2, relative to 293 K being (0.0064±0.0034) K−1. The temperature dependence of CHOx depends on the HOx number density, quenching, the relative population of the probed OH rotational level and HOx transmission from the inlet to the detection axis. The first three terms can be calculated and, in combination with the measured values of CHOx, show that HOx transmission increases with temperature. Comparisons with other instruments and the implications of this work are discussed.

Calibration methods and power cycling of double-side cooled SiC MOSFET power modules

Reliability analysis depends heavily on accurate measurements of junction temperatures for power semiconductors. It forms the foundation for precise reliability tests such as power cycling tests. One of the most established methods for junction temperature measurement in MOSFETs is the VSD(T) method. With the introduction of new module configurations such as the DSC (double-side cooled) module with cooling on both sides, conventional calibration methods for single side cooling require a critical review regarding their applicability. This article will highlight various calibration options and analyse them in regard to sensitivity and reproducibility for DSC modules. Subsequently, the most promising methods are evaluated experimentally. It was found that by heating the modules on both sides, a better temperature uniformity can be achieved than from single side heating alone. This allows to apply the VSD(T) method on DSC modules also during power cycling. A power cycling test with in situ mechanical force measurement was conducted to verify the results.

Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system

The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management. The proliferation of model complexity results in more significant uncertainty and computational requirements. However, conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples. Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search. MLPS, termed the machine learning parallel system for fast parameter search of integrated process-based models, aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness, accuracy, and stability. MLPS was constructed upon the concept of model surrogation + algorithm optimization using Ant Colony Optimization (ACO) coupled with Long Short-Term Memory (LSTM). The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0, and the average absolute percent bias decreases from 124.3% to 8.8%. The model outputs closely align with the monitoring data, and parameter calibration time is reduced by 89.94%. MLPS enables the efficient optimization of integrated process-based models, facilitating the application of highly precise complex models in environmental management. The design of MLPS also presents valuable insights for optimizing complex models in other fields.

Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID

We propose an automated computational algorithm for simultaneous model selection and parameter identification for the hyperelastic mechanical characterization of biological tissue and validate it on experimental data stemming from human brain tissue specimens. Following the motive of the recently proposed computational framework EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery) and in contrast to conventional parameter calibration methods, we construct an extensive set of candidate hyperelastic models, i.e., a model library including popular models known from the literature, and develop a computational strategy for automatically selecting a model from the library that conforms to the available experimental data while being represented as an interpretable symbolic mathematical expression. This computational strategy comprises sparse regression, i.e., a regression problem that is regularized by a sparsity promoting penalty term that filters out irrelevant models from the model library, and a clustering method for grouping together highly correlated and thus redundant features in the model library. The model selection procedure is driven by data stemming from mechanical tests under different deformation modes, i.e., uniaxial compression/tension and simple torsion. The data is acquired through conventional mechanical tests that deliver labeled one-dimensional data pairs, and thus the method can be interpreted as a supervised counterpart to the originally proposed EUCLID that is informed by full-field displacement data and global reaction forces. The proposed method is verified on synthetical data with artificial noise. In addition, we present for the first time an experimental investigation of the EUCLID framework by validating the proposed method on experimental data acquired through mechanical tests of human brain specimens, proving that the method is capable of discovering hyperelastic models that exhibit both high fitting accuracy to the data as well as concise and thus interpretable mathematical representations.

Calibration Method for Airborne Infrared Optical Systems in a Non-Thermal Equilibrium State.

Airborne infrared optical systems equipped with multiple cooled infrared cameras are commonly utilized for quantitative radiometry and thermometry measurements. Radiometric calibration is crucial for ensuring the accuracy and quantitative application of remote sensing camera data. Conventional radiometric calibration methods that consider internal stray radiation are usually based on the assumption that the entire system is in thermal equilibrium. However, this assumption leads to significant errors when applying the radiometric calibration results in actual mission scenarios. To address this issue, we analyzed the changes in optical temperature within the system and developed a simplified model to account for the internal stray radiation in the non-thermal equilibrium state. Building upon this model, we proposed an enhanced radiometric calibration method, which was applied to the absolute radiometric calibration procedure of the system. The radiometric calibration experiment, conducted on the medium-wave channel of the system within a temperature test chamber, demonstrated that the proposed method can achieve a calibration accuracy exceeding 3.78% within an ambient temperature range of -30 °C to 15 °C. Additionally, the maximum temperature measurement error was found to be less than ±1.01 °C.

Use Cases and Methods of Virtual ADAS/ADS Calibration in Simulation

Integration, testing, and release of complex Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) is one of the main challenges in the field of automated driving. In order for the systems to be accepted by customers and to compete in the market, they have to feature functional, comfortable, safe, efficient, and natural driving behavior. The calibration process acquires increasing importance in the achievement of this objective. Complex ADAS/ADS require the optimization of interacting calibration parameters in a large number of different scenarios—a task that can hardly be performed with feasible effort and cost using conventional calibration methods. Virtual calibration in simulation enables reproducible and automated testing of different data sets of calibration parameters in various scenarios. These capabilities facilitate different use cases to extend the conventional calibration process of ADAS/ADS through virtual testing. This paper discusses the different use cases of virtual calibration and methods to achieve the desired objectives. A special focus is on a multi-scenario-level method that can be used to iteratively calibrate ADAS/ADS for optimal behavior in a variety of scenarios, resulting in a more comfortable, safe, and natural behavior of the system and still a feasible number of test cases. The presented methods are implemented for the virtual calibration of an Adaptive Cruise Control model for evaluation.

The effect of functional calibration methods on gait kinematics in adolescents with idiopathic rotational deformity of the femur

BackgroundDue to anatomical deviations, assumptions of the conventional calibration method for gait analysis may be violated in individuals with rotational deformities of the femur. Functional calibration methods were compared with conventional methods in this group for 1) localization of the hip joint center and orientation of the knee axis, and 2) gait kinematics. MethodsTwenty-four adolescents with idiopathic rotational deformity of the femur underwent gait analysis and a CT scan. During standing, distance between hip joint centers and knee axis orientation were compared between calibration methods, with CT serving as reference for hip joint center estimation. Gait kinematics were compared using statistical parametric mapping. FindingsThe conventional calibration method estimated the hip joint center closer to the CT reference (4±12 mm more lateral) than the functional calibration method (26 ± 20 mm more lateral). Orientation of the knee joint axis was 2.6° less internal in the functional calibration method. During gait, statistical parametric mapping revealed significantly more hip flexion, less external hip rotation during the swing phase, less knee varus-valgus motion, and larger knee flexion angles when applying the functional method. InterpretationFunctional calibration methods were less accurate in determining the hip joint center location than the conventional calibration method and resulted in a knee joint axis that was less internally rotated. Importantly, there was less knee joint angle crosstalk during gait when using the functional method. Although differences between methods on gait kinematics were within clinically acceptable limits for the sagittal plane, relatively larger differences on transversal hip kinematics may hold clinical importance.

Noniterative Generalized Camera Model for Near-Central Camera System.

This paper proposes a near-central camera model and its solution approach. 'Near-central' refers to cases in which the rays do not converge to a point and do not have severely arbitrary directions (non-central cases). Conventional calibration methods are difficult to apply in such cases. Although the generalized camera model can be applied, dense observation points are required for accurate calibration. Moreover, this approach is computationally expensive in the iterative projection framework. We developed a noniterative ray correction method based on sparse observation points to address this problem. First, we established a smoothed three-dimensional (3D) residual framework using a backbone to avoid using the iterative framework. Second, we interpolated the residual by applying local inverse distance weighting on the nearest neighbor of a given point. Specifically, we prevented excessive computation and the deterioration in accuracy that may occur in inverse projection through the 3D smoothed residual vectors. Moreover, the 3D vectors can represent the ray directions more accurately than the 2D entities. Synthetic experiments show that the proposed method can achieve prompt and accurate calibration. The depth error is reduced by approximately 63% in the bumpy shield dataset, and the proposed approach is noted to be two digits faster than the iterative methods.