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The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR–camera's external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods. [ABSTRACT FROM AUTHOR]

For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground–object–air cooperation mechanism, termed the "ground surface simulation field (GSSF)—mobile robot -photoelectric transmitter station (PTS)". Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot. [ABSTRACT FROM AUTHOR]

As the basic application of fiber optic sensing technology, strain measurement accuracy as a key index needs to be further calibrated and analyzed. In this paper, accuracy calibration experiments and the related analyses of two fiber-optic sensing technologies, the fiber-optic grating (FBG) and optical frequency domain reflectometry (OFDR), are carried out using a standard beam of equal strength and a mature resistive strain gauge (ESG). The fiber-optic single-point strain data for loading and unloading changes of the beams of equal strength show good continuity and linearity, with good cyclic stability, and the error in the strain test data is less than 2% after repeated loading. At the same time, using finite element theory to analyze the data and using the measured data error within 5%, a good strain test curve linearity is achieved and R2 is better than 0.998. After repeated loading and unloading tests, it is verified that the fiber grating and the distributed optical fiber in the strain test have good stability in repeatability accuracy. The calibration experiments and data analysis in this paper further illustrate the three sensing technologies in determining the strain test accuracy and the advantages and disadvantages of the indicators, and the development of the fiber optic sensing technology application provides basic technical support. [ABSTRACT FROM AUTHOR]

Existing galvanometer-based laser-scanning systems are challenging to apply in multi-scale 3D reconstruction because of the difficulty in achieving a balance between a high reconstruction accuracy and a wide reconstruction range. This paper presents a novel method that synchronizes laser scanning by switching the field-of-view (FOV) of a camera using multi-galvanometers. Beyond the advanced hardware setup, we establish a comprehensive geometric model of the system by modeling dynamic camera, dynamic laser, and their combined interaction. Furthermore, since existing calibration methods mainly focus on either dynamic lasers or dynamic cameras and have certain limitations, we propose a novel high-precision and flexible calibration method by constructing an error model and minimizing the objective function. The performance of the proposed method was evaluated by scanning standard components. The results show that the proposed 3D reconstruction system achieves an accuracy of 0.3 mm when the measurement range is extended to 1100 mm × 1300 mm × 650 mm. This demonstrates that for meter-scale reconstruction ranges, a sub-millimeter measurement accuracy is achieved, indicating that the proposed method realizes multi-scale 3D reconstruction and simultaneously allows for high-precision and wide-range 3D reconstruction in industrial applications. [ABSTRACT FROM AUTHOR]

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment. [ABSTRACT FROM AUTHOR]

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands. [ABSTRACT FROM AUTHOR]

In order to investigate the contact characteristics of the mechanical parts of the brewing robot with wine lees particles, it is essential to calibrate the parameters of the discrete elemental model of wine lees particles. This paper proposes a method based on tests of the angle of repose. The simulation test is conducted to establish a regression model and combined with physical tests to find optimization. The contact model used in simulation modeling is Hertz-Mindlin with Johnson-Kendall-Roberts. Not all discrete element model parameters of wine lees particles have a significant impact on the angle of repose, so screening through Plackett-Burman Design is performed. The results indicate that the restitution coefficient between wine lees particles and restitution coefficient between wine lees particle and steel plate have a significant impact on angle of repose. Additionally, another parameter that is difficult to obtain, namely surface energy (JKR), also plays a crucial role. The optimal value interval for these three parameters is determined by the steepest ascent test, and a linear regression model for angle of repose is built through Box-Behnken Design. The optimal values obtained are as follows: restitution coefficient between wine lees particles—0.603; restitution coefficient between wine lees particle and steel plate—0.595; JKR—0.083. Finally, in order to verify the accuracy of calibrated parameters, simulation verification tests are carried out which show that there is only a relative error rate at 0.18% between simulated angle of repose and actual angle of repose, indicating that accurate calibration parameters were achieved. This study can provide reference for selecting discrete element model parameters for wine lees particles in future research endeavors. [ABSTRACT FROM AUTHOR]

The development of machine tools in the last twenty years includes, among other things, the application of mechanisms with a non-linear kinematic structure as the mechanical basis of machines. This results in significant improvements in kinematic characteristics and problems related to non-linear dependencies of the accuracy of the drive elements and the realization of movement in the machine's external coordinates. The paper presents an approach to machine tool calibration based on the original O-X glide mechanism based on the ISO 230-4 standard with the mono- and bi-directional compensation of systematic errors and adaptation to the specifics of the mechanism's kinematics. A machine tool prototype was designed and built for the research presented in the paper. The obtained results indicate the possibility of applying the existing recommendations and standards for testing the accuracy of machine tools with the need to correct the methodology by using linear and non-linear kinematic structures in machine tools. [ABSTRACT FROM AUTHOR]

The fluorescence quantitative polymerase chain reaction (qPCR) instrument has been widely used in molecular biology applications, where the reliability of the qPCR performance directly affects the accuracy of its detection results. In this paper, an integrated, physics-based calibration device was developed to improve the accuracy and reliability of qPCR, realizing the calibration of qPCR instruments' standard curve through physical simulations. With this calibration device, the collected temperature was used as the control signal to alter the fluorescence output, which allowed different probes to simulate the Ct values corresponding to samples with varying initial concentrations. The temperature and optical performance of this calibration device were tested, followed by a comparative analysis comparing the on-machine test results with standard substances to assess the linearity and uniformity of the Ct values of the measured qPCR instrument. It has been proven that this physical calibration device can effectively replace the biochemical standard substance to carry out comprehensive calibration of the temperature and optical parameters of the qPCR instrument and provide a more reliable method for the periodic calibration and quality control of the qPCR instrument. This contributes to the accuracy and reliability of fluorescence qPCR instruments in the field of molecular biology. [ABSTRACT FROM AUTHOR]

Due to nonideal residue amplification, the limited resolution of pipelined analog-to-digital converters (ADCs) has become a popular research topic for ADC designers. High-gain and high-speed amplifiers usually consume too much power for a decent ADC. Hence, this paper proposes a fast and cost-effective foreground calibration strategy for cyclic-pipelined ADCs. The calibration strategy compensates for the gain error due to inter-stage residual amplification, which alleviates the DC gain requirement for internal amplifiers. Unlike other digital calibrations, the proposed scheme is implemented with a cyclic-pipelined structure, and only one parameter needs to be calibrated, whose value can be feasibly calculated by the Fix-Point Iteration algorithm. The proposed calibration scheme is implemented in an area-efficient 16-bit, 2 MS/s cyclic-pipelined ADC, fabricated in 180 nm CMOS technology. The ADC is designed and realized by cycling a 6-bit sub-ADC four times with 1-bit redundancy each time. The calibration algorithm manages to recover the sampled data to 93.85 dB spurious free dynamic range (SFDR) even with a 57.8 dB-DC-gain amplifier. The total power consumption of ADC is 17.92 mW and it occupies an active area of 1.8 mm2. [ABSTRACT FROM AUTHOR]

X-ray fluorescence (XRF) core scanners are commonly used for fine-scale geochemical analysis in sediment studies, but data are semi-quantitative and require calibration to convert geochemical element counts to concentrations. Application of XRF core scanning in soil science remains largely untapped. This study employed an ITRAX core scanner to scan grassland soil cores and developed a novel calibration method based on a chemometric approach to characterise soil geochemistry. As soil samples are collected based on depth sampling, this study investigated whether higher resolution element concentrations could be inferred from lower resolution reference samples and if regression models from multiple cores could apply to a new core at the same resolution. Reference concentrations were obtained for all cores at 10 cm intervals, with validation conducted at 1 cm for a single core. Two calibration curve types were proposed: one based on the single core's 10 cm data to validate references at 1 cm intervals; and another using all cores, with each core serving as a test item after exclusion from the training set. Various preprocessing measures and feature selection techniques were tested. Results showed successful calibration for elements Ca, P, Zn, Sr, and S, with high R 2 values of 0.94, 0.93, 0.93, 0.92 and 0.91, respectively. The study presents a novel method for calibrating XRF core scanning element counts, demonstrating its potential for high-resolution soil analysis. [ABSTRACT FROM AUTHOR]

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments. [ABSTRACT FROM AUTHOR]

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data. [ABSTRACT FROM AUTHOR]

Two shape-sensing algorithms, the calibration matrix (CM) method and the inverse Finite Element Method (iFEM), were compared on their ability to accurately reconstruct displacements, strains, and loads and on their computational efficiency. CM reconstructs deformation through a linear combination of known load cases using the sensor data measured for each of these known load cases and the sensor data measured for the actual load case. iFEM reconstructs deformation by minimizing a least-squares error functional based on the difference between the measured and numerical values for displacement and/or strain. In this study, CM is covered in detail to determine the applicability and practicality of the method. The CM results for several benchmark problems from the literature were compared to the iFEM results. In addition, a representative aerospace structure consisting of a twisted and tapered blade with a NACA 6412 cross-sectional profile was evaluated using quadratic hexahedral solid elements with reduced integration. Both methods assumed linear elastic material conditions and used discrete displacement sensors, strain sensors, or a combination of both to reconstruct the full displacement and strain fields. In our study, surface-mounted and distributed sensors throughout the volume of the structure were considered. This comparative study was performed to support the growing demand for load monitoring, specifically for applications where the sensor data is obtained from discrete and irregularly distributed points on the structure. In this study, the CM method was shown to achieve greater accuracy than iFEM. Averaged over all the load cases examined, the CM algorithm achieved average displacement and strain errors of less than 0.01%, whereas the iFEM algorithm had an average displacement error of 21% and an average strain error of 99%. In addition, CM also achieved equal or better computational efficiency than iFEM after initial set-up, with similar first solution times and faster repeat solution times by a factor of approximately 100, for hundreds to thousands of sensors. [ABSTRACT FROM AUTHOR]

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging. [ABSTRACT FROM AUTHOR]

The non-scanning radiometer with short-wavelength (SW: 0.2–5.0 μm) and total-wavelength (TW: 0.2–50.0 μm) channels is the primary payload of the Integrated Earth–Moon Radiation Observation System (IEMROS), which is designed to provide comprehensive Earth radiation measurements and lunar calibrations at the L1 Lagrange point of the Earth–Moon system from a global perspective. This manuscript introduces a radiometer preflight calibration methodology, which involves background removal and is validated using accurate and traceable reference sources. Simulated Earth view tests are performed to evaluate repeatability, linearity, and gain coefficients over the operating range. Both channels demonstrate repeatability uncertainties better than 0.34%, indicating consistent and reliable measuring performance. Comparative polynomial regression analysis confirms significant linear response characteristics with two-channel nonlinearity less than 0.20%. Gain coefficients are efficiently determined using a two-point calibration approach. Uncertainty analysis reveals an absolute radiometric calibration accuracy of 0.97% for the SW channel and 0.92% for the TW channel, underscoring the non-scanning radiometer's capability to provide dependable global Earth radiation budget data crucial to environmental and climate studies. [ABSTRACT FROM AUTHOR]

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV. [ABSTRACT FROM AUTHOR]

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities. [ABSTRACT FROM AUTHOR]

To provide ongoing continuity for the ocean, cloud, and aerosol science data records, NASA will launch the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission on 8 February 2024. The Ocean Color Instrument (OCI) is the primary sensor onboard PACE and will provide ocean color science data to continue the data sets collected by heritage sensors MODIS, SeaWiFS, and VIIRS, but with increased spectral coverage and improved accuracy. The OCI is a grating spectrometer with hyperspectral coverage from the ultraviolet (about 315 nm) to near-infrared (about 900 nm), with additional filtered channels in the short-wave infrared (940 nm–2260 nm). A rigorous ground test program was conducted to calibrate the instrument and ensure that the calibration can be transferred to on-orbit operations in order to achieve the high levels of accuracy demanded by the science community. Some calibration parameters, such as polarization sensitivity, can only be measured during pre-launch testing. Tests were performed to measure the Mueller matrix components necessary to correct polarized scenes encountered on orbit. Measurements covered all spectral bands and a series of telescope scan angles encompassing the expected on-orbit scan range. The sensitivity (linear diattenuation) was measured above 340 nm to be below 0.6%, except at wavelengths, and was characterized as better than 0.1%. Below 340 nm, the sensitivity can be much higher, but this is not expected to affect the science data significantly. These results indicate that any polarized scenes encountered on orbit can be corrected with a high degree of confidence. [ABSTRACT FROM AUTHOR]

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionization. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature and how typical errors impact the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical vector network analyzer (VNA) measurement error in the magnitude of the reflection coefficients of the antenna, receiver, and open cable, which are 0.001, 0.001, and 0.002 (linear), respectively, would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48 ° , 0.78 ° , or 0.15 ° in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal. [ABSTRACT FROM AUTHOR]

The preparation of matrix-matched material for elemental quantitative analysis in rice flour matrix is proposed here for the first time as part of a feasibility study using the SN-ICP-MS and LA-ICP-MS methods. It was prepared via the spiking process in colloidal solution of rice flour with different levels of arsenic (As), cadmium (Cd) and lead (Pb), followed by drying in a climatic chamber. Comparative studies of the results on external calibration and gravimetric standard addition ICP-MS approaches through the use of calibration standard solutions were discussed. Method bias from the external calibration method was investigated, demonstrating the systematic effect arising from the sample matrix. Characterizing the concentration of measurands was then reasonably proposed using the gravimetric standard addition ICP-MS. Using powdered rice matrix reference material for ICP-MS calibration following acid digestion, the study showed a good agreement of recovery studies. A feasibility study of the LA-ICP-MS method as a direct solid analysis performed on the matrix-matched standard was then discussed. In the study, large fluctuation of signals was found for constructing calibration curve, generating poor linearity, especially for As and Pb, although yttrium (Y) as internal standard was applied. This might be ascribed to a limited microscale of homogeneity, and particularly laser-induced preferential evaporation of volatile elements. Using a number of measured data points, the mean and median were statistically recommended to improve precision. An attempt to use of similar matrix in both standard and sample is a critical point to consider to minimize the elemental fractionation effect. The proposed approach to prepare matrix-matched material could be a potential means for achieving elemental quantitation. [ABSTRACT FROM AUTHOR]

In this work, we present a compact, bifunctional chip-based sensor setup that measures the temperature and electrical conductivity of water samples, including specimens from rivers and channels, aquaculture, and the Atlantic Ocean. For conductivity measurements, we utilize the impedance amplitude recorded via interdigitated electrode structures at a single triggering frequency. The results are well in line with data obtained using a calibrated reference instrument. The new setup holds for conductivity values spanning almost two orders of magnitude (river versus ocean water) without the need for equivalent circuit modelling. Temperature measurements were performed in four-point geometry with an on-chip platinum RTD (resistance temperature detector) in the temperature range between 2 °C and 40 °C, showing no hysteresis effects between warming and cooling cycles. Although the meander was not shielded against the liquid, the temperature calibration provided equivalent results to low conductive Milli-Q and highly conductive ocean water. The sensor is therefore suitable for inline and online monitoring purposes in recirculating aquaculture systems. [ABSTRACT FROM AUTHOR]

We discuss how the dual-wavelength differential detection (DWDD) of fiber Bragg grating sensors can be used to build standardized high-resolution, high-accuracy, large-measurement-range, multichannel instruments and associated sensors. We analyze the system resolution and experimentally show that the high signal-to-noise ratio can allow the design of sensors with a ratio of range to resolution superior to 14 bits, and temperature measurement ranges of more than 180 °C. We propose a scheme for real-time signal correction to cancel the drift of the instrument using two internal reference sensors, and a calibration method using centralized golden sensors that allows traceability to international standards for all instruments and sensors, allowing the creation of a global sensor/instrument ecosystem. [ABSTRACT FROM AUTHOR]

In the context of autonomous driving, the fusion of LiDAR and camera sensors is essential for robust obstacle detection and distance estimation. However, accurately estimating the transformation matrix between cost-effective low-resolution LiDAR and cameras presents challenges due to the generation of uncertain points by low-resolution LiDAR. In the present work, a new calibration technique is developed to accurately transform low-resolution 2D LiDAR points into camera pixels by utilizing both static and dynamic calibration patterns. Initially, the key corresponding points are identified at the intersection of 2D LiDAR points and calibration patterns. Subsequently, interpolation is applied to generate additional corresponding points for estimating the homography matrix. The homography matrix is then optimized using the Levenberg–Marquardt algorithm to minimize the rotation error, followed by a Procrustes analysis to minimize the translation error. The accuracy of the developed calibration technique is validated through various experiments (varying distances and orientations). The experimental findings demonstrate that the developed calibration technique significantly reduces the mean reprojection error by 0.45 pixels, rotation error by 65.08%, and distance error by 71.93% compared to the standard homography technique. Thus, the developed calibration technique promises the accurate transformation of low-resolution LiDAR points into camera pixels, thereby contributing to improved obstacle perception in intelligent autonomous driving systems. [ABSTRACT FROM AUTHOR]

In this work, we present an innovative calibration technique leveraging differentiable programming to enhance energy resolution and reduce the energy scale systematic uncertainty in X-ray spectroscopic experiments. This approach is demonstrated using synthetic data and is applicable in general to various spectroscopic measurements. This method extends the scope of differentiable programming for calibration, employing Kernel Density Estimation (KDE) to achieve a target Probability Density Function (PDF) for a fully differentiable model of the calibration. To assess the effectiveness of the calibration, we conduct a toy simulation replicating the entire detector response chain and compare it with a standard calibration. This ensures a robust and reliable calibration methodology, holding promise for improving energy resolution and providing a more versatile and efficient approach without the need for extensive fine-tuning. [ABSTRACT FROM AUTHOR]

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems. [ABSTRACT FROM AUTHOR]

For the application of Delta robots with a 3-R(RPaR) configuration in machining tasks, this paper constructed a 54-parameter kinematic error model and a simplified dynamic model incorporating an integrated joint's position error and friction, respectively. Utilizing Singular Value Decomposition (SVD) of the Linear Model Coefficient Matrix (LMCM) and the coefficient chart, a criterion for identifiability of error components is established. For good identification results, the optimal measurement surface with Fourier series form is obtained using a combination of the Hook–Jeeves Direct Search Algorithm (DSA) and Inner Point Method (IPM). The friction coefficients and other dynamic parameters are obtained through fitting the integrated joint torque-angle pairs measured along specific trajectories using nonlinear least squares regression. The validation of the calibration process is conducted through simulations and experiments. The calibration results provide a foundation for the precise control of integrated joints and the high-precision motion of robots. [ABSTRACT FROM AUTHOR]

A power-efficient 16-bit 1-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) is presented in this paper. High-bit sampling makes the bridge capacitance in the digital-to-analog converter (DAC) a unit one, eliminating fractional capacitance mismatch. The high-precision comparator is composed of a four-stage preamplifier and a strong-arm latch, with auto-zeroing used to mitigate input offset further. Digital foreground calibration based on low-bit weight is implemented to correct DAC capacitance mismatch. The post-layout simulation results show that the core ADC achieves 95.61 dB SNDR and 105.1 dB SFDR with calibration, consuming 5.4 mW power under a 3.3 V supply voltage, corresponding to a Schreier figure of merit (FoM) of 175.3 dB. The ADC core area is 1.06 mm2 in the 180 nm CMOS technology. [ABSTRACT FROM AUTHOR]

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process. [ABSTRACT FROM AUTHOR]

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added. [ABSTRACT FROM AUTHOR]

The GaoFen (GF)-5-02 satellite is one of the new generations of hyperspectral observation satellites launched by China in 2021. The directional polarimetric camera (DPC) is an optical sensor onboard the GF-5-02 satellite. The precipitable water vapor (PWV) is a key detection parameter of DPC. However, the existing PWV data developed using DPC data have significant errors due to the lack of the timely calibration of the two bands (865, 910 nm) of DPC used for PWV retrieval. In order to acquire DPC PWV data with smaller errors, a calibration method is developed for these two bands. The method consists of two parts: (1) calibrate the 865 nm band of the DPC using the cross-calibration method, (2) calibrate the 910 nm band of the DPC according to the calibrated 865 nm band of the DPC. This method effectively addresses the issue of the absence of a calibration method for the water vapor absorption band (910 nm) of the DPC. Regardless of whether AERONET PWV data or SuomiNET PWV data are used as the reference data, the accuracy of the DPC PWV data developed using calibrated DPC data is significantly superior to that of the DPC PWV data retrieved using data before recalibration. This means that the calibration method for the NIR bands of the DPC can effectively enhance the quality of DPC PWV data. [ABSTRACT FROM AUTHOR]

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration. [ABSTRACT FROM AUTHOR]

A phenomenological material model has been developed to facilitate the efficient numerical analysis of fiber-reinforced high-performance concrete (HPC). The formulation integrates an elasto-plastic phase-field model for simulating fractures within the HPC matrix, along with a superimposed one-dimensional elasto-plasticity model that represents the behavior of the embedded fibers. The Drucker–Prager plasticity and one-dimensional von-Mises plasticity formulations are incorporated to describe the nonlinear material behavior of both the HPC matrix and the fibers, respectively. Specific steps are undertaken during the development and calibration of the phenomenological material model. In the initial step, an experimental and numerical analysis of the pullout test of steel fibers embedded in an HPC matrix is conducted. This process is used to calibrate the micro-mechanical model based on the elasto-plastic phase-field formulation for fracture. In the subsequent step, virtual experiments based on an ellipsoidal unit cell, also with the resolution of fibers (used as a representative volume element, RVE), are simulated to analyze the impact of fiber–matrix interactions and their physical properties on the effective material behavior of fiber-reinforced HPC. In the final step, macroscopic boundary value problems (BVPs) based on a cuboid are simulated on a single scale using the developed phenomenological material model. The resulting macroscopic stress–strain characteristics obtained from both types of simulations, namely simulations of virtual experiments and macroscopic BVPs, are compared. This comparison is utilized for the calibration of material parameters to obtain a regularized solution and to assess the effectiveness of the presented phenomenological material model. [ABSTRACT FROM AUTHOR]

High-resolution melting (HRM) is a cost-efficient tool for targeted DNA methylation analysis. HRM yields the average methylation status across all CpGs in PCR products. Moreover, it provides information on the methylation pattern, e.g., the occurrence of monoallelic methylation. HRM assays have to be calibrated by analyzing DNA methylation standards of known methylation status and mixtures thereof. In general, DNA methylation levels determined by the classical calibration approach, including the whole temperature range in between normalization intervals, are in good agreement with the mean of the DNA methylation status of individual CpGs determined by pyrosequencing (PSQ), the gold standard of targeted DNA methylation analysis. However, the classical calibration approach leads to highly inaccurate results for samples with heterogeneous DNA methylation since they result in more complex melt curves, differing in their shape compared to those of DNA standards and mixtures thereof. Here, we present a novel calibration approach, i.e., temperature-wise calibration. By temperature-wise calibration, methylation profiles over temperature are obtained, which help in finding the optimal calibration range and thus increase the accuracy of HRM data, particularly for heterogeneous DNA methylation. For explaining the principle and demonstrating the potential of the novel calibration approach, we selected the promoter and two enhancers of MGMT, a gene encoding the repair protein MGMT. [ABSTRACT FROM AUTHOR]

The object of the upgrade presented in this paper was an older analogue-based universal testing machine preferably designed for tensile testing. The objective of the upgrade was to create a new digitized measurement chain capable of also operating in the mode of simple fatigue tests with cyclic tensile stresses. The upgrade of the equipment mainly included the processes of calibration, creation of related calibration jigs, creation of transformation dependencies and digitization, creation or completion of missing parts of chain-signal conditioning modules, A/D converters, special jigs, etc., as elements of the experimental hardware. The degree of correctness of the calibration and of the transformation dependencies created was verified by regression analysis, and this was verified by simple correlation analysis. The correctness of the proposed modifications, was verified on the basis of the fatigue tests performed for cyclic loading. Thus, it was possible to design, develop and functionally verify a new measurement chain based on an older universal testing machine. Thus, a partially digitized pulsator was created for the possibility of fatigue testing in a technically or functionally limited mode, mainly for educational purposes as a temporary replacement for the order of magnitude more expensive commercially produced test systems. [ABSTRACT FROM AUTHOR]

The precise calibration of a LiDAR-camera system is a crucial prerequisite for multimodal 3D information fusion in perception systems. The accuracy and robustness of existing traditional offline calibration methods are inferior to methods based on deep learning. Meanwhile, most parameter regression-based online calibration methods directly project LiDAR data onto a specific plane, leading to information loss and perceptual limitations. A novel network, DPCalib, a dual perspective view network that mitigates the aforementioned issue, is proposed in this paper. This paper proposes a novel neural network architecture to achieve the fusion and reuse of input information. We design a feature encoder that effectively extracts features from two orthogonal views using attention mechanisms. Furthermore, we propose an effective decoder that aggregates features from two views, thereby obtaining accurate extrinsic parameter estimation outputs. The experimental results demonstrate that our approach outperforms existing SOTA methods, and the ablation experiments validate the rationality and effectiveness of our work. [ABSTRACT FROM AUTHOR]

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory. [ABSTRACT FROM AUTHOR]

In this paper, a six-degree-of-freedom analog tactile probe with a new, simple, and robust mechanical design is presented. Its design is based on the use of one elastomeric ring that supports the stylus carrier and allows its movement inside a cubic measuring range of ±3 mm. The position of the probe tip is determined by three low-cost, noncontact, 2D PSD (position-sensitive detector) sensors, facilitating a wider application of this probe to different measuring systems compared to commercial ones. However, several software corrections, regarding the size and orientation of the three LED light beams, must be carried out when using these 2D sensors for this application due to the lack of additional focusing or collimating lenses and the very wide measuring range. The development process, simulation results, correction models, experimental tests, and calibration of this probe are presented. The results demonstrate high repeatability along the X-, Y-, and Z-axes (2.0 µm, 2.0 µm, and 2.1 µm, respectively) and overall accuracies of 6.7 µm, 7.0 µm, and 8.0 µm, respectively, which could be minimized by more complex correction models. [ABSTRACT FROM AUTHOR]

The precision requirements for aeroengine blade machining are exceedingly stringent. This study aims to improve the accuracy of existing aeroengine blade measurement methods while achieving comprehensive measurement. Therefore, this study proposes a new concentric ring calibration method and designs a multi-layer concentric ring calibration plate. The effectiveness of this calibration method was verified through actual testing of standard ball gauges. Compared with the checkerboard-grid calibration method, the average deviation of the multilayer concentric ring calibration method for measuring the center distance of the standard sphere is 0.02352, which improves the measurement accuracy by 3–4 times. On the basis of multi-layer concentric ring calibration, this study builds a fringe projection profiler based on the three-frequency twelve-step phase shift method. Compared with the CMM, the average deviation of the blade chord length measured by this solution is 0.064, which meets the measurement index requirements of aeroengine fan blades. [ABSTRACT FROM AUTHOR]

Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young's modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure's design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25–50% overestimated design stiffness of internal main girders. [ABSTRACT FROM AUTHOR]

Standard beams are mainly used for the calibration of strain sensors using their load reconstruction models. However, as an ill-posed inverse problem, the solution to these models often fails to converge, especially when dealing with dynamic loads of different frequencies. To overcome this problem, a piecewise Tikhonov regularization method (PTR) is proposed to reconstruct dynamic loads. The transfer function matrix is built both using the denoised excitations and the corresponding responses. After singular value decomposition (SVD), the singular values are divided into submatrices of different sizes by utilizing a piecewise function. The regularization parameters are solved by optimizing the piecewise submatrices. The experimental result shows that the MREs of the PTR method are 6.20% at 70 Hz and 5.86% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits MREs of 28.44% and 29.61% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates MREs of 29.98% and 18.42% at the same frequencies. Furthermore, the PREs of the PTR method are 3.54% at 70 Hz and 3.73% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits PREs of 27.01% and 26.88% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates PREs of 29.50% and 15.56% at the same frequencies. All in all, the method proposed in this paper can be extensively applied to load reconstruction across different frequencies. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Suite-Nadir (OMS-N) instrument is the first hyperspectral remote sensor in the ultraviolet band of China's Fengyun series satellites. It can be used to detect several kinds of atmospheric constituents. This paper describes the prelaunch spectral calibration of the OMS-N onboard FengYun 3F. Several critical spectral parameters including the spectral resolution, spectral dispersion, and the instrument spectral response function were determined through laser-based measurements. A secondary peak of the instrument spectral response function from the short wavelength side of the ultraviolet band was found, and the possible influence on data applications was analyzed using a reference solar model and radiative transfer model. The results indicate that the spectral resolution and spectral accuracy of OMS-N meet the mission requirements. However, the asymmetries in the instrument spectral response function in the ultraviolet band were found near nadir rows, which are expressed as the "asymmetric central peak" and "secondary peak". The analysis results show that if the influences of the instrument spectral response function "asymmetric central peak" and "secondary peak" in the ultraviolet band are ignored, they will bring an error as large as 5% at the center of the absorption line. [ABSTRACT FROM AUTHOR]

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands. [ABSTRACT FROM AUTHOR]

Passive differential optical absorption spectroscopy (DOAS) is widely used to monitor the three-dimensional distribution of atmospheric pollutants. However, the observational and retrieval accuracy of this technique is significantly influenced by the precise wavelength calibration of solar spectra. Current calibration methods face challenges in automation when dealing with complex remote-sensing conditions. We introduce a novel automatic wavelength calibration algorithm for passive DOAS based on sequence-matching technology to estimate the spectral parameters of the spectrometer channels, integrating advanced processing measures such as feature structure enhancement and sub-pixel interpolation. These measures significantly reduce the dependency on reference spectrum resolution and accurately correct even minor spectral shifts. We perform sensitivity experiments using synthetic spectra to determine optimal retrieval configurations, followed by field tests at four cities on the Yangtze River Delta, China, to calibrate and compare passive DOAS instruments of various resolutions. Comparative verification in these field studies demonstrated that our algorithm was suitable for rapid spectral calibration within a wider resolution range of 0.03 nm to 0.1 nm with a wavelength inversion error < 0.01 nm. This highlights the applicability and calibration precision of our algorithm. [ABSTRACT FROM AUTHOR]

Bean production is at risk due to climate change, declining water resources, and inadequate crop management. To address these challenges, dynamic models that predict crop growth and development can be used as fundamental tools to generate basic and applied knowledge such as production management and decision support. This study aimed to calibrate and evaluate the SIMPLE model under irrigation conditions for a semi-arid region in north-central Mexico and to simulate thermal time, biomass (Bio), and grain yield (GY) of common beans cv. 'Pinto Saltillo' using experimental data from four crop evapotranspiration treatments (ETct) (I50, I75, I100, and I125) applied during the 2020 and 2021 growing seasons. Both experiments were conducted in a randomized complete block design with three replicates. Model calibration was carried out by posing and solving an optimization problem with the differential-evolution algorithm with 2020 experimental data, while the evaluation was performed with 2021 experimental data. For Bio, calibration values had a root-mean-square error and Nash and Sutcliffe's efficiency of <0.58 t ha−1 and >0.93, respectively, while the corresponding evaluation values were <1.80 t ha−1 and >0.89, respectively. The I50 and I100 ETct had better fit for calibration, while I50 and I75 had better fit in the evaluation. On average, the model fitted for the predicted GY values had estimation errors of 37% and 22% for the calibration and evaluation procedures, respectively. Therefore, an empirical model was proposed to estimate the harvest index (HI), which produced, on average, a relative error of 6.9% for the bean-GY estimation. The SIMPLE model was able to predict bean biomass under irrigated conditions for these semi-arid regions of Mexico. Also, the use of both crop Bio and transpiration simulated by the SIMPLE model to calculate the HI significantly improved GY prediction under ETct. However, the harvest index needs to be validated under other irrigation levels and field experiments in different locations to strengthen the proposed model and design different GY scenarios under water restrictions for irrigation due to climate change. [ABSTRACT FROM AUTHOR]

Simple Summary: Use of cancer biomarkers for tumor aggressiveness is an unmet clinical need. Differentiation of high-risk versus low-risk tumors may guide physicians to select appropriate treatment strategies tailored to the risk level of individual patients. This study aimed to evaluate the value of an optical redox imaging technique for differentiating a human melanoma mouse xenograft model with a high risk of metastasis from a low-risk mouse model. Several imaging indices were found to be significantly different between the two models. The high-risk model was found to be in a more oxidized status and to have a higher intratumor redox heterogeneity. These findings may inform further research development of the optical redox imaging approach into translatable cancer biomarkers in the future. To develop imaging biomarkers for tumors aggressiveness, our previous optical redox imaging (ORI) studies of the reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavoproteins (Fp, containing flavin adenine dinucleotide, i.e., FAD) in tumor xenografts of human melanoma associated the high optical redox ratio (ORR = Fp/(Fp + NADH)) and its heterogeneity to the high invasive/metastatic potential, without having reported quantitative results for NADH and Fp. Here, we implemented a calibration procedure to facilitate imaging the nominal concentrations of tissue NADH and Fp in the mouse xenografts of two human melanoma lines, an indolent less metastatic A375P and a more metastatic C8161. Images of the redox indices (NADH, Fp, ORR) revealed the existence of more oxidized areas (OAs) and more reduced areas (RAs) within individual tumors. ORR was found to be higher and NADH lower in C8161 compared to that of A375P xenografts, both globally for the whole tumors and locally in OAs. The ORR in the OA can differentiate xenografts with a higher statistical significance than the global averaged ORR. H&E staining of the tumors indicated that the redox differences we identified were more likely due to intrinsically different cell metabolism, rather than variations in cell density. [ABSTRACT FROM AUTHOR]

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions. [ABSTRACT FROM AUTHOR]

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets. [ABSTRACT FROM AUTHOR]

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model's accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts. [ABSTRACT FROM AUTHOR]

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose. [ABSTRACT FROM AUTHOR]