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While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration ( g ) is common among commercially available sensors. In this brief report, we demonstrate how we measured the offset and other errors in three different off-the-shelf wearable sensors available to professionals and how they affected a threshold-based movement detection algorithm for the quantification of infant leg movement. We describe how to calibrate and correct for these offsets and how conducting this improves the reproducibility of results across sensors.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 10 8 -1.72 × 10 8 Pa, and 5.9 × 10 6 -6.7 × 10 6 N/m 3 , respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 10 8 Pa, and a normal stiffness of 6.285 × 10 6 N/m 3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

This work is focused on developing a self-calibration algorithm for an orientation estimation of cattle movements based on a quaternion Kalman filter. The accelerometer signals in the earth's frame provide more information to confirm that the cow is performing a jump to mount another cow. To obtain the measurements in the earth's frame, we propose a self-calibration method based on a strapdown inertial navigation system (SINS), which does not require intervention by the user once deployed in the field. The self-calibration algorithm uses a quaternion-based Kalman filter to predict the angular orientation with bias correction, and update it based on the measurements of accelerometers and magnetometers. The paper also depicts an alternate update to adjust the inclination using only the accelerometer measurements. We conducted experiments to compare the accuracy of the orientation estimation when the body moves similarly to cow mount movements. The comparison is between the proposed self-calibration algorithm with the IvenSense MPU9250 and Bosch BNO055 and the quaternion attitude estimation provided in the BNO055 . The auto-calibrating algorithm presents a mean error of 0.149 rads with a mean consumption of 308.5 mW, and the Bosch algorithm shows an average error of 0.139 rads with a mean consumption of 307.5 mW. When we executed this algorithm in an MPU9250 , the average error was 0.077 rads, and the mean consumption was 277.7 mW.

LiDAR sensors, pivotal in various fields like agriculture and robotics for tasks such as 3D object detection and map creation, are increasingly coupled with thermal cameras to harness heat information. This combination proves particularly effective in adverse conditions like darkness and rain. Ensuring seamless fusion between the sensors necessitates precise extrinsic calibration. Our innovative calibration method leverages human presence during sensor setup movements, eliminating the reliance on dedicated calibration targets. It optimizes extrinsic parameters by employing a novel evolutionary algorithm on a specifically designed loss function that measures human alignment across modalities. Our approach showcases a notable 4.43% improvement in the loss over extrinsic parameters obtained from target-based calibration in the FieldSAFE dataset. This advancement reduces costs related to target creation, saves time in diverse pose collection, mitigates repetitive calibration efforts amid sensor drift or setting changes, and broadens accessibility by obviating the need for specific targets. The adaptability of our method in various environments, like urban streets or expansive farm fields, stems from leveraging the ubiquitous presence of humans. Our method presents an efficient, cost-effective, and readily applicable means of extrinsic calibration, enhancing sensor fusion capabilities in the critical fields reliant on precise and robust data acquisition.

Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable magnetisation signal changes during the experiment. We hypothesised that, despite the complex and inherently nonlinear electrodynamic physics underlying coil loading and spatial variation, simple linear regression would be sufficient to accurately predict X-nuclear transmit gain based on concomitantly acquired data from the proton body coil. We collected data across 156 scan visits at two sites as part of ongoing studies investigating sodium, hyperpolarised carbon, and hyperpolarised xenon. We demonstrate that simple linear regression is able to accurately predict sodium, carbon, or xenon transmit gain as a function of position and proton gain, with variation that is less than the intrasubject variability. In conclusion, sites running multinuclear studies may be able to remove the time-consuming need to separately acquire X-nuclear reference power calibration, inferring it from the proton instead.

Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.

This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilizes the chopping, correlated double sampling, and dynamic element matching techniques to mitigate the effects of process bias and nonideal device characteristics on measurement accuracy. Furthermore, based on the principle of charge conservation, the dynamic range utilization of the ADC increases. We propose a neural network that uses a multilayer convolutional perceptron to calibrate the sensor output results. Using the algorithm, the sensor achieves an inaccuracy of ±0.11 °C (3σ), exceeding the accuracy of ±0.23 °C (3σ) achieved without calibration. We implement the sensor in a 0.18 µm CMOS process, occupying an area of 0.42 mm 2 . It achieves a resolution of 0.01 °C and has a conversion time of 24 ms.

Hematocrit (HCT) is a crucial parameter for both adult and pediatric patients, indicating potentially severe pathological conditions. Most common methods for HCT assessment are microhematocrit and automated analyzers; however, developing countries present specific needs often not addressed by these technologies. Paper-based devices can be suitable for those environments being inexpensive, rapid, easy to use, and portable. The aim of this study is to describe and validate against a reference method, a novel HCT estimation method based on penetration velocity in lateral flow test strips complying with the requirements in low- or middle-income country (LMIC) scenarios. To calibrate and test the proposed method, 145 blood samples of 105 healthy neonates with gestational age greater than 37 weeks were collected (29 calibration set, 116 test set) in the range of HCT values (31.6-72.5%). The time difference (Δt) from the whole blood sample loading into the test strip instant till the nitrocellulose membrane saturation instant was measured by a reflectance meter. A nonlinear relation was observed between HCT and Δt and was estimated by a third-degree polynomial equation (R 2 = 0.91) valid in 30% to 70% HCT interval. The proposed model was subsequently used to estimate HCT values on the test set showing a good agreement between the estimated HCT and the HCT measured by the reference method (r = 0.87, p < 0.001), with a low mean difference of 0.53 ± 5.04% and a slight trend of overestimation for higher hematocrit values. The mean absolute error was 4.29%, while the maximum absolute error was 10.69%. Although the proposed method did not present a sufficient accuracy to be used for diagnostic purposes, it could be suitable as a fast, low-cost, easy-to-use screening tool especially in LMIC scenarios.

The paper suggests a new approach to calibration of a micromechanical inertial measurement unit. The data are collected on a simple rotating turntable with horizontal (or close to) rotation axis. For such a turntable, an electric screwdriver with fairly low rotation rate can be used. The algorithm is based on the Fourier transform applied to the rotation experimental data, implemented as FFT. The frequencies and amplitudes of the spectral peaks are calculated and collected in a small set of data, and calibration is done explicitly with these data. Calibration of an accelerometer triad and choosing the IMU coordinate frame are reduced to approximating the collected data with an ellipsoid in three dimensions. With rotation frequency calculated as the peak frequency of accelerometer readings, calibration of the gyros is a straightforward linear least square problem. The algorithm is purely algebraic, requires no iterations and no initial guess on the parameters, and thus encounters no convergence problems. The algorithm was tested both with simulated and experimental data, with some promising results.

Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. Recently, many new algorithms have been proposed for the model transfer of spectra collected by the same types of instruments under different situations. However, in a practical scenario, we need to deal with model transfer between different types of instruments. To expand model applicability, we must develop a method that could transfer spectra acquired from different types of NIR spectrometers with different wavenumbers or absorbance. Therefore, in our study, we propose a new methodology based on improved principal component analysis (IPCA) for calibration transfer between different types of spectrometers. We adopted three datasets for method evaluation, including public pharmaceutical tablets (dataset 1), corn data (dataset 2), and the spectra of eight batches of samples acquired from the plasma ethanol precipitation process collected by FT-NIR and MicroNIR spectrometers (dataset 3). In the calibration transfer for public datasets, IPCA displayed comparable results with the classical calibration transfer method using piecewise direct standardization (PDS), indicating its obvious ability to transfer spectra collected from the same types of instruments. However, in the calibration transfer for dataset 3, our proposed IPCA method achieved a successful bi-transfer between the spectra acquired from the benchtop and micro-instruments with/without wavelength region selection. Furthermore, our proposed method enabled improvements in prediction ability rather than the degradation of the models built with original micro spectra. Therefore, our proposed method has no limitations on the spectrum for model transfer between different types of NIR instruments, thus allowing a wide application range, which could provide a supporting technology for the practical application of NIR spectroscopy.

A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous operation status. Firstly, the total current signal of the collected equipment was filtered to extract the processing segment data. The processing segment data were then used to manually calibrate the feature vector of the equipment for specific parts and processes, and the feature vector was used as a reference to match with the real-time electric current data on the edge device to identify and obtain the processing start time, processing end time, and anomalous marks for each part. Finally, the information was uploaded to further obtain the part processing time, loading and unloading standby time, and the cause of the anomaly. To verify the reliability of the method, a prototype system was built, and extensive experiments were conducted on many different types of equipment in an auto parts manufacturer. The experimental results show that the proposed monitoring algorithm based on the calibration vector can stably and effectively identify the production information of each part on an independently developed edge device.

To improve the stability of the bridge structure, we detect bolts in the bridge which cause the symmetry failure of the bridge center. For data acquisition, bolts are small-scale objects under complex background in images, and their feature expression ability is limited. Due to those questions, we propose a new bolt positioning detection based on improved YOLOv5 for bridge structural health monitoring. This paper makes three major contributions. Firstly, according to the calibration anchor boxes of bolts, the size and proportion parameters of the initial anchor boxes are optimized by K-means++ clustering algorithm to solve the initial clustering problem of anchor boxes in object detection. Second, the hypercolumn (HC) technique fuses the low-level global features of the trunk and the high-level local features of three different scales to solve the problem of the inefficient distribution of anchors and insufficient extraction of classification features. In this way, we improve the detection accuracy and speed of bolt detection. Finally, we establish a dataset of bridge bolts through network collection and public datasets, including 1494 images. We compare and verify the new method in the collected bolt dataset. The experimental results show that the precision (P) of the improved YOLOv5x is up to 87.3%, and the average precision (AP) is up to 86.3%, which are 6.5% and 5.9% higher than the original YOLOv5x, respectively.

This paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.

A key challenge in further improving infrared (IR) sensor capabilities is the development of efficient data pre-processing algorithms. This paper addresses this challenge by providing a mathematical model and synthetic data generation framework for an uncooled IR sensor. The developed model is capable of generating synthetic data for the design of data pre-processing algorithms of uncooled IR sensors. The mathematical model accounts for the physical characteristics of the focal plane array, bolometer readout, optics and the environment. The framework permits the sensor simulation with a range of sensor configurations, pixel defectiveness, non-uniformity and noise parameters.

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors' mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry-Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained 0.29-0.36 fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between 0.01 and 0.05 on average and the spectral angle between 1.5 and 3.2 degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker.

The present work proposes a method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. This work focuses on characterize, calibrate, and compare Cartographer, Gmapping, HECTOR-SLAM, KARTO-SLAM, and RTAB-Map SLAM algorithms. There were four metrics in place: pose error, map accuracy, CPU usage, and memory usage; from these four metrics, to characterize them, Plackett-Burman and factorial experiments were performed, and enhancement after characterization and calibration was granted using hypothesis tests, in addition to the central limit theorem.

UAVs are sensor platforms increasingly used in precision agriculture, especially for crop and environmental monitoring using photogrammetry. In this work, light drone flights were performed on three consecutive days (with different weather conditions) on an experimental agricultural field to evaluate the photogrammetric performances due to colour calibration. Thirty random reconstructions from the three days and six different areas of the field were performed. The results showed that calibrated orthophotos appeared greener and brighter than the uncalibrated ones, better representing the actual colours of the scene. Parameter reporting errors were always lower in the calibrated reconstructions and the other quantitative parameters were always lower in the non-calibrated ones, in particular, significant differences were observed in the percentage of camera stations on the total number of images and the reprojection error. The results obtained showed that it is possible to obtain better orthophotos, by means of a calibration algorithm, to rectify the atmospheric conditions that affect the image obtained. This proposed colour calibration protocol could be useful when integrated into robotic platforms and sensors for the exploration and monitoring of different environments.

Multi-sensor fusion is important in the field of autonomous driving. A basic prerequisite for multi-sensor fusion is calibration between sensors. Such calibrations must be accurate and need to be performed online. Traditional calibration methods have strict rules. In contrast, the latest online calibration methods based on convolutional neural networks (CNNs) have gone beyond the limits of the conventional methods. We propose a novel algorithm for online self-calibration between sensors using voxels and three-dimensional (3D) convolution kernels. The proposed approach has the following features: (1) it is intended for calibration between sensors that measure 3D space; (2) the proposed network is capable of end-to-end learning; (3) the input 3D point cloud is converted to voxel information; (4) it uses five networks that process voxel information, and it improves calibration accuracy through iterative refinement of the output of the five networks and temporal filtering. We use the KITTI and Oxford datasets to evaluate the calibration performance of the proposed method. The proposed method achieves a rotation error of less than 0.1° and a translation error of less than 1 cm on both the KITTI and Oxford datasets.

A slope analog-to-digital converter (ADC) amenable to be fully implemented on a digital field programmable gate array (FPGA) without requiring any external active or passive components is proposed in this paper. The amplitude information, encoded in the transition times of a standard LVDS differential input-driven by the analog input and by the reference slope generated by an FPGA output buffer-is retrieved by an FPGA time-to-digital converter. Along with the ADC, a new online calibration algorithm is developed to mitigate the influence of process, voltage, and temperature variations on its performance. Measurements on an ADC prototype reveal an analog input range from 0.3 V to 1.5 V, a least significant bit (LSB) of 2.6 mV, and an effective number of bits (ENOB) of 7.4-bit at 600 MS/s. The differential nonlinearity (DNL) is in the range between -0.78 and 0.70 LSB, and the integral nonlinearity (INL) is in the range from -0.72 to 0.78 LSB.

It is possible to construct cost-efficient three-dimensional (3D) or four-dimensional (4D) scanning systems using multiple affordable off-the-shelf RGB-D sensors to produce high-quality reconstructions of 3D objects. However, the quality of these systems' reconstructions is sensitive to a number of factors in reconstruction pipelines, such as multi-view calibration, depth estimation, 3D reconstruction, and color mapping accuracy, because the successive pipelines to reconstruct 3D meshes from multiple active stereo sensors are strongly correlated with each other. This paper categorizes the pipelines into sub-procedures and analyze various factors that can significantly affect reconstruction quality. Thus, this paper provides analytical and practical guidelines for high-quality 3D reconstructions with off-the-shelf sensors. For each sub-procedure, this paper shows comparisons and evaluations of several methods using data captured by 18 RGB-D sensors and provide analyses and discussions towards robust 3D reconstruction. Through various experiments, it has been demonstrated that significantly more accurate 3D scans can be obtained with the considerations along the pipelines. We believe our analyses, benchmarks, and guidelines will help anyone build their own studio and their further research for 3D reconstruction.

To solve the problem of inflexibility of offline hand-eye calibration in "eye-in-hand" modes, an online hand-eye calibration method based on the ChArUco board is proposed in this paper. Firstly, a hand-eye calibration model based on the ChArUco board is established, by analyzing the mathematical model of hand-eye calibration, and the image features of the ChArUco board. According to the advantages of the ChArUco board, with both the checkerboard and the ArUco marker, an online hand-eye calibration algorithm based on the ChArUco board is designed. Then, the online hand-eye calibration algorithm, based on the ChArUco board, is used to realize the dynamic adjustment of the hand-eye position relationship. Finally, the hand-eye calibration experiment is carried out to verify the accuracy of the hand-eye calibration based on the ChArUco board. The robustness and accuracy of the proposed method are verified by online hand-eye calibration experiments. The experimental results show that the accuracy of the online hand-eye calibration method proposed in this paper is between 0.4 mm and 0.6 mm, which is almost the same as the offline hand-eye calibration accuracy. The method in this paper utilizes the advantages of the ChArUco board to realize online hand-eye calibration, which improves the flexibility and robustness of hand-eye calibration.

The camera is the main sensor of vison-based human activity recognition, and its high-precision calibration of distortion is an important prerequisite of the task. Current studies have shown that multi-parameter model methods achieve higher accuracy than traditional methods in the process of camera calibration. However, these methods need hundreds or even thousands of images to optimize the camera model, which limits their practical use. Here, we propose a novel point-to-point camera distortion calibration method that requires only dozens of images to get a dense distortion rectification map. We have designed an objective function based on deformation between the original images and the projection of reference images, which can eliminate the effect of distortion when optimizing camera parameters. Dense features between the original images and the projection of the reference images are calculated by digital image correlation (DIC). Experiments indicate that our method obtains a comparable result with the multi-parameter model method using a large number of pictures, and contributes a 28.5% improvement to the reprojection error over the polynomial distortion model.

To improve the robustness and accuracy of the corner-detection algorithm, this paper proposes a camera-calibration method based on the EDLines algorithm for the automatic detection of chessboard corners. The EDLines algorithm is initially used to perform straight-line detection on the calibration image. The features of the broken straight lines at the corners are then used to filter the straight lines and remove the background straight lines outside the chessboard. The pixels in the rectangular area around the filtered straight line are sorted by the gray gradient. After using the sorted results to fit the straight line, the coordinates of the intersection of the straight lines are taken as the initial coordinates of the corners and perform subpixel optimization on them. Finally, the corner points are sorted by the conversion between pixel-coordinate systems. The camera exposure time changes and complex imaging-background experiments show that the algorithm has no missed detection and redundancy in corner detection. The average reprojection error is found to be less than 0.05 pixels, which can be used in actual calibration.

The use of autonomous underwater vehicles (AUV) has increased in a wide range of sectors, including the oil and gas industry, military, and marine research. The AUV capabilities to operate without a direct human operator and untethered to a support vessel are features that have aroused interest in the marine environment. The localization of AUV is significantly affected by the initial alignment and the calibration of the navigation sensors. In this sense, this paper proposes a thorough observability analysis applied to the latter problem. The observability analysis is carried out considering three types of sensor fusion integration and a set of maneuvers, and the results are validated through numerical simulations. As main contribution of this paper, it is shown how the addition of position errors in the observation vector can decouple some gyro and accelerometer biases from the latitude and altitude errors, particularly in the stationary observability analysis. The influence of oscillations in the diving plane and typical AUV maneuvers are analyzed, showing their relative impacts on the degree of observability of the inertial measurement unit (IMU)/Doppler velocity log (DVL) misalignment and DVL scale factor error. Finally, the state's estimation accuracy is also analyzed, showing the limitation of the degree of observability as an assessment tool for the estimability of the states.

The perception module plays an important role in vehicles equipped with advanced driver-assistance systems (ADAS). This paper presents a multi-sensor data fusion system based on the polarization color stereo camera and the forward-looking light detection and ranging (LiDAR), which achieves the multiple target detection, recognition, and data fusion. The You Only Look Once v4 (YOLOv4) network is utilized to achieve object detection and recognition on the color images. The depth images are obtained from the rectified left and right images based on the principle of the epipolar constraints, then the obstacles are detected from the depth images using the MeanShift algorithm. The pixel-level polarization images are extracted from the raw polarization-grey images, then the water hazards are detected successfully. The PointPillars network is employed to detect the objects from the point cloud. The calibration and synchronization between the sensors are accomplished. The experiment results show that the data fusion enriches the detection results, provides high-dimensional perceptual information and extends the effective detection range. Meanwhile, the detection results are stable under diverse range and illumination conditions.

The Sunrise missions consist of observing the magnetic field of the sun continuously for a few days from the stratosphere. In these missions, a balloon supporting a telescope and associated instrumentation, including a Tunable Magnetograph (TuMag), is lifted into the stratosphere. In the camera of this instrument, the image sensor sends its data to a Field Programmable Gate Array (FPGA) using eight transmission channels. These channels must be previously calibrated for a correct delivery of the image. For this mission, the FPGA has been exchanged for a newer and larger one, so the firmware has been adapted to the new device. In addition, the calibration algorithm has been parallelized as the main innovation of this work, taking advantage of the increase in logic resources of the new FPGA, in order to minimize the calibration time of the channels. The algorithm has been implemented specifically for this instrument without using the Input Serial Deserializer (ISERDES) Intellectual Property (IP), since this IP does not support the deserialization of the data sent by the image sensor to the FPGA.

This paper presents a methodology for classifying train passages into different types with a weigh-in-motion (WIM) system to allow the calibration of railway fatigue load models and identify individual vehicles from the measurements for the continuous calibration of railway WIM stations from in-service trains. The quality assurance of the measured responses is demonstrated using statistical methods. This paper discusses the measurement station, the method used for processing the raw data, the algorithm used to identify the train types and vehicles automatically, and the limits of the obtained load spectra. The measurement errors are demonstrated to be satisfying for use in fatigue load model calibration. Furthermore, this paper proposes actions for accurately obtaining the actual traffic conditions and describes the future work required in this area.

For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.

This paper presents the design of a low-cost, compact instrumentation system to enable six degree of freedom motion tracking of acetal bricks within an experimental model of a cracked Advanced Gas-Cooled Reactor (AGR) core. The system comprises optical and inertial sensors and capitalises on the advantages offered by data fusion techniques. The optical system tracks LED indicators, allowing a brick to be accurately located even in cluttered images. The LED positions are identified using a geometrical correspondence algorithm, which was optimised to be computationally efficient for shallow movements, and complex camera distortions are corrected using a versatile Incident Ray-Tracking calibration. Then, a Perspective-Ray-based Scaled Orthographic projection with Iteration (PRSOI) algorithm is applied to each LED position to determine the six degree of freedom pose. Results from experiments show that the system achieves a low Root Mean Squared (RMS) error of 0.2296 mm in x, 0.3943 mm in y, and 0.0703 mm in z. Although providing an accurate measurement solution, the optical tracking system has a low sample rate and requires the line of sight to be maintained throughout each test. To increase the robustness, accuracy, and sampling frequency of the system, the optical system can be augmented with an Inertial Measurement Unit (IMU). This paper presents a method to integrate the optical system and IMU data by accurately timestamping data from each set of sensors and aligning the two coordinate axes. Once miniaturised, the developed system will be used to track smaller components within the AGR models that cannot be tracked with current instrumentation, expanding reactor core modelling capabilities.

This paper proposes a new technique for performing 3D static-point cloud registration after calibrating a multi-view RGB-D camera using a 3D (dimensional) joint set. Consistent feature points are required to calibrate a multi-view camera, and accurate feature points are necessary to obtain high-accuracy calibration results. In general, a special tool, such as a chessboard, is used to calibrate a multi-view camera. However, this paper uses joints on a human skeleton as feature points for calibrating a multi-view camera to perform calibration efficiently without special tools. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D joint set obtained through pose estimation as feature points. Since human body information captured by the multi-view camera may be incomplete, a joint set predicted based on image information obtained through this may be incomplete. After efficiently integrating a plurality of incomplete joint sets into one joint set, multi-view cameras can be calibrated by using the combined joint set to obtain extrinsic matrices. To increase the accuracy of calibration, multiple joint sets are used for optimization through temporal iteration. We prove through experiments that it is possible to calibrate a multi-view camera using a large number of incomplete joint sets.

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s , was used as criteria to determine the fitting performance. The Prediction Sum of Squares ( PRESS ) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation's fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance.

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri . Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R 2 ), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori , miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.

Direct visual odometry algorithms assume that every frame from the camera has the same photometric characteristics. However, the cameras with auto exposure are widely used outdoors as the environment often changes. The vignetting also affects the pixel's brightness on different frames, even if the exposure time is fixed. We propose an online vignetting correction and exposure time estimation method for stereo direct visual odometry algorithms. Our method works on a camera that has a gamma-like response function. The inverse vignetting function and exposure time ratio between neighboring frames are estimated. Stereo matching is used to select correspondences between the left image and right image in the same frame at the initialization step. Feature points are used to pick the correspondences between different frames. Our method provides static correction results during the experiments on datasets and a stereo camera.

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe. From the observed results, it can be seen that the unsupervised method does not require previous calibration of target dimensions, histogram, and distances involved in positioning. The algorithm showed less sensitivity in the extraction of sharpness characteristics regarding the number of edges and greater sensitivity to the gradient, allowing unforeseen operation scenarios, even in small sharpness variations, and robust response to variance local, temporal, and geophysical of the magnetic declination, not needing luminosity after scanning, with the two freedom of degrees of the rotation.

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm's potential, a novel localization-and-tracking system is presented to estimate a target's arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

Flexible magnetic-field-based tactile sensors (FMFTS) have numerous advantages including low cost, ease of manufacture, simple wiring, high sensitivity, and so on. Flexible magnetic-field-based tactile sensors need to be calibrated before use to build accurate mapping between contact force and magnetic field intensity measured by magnetic sensors; however, when considering remanence inconsistency of magnetic source, each FMFTS needs to be calibrated independently to enhance accuracy, and the complex preparation prevents FMFTS from being used conveniently. A robust estimation method of contact force and location that can tolerate remanence inconsistency of magnetic source in FMFTS is proposed. Firstly, the position and orientation of magnetic source were tracked using the Levenberg-Marquart algorithm, and the tracking results were insensitive to the remanence of magnetic source with appropriate cost function. Secondly, the mapping between magnitude and location of contact force and position and orientation of magnetic source was built with calibration of one sensor; the mapping only depends on the structural response of flexible substrate, and thus can be extended to estimate external force and location for other sensors with the same structure. The proposed method was evaluated in both simulations and experiments, and the results confirm that the estimation of magnitude and location of external force for FMFTS with the same structure and different remanence could reach acceptable accuracy, depending on single calibration. The proposed method can be used to simplify the calibration procedure and remove the barrier for large-scale application of FMFTS and replacement of damaged FMFTS.

The correct diagnosis and recognition of crop diseases play an important role in ensuring crop yields and preventing food safety. The existing methods for crop disease recognition mainly focus on accuracy while ignoring the algorithm's robustness. In practice, the acquired images are often accompanied by various noises. These noises lead to a huge challenge for improving the robustness and accuracy of the recognition algorithm. In order to solve this problem, this paper proposes a residual self-calibration and self-attention aggregation network (RCAA-Net) for crop disease recognition in actual scenarios. The proposed RCAA-Net is composed of three main modules: (1) multi-scale residual module, (2) feedback self-calibration module, and (3) self-attention aggregation module. Specifically, the multi-scale residual module is designed to learn multi-scale features and provide both global and local information for the appearance of the disease to improve the performance of the model. The feedback self-calibration is proposed to improve the robustness of the model by suppressing the background noise in the original deep features. The self-attention aggregation module is introduced to further improve the robustness and accuracy of the model by capturing multi-scale information in different semantic spaces. The experimental results on the challenging 2018ai&#95;challenger crop disease recognition dataset show that the proposed RCAA-Net achieves state-of-the-art performance on robustness and accuracy for crop disease recognition in actual scenarios.

The calibration of three-axis magnetic field sensors is reviewed. Seven representative algorithms for in-situ calibration of magnetic field sensors without requiring any special piece of equipment are reviewed. The algorithms are presented in a user friendly, directly applicable step-by-step form, and are compared in terms of accuracy, computational efficiency and robustness using both real sensors' data and artificial data with known sensor's measurement distortion.

This paper summarized the research status, imaging model, systems calibration, distortion correction, and panoramic expansion of panoramic vision systems, pointed out the existing problems and put forward the prospect of future research. According to the research status of panoramic vision systems, a panoramic vision system with single viewpoint of refraction and reflection is designed. The systems had the characteristics of fast acquisition, low manufacturing cost, fixed single-view imaging, integrated imaging, and automatic switching depth of field. Based on these systems, an improved nonlinear optimization polynomial fitting method is proposed to calibrate the monocular HOVS, and the binocular HOVS is calibrated with the Aruco label. This method not only improves the robustness of the calibration results, but also simplifies the calibration process. Finally, a real-time method of panoramic map of multi-function vehicle based on vcam is proposed.

Visual inspection is an important task in manufacturing industries in order to evaluate the completeness and quality of manufactured products. An autonomous robot-guided inspection system was recently developed based on an offline programming (OLP) and RGB-D model system. This system allows a non-expert automatic optical inspection (AOI) engineer to easily perform inspections using scanned data. However, if there is a positioning error due to displacement or rotation of the object, this system cannot be used on a production line. In this study, we developed an automated position correction module to locate an object's position and correct the robot's pose and position based on the detected error values in terms of displacement or rotation. The proposed module comprised an automatic hand-eye calibration and the PnP algorithm. The automatic hand-eye calibration was performed using a calibration board to reduce manual error. After calibration, the PnP algorithm calculates the object position error using artificial marker images and compensates for the error to a new object on the production line. The position correction module then automatically maps the defined AOI target positions onto a new object, unless the target position changes. We performed experiments that showed that the robot-guided inspection system with the position correction module effectively performed the desired task. This smart innovative system provides a novel advancement by automating the AOI process on a production line to increase productivity.

Global localization is a fundamental ability for mobile robots. Considering the limitation of single type of sensor, fusing measurements from multiple sensors with complementary properties is a valuable task for study. In this paper, we propose a decoupled optimization-based framework for global-local sensor fusion, which fuses the intermittent 3D global positions and high-frequent 6D odometry poses to infer the 6D global localization results in real-time. The fusion process is formulated as estimating the relative transformation between global and local reference coordinates, translational extrinsic calibration, and the scale of the local pose estimator. We validate the full observability of the system under general movements, and further analyze the degenerated movement patterns where some related system state would be unobservable. A degeneration-aware sensor fusion method is designed which detects the degenerated directions before optimization, and adds constraints specifically along these directions to relieve the effect of the noise. The proposed degeneration-aware global-local sensor fusion method is validated in both simulation and real-world datasets with different sensor configurations, and shows its effectiveness in terms of accuracy and robustness compared with other decoupled sensor fusion methods for global localization.

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the "Manumeter"). The "HAND" (Hand Activity estimated by Nonlinear Detection) algorithm assigns a "HAND count" by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants' Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.

Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device's error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.

Scanning electron microscopes (SEMs) are versatile imaging devices for the micro- and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen's two-dimensional (2D) properties are provided by the acquired images, detailed morphological characterizations require knowledge about the three-dimensional (3D) surface structure. To overcome this limitation, a reconstruction routine is presented that allows the quantitative depth reconstruction from SEM image sequences. Based on the SEM's imaging properties that can be well described by an affine camera, the proposed algorithms rely on the use of affine epipolar geometry, self-calibration via factorization and triangulation from dense correspondences. To yield the highest robustness and accuracy, different sub-models of the affine camera are applied to the SEM images and the obtained results are directly compared to confocal laser scanning microscope (CLSM) measurements to identify the ideal parametrization and underlying algorithms. To solve the rectification problem for stereo-pair images of an affine camera so that dense matching algorithms can be applied, existing approaches are adapted and extended to further enhance the yielded results. The evaluations of this study allow to specify the applicability of the affine camera models to SEM images and what accuracies can be expected for reconstruction routines based on self-calibration and dense matching algorithms.

In this paper, the application of Augmented Reality (AR) for the control and adjustment of robots has been developed, with the aim of making interaction and adjustment of robots easier and more accurate from a remote location. A LeapMotion sensor based controller has been investigated to track the movement of the operator hands. The data from the controller allows gestures and the position of the hand palm's central point to be detected and tracked. A Kinect V2 camera is able to measure the corresponding motion velocities in x, y, z directions after our investigated post-processing algorithm is fulfilled. Unreal Engine 4 is used to create an AR environment for the user to monitor the control process immersively. Kalman filtering (KF) algorithm is employed to fuse the position signals from the LeapMotion sensor with the velocity signals from the Kinect camera sensor, respectively. The fused/optimal data are sent to teleoperate a Baxter robot in real-time by User Datagram Protocol (UDP). Several experiments have been conducted to test the validation of the proposed method.

Demand for indoor navigation systems has been rapidly increasing with regard to location-based services. As a cost-effective choice, inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) systems have been developed for years because they do not require external devices to be installed in the environment. In this paper, we propose a PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems. Since the IMU is mounted on a part of the upper body, the framework of the zero-velocity update cannot be applied because there are no periodical moments of zero velocity. Therefore, we propose a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest. In addition, we integrated the idea of an efficient map-matching algorithm based on particle filtering into our system to improve positioning and heading accuracy. Since our system was designed for 3D navigation, which can estimate position in a multifloor building, we used a barometer to update pedestrian altitude, and the components of our map are designed to explicitly represent building-floor information. With our complete PDR system, we were awarded second place in 10 teams for the IPIN 2018 Competition Track 2, achieving a mean error of 5.2 m after the 800 m walking event.

Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to study the baseline characteristics of an in-house-built portable medical NIRS device over a long time (>3.5 h). We found that the measured baselines all formed perfect polynomial functions on phantom tests mimicking human bodies, which were identified by recent NIRS studies. More importantly, our study shows that the fourth-order polynomial function acted to distinguish performance with stable and low-computation-burden fitting calibration (R-square >0.99 for all probes) among second- to sixth-order polynomials, evaluated by the parameters R-square, sum of squares due to error, and residual. This study provides a straightforward, efficient, and quantitatively evaluated solution for online baseline removal for hemodynamic monitoring using NIRS devices., Competing Interests: The authors declare no conflict of interest.