Automatic Calibration Procedure Research Articles

FLORIS and CLORIS: Hybrid source and network localization based on ranges and video

Abstract We propose hybrid methods for localization in wireless sensor networks fusing noisy range measurements with angular information (extracted from video). Compared with conventional methods that rely on a single sensed variable, this may pave the way for improved localization accuracy and robustness. We address both the single-source and network (i.e., cooperative multiple-source) localization paradigms, solving them via optimization of a convex surrogate. The formulations for hybrid localization are unified in the sense that we propose a single nonlinear least-squares cost function, fusing both angular and range measurements. We then relax the problem to obtain an estimate of the optimal positions. This contrasts with other hybrid approaches that alternate the execution of localization algorithms for each type of measurement separately, to progressively refine the position estimates. Single-source localization uses a semidefinite relaxation to obtain a one-shot matrix solution from which the source position is derived through factorization. Network localization uses a different approach where sensor coordinates are retained as optimization variables, and the relaxed cost function is efficiently minimized using fast iterations based on Nesterov’s optimal method. Further, an automated calibration procedure is developed to express range and angular information, obtained through different devices, possibly deployed at different locations, in a single consistent coordinate system. This drastically reduces the need for manual calibration that would otherwise negatively impact the practical usability of hybrid range/video localization systems. We develop and test, both in simulation and experimentally, the new hybrid localization algorithms, which not only overcome the limitations of previous fusing approaches, but also compare favourably to state-of-the-art methods, even outperforming them in some scenarios.

Automatic Setting Procedure for Exoskeleton-Assisted Overground Gait: Proof of Concept on Stroke Population.

Stroke-related locomotor impairments are often associated with abnormal timing and intensity of recruitment of the affected and non-affected lower limb muscles. Restoring the proper lower limbs muscles activation is a key factor to facilitate recovery of gait capacity and performance, and to reduce maladaptive plasticity. Ekso is a wearable powered exoskeleton robot able to support over-ground gait training. The user controls the exoskeleton by triggering each single step during the gait cycle. The fine-tuning of the exoskeleton control system is crucial—it is set according to the residual functional abilities of the patient, and it needs to ensure lower limbs powered gait to be the most physiological as possible. This work focuses on the definition of an automatic calibration procedure able to detect the best Ekso setting for each patient. EMG activity has been recorded from Tibialis Anterior, Soleus, Rectus Femoris, and Semitendinosus muscles in a group of 7 healthy controls and 13 neurological patients. EMG signals have been processed so to obtain muscles activation patterns. The mean muscular activation pattern derived from the controls cohort has been set as reference. The developed automatic calibration procedure requires the patient to perform overground walking trials supported by the exoskeleton while changing parameters setting. The Gait Metric index is calculated for each trial, where the closer the performance is to the normative muscular activation pattern, in terms of both relative amplitude and timing, the higher the Gait Metric index is. The trial with the best Gait Metric index corresponds to the best parameters set. It has to be noted that the automatic computational calibration procedure is based on the same number of overground walking trials, and the same experimental set-up as in the current manual calibration procedure. The proposed approach allows supporting the rehabilitation team in the setting procedure. It has been demonstrated to be robust, and to be in agreement with the current gold standard (i.e., manual calibration performed by an expert engineer). The use of a graphical user interface is a promising tool for the effective use of an automatic procedure in a clinical context.

Surrogate modeling-based calibration of hydrodynamic river model parameters

As opposed to other disciplines, automated calibration procedures are not common practice for full hydrodynamic river models, mainly because of the long computation times impeding the accurate assessment of parameter values. Default or text-book values are therefore often used. This paper introduces a methodology to optimize hydrodynamic model parameter values, based on the use of a surrogate conceptual model. Thanks to the spatial lumping and the explicit calculation schemes of these conceptual models, very short calculation times and a large number of simulation runs can be achieved. The surrogate model is coupled with the Shuffled Complex Evolution Metropolis algorithm of the University of Arizona (SCEM-UA) to identify the optimal parameter sets and their uncertainty. Afterwards, the optimized parameter values are transferred to the full hydrodynamic model. The methodology is demonstrated on a case study of the river Molenbeek in Belgium, using streamflow, water level and gate level observations. Results show a decrease of the hydrodynamic model residuals by about 60 percent.

Calibration of the LHAASO-KM2A electromagnetic particle detectors using charged particles within the extensive air showers

In the Large High Altitude Air Shower Observatory (LHAASO), one square kilometer array (KM2A), with 5242 electromagnetic particle detectors (EDs) and 1171 muon detectors (MDs), is designed to study ultra-high energy gamma-ray astronomy and cosmic ray physics. The remoteness and numerous detectors extremely demand a robust and automatic calibration procedure. In this paper, a self-calibration method which relies on the measurement of charged particles within the extensive air showers is proposed. The method is fully validated by Monte Carlo simulation and successfully applied in a KM2A prototype array experiment. Experimental results show that the self-calibration method can be used to determine the detector time offset constants at the sub-nanosecond level and the number density of particles collected by each ED with an accuracy of a few percents, which are adequate to meet the physical requirements of LHAASO experiment. This software calibration also offers an ideal method to realtime monitor the detector performances for next generation ground-based EAS experiments covering an area above square kilometers scale.

SeNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

Performance of the dynamically dimensioned search algorithm: influence of parameter initialization strategy when calibrating a physically based hydrological model

Abstract This study presents an evaluation of the performance of the dynamically dimensioned search (DDS) algorithm when calibrating the hydrological component of the Visualizing Ecosystems for Land Management Assessments (VELMA) ecohydrological model. Two calibration strategies were tested for the initial parameter values: (1) a ‘high-cost strategy’, where 100 sets of initial parameter values were randomly chosen within the overall parameter space, and (2) a ‘low-cost strategy’, where a unique set of initial parameter values was derived from the available field data. Both strategies were tested for six different values of the maximum number of model evaluations ranging between 100 and 10,000. Results revealed that DDS is able to converge rapidly to a good parameter calibration solution of the VELMA hydrological component regardless of the parameter initialization strategy used. The accuracy and convergence efficiency of the DDS algorithm were, however, slightly better for the low-cost strategy. This study suggests that initializing the parameter values of complex physically based models using information on the watershed characteristics can increase the efficiency of the automatic calibration procedures.

Time-Resolved Gravimetric Method To Assess Degassing of Roasted Coffee

During the roasting of coffee, thermally driven chemical reactions lead to the formation of gases, of which a large fraction is carbon dioxide (CO2). Part of these gases is released during roasting while part is retained inside the porous structure of the roasted beans and is steadily released during storage or more abruptly during grinding and extraction. The release of CO2 during the various phases from roasting to consumption is linked to many important properties and characteristics of coffee. It is an indicator for freshness, plays an important role in shelf life and in packaging, impacts the extraction process, is involved in crema formation, and may affect the sensory profile in the cup. Indeed, and in view of the multiple roles it plays, CO2 is a much underappreciated and little examined molecule in coffee. Here, we introduce an accurate, quantitative, and time-resolved method to measure the release kinetics of gases from whole beans and ground coffee using a gravimetric approach. Samples were placed in a container with a fitted capillary to allow gases to escape. The time-resolved release of gases was measured via the weight loss of the container filled with coffee. Long-term stability was achieved using a customized design of a semimicro balance, including periodic and automatic zero value measurements and calibration procedures. The novel gravimetric methodology was applied to a range of coffee samples: (i) whole Arabica beans and (ii) ground Arabica and Robusta, roasted to different roast degrees and at different speeds (roast air temperatures). Modeling the degassing rates allowed structural and mechanistic interpretation of the degassing process.

Does the Temporal Resolution of Precipitation Input Influence the Simulated Hydrological Components Employing the SWAT Model?

AbstractThis study aimed to evaluate the influence of sub‐daily precipitation time steps on model performance and hydrological components by applying the Green and Ampt infiltration method using the Soil and Water Assessment Tool (SWAT). Precipitation was measured at a resolution of 0.1 mm and aggregated to 5‐, 15‐, 30‐, and 60‐min time steps. Daily discharge data over a 10‐year period were used to calibrate and validate the model. Following a global sensitivity analysis, relevant parameters were optimized through an automatic calibration procedure using SWAT‐CUP for each time step. Daily performance statistics were almost equal among all four time steps (NSE ≈ 0.47). Discharge mainly consisted of groundwater flow (55%) and tile flow (42%), in reasonable proportions for the investigated catchment. In conclusion, model outputs were almost identical, showing simulations responded nearly independently of the chosen precipitation time step. This held true for (1) the selection of sensitive parameters, (2) performance statistics, (3) the shape of the hydrographs, and (4) flow components. However, a scenario analysis revealed that the precipitation time step becomes important when saturated hydraulic conductivities are low and curve numbers are high. The study suggests that there is no need in using precipitation time steps <1 h for lowland catchments dominated by soils with a low surface runoff potential if daily flow values are being considered. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.

An automated vision system for measurement of zebrafish length using low-cost orthogonal web cameras

Fish size inspection is becoming increasingly important in aquaculture applications and research including growth monitoring, an early indication of health problems and others. However, sizing of small fish has not yet been thoroughly explored in the literature due to challenges associated with rapid and unpredictable changes in the swimming direction of such fish. This paper presents a new low-cost computer vision system for estimating the length of small fish length using dual synchronized orthogonal webcams. The contour and location of the fish body are identified through continuous capturing of front and side images of the fish population under study. A mathematical model that accounts for the projection error that is mainly caused by the depth from the camera and light refraction is derived and implemented in this study. An automatic calibration procedure is also suggested to account for light-refraction during the fish length measurements. Similarly, a camera distance calibration is performed experimentally and considered throughout the estimation process of the fish length. The performance of the developed vision system is assessed experimentally using individual free-swimming adult zebrafish. The obtained results for the subjects under test have demonstrated an average estimation error of approximately 1%. Such a relatively high estimation performance demonstrates the validity of the proposed model and compares favourably to the state of the art of small-fish sizing.

Differential temperature sensor with high sensitivity, wide dynamic range and digital offset calibration

The goal of this paper is twofold: first to add together all different causes that can alter the offset of a differential temperature sensor and, second, to present a new differential temperature sensor architecture that can digitally compensate for this behavior and therefore extends the sensor dynamic range. Measurements performed on a 65nm CMOS differential temperature sensor are presented to illustrate the discussion. As evolution of the state of the art, an automatic calibration procedure and the new sensor topology is presented. With this new topology, not only the thermal offset can be digitally calibrated, but the application field of differential temperature sensors is widened, being now suitable for use in measurements where both wide input range and high differential sensitivity are required.

Experimental evaluation of a sEMG-based control for elbow wearable assistive devices during load lifting tasks.

In this work, a surface skin electromyography(sEMG)-based control solution for elbow wearable assistive devices during load lifting tasks is presented. The goal of the controller consists in limiting the user's muscle activity during the task execution, in such a way that the assistive device can partially compensate the load-related biceps muscle effort. Since sEMG-driven control strategies based on the estimation of the joint torques generally requires complex task- and subject-dependent training sessions for tuning the control algorithms, here a more direct control approach is proposed, based on a muscle activity error related proportional-integral action together with an double-threshold activation logic. The controller's parameters are easily set by means of a fast, online and automatic subject calibration procedure, ensuring a simple adjustability to different users. An experimental phase has been conducted in order to evaluate the sEMG-based control performance involving four healthy subjects, using as wearable assistive device a twisted string action module, which is particularly suitable for assistive applications because of its lightness and compactness. Results show that the control strategy is able to successfully limit the EMG activity of the subjects during the lifting tasks, providing preliminary outcomes and promising possibilities for the use of twisted string-based technologies to assist human joints and muscles.

Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking.

Eye-movements are the only directly observable behavioural signals that are highly correlated with actions at the task level, and proactive of body movements and thus reflect action intentions. Moreover, eye movements are preserved in many movement disorders leading to paralysis (or amputees) from stroke, spinal cord injury, Parkinson's disease, multiple sclerosis, and muscular dystrophy among others. Despite this benefit, eye tracking is not widely used as control interface for robotic interfaces in movement impaired patients due to poor human-robot interfaces. We demonstrate here how combining 3D gaze tracking using our GT3D binocular eye tracker with custom designed 3D head tracking system and calibration method enables continuous 3D end-point control of a robotic arm support system. The users can move their own hand to any location of the workspace by simple looking at the target and winking once. This purely eye tracking based system enables the end-user to retain free head movement and yet achieves high spatial end point accuracy in the order of 6 cm RMSE error in each dimension and standard deviation of 4 cm. 3D calibration is achieved by moving the robot along a 3 dimensional space filling Peano curve while the user is tracking it with their eyes. This results in a fully automated calibration procedure that yields several thousand calibration points versus standard approaches using a dozen points, resulting in beyond state-of-the-art 3D accuracy and precision.

A novel calibration method for industrial AGVs

We propose a novel calibration method for industrial Automated Guided Vehicles (AGVs) adopting the tricycle wheeled robot model and equipped with an on-board exteroceptive sensor. The method simultaneously estimates the calibration parameters for the odometry and the exteroceptive sensor using only the input commands and the sensor egomotion of the robot while executing segment paths. Two AGV models, both relevant to industrial practice, are considered: the standard tricycle model and an asymmetric one that takes into account the different weight distribution in forward and backward motions typical of industrial AGVs. The parameters of the standard model comprise the steering offset and driving scale, which measure the angular offset of the tricycle steering wheel and the distance increment corresponding to an encoder tick, and the three parameters representing the sensor pose. The asymmetric model adopts different values for the steering offset in forward and backward motions to account for the different weight distribution. Closed-form or compact solutions are provided for both problem formulations. The observability of the calibration procedure is also formally proved. The proposed automated calibration procedure has been implemented on industrial AGVs, leading to estimation of the parameters in about 12 min, a significant improvement compared with one hour or more required by manual AGV calibration. Experiments with AGVs of various sizes in warehouses have assessed the effectiveness and numerical stability of the proposed approach. The precision of calibration parameters has been found to be about 0.1∘ for angles and 6 mm for positions. Parameters obtained via the proposed automated calibration procedure have allowed different AGVs to accurately stop at the desired operation points.

Hydrological model application under data scarcity for multiple watersheds, Java Island, Indonesia

Abstract Study region Java Island, Indonesia. Study focus The Indonesian island of Java is home to more than half of Indonesia’s population and routinely experiences water related natural disasters. This study represents a first step towards skillful hydrologic prediction and hydrologically-informed mitigation strategies. This is the first study to collate a comprehensive suite of hydrometeorological observations and systematically identify Variable Infiltration Capacity (VIC) Land Surface Model (LSM) parameters on Java to create a set of benchmark simulations. New hydrological insights for the region Quality control procedures revealed inconsistencies between precipitation and streamflow with only five watersheds possessing data of suitable quality. Simulations and observations confirmed that both precipitation and streamflow variability increase eastward on the island and that rainfall-runoff response was most frequently dominated by baseflow, rather than surface runoff. The most sensitive VIC parameters were identified and then calibrated with an automatic calibration procedure. In the calibration period, model performance was generally deemed satisfactory with Nash Sutcliffe Efficiency (NSE) between 0.31 to 0.89, whereas the validation period exhibited poorer performance than expected (0.07

Auto-Calibration Methods of Kinematic Parameters and Magnetometer Offset for the Localization of a Tracked Mobile Robot

This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on a tracked electrical mobile robot. The mobile robot is aimed to be adopted as a tool to help humanitarian demining operations.

Improved 3D displacement measurements method and calibration of a combined fringe projection and 2D-DIC system

An improved measurement method and an automatic calibration procedure are proposed for a combined 2D Digital Image Correlation and Fringe Projection system that allows measuring in- and out-of-plane displacement maps with only one image at each deformation stage of a specimen. The proposed method increases the accuracy and range of the out-of-plane displacements by taking into account the divergences of both the projected fringes (uncollimated) and the camera (with non-zero FOV). The calibration is performed automatically by acquiring a sequence of images of a reference plane by displacing perpendicular to it the camera and fringe projector with a motorized translation stage. The acquired images are then used to obtain a fringe function for each pixel and the necessary parameters required for the correction of the in-plane displacements. Furthermore, a closed form expression is obtained that relates the out-of-plane displacements with the shifted phase at each pixel for a given experimental set-up. This expression is in good agreement with the fringe function obtained by fitting a simple 2nd order polynomial to the experimental obtained calibration data. Finally, the polynomial approach is proposed as a fringe function because it avoids the errors in the determination of the required parameters of the theoretical expression as well as some small misalignment or aberration effects.

Sky camera geometric calibration using solar observations

Abstract. A camera model and associated automated calibration procedure for stationary daytime sky imaging cameras is presented. The specific modeling and calibration needs are motivated by remotely deployed cameras used to forecast solar power production where cameras point skyward and use 180° fisheye lenses. Sun position in the sky and on the image plane provides a simple and automated approach to calibration; special equipment or calibration patterns are not required. Sun position in the sky is modeled using a solar position algorithm (requiring latitude, longitude, altitude and time as inputs). Sun position on the image plane is detected using a simple image processing algorithm. The performance evaluation focuses on the calibration of a camera employing a fisheye lens with an equisolid angle projection, but the camera model is general enough to treat most fixed focal length, central, dioptric camera systems with a photo objective lens. Calibration errors scale with the noise level of the sun position measurement in the image plane, but the calibration is robust across a large range of noise in the sun position. Calibration performance on clear days ranged from 0.94 to 1.24 pixels root mean square error.

Suomi NPP VIIRS Reflective Solar Bands Operational Calibration Reprocessing

Radiometric calibration coefficients for the VIIRS (Visible Infrared Imaging Radiometer Suite) reflective solar bands have been reprocessed from the beginning of the Suomi NPP (National Polar-orbiting Partnership) mission until present. An automated calibration procedure, implemented in the NOAA (National Oceanic and Atmospheric Administration) JPSS (Joint Polar Satellite System) operational data production system, was applied to reprocess onboard solar calibration data and solar diffuser degradation measurements. The latest processing parameters from the operational system were used to include corrected solar vectors, optimized directional dependence of attenuation screens transmittance and solar diffuser reflectance, updated prelaunch calibration coefficients without an offset term, and optimized Robust Holt-Winters filter parameters. The parameters were consistently used to generate a complete set of the radiometric calibration coefficients for the entire duration of the Suomi NPP mission. The reprocessing has demonstrated that the automated calibration procedure can be successfully applied to all solar measurements acquired from the beginning of the mission until the full deployment of the automated procedure in the operational processing system. The reprocessed calibration coefficients can be further used to reprocess VIIRS SDR (Sensor Data Record) and other data products. The reprocessing has also demonstrated how the automated calibration procedure can be used during activation of the VIIRS instruments on the future JPSS satellites.

Sparse Weighted-Norm Minimum Variance Portfolios

We propose to impose a weighted l1 and squared l2 norm penalty on the portfolio weights to improve out-of-sample (OOS) performances of portfolio optimization when the number of assets becomes large. We show that under certain conditions, the realized risk of the optimal minimum variance portfolio (MVP) obtained from the strategy can asymptotically be lower than those of benchmark portfolios with a high probability. Our theoretical results imply that penalty parameters for the weighted-norm penalty can be specified as a simple function of the number of assets and sample size. With the theoretical results, we also develop an automatic calibration procedure for choosing the penalty parameters. We demonstrate superior OOS performances of the weighted-norm MVP with two real data sets. Finally, we propose several alternative norm penalties and show that their OOS performances are comparable to the weighted-norm strategy.