Auto-calibration Method Research Articles

Development and application of distributed rainfall-runoff model using MPI technique

A lumped model, in general, is expressed by ordinary differential equations, which does not take into account the spatial variability of processes, input, boundary conditions and watershed geometric characteristics. For this reason, recently distributed run-off models have been developed to represent the variability in physical watershed characteristics such as topography, land use, and rainfall properties. However, the distributed rainfall-runoff model requires a lot of time and effort to generate input data. Also, it takes a lot of time to calculate the discharge using a numerical analysis based on kinematic wave theory in runoff process. Therefore, most river basins that use the distributed model are of limited scales, such as small river basins. Especially, the necessity for an integrated watershed management system has been increasing due to changes in watershed management concepts and discharge calculations for whole river basins, including the upstream and downstream of dams. In this study, a distributed rainfall-runoff model using Message Passing Interface (MPI) technique was developed to decrease the calculation time needed for the application of large scale watersheds. This model, which uses a parallelized based K-water hydrologic & hydraulic Distributed RUnoff Model (K-DRUM), can simulate temporal changes and the spatial distribution of flood discharge by taking into consideration radar grid rainfall and grid based hydrological parameters. The model was applied to the Geum River Basin, which include the Yongdam and Daecheong Dam Watersheds located on the Korean Peninsula. The results were calculated between a single domain and a divided small domain. They were compared to analyze the application effects of the parallelization technique. As a result, a maximum of 10 times the amount of calculation time was saved by using parallelization code rather than a single processor. Also, problems related to the running time and inaccurate settings that typically occurred using the existing trial and error method were solved by applying an auto calibration method in setting initial soil moisture conditions. As the result, the calculation results showed a good agreement with the observed data.

Methods for geometrical video projector calibration

In this paper we present two methods to geometrically calibrate a video projector using a markerless planar surface. The first method assumes a partial knowledge on the camera parameters, whereas the second method consists in an auto-calibration method with no assumption on the parameters of the camera. Instead, the auto-calibration is performed by identifying a roughly fronto-parallel pose of the camera w.r.t. the projection surface. The fact that camera calibration is not needed increases the usability of the methods and at the same time eliminates one potential source of inaccuracy, since errors in the camera calibration would otherwise inevitably propagate through the projector calibration. Not using a printed pattern as most existing methods do is another gain in accuracy and ease of use. As depicted by our experiments, both methods enjoy a good stability and give good results when compared against existing methods.

Design Considerations for Autocalibrations of Wide-BandΔΣFractional-NPLL Synthesizers

Autocalibration of VCO frequency and loop gain is an essential process in PLL frequency synthesizers. In a wide tuning-range fractional-NPLL frequency synthesizer, high-speed and high-precision automatic calibration is especially important for shortening the lock time and improving the phase noise. This paper reviews the design issues of the PLL auto-calibration and discusses on the limitations of the previous techniques. A very simple and efficient auto-calibration method based on a high-speed frequency-to-digital converter (FDC) is proposed and verified through simulations. The proposed method is highly suited for a very wide-bandΔΣfractional-NPLL.

Hydrologic Modeling of the Bouregreg Watershed (Morocco) Using GIS and SWAT Model

The study of water resources at watershed scale is widely adopted as approach to manage, assess and simulate these important natural resources. The development of remote sensing and GIS techniques has allowed the use of spatially and physically based hydrologic models to simulate as simply and realistically as possible the functioning of watershed systems. Indeed, the major constraint that has hindered the expansion use of these tools was the unavailability or scarcity of data especially in the developing countries. In this context, the objective of this study is to model the hydrology in the Bouregreg basin, located at the north-central of Morocco, using the Soil and Water Assessment Tool (SWAT) in order to understand and determine the different watershed hydrological processes. Thus, it aims to simulate the stream flow, establish the water balance and estimate the monthly volume inflow to SMBA dam situated at the basin outlet. The ArcSWAT interface implemented in the ArcGIS software was used to delineate the basin and its sub-components, combine the data layers and edit the model database. The model parameters were analyzed, ranked and adjusted for hydrologic modeling purposes using daily temporal data series. They were calibrated using an auto-calibration method based on a Shuffled Complex Evolution Algorithm from 1989 to 1997 and validated from 1998 to 2005. Based on statistical indicators, the evaluation indicates that SWAT model had a good performance for both calibration and validation periods in Bouregreg Watershed. In fact, the model showed a good correlation between the observed and simulated monthly average river discharge with R² and Nash coefficient of about 0.8. The water balance components were correctly estimated and the SMBA dam inflow was successfully reproduced with R² of 0.9. These results revealed that if properly calibrated, SWAT model can be used efficiently in semi-arid regions to support water management policies.

FOUR-OCTAVE SIX-PORT RECEIVER AND ITS CALIBRATION FOR BROADBAND COMMUNICATIONS AND SOFTWARE DEFINED RADIOS

This paper presents a software defined radio six-port receiver for a novel broadband mobile communications system. The prototype covers the frequency range from 0.3 GHz to 6 GHz, and operates with up to 100 MHz-wide channels. The multi-band and multi-mode demodulation capabilities of the six-port architecture have been experimentally demonstrated. The six-port receiver has been satisfactorily proved for high data rates (up to 93.75 Mb/s, limited by the available test instruments). An efficient six-port auto-calibration method suitable for large instantaneous bandwidth systems is presented and validated.

Joint application of autocalibration, superresolution and blind multipath estimation with an experimental array

Many sophisticated high-resolution methods have been proposed. The study describes the practical experience in the combined application of superresolution and autocalibration methods for the resolution of multipath signals. An experimental linear array antenna has been used to perform superresolution after applying autocalibration to signals from one or several radiating sources, for example base stations for mobile communication. In case of multipath propagation, the line-of-sight signals are extracted by space-time processing. These line-of-sight signals are then used as a reference for blind channel estimation and are applied to obtain superresolution of sources and reflectors in azimuth and in differential delay. The suggested methods are tested with the real data.

Auto-Calibration Method to Determine Camera Pose for Stereovision-Based Off-Road Vehicle Navigation

Stereo cameras have been used as perception sensors for agricultural vehicle navigation for years. One problem impeding their broader application is the difficulty of calibrating the installation poses of a camera using conventional measuring tools, especially when such a system is used in ill-structured agricultural field environments. The research reported in this paper was aimed to develop an automated calibration method for determining the camera installation pose with respect to a vehicle frame. Based on this method, a binocular stereo camera acquired a sequence of field scenery images as the vehicle moved straight forward for a short distance on a relatively flat surface. An image processing algorithm has been developed to detect some static feature points in the ground image and track their three-dimensional (3D) relative motions with respect to the moving vehicle. A plane best fitting to those detected ground features was then used to determine the camera roll and pitch angles, and the tracked motions of those feature points were used to estimate the camera yaw. Field test results validated that the developed auto-calibration method was capable of determining the camera installation pose at a calibration accuracy of ±1° over an approximately 10 m of vehicle traveling distance. The calibrated poses could be used to compensate for the navigation errors induced by camera misalignment.

Autocalibration algorithm for mutual coupling of planar array

With few exceptions, high resolution direction of arrival (DOA) estimation algorithms suffer a lot from the mutual coupling between different array elements. In order to compensate this effect, two autocalibration methods are proposed for the 2-D DOA estimation on a planar array. Both of them can estimate the DOAs without any iterative process or calibration sources. We prove that the effect of mutual coupling can be automatically eliminated by the inherent mechanism of the proposed methods. Moreover, they can also achieve an obvious performance improvement even when there is a coupling model mismatch. After getting the DOA estimations, two methods are proposed to estimate the mutual coupling coefficients. Moreover, these coefficient estimates can be used to further improve the accuracy of DOA estimations. The Cramer–Rao lower bound (CRB) for the parameter estimations are also presented as the benchmark. Simulation results demonstrate the effectiveness of the proposed algorithms.

Apparatus and method of auto frequency calibration for transducer

An apparatus for automatically calibrating a transducer are disclosed in the present invention, the apparatus comprising: an oscillator, for generating a system frequency; a micro controller, being coupled to the oscillator, for receiving the system frequency; a counting unit, being installed inside the micro processor, for generating at least a clock signal according to the system frequency; a transformer, whose input terminal is being coupled to the counting unit, for receiving the clock signal and transferring the clock signal to be a plurality of exciting signals, the output terminal feedbacks and couples the exciting signals to the interrupting signal input terminal of the micro controller, wherein the a plurality of exciting signals comprising at least a primary oscillating signal and at least a secondary oscillating signal and the amount of the clock signal equals to that of the primary oscillating signal; and a transducer, whose input terminal is being coupled to the transformer, and the transducer is being excited by the exciting signals to generate an output signal. Thus, the micro controller can be dynamically calibrating the frequency of the exciting signal according to the output signal variation of the transducer, and the best transmitting effect can be achieved without being suffered from the variation of external environment such as the temperature, humidity change.

Pollution load modelling in sewer systems: an approach of combining long term online sensor data with multi-objective auto-calibration schemes

Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002.In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two surface accumulation-wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. Results from multi-objective calibration show better robustness in validation events than single-objective calibration. The build-up wash-off approach using the basic wash-off equation gives the best correlations between measured data and simulation results.

Yarn Diameter Measurements Using Coherent Optical Signal Processing

A method to measure variations in yarn diameter using coherent optical signal processing based on a single photodiode plus additional electronics is described. The approach enables us to quantify yarn irregularities associated with diameter variations which are linearly correlated with yarn mass variations. A robust method of system auto-calibration, eliminating the need for a temperature and humidity controlled environment, is also demonstrated. Two yarns that span the diameter ranges commonly used in the textile industry were used to verify the system linearity and ascertain its resolution. The results obtained have been verified using image analysis. Moreover, a diameter characterization was performed under real-world conditions for three types of yarns and a correlation with capacitive measurements is also presented. The system with sensitivity of 0.034 V/mm2 is able to detect minute variations of yarn diameter and characterize irregularities starting at very low thresholds (commercial systems generally characterize variations of 30% or greater relative to the average value).

Sewer modelling based on highly distributed calibration data sets and multi-objective auto-calibration schemes

Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two build-up wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. However, until now, convective storms have not been convincingly represented. The build-up wash-off approach using the basic wash-off equation shows the best correlations between measured data and simulation results. As one of the chosen objectives for the multi-objective optimisation reacted highly sensitively to measurement errors, additional improvements can be expected after refining the criteria used in this algorithm.

Key-Frame Selection and an LMedS-Based Approach to Structure and Motion Recovery

Auto-calibration for structure and motion recovery can be used for match move where the goal is to insert synthetic 3D objects into real scenes and create views as if they were part of the real scene. However, most auto-calibration methods for multi-views utilize bundle adjustment with non-linear optimization, which requires a very good starting approximation. We propose a novel key-frame selection measurement and LMedS (Least Median of Square)-based approach to estimate scene structure and motion from image sequences captured with a hand-held camera. First, we select key-frames considering the ratio of number of correspondences and feature points, the homography error and the distribution of corresponding points in the image. Then, by using LMedS, we reject erroneous frames among the key-frames in absolute quadric estimation. Simulation results demonstrated that the proposed method can select suitable key-frames efficiently and achieve more precise camera pose estimation without non-linear optimization.

Autocalibration of spectral-domain optical coherence tomography spectrometers for in vivo quantitative retinal nerve fiber layer birefringence determination

Accurate wavelength assignment of each spectral element for spectral-domain optical coherence tomography (SD-OCT) and optical frequency domain imaging (OFDI) is required for proper construction of biological tissue cross-sectional images. This becomes more critical for functional extensions of these techniques, especially in polarization-sensitive optical coherence tomography (PS-OCT), where incorrect wavelength assignment between the two orthogonal polarization channels leads to polarization artifacts. We present an autocalibration method for wavelength assignment that does not require separate calibration measurements and that can be applied directly on actual data. Removal of the birefringence artifact is demonstrated in a PS-OCT system with picometer accuracy in the relative wavelength assignment, resulting in a residual phase error of 0.25 deg/100 microm. We also demonstrate, for the first time, a quantitative birefringence map of an in vivo human retinal nerve fiber layer.

Experimental study on the sensitivity of autocalibration to projective camera model parameters

Existing methods of 3-D object modeling and recovering 3-D data from uncalibrated 2-D images are subject to errors introduced by assumptions about camera parameters and mismatches in finding point pairs in the images. In this study, we experimentally evaluate the effect of each of these assumptions together with the inaccuracy in the measurements in the images. Sensitivity of reconstruction errors to inaccuracies in the estimation of camera parameters and mismatches due to noise in input data is measured using a linear and two nonlinear autocalibration methods for a projective camera. Our experimental results show that some assumptions such as a vanishing skew can be safely made; however, other parameters such as principal point location are quite sensitive to wrong assumptions.

Auto‐calibrated parallel spiral imaging

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is defined along radial directions so that missing spiral interleaves can be estimated directly from neighboring interleaves. This kernel is invariant along the radial direction but varies azimuthally. Therefore, the k-space is divided into angular sectors and sector-specific kernels are used. It is demonstrated experimentally that relatively few sectors are sufficient for accurate reconstruction, allowing for efficient implementation. The interpolation kernels can be derived either from a separate calibration scan or self-calibration data available with a dual-density spiral acquisition. The reconstruction method is implemented with two sampling strategies and experimentally demonstrated to be robust.

Virtual off-line auto calibrating position and current sensors for switched-reluctance-motor drives

To obtain high-performance motor drives, modern control strategies should be employed in the drive system. These techniques are inherently dependent to the measurement devices, or sensors, operating properly. In particular, switched-reluctance-motor drives need position and current sensors to demonstrate full torque-control capability. This requirement yields more expensive and complex drive systems. On the other hand, when these sensors fail, the controller should be able to recover and handle the proper operation of the drive system. This fact necessitates back-up systems to support the operational drive in case of sensor failure, especially for life-dependent applications such as automotive systems. In the paper, novel virtual position and current sensors are designed based on modelling of magnetic double-saliency characteristics in switched-reluctance-motor drives. This approach does not suffer from unpredicted variations of spatial distribution of magnetic field since a practical autocalibration method is designed to tune the virtual sensors. The proposed virtual autocalibrating sensors are implemented fully on an experimental SRM drive based on a fixed-point digital signal processor. The experimental results verify the capability and high performance of the proposed approach.

Gas sensor network for air-pollution monitoring

This paper describes the development of a gas sensor system to be used as a sensing node to form a dense real-time environmental monitoring network. Moreover, a new auto-calibration method is proposed to achieve the maintenance-free operation of the sensor network. The network connectivity can be used not only for data collection but also for the calibration and diagnosis of the sensors since the measured pollutant concentrations can be easily compared through the network with nearby sensors and governmental monitoring stations. Different pollutant concentrations are usually monitored at different sites. However, a case study on local NO 2 distribution has shown that there exists a special condition under which pollutant concentrations become low and uniform in a certain local area. The baseline of the gas sensor response can be adjusted in this special occasion using the pollutant concentration values reported from the neighboring environmental monitoring stations. The experimental result has shown that NO 2 concentration can be measured with sufficient accuracy by incorporating appropriate temperature and humidity compensation into calibration curves. Moreover, a case study on auto-calibration demonstrates its effectiveness in keeping the measurement accuracy of the sensor system in long-term operation.

PROBLEMS AND POTENTIAL OF AUTOCALIBRATING A HYDROLOGIC MODEL

An investigation was conducted to evaluate strengths and limitations of manual calibration and the existingautocalibration tool in the watershed-scale model referred to as the Soil and Water Assessment Tool (SWAT). Performanceof the model was tested on the Little River Experimental Watershed in Georgia and the Little Washita River ExperimentalWatershed in Oklahoma, both USDA-ARS watersheds. A long record of multi-gauge streamflow data on each of thewatersheds was used for model calibration and validation. Model performance of the streamflow response in SWAT wasassessed using a six-parameter manual calibration based on daily mass balance and visual inspection of hydrographs andduration of daily flow curves, a six-parameter autocalibration method based on the daily sum of squares of the residuals afterranking objective function (referred to as SSQRauto6), a six-parameter method based on the daily sum of squares of residuals(SSQauto6), and an eleven-parameter method based on the daily sum of square of residuals (SSQauto11). Results show thatfor both watersheds, manual calibration generally outperformed the autocalibration methods based on percent bias (PBIAS)and simulation of the range in magnitude of daily flows. For the calibration period on Little River subwatershed F, PBIASwas 0.0%, -24.0%, -21.5%, and +29.0% for the manual, SSQRauto6, SSQauto6, and SSQauto11 methods, respectively.Based on the coefficient of efficiency (NSE), the SSQauto6 and SSQauto11 methods gave substantially better results thanmanual calibration on the Little River watershed. On the Little Washita watershed, however, the manual approach generallyoutperformed the automated methods, based on the NSE error statistic. Results of this study suggest that the autocalibrationoption in SWAT provides a powerful, labor-saving tool that can be used to substantially reduce the frustration and uncertaintythat often characterize manual calibrations. If used in combination with a manual approach, the autocalibration tool showspromising results in providing initial estimates for model parameters. To maintain mass balance and adequately representthe range in magnitude of output variables, manual adjustments may be necessary following autocalibration. Caution mustalso be exercised in utilizing the autocalibration tool so that the selection of initial lower and upper ranges in the parametersresults in calibrated values that are representative of watershed conditions.

Auto-calibration of an SMT machine by machine vision

An SMT machine has many working coordinate frames—the fiducial mark camera frame, component camera frame, machine table frame, PCB frame, and reference frame. Because of many influences such as mechanical dimension errors, machine assembling errors, and camera lens distortions, all frames on the SMT machine must be calibrated to compensate for these machine errors. This paper applies machine vision techniques to auto-calibrate an SMT machine, including frame transformations and nozzle compensation, bringing the accuracy of this system to within ±0.1 mm. The coordinate mapping from the camera frame to machine table frame is a quadratic form, while the other frame mappings use linear forms. The merits for this machine’s vision-based, auto-calibration methods are: (1) It has little calibration time, (2) It does not need expensive calibration instruments, and (3) Its expense is very low.