Auto Calibration Research Articles

Isotonic Regression for Variance Estimation and Its Role in Mean Estimation and Model Validation

We study isotonic regression which is a nonparametric rank-preserving regression technique. Under the assumption that the variance function of a response is monotone in its mean functional, we investigate a novel application of isotonic regression as an estimator of this variance function. Our proposal of variance estimation with isotonic regression is used in multiple classical regression problems focused on mean estimation and model validation. In a series of numerical examples, we (1) explore the power variance parameter of the variance function within Tweedie’s family of distributions, (2) derive a semi-parametric bootstrap under heteroscedasticity, (3) provide a test for auto calibration, (4) explore a quasi-likelihood approach to benefit from best-asymptotic estimation, and (5) deal with several difficulties under Lognormal assumptions. In all of these problems we verify that the variance estimation with isotonic regression is essential for proper mean estimation and beneficial compared to traditional statistical techniques based on local polynomial smoothers.

Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.

Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled data. Although unsupervised or self-supervised deep learning methods have emerged to address the limitations of supervised deep learning approaches, they still require a database of images. In contrast, scan-specific deep learning methods learn and reconstruct using only the sub-sampled data from a single scan. Here, we introduce Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion (DNLINV) that does not require an auto calibration scan region. DNLINV utilizes a Deep Image Prior-type generative modeling approach and relies on approximate Bayesian inference to regularize the deep convolutional neural network. We demonstrate our approach on several anatomies, contrasts, and sampling patterns and show improved performance over existing approaches in scan-specific calibrationless parallel imaging and compressed sensing.

Freely expandable visual measurement network for large scale space based on auto calibration method

Freely expandable visual measurement network for large scale space based on auto calibration method

High-precision scanning system for complete 3D goat udder and teat imaging, and analysis of morphological traits

Currently, udder scoring for goat udder phenotyping in France is made by a specialized technician trained to score 5 traits for the udder and 4 for teats, only one of which being measured (teat length). It is a hard work in big flocks, and only done one time in the life of goats. Recent works underlined a clear degradation of udder/teat shape with parity and an increasing proportion of morphological and functional unbalance of udders. Thus, for improved genetic selection, we must increase the number and quality of quantitative udder traits measured throughout the productive lifespan of dairy goats. To achieve this goal, we aimed to design a 3D Scanner device to scan the entire udder and to produce high definition numeric images. The device developed is easy to transport in a van and consists of a portable corridor cage in which the goats enter freely. Goats are then blocked less than 2 min while the images were taken. We used 4 Intel Realsense depth D415 cameras mounted on a mobile trolley situated 60 cm under and on the two sides of the cage and sliding from the front to the back of the animal in 10 s. The D415 has a baseline of 55 mm which allows the depth error to be less than 2% at 2 m. The device has an auto calibration internal software that ensure good evaluation of distances with one-millimeter depth precision at 50 cm. The reconstruction of the 3D Image from these 4 sources was done by the RecFusion® software. The image resolution obtained is 1280 * 720 pixels and real-time scene capture is done at up to 90 frames per second (900 images/camera/run/goat). The size of voxel generated was 2 mm. The first prototype was tested on 260 goats of different parity and udder shape and produced well-defined 3D images on which we can measure dimensions with Metrux2α® software after manual positioning of references points. The first validation steps wad done on a soccer ball and on 10 goats for comparison of manual scoring and Image analysis. Machine learning algorithms are currently being developed for the purpose of automatic measurement on these images. The actual precision of the automatic measurement will be assessed following the completion of this final phase. Nevertheless, this scanner shows great promise for future implementation within the goat selection scheme, as it enables swift, objective, and quantitative evaluation of udder and teat characteristics.

Next generation of flow analysis is based on flow programming

Concepts of air Segmented Flow Analysis (SFA) and Flow Injection Analysis (FIA) are discussed and compared with the performance of a new method based on comprehensive flow programming. The programmable Flow Injection (pFI), miniaturized on the lab-on-valve platform, can perform tasks that can not be done in any other way, such as Auto calibration by a single standard solution or analysis in a flow-batch mode that exactly simulates the traditional manual batch technique. Performance of pFI is documented by examples of nutrient determinations in sea water.

A Study on the Parameter Selection of Bat Algorithm in in Optimizing Parameters in Camera Auto Calibration Problem

In camera auto calibration, the major goal is to discover intrinsic parameter values that minimize the cost function. This study proposes to implement Bat algorithm, a stochastic optimization technique, to determine the optimum intrinsic parameter values. Each bat in the Bat Algorithm represents a potential solution to the issue, and each dimension in the Bat Algorithm's search space represents one of the basic parameters: skew, focal length, and magnification factor. The Kruppa's equation is the basis for the cost function in this study. By studying the echolocation behavior of the microbats, the bats will try to improve the fitness with each iteration. The Bat Algorithm's performance is evaluated using a case study from a database from Le2i Universite de Bourgoune. This paper studies the correlation of different parameters selection in Bat Algorithm in solving the camera auto-calibration problem. Finding shows that Bat Algorithm produces output that as expected as theory of Computational Intelligence suggested.

Precise Calibration of the Continuous Surface Cap Model for Concrete Simulation

The Continuous Surface Cap Model (CSCM) is one of the most widely used concrete models in LS-DYNA. The model is capable of capturing many important nonlinear mechanical behaviors of concrete well. The model has a built-in auto calibration procedure based on CEB-FIP code data. However, the built-in calibration procedure estimates material properties with significant errors, especially for tensile strength. Our study highlights the imperfection of the built-in automated material calibration procedure by the example of one-element uniaxial tension and compression tests. A calibration procedure is proposed, which significantly improves the accuracy of the material properties calculation: tensile and compressive strength and fracture energy. It is shown that the model with the proposed calibration procedure can describe the structure defamations and the fracture zone patterns more accurately.

Tensile-Force-Resilient Biomedical Front-End Circuits Employing Auto-Calibrated Omni-Directional Thin-Film Transistors

Flexible electronics based on thin film transistors (TFTs) are expected to deliver important benefits in the mobile healthcare field. Nevertheless, the mechanical strain caused by the respiration or movement of organisms seriously affects the characteristics of the TFTs, and thus deteriorates the performance of TFT-based circuits and systems. Taking indium-gallium-zinc-oxide (IGZO) TFT as an example, this paper proposes a tensile-force-resilient biomedical front-end system based on omni-directional thin film transistors (OD TFTs). The proposed OD TFT is a combination of dual gate TFTs with optimized layout, whose force-insensitive axis can be adjusted by the biasing of top gate. Based on this, an auto calibration technique in front-end system is introduced, which can automatically adjust the top gate biasing of OD TFTs according to the Poisson's ratio of substrate and the direction of external force, so as to align the force-insensitive axis along the direction of external force. Employing the OD TFTs in circuits and the auto calibration technique, the force resilience of the flexible electronic system is significantly improved in practical applications. The simulations of front-end system on acquiring electrocardiogram (ECG) and electromyogram (EMG) signals are performed. The results show that the proposed front-end system with auto calibration provides an increase of SNR from initial -19.91 dB and -25.89 dB without calibration to 17.53 dB and 1.32 dB in two cases, respectively.

Auto Calibration of Camera and LiDAR attached to Micro Mobility

本稿では小型モビリティに搭載したカメラとLiDARの相対位置を小型モビリティ移動時の計測データから算出する自動キャリブレーション手法を提案する．本手法は小型モビリティ移動時のカメラ撮影画像からVisual-SLAMにより算出したカメラ移動情報と，LiDAR計測点群からLiDAR-SLAMにより算出したLiDAR移動情報のみを用いて相対位置を算出する．一般的な手動キャリブレーション手法と異なり，キャリブレーションボード等の特定対象物の計測データが不要なため，小型モビリティ使用時の計測データから自動的に相対位置が算出可能となる．自動キャリブレーションはカメラ移動情報の算出精度低下に伴って，カメラとLiDARの相対位置の算出精度も低下する．今回は自動キャリブレーションに使用するカメラ移動情報の選定と，カメラ撮影画像とLiDAR計測点群との対応関係を用いた反復計算により，相対位置の算出精度が向上することを確認した．

Proposal and Verification of Auto Calibration Technique for Bias Control Circuit Connecting to Built-In Optical Power Monitor in Imperfect IQ-Modulator

We show that imperfection in an IQ-modulator degrades the accuracy of the auto bias control (ABC) circuit connected to the modulator's complementary port. Theoretical analyses show that the IQ-modulator constructed by a nested Mach-Zehnder modulator with a low extinction ratio can distort a constellation of modulated light observed at the complementary port. We propose an auto calibration technique for the ABC circuit that can effectively suppress this degradation. Experimental results using 32-Gbaud, 16-QAM signals showed the measured Q-factor improved by 0.5dB with our proposed technique.

Temperature invariant and high precision absolute rotary encoder using photocells on visible light spectrum

This paper introduces temperature invariant absolute rotatory encoder using photocells on the visible light spectrum. A multivariate function is proposed to remove error arising on photocells through temperature variance and a small change in light intensity. Multiple experiments are conducted in the controlled environment to prove proposed multivariate function. Encoder introduced in this paper is more scalable than grey code encoder available in the market. Dynamic auto calibration is also proposed to make encoders more reliable with time.

A conceptual LUTI model based on neural networks

This paper deals with Land-use-Transport-Interaction (LUTI) and presents a conceptual model of a data driven LUTI modelling tool based on neural networks. A starting point is the general opinion of experts on the state of the art LUTI models; currently used land use transportation models are too aggregate in substance. Therefore to enhance the interaction between transport and land-use, researchers propose the refinement of the models; internalising more comprehensive relationships. However, the lack of a good theoretical framework impedes the development and consequently the use of these models on a large scale. This fact has fuelled questions: (i) does transport planning need these comprehensive descriptions of land use; (ii) is it necessary to disaggregate and refine the models further in order to be able to do consistent transport planning; (iii) which land use characteristics should at least be internalised; and (iv) which modelling method is suitable for implementation. Based on literature, the hypothesis is that the first question can be answered by ’no’ and no further refinements are needed. Literature shows that the main drivers for land-use changes are the location choice of both households and firms. Therefore only these two building blocks are used in relation with the transport component. Artificial neural networks (ANNs) will be used as the modelling technique. ANNs are data driven techniques that find relationships in data during an auto calibration process. Therefore ANNs can work without having a sound theoretical framework. This characteristic offers possibilities for LUTI modelling that lacks a sound theoretical framework. The research leads to a conceptual model. A literature review shows that the individual building blocks of the conceptual LUTI model can be modelled using neural networks. However, an integration of the building blocks has not been established yet. Further research has to result in the actual implementation of the proposed model.

A real-time auto calibration technique for stereo camera

Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision. Usually, to get accurate 3D results the camera should be manually calibrated accurately as well. This paper proposes a robust approach to auto calibration stereo camera without intervention of the user. There are several methods and techniques of calibration that have been proven, in this work, we exploiting the geometric constraint, namely, the epipolar geometry. We specifically focus to use seven techniques for features extraction (SURF, BRISK, FAST, FREAK, MinEigen, MSERF, and SIFT), however, tries to establish the correspondences between points extracted in stereo images with various matching techniques (SSD, SAD, Hamming). Then we exploit the fundamental matrix to estimate the epipolar line by choosing the perfect eight-point algorithms (Norm8Point, LMedS, RANSAC, MSAC, and LTS). A large number of experiments have been carried out, and very good results have been obtained by comparison and choice of the perfect technique in every stage.

Calibration of systems for quantitative fluorescence analysis of thin layers.

Imaging fluorescence analysis is a powerful tool for the characterization of thin functional layers. Due to the development of new components such as cost-efficient and long life diode lasers and LEDs as well as sensitive cameras, the number of industrial in situ sensors based on fluorescence analysis technology increased rapidly in recent years. Of crucial importance for all these new sensors are efficient and robust methods for calibration. Although there are many examples for the calibration of laboratory setups for single specialized applications, there is no standardized method for the traceable device independent calibration of imaging fluorescence systems. This paper presents the evaluation of five different methods for the calibration of systems for quantitative fluorescence analysis. Each method is applied for the calibration of an imaging fluorescence laser scanner. In addition to characterizing the precision of the methods, the work analyzes the usability of the methods for different applications. The results show for the first time that a calibrated IR point sensor can be used for the auto calibration of high resolution imaging inline fluorescence sensors. In addition, we present a novel method for the transfer of calibration data between analysis systems with different optical setups by using a solid material fluorescence standard.

Developing heuristic multi-criteria auto calibration method for continuous HEC-HMS in snow-affected catchment

ABSTRACTTwo search methods are currently available for calibrating the HEC-HMS program: the Univariate Gradient (UG) and the Nelder Mead (NM) methods; both having poor performance in continuous models with snowmelt module. In this paper, a novel approach using Melody Search algorithm (MeS) is presented for auto-calibration of HEC-HMS and compared to Self-adaptive Global Harmony Search (SGHS) and Genetic Algorithms (GA). The results show that the suggested approach is able to overcome the poor performance of aforementioned methods yielding more enhanced solutions. While NM and UG reach a Nash Sutcliffe Index (NSE) of −0.33 and 0.105, GA, SGHS and MeS have been able to reach to 0.526, 0.635, and 0.674 respectively. In addition, the improving power of algorithms was tested by starting from the final solution found by the other methods. It was noticed that in all cases MeS and to some degree SGHS are able to substantially improve the solutions found by either UG or NM. Interestingly, while SGHS was able to slightly improve the solution of MeS algorithm, MeS made a substantial improvement on the solution reached by the SGHS. Finally, the novel MeS based multi-objective algorithm proposed here, i.e. NSMeS, and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are compared with different combinations of objective functions where NSMeS demonstrated better performance.

Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere

Abstract Calibration in the robotic belt grinding of complex blades is considered as one of the key bottlenecks of measurement accuracy. To enhance the accuracy of robotic calibration system, an improved method is proposed in this paper to calibrate the tool (grinding machine) frame and workpiece (aero-engine blade) frame by holding the ruby probe as the main calibration tool. Firstly, the sphere-to-sphere method replacing the traditional point-to-point method is put forward to calibrate the flexible and fixed probe frame. Secondly, the calibrated flexible and fixed probe frame is employed to precisely seek the origin point of tool frame and then to calibrate it accurately. Thirdly, both the rough calibration (manual calibration) and fine calibration (auto calibration) are adopted to calibrate the workpiece frame, the resulting translation and rotation errors are controlled at small values to improve the calibration accuracy. Finally, a typical case on robotic belt grinding of aero-engine blade is conducted to validate the calibration results of the robotic belt grinding system (RBGS).

Autocalibration of gyroscope based two-step regulator for autonomous vehicles.

In this article was presented an auto calibration algorithm based on a two-step regulator and gyroscope readings. Unable to use PID, as the PWM signal causes the motors to overheat, it was decided to introduce a two-step algorithm. This type of controller reaches the setpoint much faster, though an error is present. Gyroscope is used to sample angular speed and then integrate these values to get the angle. To calibrate the offsets the robot performs a calibration run during which it measures and averages the error on each gyroscope axis while no movement is present. Afterwards it makes a characteristics of braking angle and angular speed. Interpolated polynomial from 13 samples allows to designate the point in time where the braking has to start to achieve the desired angle. The calibration aims for adjusting the parameters of movement depending on the surface it runs on. Additional check is made using encoders to estimate the accuracy of gyroscope output.

倍率の異なる複数カメラの自動校正方法に関する研究(セッション4,学生研究発表会)

倍率の異なる複数カメラの自動校正方法に関する研究(セッション4,学生研究発表会)

A modified closed-loop auto frequency calibration technique for a 1.571-GHz integer-N PLL

ABSTRACTIn this paper, a modified closed-loop auto frequency calibration technique (MCL-AFC) is adopted in an integer-N phase-locked loop (PLL) for GPS-L1 application. The ignorance of circuit initial conditions setting in the closed-loop AFC may cause the start-up trap and long frequency calibration time. To solve these problems, the MCL-AFC technique is introduced. The process of MCL-AFC is listed below: first, initialisation process is only used for start-up of PLL; second, closed-loop voltage comparison process and open-loop switching process will take place alternately until optimum frequency control words are obtained. Tuning voltage searching range is reduced by half during the voltage comparison process since VCO’s tuning voltage is set to half of supply voltage through switching process. The MCL-AFC circuit is implemented in a 1-poly 6-metal 180 nm CMOS process and its chip area is 0.0167 mm2. The measured locked output frequency of the PLL is 1.571 GHz and the out-band phase noise is −131.9dBc/Hz at 1 MHz. The calibration time of PLL with MCL-AFC circuit is reduced to only 5µs while whole locking time is about 10.2µs.

NAC Measurement Technique on High Parallelism Probe Card with Protection Resistors

In this paper, a novel time-domain measurement technique on a high parallelism probe card with protection resistors installed is proposed. The measured signal amplitude decreases when the measurement is performed by Needle Auto Calibration (NAC) probing on a high parallelism probe card with installed resistors. Therefore, the original signals must be carefully reconstructed, and the compensation coefficient, which is related to the number of channel branches and the value of protection resistors, must be introduced. The accuracy of the reconstructed signals is analyzed based on the varying number of channel branches and various protection resistances. The results demonstrate that the proposed technique is appropriate for evaluating the overall signal performance of probe cards with Automatic Test Equipment (ATE), which enhances the efficiency of probe card performance test dramatically.