High-precision Estimation Research Articles

Embedded Fluorescence Lifetime Determination for High-Throughput, Low-Photon-Number Applications

Time-resolved fluorescence (TRF) analysis is considered to be among the primary research tools in biochemistry and biophysics. One application of this method is the investigation of biomolecular interactions with promising applications for biosensing. For the latter context, time-correlated single photon counting (TCSPC) is the most sensitive, hence preferred implementation of TRF. However, high throughput applications are presently limited by the maximum achievable photon acquisition rate, and even more by the data processing rate. The latter rate is actually limited by the computational complexity to estimate accurately the fluorescence lifetime from TCSPC data. Here we propose a solution that would enable the implementation of TRF detection for fluorescence-activated droplet sorting (FADS), a particularly high throughput, microfluidic-based technology that can be used for drug discovery and thus help finding new cures for diseases. Most fluorescence lifetime algorithms require a large number of detected photons for an accurate lifetime computation. This paper presents an implementation based on a maximum likelihood estimator (MLE), enabling high precision estimation with a limited number of detected photons, significantly reducing the total measurement time. This speedup rapidly increases the input data rate. As a result, off-the-shelf embedded products cannot handle the data rates produced by current TCSPC units that are used to measure the fluorescence. Therefore, a configurable real-time capable hardware architecture is implemented on a field-programmable gate array (FPGA) that can handle the data rates of future TCSPC units, rendering high throughput droplet sorting with microfluidics possible. The presented hardware architecture is validated with experimental input data and produces high precision results.

INTEGRATING DEPTH AND IMAGE SEQUENCES FOR PLANETARY ROVER MAPPING USING RGB-D SENSOR

Abstract. RGB-D camera allows the capture of depth and color information at high data rates, and this makes it possible and beneficial integrate depth and image sequences for planetary rover mapping. The proposed mapping method consists of three steps. First, the strict projection relationship among 3D space, depth data and visual texture data is established based on the imaging principle of RGB-D camera, then, an extended bundle adjustment (BA) based SLAM method with integrated 2D and 3D measurements is applied to the image network for high-precision pose estimation. Next, as the interior and exterior elements of RGB images sequence are available, dense matching is completed with the CMPMVS tool. Finally, according to the registration parameters after ICP, the 3D scene from RGB images can be registered to the 3D scene from depth images well, and the fused point cloud can be obtained. Experiment was performed in an outdoor field to simulate the lunar surface. The experimental results demonstrated the feasibility of the proposed method.

High-precision natural dose rate estimates through beta counting

The beta-particle emission from a sediment or rock sample can be measured very precisely using beta-counting instruments. The observed count rate is largely a function of the radionuclide concentration in the sample, so has the potential to provide a precise estimate of the natural radiation dose rate. However, the count rate is also sensitive to the attenuation of beta particles in the sample, and the relative proportions of the different radionuclide sources. Here we devise a correction for the self-attenuation effect using dilution analysis, and show that imprecise prior knowledge of radionuclide activity is sufficient for calculation of an accurate combined beta-plus-gamma dry dose rate. The method is tested on a selection of archive samples, and compared with results from high-resolution gamma-spectrometry. We show that with counting uncertainty ∼2%, and calibration uncertainty ∼2%, the total random uncertainty of the beta-plus-gamma dry dose rate is less than 3%. For most natural sediments, this level of precision equal to, or better than, that obtainable with other methods.

QCD parameter correlations from heavy quarkonia

Correlations between the QCD coupling [Formula: see text], the gluon condensate [Formula: see text] and the [Formula: see text], [Formula: see text]-quark running masses [Formula: see text] in the [Formula: see text]-scheme are explicitly studied (for the first time) from the (axial-)vector and (pseudo)scalar charmonium and bottomium ratios of Laplace sum rules (LSR) evaluated at the [Formula: see text]-subtraction stability point where perturbative (PT) @N2LO, N3LO and [Formula: see text] @NLO corrections are included. Our results clarify the (apparent) discrepancies between different estimates of [Formula: see text] from [Formula: see text] sum rule and also show the sensitivity of the sum rules on the choice of the [Formula: see text]-subtraction scale which does not permit a high-precision estimate of [Formula: see text]. We obtain from the (axial-)vector [respectively (pseudo)scalar] channels: [Formula: see text] [respectively [Formula: see text] GeV4, [Formula: see text] [respectively 1266(16)] MeV and [Formula: see text] MeV. Combined with our recent determinations from vector channel, one obtains the average: [Formula: see text] MeV and [Formula: see text] MeV. Adding the two above values of the gluon condensate to different previous estimates in Table 1, one obtains the 2018 sum rule average: [Formula: see text] GeV4. The mass-splittings [Formula: see text] give @N2LO: [Formula: see text] in good agreement with the world average.

Improved prediction of GPS satellite clock sub-daily variations based on daily repeat

High-precision estimates of GPS satellite clock errors reveal systematic sub-daily clock bias variations on the order of 1 ns. The low noise levels in the Rubidium Atomic Frequency Standards onboard GPS IIR, IIR-M, and IIF satellites provide visibility of these small, but systematic behaviors. Prior studies have reported on this phenomenon and sought to characterize the specific frequency components present and to identify potential causes of the observed periodic variations. Our research focuses on the repeatability of the clock variations and the potential for using observed variations directly to predict future clock behavior. Results are presented and compared to IGS Ultra-rapid and broadcast message predictions for all operating GPS satellite clocks for a 1-month period in July 2017. During this time, the accuracy of the proposed sub-daily variation prediction is better than 0.15 ns (RMS) for 8 out of 9 GPS Block IIF Rb clocks and under 0.3 ns (RMS) for most GPS IIR and IIR-M Rb clocks. This approach is complementary to existing techniques for estimating longer-term clock rates and drift and can be combined with them to improve the fidelity of predictive satellite clock models for real-time GPS position, navigation, and timing applications.

On the performance of the noise power spectrum from the gain-corrected radiography images.

Fixed pattern noise due to nonuniform amplifier gains and scintillator sensitivity should be alleviated in radiography imaging to acquire low-noise x-ray images from detectors. Here, the noise property of the detector is usually evaluated observing the noise power spectrum (NPS). A gain-correction scheme, in which uniformly illuminated images are averaged to design a gain map, can be applied to alleviate the fixed pattern noise problem. The normalized NPS (NNPS) of the gain-corrected image decreases as the number of images for the average increases and converges to an infimum, which can be achieved if the fixed pattern noise is completely removed. If we know the NNPS infimum of the detector, then we can determine the performance of the gain-corrected images compared with the achievable lower bound. We first construct an image-formation model considering the nonuniform gain and then consider two measurement methods based on subtraction and division to estimate the NNPS infimum of the detector. In order to obtain a high-precision NNPS infimum estimate, we consider a time-averaging method. For several flat-panel radiography detectors, we constructed the NNPS infimum measurements and compared them with NNPS values of the gain-corrected images. We observed that the NNPS values of the gain-corrected images approached the NNPS infimum as the number of images for the average increased.

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table by Using Simulated Annealing Algorithm

In order to study the anti-shock performance of equipment under different working conditions, a hydraulic servo shaking table is used to replicate the desired motion with high fidelity. However, the sinusoidal acceleration response waveform is not a pure sinusoid due to the inherent nonlinearities within the system. The generating higher harmonic deteriorates the control performance and leads to system instability. To suppress the harmonic distortion and accurately estimate harmonic information, the harmonic estimation scheme based on simulated annealing algorithm is proposed. The sum of error square between actual value and estimated value is defined as the objective function. The amplitude and phase of each harmonic can be directly extracted when the objective function is minimized. Simulation and experimental results indicate that the proposed algorithm has good convergence performance and high estimation precision.

Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

Machined surfaces are rough from a microscopic perspective no matter how finely they are finished. Surface roughness is an important factor to consider during production quality control. Using modern techniques, surface roughness measurements are beneficial for improving machining quality. With optical imaging of machined surfaces as input, a convolutional neural network (CNN) can be utilized as an effective way to characterize hierarchical features without prior knowledge. In this paper, a novel method based on CNN is proposed for making intelligent surface roughness identifications. The technical scheme incorporates there elements: texture skew correction, image filtering, and intelligent neural network learning. Firstly, a texture skew correction algorithm, based on an improved Sobel operator and Hough transform, is applied such that surface texture directions can be adjusted. Secondly, two-dimensional (2D) dual tree complex wavelet transform (DTCWT) is employed to retrieve surface topology information, which is more effective for feature classifications. In addition, residual network (ResNet) is utilized to ensure automatic recognition of the filtered texture features. The proposed method has verified its feasibility as well as its effectiveness in actual surface roughness estimation experiments using the material of spheroidal graphite cast iron 500-7 in an agricultural machinery manufacturing company. Testing results demonstrate the proposed method has achieved high-precision surface roughness estimation.

On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems

This paper considers the filtering problem for a class of multi-input multi-output systems with nonlinear time-varying uncertain dynamics, random process andmeasurement noise. An extended state based Kalman filter, with the idea of timely estimating the unknown dynamics, is proposed for better robustness and higher estimation precision. The stability of the proposed filter is rigorously proved for nonlinear time-varying uncertain system with weaker stability condition than the extended Kalman filter, i.e., the initial estimation error, the uncertain dynamics and the noises are only required to be bounded rather than small enough. Moreover, quantitative precision of the proposed filter is theoretically evaluated. The proposed algorithm is proved to be the asymptotic unbiased minimum variance filter for constant uncertainty. The simulation results of some benchmark examples demonstrate the feasibility and effectiveness of the method.

Strategies to Obtain Subpitch Precision in Lateral Motion Estimation in Ultrasound Elastography.

In elastography, conventional linear array (CLA)-based RF data acquisition provides more accurate displacement measurements in the direction of beam propagation (axial direction) when compared to the perpendicular direction (lateral). Obtaining good quality lateral displacement estimates in ultrasound (US) elastography will lead to several benefits such as obtaining accurate inverse solutions, improving shear strain elastogram quality, getting good quality poroelastograms, and obtaining reliable rotation elastograms. For accomplishing high-precision lateral displacement estimation (LDE), one of the popular methods is by interpolating additional A-lines in between neighboring RF A-lines. We describe a method wherein true RF A-lines (not interpolated) are acquired and augmented at subpitch locations using CLA transducer, instead of interpolating the data, and using this new frame data for further image formation and/or processing to yield better lateral resolution and LDE. We demonstrate the proposed method by translating the US beam of CLA transducer in subpitch range by the following two approaches: 1) actuator-assisted beam translation and 2) electronic translation of subaperture of a CLA by activating odd and even number of consecutive elements sequentially, referred to as electronic beam translation. The performances of the different methods were studied through simulations and experiments on phantoms. The results demonstrate that these methods yield better quality LDE compared to those obtained from interpolation of RF A-lines. These methods may provide affordable ways to obtain subpitch precision LDE using CLA.

A high accuracy method for pose estimation based on rotation parameters

In this paper, A high-precision pose estimation method based on rotation parameters is proposed. By introducing the parameterization of rotation matrix, we first formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Then, based on Gröbner basis method, we solve the solution of polynomial equation directly, and then the attitude is calculated by back-substitution method. More importantly, one of the main advantages of our method is scalability, because the order of the polynomial equations we solve is independent of the number of points involved in the estimation. Finally, we compare the performance of our algorithm with the leading PnP method, both in simulations and experiments, and show that the proposed method achieves the accuracy of approaching the OPnP (optimal solution to the PnP problem).

Real-Time Contour-Error Estimation Methods for Three-Dimensional Free-Form Parametric Curves in Contour-Following Tasks

It is crucial to control the contour error in curved contour-following tasks caused by reasons such as servo delay and external disturbance. Contour-error estimation plays as a precondition for its further control. Existing methods can hardly keep well estimation accuracy for high-speed following of free-form curves with sharp corners, especially for three-dimensional curves. Consequently, this paper presents three high-precision real-time contour-error estimation methods for spatial free-form parametric curved contour following. By generating and updating the backstepping point according to the tangential tracking error, a multiple tangential approximation method is presented first. Then, a spatial circular approximation method is given by means of approximating the actual-position nearby region of the desired contour with a spatial circle. Finally, via modification of the Newton method so as to improve its stability without sacrificing of its fast convergence property, an initial value regeneration-based Newton algorithm is proposed for contour-error estimation. All of the presented methods take both estimation precision and calculation burden into consideration, and possess their own advantages. Using these algorithms, the contour error can be rapidly estimated in vector form with a high accuracy. Simulation and experimental results demonstrate the feasibility and the superiority of the presented algorithms.

Newton-ILC Contouring Error Estimation and Coordinated Motion Control for Precision Multiaxis Systems With Comparative Experiments

This paper proposes a Newton extremum seeking algorithm based iterative learning coordinated control (Newton-ILC) strategy for contouring motion accuracy of precision multiaxial systems. Specifically, as the contouring error estimation is critically important for coordinated contouring motion control, a cost function is constructed based on the reference contour, as well as the current position, and the minimal value of the function is searched to determine the contouring error point through an offline Newton algorithm. Consequently, high precision estimation of the contouring error can be achieved, even under extreme contouring tasks with high speed, large curvature, and sharp corners. The calculated contouring error is then projected to each axis, and the axial contouring errors are controlled by iterative learning method, while the learning results will be used to adjust the axial position reference commands for contouring accuracy improvement. Comparative experiments are conducted on a biaxial linear motor stage to validate the practical effectiveness of the proposed Newton-ILC strategy. The experimental results illustrate that the proposed Newton-ILC achieves not only nearly perfect contouring error estimation but obvious improvement of contouring accuracy as well after a few iterations. Particularly, in some extreme cases such as large initial tracking error, high speed, large curvature, and sharp corners, the proposed Newton-ILC strategy can achieve rather excellent contouring performance when compared with individual axis control, conditional cross-coupled control, and cross-coupled iterative learning coordinated control (CCILC) methods.

A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions

Many available signals in the real world are usually weak with impulse noises and/or outliers, and we also need to have higher estimation precision in applications. Our focus of attention is pretty much on integrating robustness and accuracy under lower signal to noise ratio (SNR) with impulse noises. Although traditional fractional adaptive time delay estimation (TDE) methods have higher precision, the results of estimation are unreasonable when the signals contain some impulse noises. While, most proposed robust algorithms later can work well mainly with high SNR. In this paper, considering the practical problem in equipment fault acoustic localization based on TDE methods, an improved robust fractional adaptive time delay estimation method is addressed facing lower SNR conditions. First, the impulse noises are modeled as Alpha stable distribution, and the integer part of TDE is getting by using covariate correlation approach. Then, the integer estimation value is used as initial parameter value of time delay. Covariant sequence is the input of time delay estimator. Next, fractional TDE value is adaptive obtained by iteration under minimum average <i>p</i> norm criterion. Covariant sequence weakens irrelevant noises, meanwhile preserves time delay information between original sequences. Computer simulations and comparative experiments show that improved method has better estimation results. This method is robust and higher precision, and especially under impulse environment and low SNR conditions.

High Precision Estimate of Dry Matter Weight in Soybean by Proximal Remote Sensing

High Precision Estimate of Dry Matter Weight in Soybean by Proximal Remote Sensing

Study on a Kalman Filter based PID Controller

This study proposes a self-tuning PID controller design method based on a Kalman filter. Recently, data-driven controller tuning methods that can directly tune control parameters by closed-loop data without system models have been received much attention as convenient tuning approaches. On the other hand, in parameter estimation problems, the Kalman filter that can obtain high-precision estimation results has been applied in many research/industrial area. In this paper, a data-driven PID parameters tuning problem that is derived based on a PID control law is resolved as a Kalman filtering problem, and a self-tuning PID controller based on the Kalman filter is proposed. The effectiveness of the proposed method is evaluated by simulation and experimental examples.

A new method for estimation of orthogonal and phase deviations of constant-envelope signals

In this paper it is shown that orthogonal deviation increases the ranging error of navigation signals, and a method for the estimation of orthogonal and phase deviation of the I and Q components of a constant-envelope signal is proposed. A measurement process is introduced and the measurement accuracy for different signal-to-noise ratios and data lengths is provided. Moreover, in the process of evaluation, a very important conclusion is made: when a digital signal is filtered, the filter bandwidth does not affect measurement accuracy. The corresponding proof is given. The accuracy of the proposed method and measurement is verified by simulations. In simulations, an expectation-maximum (EM) algorithm was used to estimate the constellation coordinates, and the shortcomings of the EM algorithm in high-precision parameter estimation were determined and the corresponding corrections made. Finally, the proposed method was used to estimate both orthogonal deviation and phase deviation of a real satellite signal.

A robust complex-domain state estimator using synchrophasor measurements

Phasor measurement units (PMUs) have been widely deployed in modern power grids, which provides a great opportunity for high-precision and real-time power system state estimation. If power networks are completely observable by PMUs, linear state estimation can be achieved with massive synchronous data. In this paper, the analysis of phasor error is presented and a complex number weighted least squares (CWLS) algorithm for state estimation is proposed, which shows high computational efficiency and stronger robustness to phasor errors. Meanwhile, self-adaptive weight and bad data identification techniques are developed to enhance the CWLS’s performance. Test results on various IEEE test systems and the realistic power grid validate the effectiveness and superiority of the proposed CWLS algorithm, outperforming the conventional weighted least squares (WLS) state estimation method as well as other existing industrial-grade approaches.

Synergistic real-time compensation of tracking and contouring errors for precise parametric curved contour following systems

The tracking and contouring errors are inevitable in real computer numerical control contour following because of the reasons such as servo delay and dynamics mismatch. In order to improve the motion accuracy, this paper proposes a synergistic real-time compensation method of tracking and contouring errors for precise parametric curve following of the computer numerical control systems. The tracking error for each individual axis is first compensated, by using the feed-drive models with the consideration of model uncertainties, to enhance the tracking performances of all axes. Further, the contouring error is estimated and compensated to improve the contour accuracy directly, where a high-precision contouring-error estimation algorithm, based on spatial circular approximation of the desired contour neighboring the actual motion position, is presented. Considering that the system structure is coupled after compensation, the stability of the coupled system is analyzed for design of the synergistic compensator. Innovative contributions of this study are that not only the contouring-error can be estimated with a high precision in real time, but also the tracking and contouring performances can be simultaneously improved although there exist modeling errors and disturbances. Simulation and experimental tests demonstrate the effectiveness and advantages of the proposed method.

Data-driven fault prediction and anomaly measurement for complex systems using support vector probability density estimation

To quantitatively monitor the state of complex system, a data-driven fault prediction and anomaly degree measurement method based on probability density estimation is studied in this paper. First, an anomaly index is introduced and defined to measure the anomaly degree of samples. Then By improving the form of constraint condition, a single slack factor multiple kernel support vector machine probability density estimation model is presented. As a result, the scale of object function and the solution number are all reduced, and the computational efficiency of the presented model is greatly enhanced. On the other hand, as the introduction of multiple kernel functions, a multiple kernel matrix with better data mapping performance is obtained, which can well solve the composite probability density estimation for uncoupled data. The simulation test shows that the presented model has higher estimation precision and speed. The experiments on complex system fault prediction also show that the system’s anomaly degree can be quantitatively and accurately measured by the anomaly index gained from the prediction results, which can effectively improve the fault prediction precision and increase the prediction advances.