Reduce Measurement Noise Research Articles

Optimal multiple fringe pattern composition by weighted complex amplitude fusion in fringe projection profilometry

This paper presents an optimal multiple fringe pattern composition method for 3D shape measurement of high-dynamic-range (HDR) objects using fringe projection profilometry (FPP). With the inverse variance weighting theory, we take the square of the modulation intensities of the fringe pattern images with different intensity levels as the weights to obtain the composited phase of fringe patterns by weighted complex amplitude fusion, which improves the measurement precision of HDR objects. Additionally, we integrate HDR 3D shape measurement and temporal noise reduction into a unified framework by utilizing weighted complex amplitude fusion to completely measure translucent objects with specular reflections. Simulations and experiments demonstrate that our method can achieve higher measurement precision and is resistant to the time-varying ambient light.

Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection.

Smoothing is a widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjustive smoothing method is developed that retains sharp peaks and less distorted signals. The proposed method uses only one parameter that determines the global smoothness of data, while balancing the local smoothness using the data itself. Simulation and real experiments in comparison with existing convolution-based smoothing methods indicate both qualitatively and quantitatively improved noise reduction performance in practical scenarios. We also discuss parameter selection and demonstrate an application for the automated smoothing and detection of a given number of peaks from noisy measurement data.

Enabling the characterization of the nonlinear electrokinetic properties of particles using low voltage.

Insulator-based electrokinetically driven microfluidic devices stimulated with direct current (DC) voltages are an attractive solution for particle separation, concentration, or isolation. However, to design successful particle manipulation protocols, it is mandatory to know the mobilities of electroosmosis, and linear and nonlinear electrophoresis of the microchannel/liquid/particle system. Several techniques exist to characterize the mobilities of electroosmosis and linear electrophoresis. However, only one method to characterize the mobility of nonlinear electrophoresis has been thoroughly assessed, which generally requires DC voltages larger than 1000 V and measuring particle velocity in a straight microchannel. Under such conditions, Joule heating, electrolysis, and the DC power source cost become a concern. Also, measuring particle velocity at high voltages is noisy, limiting characterization quality. Here we present a protocol-tested on 2 μm polystyrene particles-for characterizing the mobility of nonlinear electrophoresis of the liquid/particle system using a DC voltage of only 30 V and visual inspection of particle dynamics in a microchannel featuring insulating obstacles. Multiphysics numerical modelling was used to guide microchannel design and to correlate particle location during an experiment with electric field intensity. The method was validated against the conventional characterization protocol, exhibiting excellent agreement while significantly reducing measurement noise and experimental complexity.

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study.

In the analysis of electroencephalography (EEG), entropy can be used to quantify the rate of generation of new information. Entropy has long been known to suffer from variance that arises from its calculation. From a sensor's perspective, calculation of entropy from a period of EEG recording can be treated as physical measurement, which suffers from measurement noise. We showed the feasibility of using Kalman filtering to reduce the variance of entropy for simulated signals as well as real-world EEG recordings. In addition, we also manifested that Kalman filtering was less time-consuming than moving average, and had better performance than moving average and exponentially weighted moving average. In conclusion, we have treated entropy as a physical measure and successfully applied the conventional Kalman filtering with fixed hyperparameters. Kalman filtering is expected to be used to reduce measurement noise when continuous entropy estimation (for example anaesthesia monitoring) is essential with high accuracy and low time-consumption.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Abstract In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs

Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used. To address this issue, this paper introduces a hybrid dual-scale neural network model based on the generalized regression multi-model and cubature information filter (GRMM-CIF) framework. We have established the GRMM-CIF filtering structure to differentiate motion modes and reduce measurement noise. Furthermore, considering trajectory datasets and rates of motion change, a neural network at different scales will be designed. We propose the dual-scale bidirectional long short-term memory (DS-Bi-LSTM) algorithm to address prediction delays in a multi-model context. Additionally, we employ scale sliding windows and threshold-based decision-making to achieve dual-scale trajectory reconstruction, ultimately enhancing tracking accuracy. Simulation results confirm the effectiveness of our approach in handling the uncertainty of UAV motion and achieving precise estimations.

Reply to “Comment on “So you think you can DAS? A viewpoint on delay-and-sum beamforming” - Think twice before f-numbering”, published by M.F. Schiffner and G. Schmitz

In a 2021 paper, we delved into the details of delay-sum beamforming (DAS) in high-frame-rate ultrasound for medical imaging [1]. We also proposed a simple and fast method of determining an f-number, which is based on the directivity of the transducer elements. In their comment, Martin F. Schiffner and Georg Schmitz argue that we mistakenly link image quality enhancement to the reduction of measurement noise. They disapprove our proposed f-number, claiming it deteriorates the signal-to-noise ratio (SNR). Based on their previous work [2], they also highlight that the f-number should be derived from the grating lobe angles. In this reply, we explain their error in the SNR argument. We also illustrate the potential drawbacks of exclusively relying on grating lobes to establish an f-number with a DAS, suggesting that alternative approaches might be worthy of consideration.

Photolithographic patterning on multi-wavelength quantum dot film of the improved conversion efficiency for digital holography.

A triple-wavelength patterned quantum dot film was fabricated for the light source of digital holography to improve both the axial measurement range and noise reduction. The patterned quantum dot film was fabricated after optimizing the photolithography process condition based on the UV-curable quantum dot solution, which was capable of multiple patterning processes. In addition, an optimized pattern structure was developed by adding TiO2 nanoparticles to both the quantum dot and bank layers to increase the scattering effect for the improved photoluminescence intensity. Finally, the newly developed light source with the balanced spectral distribution was applied to the digital holography, rendering it applicable as an improved light source.

Uncertainty Inverse Analysis of Positioning Accuracy for Error Sources Identification of Industrial Robots

Uncertainties widely exist in modeling parameters of industrial robots due to wear during service. These uncertainties are too small to be directly measured and may severely affect the positioning accuracy of end-effector through multi-stage propagation. An uncertainty inverse analysis method of positioning accuracy is proposed to identify uncertainty of typical structural parameters with the selected optimal position information. The Denavit–Hartenberg method is employed to establish the kinematic model. For the probability uncertainty of modeling parameters, the inverse analysis model, which reflects propagation mapping between uncertain variables and end-effector position response is obtained by first-order second-moment method. The partial-derivative information is used to improve calculation efficiency through removing low-contributing parameters. To further reduce measurement noise interference, orthogonal matching pursuit strategy is presented to search optimal sensor placement with the objectives of maximum coefficient modulus and minimum correlation, then pseudoinverse matrix method is employed to obtain the exact solution of identification equation. Finally, numerical simulation results for 6 degrees-of-freedom robots indicate that the proposed method with improved solution efficiency can accurately identify uncertainties of joint angles within the error 5% under large measurement errors.

Design and implementation of analog tracking filter for ac signal acquisition

SummaryThis paper presents an analog tracking bandpass filter that is intended to be applied in ac signal acquisition circuits to reduce measurement noise. A fast and accurate frequency tuning technique is developed based on the phase control loop. A complete derivation process is provided to build the mathematical model describing the control loop for the tracking filter. Accordingly, a zero‐crossing detector is designed to accurately extract signal phases in the proposed loop. As a closed‐loop control system, the loop dynamics such as stability, transient response, and steady‐state error are analyzed using the derived model. From both the simulations and measurements, the developed filter is found to provide excellent performance in frequency tracking, noise reduction, and linearity compared to existing work. The maximum and minimum Q value reaches 42.2 and 14, respectively, in the frequency tuning range of 34 to 107 kHz, while the frequency tracking error is less than 1.18% at 100 kHz.

Shore power connection for offshore vessels - Measured noise reduction in port and dock

The presentation will summarize measured noise reduction for drilling, pipelay and offshore support vessels due to shore power connection. The noise level at office or storage building in distance of 100-250 m from the vessels has been registered continously through days and nights. Noise levels at nights without and with shore power connection has been compared. A drilling vessel had Lw 110 dB and a noise reduction due to shore power connection of 18 dB. A pipelay vessel had Lw 105 dB without shore power, and a noise reduction of 15 dB due to the effect of shore power. Both drilling and pipelay vessel seem to have Lw of 90-92 dB with shore power connection. Two different offshore support vessels have also been measured with sound power level Lw of 107 dB and 100 dB. The noise reduction with shore power connection is 13 dB for the first and 6-7 dB for the latter. With shore power connection, both offshore suppert vessels have Lw of 94 dB. The sound power level with shore power connection seems to be limited by the fact that ship noise level from ventilation and fans seems to be the same.

Conceptual design and demonstration of a three-color laser interferometer for noise reduction in fusion plasma measurements.

A three-color laser interferometer consisting of three Mach-Zehnder-type, one-color laser interferometers with heterodyne detection and coaxial laser beams is demonstrated. The three-color laser interferometer is considered as three sets of a two-color laser interferometer. From the two sets of the two-color laser interferometer, the value consisting only of the noise floor can be assessed. The noise floor can be reduced by subtracting the value consisting only of the noise floor from the measurement value obtained with the other two-color laser interferometer. In the case of the three lasers with wavelengths 9.25 μm, 10.59 μm, and 532nm, a 15% noise reduction was obtained compared to the two sets of the two-color laser interferometers contained in the three-color laser interferometer. The 100-Hz noise reduction by 53% was achieved, and the other frequency noises were equal to or less than the smallest noise achieved by the two-color laser interferometers. The 100-Hz noise floor is caused by the vibration noise, which remains because of the non-coaxiality of the three beams.

Characterization of measurement and instrument noise in areal surface topography measurements by the Allan deviation

Measurement and instrument noise as proposed in ISO 25178-600 and defined in ISO 25178-700 are assumed to be both stationary and randomly distributed. This paper presents the temporal and lateral Allan deviation as useful tools for deeper noise analysis in surface topography measurements. The temporal Allan deviation may be used to identify and qualify drift and spikes in the mean topography, which makes averaging over longer times no longer beneficial to reduce measurement noise. The lateral Allan deviation may reveal the application of implicit and/or explicit filtering or other correlations on surface topography measurements.

Design and implementation of high-precision voltage measurement system based on Kalman filter and automatic calibration technology

With the rapid development of science and technology, precision measurement is of increasing importance. In this paper, we discuss noise reduction and automatic calibration issues for a high-precision voltage measurement system that covers voltages from 0.1 µV to 300 V and a frequency range from 10 Hz to 300 kHz (including DC). First, a modified Kalman filter is proposed to reduce measurement noise. Then, a self-calibration circuit is constructed to eliminate the systematic errors in DC voltage (DCV) measurements. Finally, a wideband AC calibration model is developed to overcome the defects in AC voltage (ACV) measurements caused by AC characteristics. The measurement application demonstrates the effectiveness of the proposed methods----the designed voltage measurement system has a DCV measurement accuracy of better than 9 µV/V and a high ACV accuracy of 0.25 mV/V in the frequency range of 30 Hz to 30 kHz at an ambient temperature of 25 °C. In addition, the minimum DC measurement uncertainty obtained on the DCV 10 V range is 0.4 μV/V (type A) and the minimum AC measurement uncertainty obtained on the ACV 10 V range is 2 μV/V (type A).

Mitigation of Thermal Stability Concerns in FinFET Devices

Here, we developed a procedure for mitigating thermal hazards in packaged FinFET devices. A monitoring system was installed into devices, based on self-heating impact analysis in the system and device levels, to allow for the observation and alerting of chip temperature and reliability risks. A novel algorithm for reducing measurement noise by means of temperature fluctuation compensation and the filtering of invalid data is presented and demonstrated on packaged devices. The results presented in this work show that the proposed techniques make exceptional improvements to sensory accuracy. Using this methodology enables the mitigation of thermal concerns in systems, including large data servers, and accelerates development of smart resource allocation formations.

Seam detection method design and automatic calibration based on an active light coaxial visual sensor

Seam feature detection is one of the critical premises of automatic welding. However, the current seam detection method is vulnerable to complex laser environments, such as illumination interference and material defects, thereby limiting the application of active light coaxial visual sensors in seam tracking. This paper proposes a seam detection and calibration method based on an active light coaxial visual sensor in a laser welding digital twin environment. First, a vision sensor with an 808 nm auxiliary illumination and a customized lens are mounted on the laser welding head to filter out noises and capture the welding image, including the seam and melt pool. Next, a seam information extraction approach based on image processing algorithms is designed to extract seam position and seam width. In addition, an artificial neural network based Kalman filter is proposed to eliminate measurement errors and reduce measurement noise. The calibration of the seam detection parameters is a tedious process. Therefore, the calibration problem is transformed into an optimization problem. A genetic algorithm-based calibration method is proposed to automatically determine the optimum parameters of the seam detection algorithm. Finally, seam detection experiments on laser welding are conducted in both the digital twin environment and the laser welding system. The experimental results show that the proposed method is more efficient than the Hough transfer-based seam detection method. The calibration method can ensure the accuracy of the seam detection method, especially in complex and changeable welding conditions.

Experimental Study on Slosh Dynamics Estimation in a Partially Filled Liquid Container Using a Low-Cost Measurement System

A classic problem in the field of engineering and especially fluid dynamics is the liquid sloshing. Advancements in aerial robotics for agricultural purposes also drew attention towards the stability of pesticide spraying platforms under the influence of sloshing. Several mechanics models of the sloshing phenomenon had already been developed which are capable to predict the slosh within certain limits. This paper aims to identify sloshing parameters using a novel approach that utilizes a low-cost sensor. This research work utilizes Kalman filter to reduce measurement noise which is inherited by the sensor. The experimental setup used in this research consists of a rectangular container placed on a conveyor belt. Ultrasonic sensor was mounted on the top of the container, whose slosh parameters need to be identified. Sloshing data was acquired using an ultrasonic sensor in the presence of the input supplied by the motion of the conveyor. System identification is used to identify the system model. Luenberger observer and Sliding mode observers were utilized to check the accuracy of the predicted model. The RMSE value and the best fit estimation percentages were calculated for comparison of accuracy and error analysis. After several tests with different slosh levels, the recorded data is analyzed. The results support our proof of concept to measure slosh under dynamic conditions using a low cost sensor. The identified model of sloshing can be incorporated on an agriculture pesticide sparing drone for precision spraying resulting in efficient spraying of pesticide.

Measured Effect of Low-Height Solid Safety Barriers on Heavy Truck Noise

Low-height solid safety barriers (SSBs) are constructed along highways where an adequate clear zone cannot be provided between the highway and an important roadside feature. Where a roadside SSB is a permanent installation, it is possible that it also provides noise reduction benefits. Highway traffic noise sources that could be shielded by a low-height SSB include tire–pavement noise and any other noise originating close to the pavement surface, such as a low exhaust on a heavy truck. This paper reports the findings of a study examining how heavy truck noise is shielded by low-height roadside SSBs. Measurements of the maximum sound level (Lmax) of individual heavy truck pass-by events at two representative locations in Ohio were obtained at a position where a low-height SSB was present as well as at a nearby unshielded position. The results indicated that a perceptible reduction in the pass-by event Lmax (between 3 and 5 dBA) was realized for locations behind the SSBs. Variations in the measured noise reduction were associated with the line-of-sight shielding between various truck noise sources and the receiver positions. For events in which the exhaust source was shielded by the SSB, the measured noise reductions were higher at the site with the 42-in. tall SSB, although there was no difference for the site with the shorter (32-in.) SSB. It is recommended that analysts consider the potential for noise reduction associated with low-height SSBs in locations where such barriers are expected to be permanent.

An Improved Next Generation Gravity Mission

There is an opportunity to make a major reduction in the acceleration noise level for the first Next Generation Gravity Mission by replacing the accelerometers used on the GRACE Follow-On Mission by a highly simplified version of the Gravitational Reference Sensors flown very successfully on the LISA Pathfinder mission of ESA. The reduced measurement noise level can make possible much-improved measurements of the short-period and short-wavelength variations in the geopotential. This would be particularly from the along-track analysis of the results, which can permit repeat measurements about half a day apart along ground tracks within 200 km of each other over a substantial part of the globe. Such a mission would permit considerably improved testing of geophysical models for the geopotential variations due to changes in the Earth’s mass distribution.

Reliable Centralized Control of PMSM

In this paper, we introduce the development methodology of a reliable centralized control applied to a synchronous permanent magnet machine. The proposed system is nonlinear, we linearize around a point of application. The resulting model will then be used to reproduce the dynamic behavior of the machine for a reliable control. The controller is based on the standard h infinite to increase performance, reduce measurement noise, and to tolerate the outage of certain sensors. To illustrate the results, we made a comparison between a standard state feedback control and reliable h infinite robust control. The simulation results shows, that the system in case of technical placements poles loses classic performance in the presence of an outage, that the reliable centralized robust control remain satisfactory performance even in the presence of outage.