Real-time Drift Research Articles

Temperature Compensation Method for Tunnel Magnetoresistance Micro-Magnetic Sensors Through Reference Magnetic Field.

The sensitivity of Tunnel Magnetoresistance (TMR) sensors is characterized by significant temperature drift and poor sensitivity drift repeatability, which severely impairs measurement accuracy. Conventional temperature compensation techniques are often hindered by low compensation precision, inadequate real-time performance, and an inability to effectively address the issue of poor repeatability in temperature drift characteristics. To overcome these challenges, this paper introduces a novel method for suppressing temperature drift in TMR sensors. In this method, an alternating reference magnetic field is applied to TMR sensors, and the output amplitude at the frequency of the reference magnetic field is calculated to compensate the sensitivity temperature drift in real time. Temperature characteristic tests were conducted in a non-magnetic temperature test chamber, and the results revealed that the proposed method significantly reduced the TMR sensitivity drift coefficient from 985.39 ppm/°C to 59.08 ppm/°C. Additionally, the repeatability of sensitivity temperature characteristic curves was enhanced, with a reduction in root mean square error from 0.84 to 0.21. This approach effectively mitigates temperature-induced sensitivity drift without necessitating the use of a temperature sensor, and has the advantages of real-time performance and repeatability, providing a new approach for the high-precision temperature drift suppression of TMR.

Suppressing Thermal Noise to Sub-Millikelvin Level in a Single-Spin Quantum System Using Realtime Frequency Tracking.

A single nitrogen-vacancy (NV) center in a diamond can be used as a nanoscale sensor for magnetic field, electric field or nuclear spins. Due to its low photon detection efficiency, such sensing processes often take a long time, suffering from an electron spin resonance (ESR) frequency fluctuation induced by the time-varying thermal perturbations noise. Thus, suppressing the thermal noise is the fundamental way to enhance single-sensor performance, which is typically achieved by utilizing a thermal control protocol with a complicated and highly costly apparatus if a millikelvin-level stabilization is required. Here, we analyze the real-time thermal drift and utilize an active way to alternately track the single-spin ESR frequency drift in the experiment. Using this method, we achieve a temperature stabilization effect equivalent to sub-millikelvin (0.8 mK) level with no extra environmental thermal control, and the spin-state readout contrast is significantly improved in long-lasting experiments. This method holds broad applicability for NV-based single-spin experiments and harbors the potential for prospective expansion into diverse nanoscale quantum sensing domains.

An Improved Photovoltaic Power Reserve Control With Rapid Real-Time Available Power Estimation and Drift Avoidance

An Improved Photovoltaic Power Reserve Control With Rapid Real-Time Available Power Estimation and Drift Avoidance

An alternating continuous integration system for magnetic measurements for experimental advanced superconducting tokamak.

Integrators are critical instruments used for magnetic measurement systems (MMSs) in tokamaks, and, currently, the Experimental Advanced Superconducting Tokamak (EAST) has over 600 deployed. However, these integrators, designed with real-time drift compensation, will not be able to support longer pulse operations in the near future due to saturation and drift. To address these issues, this paper proposes a new alternating integration system combining analog integration with drift digital rectification. This system utilizes a microcontroller unit (MCU) to control two parallel analog integrators to work alternatively, compensate their drifts based on their respective error characteristics, and assemble the two integration segments together. The designed architecture provides highly flexible capabilities in operation modes and error correction, which make the system operation and maintenance highly automated. Performance tests on the EAST experiment site show that the prototype integrator can meet the requirements of real-time plasma control for a duration of hour-level.

Practical Real-Time Phase Drift Compensation Scheme for Quantum Communication Systems.

Quantum communication systems are susceptible to various perturbations and drifts arising from the operational environment, with phase drift being a crucial challenge. In this paper, we propose an efficient real-time phase drift compensation scheme in which only existing data from the quantum communication process is used to establish a stable closed-loop control subsystem for phase tracking. This scheme ensures the continuous operation of transmission by tracking and compensating for phase drift in the phase-encoding quantum communication system. The experimental results demonstrate the effectiveness and feasibility of the proposed scheme with an average quantum bit error rate of 1.60% and a standard deviation of 0.0583% for 16 h of continuous operation.

Development and assessment of a novel servo-controlled spraying system for real time adjustment of the orientation angle of the nozzles of a boom sprayer.

Pesticide spray drift, which is the movement of pesticide by wind to any location other than the intended area, is hazardous to human, animal, food safety and environmental health. It is not possible to completely eliminate spray drift during spraying with field crop sprayers, but spray drift can be reduced by developing new technologies. The most common methods to reduce spray drift are carrying the droplets to the target with air-assisted spraying, electrostatic spraying, preferring air induction nozzles and boom shields. With these methods, it is not possible to make a change on the sprayer depending on the wind intensity during spraying. In this study, a novel servo-controlled spraying system was designed and developed to change the nozzle orientation angle in the reverse direction of the wind current to reduce the ground spray drift in real time and automatically in a wind tunnel. The displacement in the spray pattern (Dc ) was used as a ground drift indicator for each nozzle to evaluate the spray drift. The developed system, operated by LabVIEW software, calculated different nozzle orientation angles depending on nozzle types, wind velocities and spraying pressures. Orientation angles calculated for different test conditions achieved in reduction were up to 49.01% for XR11002 nozzle, 32.82% for AIXR11002 nozzle and 32.31% for TTJ6011002 nozzle at 400 kPa spray pressure and 2.5 m s-1 wind velocity. The developed system, which has a self-decision mechanism, calculated the nozzle orientation angle instantaneously according to the wind velocity. It has been observed that the adjustable spraying nozzle system, sprayed with high precision towards the wind in the wind tunnel, and the developed system have advantages compared to conventional spraying systems. This article is protected by copyright. All rights reserved.

Error-Covariance Reset in the Multiplicative Extended Kalman Filter for Attitude Estimation

This paper presents a study of the reset step in the multiplicative extended Kalman filter (MEKF). This filter is widely used for spacecraft attitude estimation, which typically involves estimating the attitude and gyro drift in real time using external sensors such as star trackers. The basic idea of the MEKF is to use the quaternion or direction-cosine matrix as the “global” attitude parameterization and a three-component state vector for the “local” parameterization of attitude errors. The true attitude is expressed as the product of the error attitude and the estimate rather than as the sum of the error and the estimate. The reset operation moves the local error to the global variable. This reset does not add new information, but it changes the reference frame for the attitude error covariance. This results in an error-covariance reset that is very different from the measurement update of the error covariance in the MEKF. The effects of using an error-covariance reset in the MEKF are analyzed in this work. The results from this work can be applied to any application involving attitude estimation as part of its process, such as inertial navigation.

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

Handling sentiment drifts in real time twitter data streams are a challenging task while performing sentiment classifications, because of the changes that occur in the sentiments of twitter users, with respect to time. The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time. This work proposes an adaptive learning algorithm-based framework, Twitter Sentiment Drift Analysis-Bidirectional Encoder Representations from Transformers (TSDA-BERT), which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time. The framework also works on static data by converting them to data streams using the Kafka tool. The experiments conducted on real time and simulated tweets of sports, health care and financial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model, with accuracies of 91%, 87% and 90%, respectively. Though the results have been provided only for a few topics, as a proof of concept, this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

Real-Time Temperature Drift Compensation Method of a MEMS Accelerometer Based on Deep GRU and Optimized Monarch Butterfly Algorithm

In recent years, inertial sensors based on Micro-Electro-Mechanical Systems (MEMS) have become increasingly popular. They have been widely used in various fields due to their low cost, small size, and low power consumption. It seems that MEMS inertial sensors may eventually fully occupy the middle to lower end inertial navigation application market that traditional inertial sensors previously occupied. To realize the full potential of MEMS inertial sensors, one of the critical issues is their temperature drift. This paper first proposed a recursive model for real-time compensation. Then three algorithms were proposed, including a two-layer deep gated recurrent unit recurrent neural network (GRU-RNN, short GRU), deep GRU based on monarch butterfly algorithm (MBA), and deep GRU based on optimized monarch butterfly algorithm (OMBA). Each of these three algorithms is combined with the real-time compensation model to compensate for the temperature drift of a MEMS accelerometer. The experimental results proved the correctness of these three methods, and the MEMS accelerometer’s temperature drift is compensated effectively. The results indicate that the deep GRU + OMBA shows the best performance for the temperature drift compensation combined with the real-time compensation model. After deep GRU + OMBA method compensation, the angle random walk, the bias instability, the rate random walk, and rate ramp of the MEMS accelerometer were improved from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.97e^{-4} \, mg\,\,\cdot s^{\frac {1}{2}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.90e^{-4} \, mg$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.57e^{-5} \, mg/ s^{\frac {1}{2}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.82e^{-6} \, mg\,\,/ s$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.90e^{-5} \, mg\,\,\cdot s^{\frac {1}{2}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.07e^{-5} \, mg$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.12e^{-6} \, mg/ s^{\frac {1}{2}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.59e^{-8} \, mg\,\,/ s$ </tex-math></inline-formula> , respectively. Their percentage of improvement reaches 96.50% on average.

A two-stage method for real-time baseline drift compensation in gas sensors

Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

Real-Time Concept Drift Detection and Its Application to ECG Data

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world &amp;amp; time-series datasets.

Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network With Hadamard Transform and Additive Layers

In this paper, we propose a computationally efficient deep learning framework to address the issue of sensitivity drift compensation for chemical sensors. The framework estimates the underlying drift signal from sensor measurements by means of a deep neural network with a multiplication-free Hadamard transform based layer. In addition, we propose an additive neural network which can be efficiently implemented in real-time on low-cost processors. The temporal additive neural network structure performs only one multiplication per “convolution” operation. Both the regular network and the additive network can have Hadamard transform based layers that implement orthogonal transforms over feature maps and perform soft-thresholding operations in the transform domain to eliminate noise. We also investigate the use of the Discrete Cosine Transform (DCT) and compare it with the Hadamard transform. We present experimental results demonstrating that the Hadamard transform outperforms the DCT.

An analog-digital integrator for EAST long pulse discharge

As one of the key electronic instruments for the experiment of long pulse plasma discharge, the integrator is required to work stably in complex and changeable electromagnetic environments. For the integrator of real-time drift compensation in the Experimental Advanced Superconducting Tokamak (EAST), an alternating method and an integrator correction algorithm (IDC) are adapted to improve the performance of the system in real time, and the new design of integrator system is completed. The new integrator system works stably for up to 1284s in the EAST experiment and the integration drift within 1000s on the testing bench is less than 8 mV when the integration time constant is 20 ms.

An ARMA-Based Digital Twin for MEMS Gyroscope Drift Dynamics Modeling and Real-Time Compensation

The applications of Inertial Measurement Unit (IMU) based on Micro Electro Mechanical System (MEMS) are widely used because of its low-cost, small volume and low energy consumption. Unlike the accelerometers and magnetometers, which are respectively sensitive to vibrations and ferrous materials interference, MEMS type gyroscope is able to provide stable orientation information for a short period of time. Compared with these two sensors, gyroscopes are neither disturbed by vibrations nor affected by metallic materials, and therefore it would be one of the primary sensors for industrial applications. Reviewing the most recently related works, accurate attitude estimations are usually obtained by fusing gyro integration. However, it is well known that series drifts will be induced after a long-term integration. Furthermore, according to the literature and the associated experiments, they all demonstrated that MEMS type gyroscopes have strong temperature correlation because of the silicon structure. Put it simply, the temperature variations will give rise to gyro drifts severely and thereby degrade the orientation estimation precision. To address this problem, this article proposed a temperature modeling technique and a real-time drift pre-compensation procedure by applying autoregressive moving average (ARMA) technology. Different from the traditional modeling methods, the proposed method is able to describe transient drifting behaviors as well as steady state ones. Moreover, comparison studies using artificial neural networks (ANNs) are also presented. Experiments show that the proposed method provides a stable and reliable temperature-caused gyroscope drift pre-compensation. The developed method is also very suitable for the realization in a low-cost embedded system.

Compact Laser Collimation System for Simultaneous Measurement of Five-Degree-of-Freedom Motion Errors

A compact laser collimation system is presented for the simultaneous measurement of five-degree-of-freedom motion errors. The optical configuration of the proposed system is designed, and the principle of the measurement of five-degree-of-freedom errors is described in detail. The resolution of the roll and the horizontal straightness is doubled compared with other laser collimation methods. A common optical path compensation method is provided to detect light drift in real time and compensate for straightness and angle errors. An experimental setup is constructed, and a series of experiments are performed to verify the feasibility and stability of the system. Compared with commercial instruments, the pitch and yaw residuals are ± 2.5 ″ and ± 3.5 ″ without correction, and the residuals are ± 1.9 ″ and ± 2.8 ″ after correction, respectively. The comparison deviations of the horizontal straightness and vertical straightness changed from ± 4.8 μ m to ± 2.8 μm and ± 5.9 μm to ± 3.6 μm, respectively. The comparison deviation of the roll is ± 4.3 ″ . The experimental results show that the data of the five-degree-of-freedom measurement system obtained are largely the same as the measurement data of commercial instruments. The common optical path compensation can effectively improve the measurement accuracy of the system.

WDM-Based Silicon Photonic Multi-Socket Interconnect Architecture With Automated Wavelength and Thermal Drift Compensation

A silicon photonic circuit comprising all the building blocks necessary to demonstrate optical communication between two sockets interconnected through an Arrayed Waveguide Grating Router (AWGR) is reported. The article focuses on the robustness of the interconnection scheme to the unavoidable wavelength and thermal fluctuations observed in real datacenter environments. To improve the reliability of the system, a feedback control mechanism, based on contactless integrated photonic probes (CLIPP) and heater actuators, is added to the interconnection to monitor in parallel the working point of each sensitive device and keep it locked in real-time. Experimental results demonstrate successful operations in a 30 Gbit/s data routing scenario at $\text{5}\cdot \text{10}^{-11}$ bit error rate, irrespective of sudden wavelength shifts of up to 200 pm or of iterated thermal variations in a 10 $^\circ$ C temperature range, with a recovery time of around 30 ms. These results prove that AWGR-based interconnections equipped with real-time drift compensation systems can be a viable option in multi-socket layouts even in highly demanding environments.

Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time

The presence of the stochastic errors in MEMS (Micro Electro Mechanical Systems) gyroscopes makes the improvement of the measurement precision challenging. This paper addresses a novel method to estimate and compensate the random drift of MEMS gyroscopes in real time, combining unscented Kalman filter (UKF) with recurrent neural network (RNN). In the proposed method, the random drift is regarded as a generalized nonlinear autoregressive moving average (NARMA) model, and its optimal predictor is realized by a dynamic RNN. To compensate the random drift in real time, the RNN model is brought into the framework of UKF, for establishing the state equation of the improved UKF. The novelty of this paper is that a strategy is presented to guarantee the validity of the combination of UKF and RNN. The effectiveness and superiorities of the proposed method are verified by experiments.

Ultraprecise single-molecule localization microscopy enables in situ distance measurements in intact cells.

Single-molecule localization microscopy (SMLM) has the potential to quantify the diversity in spatial arrangements of molecules in intact cells. However, this requires that the single-molecule emitters are localized with ultrahigh precision irrespective of the sample format and the length of the data acquisition. We advance SMLM to enable direct distance measurements between molecules in intact cells on the scale between 1 and 20 nm. Our actively stabilized microscope combines three-dimensional real-time drift corrections and achieves a stabilization of <1 nm and localization precision of ~1 nm. To demonstrate the biological applicability of the new microscope, we show a 4- to 7-nm difference in spatial separations between signaling T cell receptors and phosphatases (CD45) in active and resting T cells. In summary, by overcoming the major bottlenecks in SMLM imaging, it is possible to generate molecular images with nanometer accuracy and conduct distance measurements on the biological relevant length scales.

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks.

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

Visual sensing based ultra-high precision motion control with compensated mechanical drift across different stages

Ultrahigh precision motion control across different stages in a multistage system plays a central role in many micro/nanoscale applications. However, it remains a challenge due to the mechanical drift. Here, we propose a visual sensing based approach to implement the cooperative motion control in a multistage platform. In this method, microsized beads are attached to individual stages and taken as visual sensing probes. By tracking the motion of visual sensing probes, the positions of individual stages are measured. The principle of the proposed method is presented, followed by the experimental demonstration of the real-time mechanical drift compensation and high-precision cooperative motion control in a home-made dual piezoelectric flexural stage platform. With the proposed method, ultrahigh precision motion control between the two stages are achieved in a 3-D manner, with closed-loop motion control resolution of 1.5, 1.3, and 4.2 nm for X , Y , and Z axes, respectively. The proposed method provides an effective approach for ultrahigh precision motion control across different stages, which is believed to be significant for nanoscale manipulation related applications.