Nonlinear Sensor Research Articles

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error (MAPE) of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

Fusion of Color Correction and HSV Segmentation Techniques for Automated Segmentation of Acute Lymphoblastic Leukemia.

This article presents an enhanced segmentation methodology for the accurate detection of acute lymphoblastic leukemia (ALL) in blood smear images. The proposed approach integrates color correction techniques with HSV color space segmentation to improve white blood cell analysis. Our method addresses common challenges in microscopic image processing, including sensor nonlinearity, uneven illumination, and color distortions. The key objectives of this study are to develop a robust preprocessing pipeline that normalizes blood smear images for consistent analysis, implement an HSV-based segmentation technique optimized for leukocyte detection, and validate the method's effectiveness across various ALL subtypes using clinical samples. The proposed technique was evaluated using real-world blood smear samples from ALL patients. Quantitative analysis demonstrates significant improvements in segmentation accuracy compared to traditional methods. Our approach shows strong capability in reliably detecting and segmenting ALL subtypes, offering the potential for enhanced diagnostic support in clinical settings.

Fixed-Time Fault-Tolerant Adaptive Neural Network Control for a Twin-Rotor UAV System with Sensor Faults and Disturbances

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller.

Good results from sensor data: Performance of machine learning algorithms for regression problems in chemical sensors

Accurately predicting unseen data, instead of mere memorization of training examples, is a critical goal of machine learning. This generalization is particularly important in the field of chemical sensors, where the ability to accurately predict the chemical properties or concentration levels of unknown samples is crucial. The paper presents a comprehensive yet accessible introduction to various machine learning concepts, highlighting the importance of model interpretability and generalization in ensuring reliable and accurate results in this context. Nonlinear sensor array data are utilized to introduce key concepts (e.g., bias-variance tradeoff) and techniques (linear models, partial least squares regression, support vector machines, k-nearest neighbors, decision trees, ensemble methods, automated machine learning, symbolic regression, and artificial neural networks), providing a solid foundation to make informed decisions when selecting machine learning techniques for sensor-specific regression applications. The results clearly indicate a number of conclusions. First, overparameterized deep feedforward neural networks show great accuracy and generalization when trained on a sufficiently large dataset. Second, symbolic regression models proved to be more accurate than deep feedforward neural networks and classical machine learning techniques on smaller datasets. Third, the performance of various machine learning models was dataset-dependent, showing the importance of comparative studies to determine the most suitable approach. It is clear that the optimal model cannot be known a priori. This paper aims to provide a starting point for investigations on the performance of different machine learning techniques in chemical sensor applications.

High sensitivity, thermal noise-driven aluminum-based resonant MEMS humidity sensor

The integration of connected devices and the Internet of Things in the era of Industry 4.0 has led to the transformation of various sectors. Sensors, particularly for humidity measurement, play a pivotal role in applications such as respiration and wound monitoring, human–machine interfaces, fuel cells, and circuit failure detection. While conventional humidity sensors dominate the market, gravimetric-based sensing offers promise but faces challenges due to complex fabrication, high cost, and nonlinearity. This study explores gravimetric platforms for humidity sensing and proposes linear and nonlinear schemes to detect humidity. The linear sensors exhibit consistent frequency shifts with humidity changes, while the nonlinear sensors introduce a turn-around point where nonlinear effects counteract mass-loading. We address this limitation by splitting the dynamic range into low and high ranges on either side of this point, thereby creating one-to-one relationships. Both sensor types demonstrate high sensitivity (from 39068 to −23568 ppm/%RH for nonlinear and 5809 ppm/%RH for linear) and repeatability, emphasizing their suitability for real-time humidity monitoring. Moreover, the thermal noise-driven resonators underlying the sensors do not require external input, thereby enhancing their suitability for low-power applications. These findings provide a foundation for innovative real-time sensing applications and offer insights into optimizing sensitivity and stability in humidity sensing and other applications.

Nonlinear dynamic transfer partial least squares for domain adaptive regression

Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets.

High-responsive nonlinear sensor by tracking Hamiltonian hopping point

High-responsive nonlinear sensor by tracking Hamiltonian hopping point

Diffusion LMS algorithm in the presence of second order nonlinearities with theoretical bounds

In this paper, we propose an algorithm for the estimation and compensation of second-order nonlinearity in Wireless Sensor Networks (WSNs) within a distributed estimation framework. First, we investigate the impact of second-order nonlinearity on the performance of the Diffusion Least Mean Square (DLMS) algorithm and derive an upper bound for the l2-norm of the error caused by nonlinearity. Next, we suggest a distributed algorithm incorporating additional nonlinearity estimation and compensation units. Furthermore, considering second-order nonlinearity, we calculate the Cramer-Rao Bound for estimating both the unknown vector and the nonlinearity coefficient vector, with the Fisher information matrix derived in a closed-form formula. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing the performance of distributed estimation in the presence of nonlinear sensors in a WSN.

Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output disturbance. The imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead zone. Because of the presence of output dead zone and the additive disturbance, the states cannot be expected to driven into an arbitrarily small neighborhood of the origin. To solve the global practical stabilization problem, we proposes a low gain-based linear dynamic output feedback law, under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set, both in finite time. Simulation results illustrate the effectiveness of our proposed control method.

Obstacle shape determination by mobile robot sensor system using GP2Y0A (Sharp) type IR distance sensors

PURPOSE. When organizing a mobile robot (MR) movement in a non-deterministic environment, the SLAM problem arises, which includes the detection of an obstacle presence by the MR sensor system, the distance to the obstacle and its shape. To solve this problem, an infrared (IR) analog distance sensor is often used, the information flow from which is relatively small and can be processed in real time using low-performance microcontrollers. However, such a sensor can only detect an obstacle and determine а distance to a certain point on its surface. The goal is to develop a method for determining both а distance to an obstacle and its shape. When setting up experiments on the use of an analog sensor of the GP2Y0A (SHARP) type, a problem was revealed associated with the occurrence of not only fluctuation noise in а data communication channel, but also artifacts – anomalous signal values appear with random periodicity. It is necessary to determine the source of such interference, propose a method for estimating its parameters and a way to minimize its influence. METHODS. To determine the shape of an obstacle, a differential method is proposed based on the use of several readings of a scanning IR distance sensor. As an indicator of the “noisiness” of the channel, it is proposed to use the number of sensor signal values that exceed the average signal value by 1σ, 2σ, 3σ, 4σ and 5σ. The use of standard methods for filtering abnormal values of a sensor signal leads to significant delays in a response of the MR control system. This is unacceptable, because at the executive level of a control system it is required to provide a "hard" real-time mode. RESULTS. The article presents the results of experiments showing the conditions for applying the differential method for determining a shape of an obstacle, the source of anomalous signal values is identified and a method for minimizing them is proposed. A method for increasing the practical use range of a nonlinear IR sensor conversion function is also proposed. CONCLUSION. The number and magnitude of abnormal values depend on a communication channel length. When using analog sensors, it is necessary to convert an output signal into digital form using analog-to-digital converters (ADCs) in an integrated design, structurally bringing the ADC as close as possible to the signal source.

Design and Non-Linearity Optimization of a Vertical Brushless Electric Power Steering Angle Sensor.

This paper presents the design and the non-linearity optimization of a new vertical non-contact angle sensor based on the electromagnetic induction principle. The proposed sensor consists of a stator part (with one solenoidal excitation coil and three sinusoidal receiver coils) and a rotor part (with six rectangular metal sheets). The receiver coil was designed based on the differential principle, which eliminates the effect of the excitation coil on the induced voltage of the receiver coil, and essentially decouples the excitation field from the eddy current field. Moreover, the induced voltages in the three receiver coils are three-phase sinusoidal signals with a phase difference of 10°, which are linearized by CLARK transformation. To minimize the sensor non-linearity, the Plackett-Burman technique was used, which identified the stator radius and the rotor blade thickness as the key factors affecting the sensor linearity. Then, the particle swarm algorithm with decreasing inertia weights was utilized to optimize the sensor linearity. A sensor prototype was made and tested in the laboratory, where the experimental results showed that the sensor non-linearity was only 0.648% and 0.645% in the clockwise and counterclockwise directions, respectively. Notably, the non-linearity of the sensor was less than -0.696% at different speeds.

Design and evaluation of a novel dual-channel complementary potentiometer for rotation measurement

The main objective of this work is to develop a novel Dual-Channel Complementary (DCC) potentiometer to enhance the measurement accuracy of the conventional rotational potentiometer. Its principle is utilizing the complementary characteristic of the signals, which exist a specific phase offset, from different channels to reduce the effect of their inherent nonlinearity on measurement accuracy, and increase the measurement accuracy. A data-driven multi-channel information fusion method has been proposed to fuse the signals of the Dual-Channel Complementary (DCC) potentiometer, which could achieve data fusion and sensor nonlinearity calibration in one go. Prototype has been developed to evaluate the efficiency of the proposed DCC principle and information fusion method. Experimental results reveal that compared to the conventional rotational potentiometer, the RMS value of the measurement error of the DCC potentiometer could achieve a reduction of about 99.5%. The overall measurement performance of the DCC potentiometer could be significantly enhanced.

Low discrepancy on non-linear sensor deployment in a time-critical linear IIoT network

Linear networks are characterized by their long and narrow topology, which transmits data in a linear manner. For time-critical linear IIoT networks, stable connectivity is paramount. A good deployment strategy ensures stable connectivity in the network and maximize the lifetime and coverage of the network. In order to achieve this goal, deployment strategies should address tunnelling effects in linear networks. The proposed method tackles three major issues: 1. minimizing tunnelling effects using non-linear sensor deployment method, 2. maximizing network coverage by introducing a modified Latin Hypercube Sequence (LHS), and 3. Minimizing data interference in the multichannel TSCH network using a neighbour count-based sensor node deployment technique. The simulation validates the efficacy of the suggested approach in prolonging the network’s lifespan by mitigating the tunnelling effect near the sink. Moreover, it optimally utilizes over 70% of the mean energy within the network system. This is a significant improvement over previous algorithms which often failed to even utilizing 40% of the energy, and many a times much lesser. In terms of network coverage, it surpasses other algorithms. Consequently, the proposed method delivers economic advantages by extending the network’s longevity, achieved through a higher number of nodes concentrated around the sink, and significantly reduces the tunnelling effect in the network.

A temperature control method of hot-bar soldering based on extended Kalman filter

PurposeThis study aims to introduce a novel technique for nonlinear sensor time constant estimation and sensor dynamic compensation in hot-bar soldering using an extended Kalman filter (EKF) to estimate the temperature of the thermocouple.Design/methodology/approachTemperature optimal control is combined with a closed-loop proportional integral differential (PID) control method based on an EKF. Different control methods for measuring the temperature of the thermode in terms of temperature control, error and antidisturbance are studied. A soldering process in a semi-industrial environment is performed. The proposed control method was applied to the soldering of flexible printed circuits and circuit boards. An infrared camera was used to measure the top-surface temperature.FindingsThe proposed method can not only estimate the soldering temperature but also eliminate the noise of the system. The performance of this methodology was exemplary, characterized by rapid convergence and negligible error margins. Compared with the conventional control, the temperature variability of the proposed control is significantly attenuated.Originality/valueAn EKF was designed to estimate the temperature of the thermocouple during hot-bar soldering. Using the EKF and PID controller, the nonlinear properties of the system could be effectively overcome and the effects of disturbances and system noise could be decreased. The proposed method significantly enhanced the temperature control performance of hot-bar soldering, effectively suppressing overshoot and shortening the adjustment time, thereby achieving precise temperature control of the controlled object.

Adaptive multipole models of optically pumped magnetometer data.

Multipole expansions have been used extensively in the Magnetoencephalography (MEG) literature for mitigating environmental interference and modelling brain signal. However, their application to Optically Pumped Magnetometer (OPM) data is challenging due to the wide variety of existing OPM sensor and array designs. We therefore explore how such multipole models can be adapted to provide stable models of brain signal and interference across OPM systems. Firstly, we demonstrate how prolate spheroidal (rather than spherical) harmonics can provide a compact representation of brain signal when sampling on the scalp surface with as few as 100 channels. We then introduce a type of orthogonal projection incorporating this basis set. The Adaptive Multipole Models (AMM), which provides robust interference rejection across systems, even in the presence of spatially structured nonlinearity errors (shielding factor is the reciprocal of the maximum fractional nonlinearity error). Furthermore, this projection is always stable, as it is an orthogonal projection, and will only ever decrease the white noise in the data. However, for array designs that are suboptimal for spatially separating brain signal and interference, this method can remove brain signal components. We contrast these properties with the more typically used multipole expansion, Signal Space Separation (SSS), which never reduces brain signal amplitude but is less robust to the effect of sensor nonlinearity errors on interference rejection and can increase noise in the data if the system is sub-optimally designed (as it is an oblique projection). We conclude with an empirical example utilizing AMM to maximize signal to noise ratio (SNR) for the stimulus locked neuronal response to a flickering visual checkerboard in a 128-channel OPM system and demonstrate up to 40 dB software shielding in real data.

Output Feedback for Nonlinear Systems With Nonlinear and Discontinuous Inputs

The paper aims to design a high-gain observer for nonlinear systems with nonlinear and discontinuous sensor signal. The ultimate boundedness and exponential stability of the estimation error of high-gain observer are presented. The performance recovery property of output feedback control using high-gain observer is demonstrated. Simulation examples are illustrated to validate the proposed results of this paper.

“Fluid nonlinear coefficient sensor” designed on 2D photonic crystal

“Fluid nonlinear coefficient sensor” designed on 2D photonic crystal

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Sensor data is an integral component of internet of things (IoT) and edge computing environments and initiatives. In IoT, almost any entity imaginable can be outfitted with a unique identifier and the capacity to transfer data over a network. The estimate problem was formulated as a min-max problem subject to system dynamics and limitations on states and disturbances within the moving horizon strategy framework. In this paper, a novel Sand Cat Swarm Optimization Based MHEF for Nonlinear LTI IOT Sensor Data Enhancement (SCSO-MHEF) is proposed. In the proposed method the MHEF is optimized using Sand Cat Swarm Optimization to enhance sensor data stability tuned by initial parameters. Simulation experiments were conducted on various and unique scenarios in various orders LTI system with IOT sensor data in order to validate the suggested approach. This method can be used to analyze systems with dynamically changing systems. The proposed SCSO-MHEF technique overall accuracy of 84.5%, 87.3 %, and 99.5 % better than Kalman Filter (KF), EKF and Moving Horizon Filter (MHEF) respectively.

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Sensor data is an integral component of internet of things (IoT) and edge computing environments and initiatives. In IoT, almost any entity imaginable can be outfitted with a unique identifier and the capacity to transfer data over a network. The estimate problem was formulated as a min-max problem subject to system dynamics and limitations on states and disturbances within the moving horizon strategy framework. In this paper, a novel Sand Cat Swarm Optimization Based MHEF for Nonlinear LTI IOT Sensor Data Enhancement (SCSO-MHEF) is proposed. In the proposed method the MHEF is optimized using Sand Cat Swarm Optimization to enhance sensor data stability tuned by initial parameters. Simulation experiments were conducted on various and unique scenarios in various orders LTI system with IOT sensor data in order to validate the suggested approach. This method can be used to analyze systems with dynamically changing systems. The proposed SCSO-MHEF technique overall accuracy of 84.5%, 87.3 %, and 99.5 % better than Kalman Filter (KF), EKF and Moving Horizon Filter (MHEF) respectively.