Nonlinear Sensor Research Articles

Control Method of Lower Limb Rehabilitation Robot Based on Nonlinear Time Series Prediction Model and Sensor Technology

Control Method of Lower Limb Rehabilitation Robot Based on Nonlinear Time Series Prediction Model and Sensor Technology

Features of the formation of nickel-containing nanoparticles on glassy carbon electrodes using electrodeposition from solutions of sodium pectate complexes

Problem statement. Currently, many leading companies in the field of electronics, radio engineering, and aerotechnics are concerned about the depletion of non-renewable resources such as platinum, gold, silver, indium and even the more common lithium, nickel and copper, which they actively use in their products. The design and synthesis of nanostructures with controlled morphology has attracted the attention of many researchers and engineers, since it is much more efficient to deposit a small amount of metal on the surface being used than to create a part entirely from this metal. There are several main methods for producing nanostructures on a surface: chemical deposition, electron beam lithography, pulsed laser deposition, electrochemical deposition and other methods. Electrochemical deposition is worth highlighting among all methods, because it makes it possible to obtain such surfaces relatively cheaply and on a large scale and control their morphology by changing deposition conditions, such as time, potential, solution pH, etc. Purpose of the study. Obtain nickel-containing nanoparticles on glassy carbon by electrodeposition from an aqueous solution of biopolymer complexes of sodium pectate with divalent nickel; to determine the influence of electrodeposition conditions, namely, the duration of deposition and the content of Ni(II) ions in sodium petectate complexes, on the morphology of the resulting surface. Results. Studies have been carried out of the influence of electrodeposition conditions on the morphology of the resulting glassy carbon electrode surface. It was found that biopolymer ligands act as a stabilizing agent for the formation of nickel-containing nanoparticles instead of a nickel-containing layer. The standard sizes of the resulting nanoparticles are in the range of 20 – 90 nm. The nickel content in the complexes, as well as the deposition time, proportionally affects the amount of deposited nanoparticles, but has little effect on their sizes. Practical significance. The results obtained make it possible to use them for controlled electrodeposition of nickel-containing nanoparticles on conducting surfaces in the development of nonlinear optical devices, LEDs, diodes, transistors, logic gates, sensors and other electronic devices.

Hydrogen-bond-modulated negative linear compressibility in a V-shaped molecular crystal.

A material with the "hidden" negative linear compressibility (NLC) will expand along a specific crystal direction upon uniformly compression to a critical pressure; such materials are thought to be promising candidates for non-linear actuators, switches and sensors. Herein, we use density functional theory (DFT) calculations to uncover the hidden NLC in a V-shaped molecular crystal, bis(5-amino-1,2,4-triazol-3-yl)methane (BATZM). The calculations indicate that the crystal is normally compressed over the pressure range of 0-3 GPa while it expands along the b-axis when the external hydrostatic pressure exceeds 3 GPa. The compressive behavior of the BATZM crystal is modulated by inter-molecular hydrogen bonds, which act as highly compressible springs at low pressures but robust struts at high pressures. Hence, the crystal prefers to compress the hydrogen bonds coupled with PLC at first and flatten the molecules, coupled with later NLC to resist the increasing external pressure. The compressive behavior of BATZM provides a strategy to design more hidden NLC materials via the rational use of the hydrogen bonds.

Nonlinear Kinetic Inductance Sensor

The concept of nonlinear kinetic inductance sensor (NKIS) of electromagnetic radiation is proposed. The idea is based on divergency of kinetic inductanceLk∼dq/dI(ℏqis a momentum of superconducting electrons,Iis a supercurrent) of hybrid superconductor/normal metal (SN) bridge at currentI*Idep(Idepis a depairing current of the hybrid) and temperatureT* much smaller than critical temperatureTc. It makes possible to have large change of phase differenceδϕalong SN bridge in current biased regime atI≃I*even for small electron temperature increase. Appearance ofδϕis accompanied by the change of the current and magnetic flux through the coupled superconducting ring which could be measured with help of superconducting quantum interference device (SQUID). In some respect proposed sensor may be considered as a superconducting counterpart of transition edge sensor (TES) those work is based on large derivativedR/dT(Ris a resistance) nearTc. Because atI≃I*SN bridge is in gapless regime there is no low boundary for frequency of detected electromagnetic radiation. Our calculations show that such a sensor can operate in single photon regime and detect single photons with frequencyν≳10 GHz. We argue that the nontrivial dependenceI(q)of SN bridge could be also used in detectors of continuous electromagnetic radiation, current and magnetic field sensors.

Dynamic nonlinear soft sensor modelling method using linear slow feature analysis and least squares support vector regression for batch processes

AbstractData‐driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low‐volume and high value‐added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data‐driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.

Gas-phase odor mixture quantification based on relative comparison method using multiple olfactory receptors

The gas-phase odor biosensors exhibit outstanding sensitivity and selectivity towards target ligands. Current odor detections, however, were only operated under one or multiple separated olfactory receptors (ORs) stimulated by a single component odor. Meanwhile, the nonlinear characteristic, drift, and aging problems in biosensors were the main barriers for better quantification performance. Here, we proposed a gas-phase odor biosensor for odor mixture quantification based on relative comparison method using cell expressing ORs, the liquid thickness control and liquid exchange maintained repeatable OR responses. We repeatedly compared the known and its adjacent unknown odor response thus mitigating the biosensor drift interference and sensor nonlinearity characteristic. We first conducted the odor quantification of one odor component in a mixture of five odors. The existence of other odors did not influence the quantification of Or56a towards geosmin component in the mixture. Then, two types of OR were utilized for quantifying odor mixture with or without adding the OR label information to the individual cells. With-label quantification was more precise and efficient than without-label one. Lastly, we simultaneously expressed two ORs on one cell line for wider detection range and quantified the mixture even if OR has cross sensitivity. We believe the odor mixture quantification methods established in this study will extend the application scenarios of odor biosensors especially in rapid on-site odor detection.

Optimization of linearity of piezoresistive pressure sensor based on pade approximation

To optimize nonlinearity and temperature drift of silicon piezoresistive pressure sensor, novel calibration algorithm based on Pade approximation is adopted in the paper. The output data of the pressure sensor is processed by the novel algorithm to remove the influences of the cross-sensitivity and the unexpected jump points. High accuracy up to 0.01% FS can be achieved based on the algorithm. Compared with the other published algorithms, the proposed algorithm shows the merits of high accuracy, less memory consumption, and low cost. The algorithm can have potential application in the field of the intelligent sensor with the requirement of high output accuracy.

2D quasi-layered material with domino structure

Interlayer coupling strength dichotomizes two-dimensional (2D) materials into layered and non-layered types. Traditionally, they can be regarded as atomic layers intrinsically linked via van der Waals (vdW) forces or covalent bonds, oriented orthogonally to their growth plane. In our work, we report a material system that differentiates from layered and non-layered materials, termed quasi-layered domino-structured (QLDS) materials, effectively bridging the gap between these two typical categories. Considering the skewed structure, the force orthogonal to the 2D QLDS-GaTe growth plane constitutes a synergistic blend of vdW forces and covalent bonds, with neither of them being perpendicular to the 2D growth plane. This unique amalgamation results in a force that surpasses that in layered materials, yet is weaker than that in non-layered materials. Therefore, the lattice constant contraction along this unique orientation can be as much as 7.7%, tantalizingly close to the theoretical prediction of 10.8%. Meanwhile, this feature endows remarkable anisotropy, second harmonic generation enhancement with a staggering susceptibility of 394.3 pm V−1. These findings endow further applications arranged in nonlinear optics, sensors, and catalysis.

High-aspect-ratio dielectric pillar with nanocavity backed by metal substrate in the infrared range.

We investigated absorption and field enhancements of shallow nanocavities on top of high-aspect-ratio dielectric pillars in the infrared range. The structure includes a high-aspect-ratio nanopillar array of high refractive index, with nano-cavities on top of the pillars, and a metal plane at the bottom. The enhancement factor of electric field intensity reaches 3180 in the nanocavities and peak absorption reaches 99%. We also investigated the finite-size effect of the presented structure to simulate real experiments. Due to its narrow absorption bandwidth 3.5 nm, it can work as a refractive index sensor with sensitivity 297.5 nm/RIU and figure of merit 85. This paves the way to directly control light field at the nanoscales in the infrared light range. The investigated nanostructure will find applications in multifunctional photonics devices such as chips for culturing cells, refractive index sensors, biosensors of single molecule detection and nonlinear sensors.

A Variance-Constrained Method to Protocol-Based $$H_{\infty }$$ State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity

A Variance-Constrained Method to Protocol-Based $$H_{\infty }$$ State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity

Assessing phase reconstruction accuracy for different nonlinear curvature wavefront sensor configurations

Assessing phase reconstruction accuracy for different nonlinear curvature wavefront sensor configurations

Nonlinear vibrational analysis and linearization error investigation in size-dependent resonant pressure microsensors

Dynamic and vibrational behavior of nonlinear resonant pressure micro sensors is studied in this paper using a proposed dynamic model. The basic part of the device is a microplate deflects due to external pressure in presence of electrostatic actuation. The frequency changes because of the microplate deformation is used to estimate the external pressure. For modeling of the micro sensor, the nonlinear governing equation of size dependent microplate in polar coordinate system and based on the modified couple stress theory is presented. Assuming specified number of Taylor expansion terms for electrostatic force, the linear and nonlinear ODE is obtained and nonlinear equation is solved both with multiple time scales method (MTS) and finite difference numerical method. Results indicate that considering finite terms of Taylor expansion (1 term for linear and three terms for MTS method) is the source of error in estimation of pressure or system stability. Using sufficient terms of Taylor Expansion, the error in linear and MTS solutions is vanished. Therefore, the limited values of microplate vibrational amplitude is extracted to increase the accuracy of the sensor in linear action. The effect of size dependency, applied voltage on nonlinear behavior and linearization error is also studied.

Measurement and Characterization of Rotational Errors of Aerostatic Bearings in Subnanometer Accuracy.

Measuring the running accuracy of aerostatic bearings is challenging because of the high-precision requirements in rotational motion. This paper presents an ultra-high precision measurement method for aerostatic bearings using atomic force microscopy (AFM) as the displacement sensor. The Donaldson reversal method was used to separate the artifact form errors from the measurement results. A measurement system was developed with the integration of an AFM module. The effects of sensor nonlinearity, environmental noise, and structural vibration on the measurement results were effectively suppressed in the system. The experimental results show that the measurement achieves up to subnanometer accuracy.

Improved spatial-temporal sound perception using active MEMS

Current microphone technology is based on a transducer concept that is the same for all end user groups, namely, passive, linear. While most of the technology-related challenges have been overcome by sophisticated post-processing algorithms, some of the current limitations of sound detection technology, especially as recognized by groups dealing with hearing impairments and losses, should be addressed at the transducer stage. In this work we present the dynamic behaviour and performance of an active, nonlinear MEMS sensor subject to acoustic stimuli and noise. Our work includes the presentation of a reduced-order model of the composite MEMS structure, relevant dynamic analyses including bifurcation and stability diagrams, as well as experimental investigations. Simulations and experimental results demonstrate cochlear-like amplification properties, meaning that soft signals are amplified significantly different from louder signals. Signals which initially are imbedded in a noise floor can be amplified without also amplifying the noise and thus providing another way of detecting sound patterns.

Passivity-Based State Estimation of Markov Jump Singularly Perturbed Neural Networks Subject to Sensor Nonlinearity and Partially Known Transition Rates

Passivity-Based State Estimation of Markov Jump Singularly Perturbed Neural Networks Subject to Sensor Nonlinearity and Partially Known Transition Rates

Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology – dubbed LC 2.0 – with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.

Automatic Landing System Design for Unmanned Fixed-Wing Vehicles via Multivariable Active Disturbance Rejection Control

Landing control of unmanned aerial vehicles (UAVs) is challenging because of the strong nonlinear dynamics, multivariable, model uncertainties, wind variations, and sensor noise. Motivated by this fact, this paper investigates an automatic landing system (ALS) that includes trajectory generation and guidance law for the first flight test of a turbine-based combined cycle technology demonstrator. Specifically, the control scheme increases the original model’s order to generate a reasonable monotone-decreasing throttle reference flare trajectory by the pseudospectral method. Subsequently, the guidance law based on innovative multivariable active disturbance rejection control is designed to robustly track the reference altitude and velocity simultaneously with high accuracy. The multivariable extended state observer (ESO) incorporated decoupling algorithm enhances the estimation capability and accuracy of potential problem in cross-coupling dynamics compared to the traditional ESO. It is proven that the closed-loop error dynamic has bounded-input bounded-output stability and an explicit upper bound is given. Numerical simulation verifies that the presented approach has better robustness and higher tracking accuracy for external disturbances and parametric uncertainties than the existing benchmark autolanding controller. Finally, flight tests show that the proposed ALS can land the vehicle effectively and safely under severe wind conditions.

A missing manufacturing process data imputation framework for nonlinear dynamic soft sensor modeling and its application

Data-driven soft sensors have been widely used in manufacturing processes for product quality prediction. However, in engineering practice, traditional linear soft sensors are considered to be insufficient when the manufacturing processes present high dimensionality as well as strong nonlinearity and dynamics. Moreover, due to the economic and technical limitations, missing data problem is widespread, which affects the generalization and accuracy of soft sensors. To overcome these issues, a missing manufacturing process data imputation framework is presented for nonlinear dynamic soft sensor modeling with the purpose of quality prediction. Specifically, the generate adversarial imputation network is introduced for data imputation, and the loss functions of discriminator and generator are reasonably designed. Subsequently, a mix gated unit combining bidirectional gated recurrent unit with bidirectional minimal gated unit is proposed for nonlinear dynamic soft sensor modeling aiming at improving the performance of quality prediction. Finally, the efficiency and flexibility of the proposed framework are demonstrated through a representative manufacturing process, the hot rolling process, in which the percentages of missing data are simulated from 20% to 80%. The ideal performance indicators, root mean square error and mean absolute error, have been obtained. It can be shown that the proposed framework can provide better prediction accuracy than the competitive methods in each scenario.

Broad-bandwidth and accurate optical vibration sensing by using FDM Φ-OTDR with linear regression analysis of multi-frequency phase responses.

We demonstrate Φ-OTDR distributed acoustic sensing (DAS) that realizes both a broad bandwidth for the vibration frequency and wide dynamic range for the vibration amplitude based on optical frequency-division-multiplexing (FDM). We enhance the sampling rate of DAS by using FDM while suppressing waveform distortion in time domain (spurious components in spectral domain) caused by sensor nonlinearity inherent in Φ-OTDR, thus improving dynamic range, with linear regression analysis of multi-frequency phase responses. The proposed method compares the phase offsets and responses of each frequency to those of a common reference frequency and uses the information to calibrate each of the different responses. We clarify the physical origin of the problem and the validity of the proposed method in both simulations and experiments. Experimental results show an improvement in dynamic range by above 8 dB on average for vibration waveforms with nɛ-order amplitudes and kHz-order frequencies over 10-km single-mode fiber.

Amplitude modulated feed-forward thermal drift compensation for linear and nonlinear current sensors

The linear and nonlinear temperature responses restrict the application of Hall current sensors operating in thermal power plants and industries. The rise in temperature of electron device-based measurement causes a significant error, leading to undesirable consequences for plant operation and relay coordination. This paper investigates four Hall current sensor types with linear and nonlinear temperature responses. The Hall current sensor, which receives the magnetic excitation from the permanent magnet, exhibits a linear temperature response, and the wire-wound design exhibits a nonlinear temperature response in the temperature range of 306–376 K. The solution scheme with amplitude modulation and thermal sensor integration with interdigitated electrode design having graphene and ZnO–KMnO4 compounds as the dielectric is also proposed. The use of amplitude modulation achieves input frequency immunity with a 0.03% K−1 improvement in the temperature response of the capacitive thermal sensor. Experimental observations confirm the validity of the thermal drift compensation scheme with a 20%–99% reduction of thermal drift error with a suitable choice of a thermal sensor.