Sensor Parameters Research Articles

Comment on the paper “Analyzing the effect of dynamic properties of materials and operating medium on sensor parameters to increase the performance of diaphragmbased static/dynamic pressure sensors, Timuçin Emre Tabaru, Şekip Esat Hayber, Journal of Computational Electronics, 2021, 20:643–657”

Some errors exist in the above paper.

A Novel-Structure LC Resonant Passive Wireless Sensor for NO2 Sensing

This work presents an LC resonant passive wireless gas sensor with a novel structure designed to mitigate the negative impact of substrate. The LC sensor antenna in the new structure, and the reader antenna, were designed and optimized utilizing HFSS software to improve the transfer efficiency. The superiority of the designed structure compared with general examples is highlighted and verified. The change in the substrate capacitance essentially makes no interference with the parameters of the LC sensor to be measured. The sensor for the new structure was prepared by combining etching and sputtering methods. The ZnO nanowires (NWs) were characterized to confirm their high purity and wurtzite crystal structure. The LC gas sensors demonstrated excellent wireless sensing performance, including a low detection limit of 0.5 ppm NO2, high response of 1.051 and outstanding stability at 180 °C. The newly developed sensor structure not only prevented interference from the substrate during gas sensing testing, but also expanded the choice of sensor substrates, playing a critical role in the development of sensors based on the LC resonance principle.

Digital mapping of soil weathering using field geophysical sensor data coupled with covariates and machine learning

Understanding the weathering intensities can provide answers for environmental issues, soil, and geoscience studies. Recently, geophysical approaches and machine learning techniques have been applied in soil science to access weathering. This study aimed to model weathering intensity using combined data from geophysical sensors, satellite images, and morphometry associated to machine learning algorithms. We also we evaluated the efficiency of nested-leave one out cross-validation applicability in a small geodata set evaluated the importance of covariates and the resulting weathering intensity map in relation to pedogeomorphological processes. Our study focused on a 184-ha area in southwest Brazil, where we conducted soil analysis at 71 sites. We applied principal component analysis and determined the ideal number of clusters to determine the classes of weathering intensity. We used six geophysical sensor parameters, including equivalent uranium, equivalent thorium, potassium 40, magnetic susceptibility, and soil apparent electrical conductivity, along with the weathering index to create the clusters. Then, four machine learning algorithms were used to infer different weathering intensities in soils formed from the same parent materials. To validate our results, we used the nested-leave-one-out-cross-validation (“nested-LOOCV”) method, which is suitable for small datasets. Our findings showed that the random forest model performed the best with three clusters as the ideal number. We also found that the geophysical data, clusterization, and machine learning algorithm contributed significantly to identifying different weathering intensities. The results indicated that weathering operated at different intensities on both the diabase/Rhodic-Nitisols and the siltite/metasiltite Rhodic and Xanthic Lixisols areas, with the highest intensities occurring in the west Xanthic Lixisols and the lowest intensities occurring in the Rhodic and Lixisols in the east area. The siltite/metamorphosed siltite and Lixisols areas presented moderate weathering rates. We found that the all-geophysical variables used were related and affected by weathering intensity, which contributed to the modeling and clusterization processes.

High-integrated micro permanent magnet linear actuator positioning system

A tunnel magnetic resistance (TMR) sensor is a magnetic detection sensor with low power consumption and high sensitivity. The TMR sensor has promising applications in the position detection of micro permanent magnet linear actuators since the surrounding magnetic field of the micro actuator in this paper will be changed by its movement. Firstly, according to the air-gap magnetic field distribution characteristics of the micro permanent magnet linear actuator and the detection principle of the TMR sensor, the integrated installation parameters of TMR sensors were determined. Then, with the help of TMR technology, the backpropagation neural network (BPNN) algorithm and improved BPNN using particle swarm optimization (PSO) algorithm were used to study the position identification strategy of the slider, and the algorithm strategy with the minimum error was selected. Finally, the experimental results show that the slider position identification strategy based on the PSO-BPNN algorithm can achieve position tracking error within 0.1 mm under the given step position tracking and sinusoidal position tracking, and the movements can be repeated well.

Thickness measurements of a Cr coating deposited on Zr-Nb alloy plates using an ECT pancake sensor

Zr-Nb alloy have been widely used as fuel rods in nuclear power plants. However, from the Fukushima nuclear accident, the weakness of the rod was revealed under harsh conditions, and research on the safety of these types of rods was conducted after the disaster. The method of depositing chromium onto the existing Zr-Nb alloy fuel rods is being considered as a means by which to compensate for the weakness of Zr-Nb alloy rods because chromium is strong against oxidation at high temperatures and has high strength. In order to secure these advantages, it is important to maintain the Cr thickness of the rods and properly inspect the rods before and during their use in power generation. Eddy current testing is a typical means of evaluating the thickness of thin metals and detecting surface defects. Depending on the size and shape of the inspected object, various eddy current sensors can be applied. In particular, because pancake sensors can be manufactured in very small sizes, they can be used for inspections even in narrow spaces, such as a nuclear fuel assembly.In this study, an eddy current technique was developed to confirm the feasibility of Cr coating thickness evaluations. After determining the design parameters of the pancake sensor by means of a FEM simulation, a FPCB pancake sensor was manufactured and the optimal frequency was selected by measuring minute changes in the Cr-coating thickness using the developed sensor.

Jitter-Caused Clutter and Drift-Caused Clutter of Staring Infrared Sensor in Geostationary Orbit.

For staring infrared sensors in geostationary orbit, the clutter caused by the high-frequency jitter and low-frequency drift of the sensor line-of-sight (LOS) is the impact of background features, sensor parameters, LOS motion characteristics, and background suppression algorithms. In this paper, the spectra of LOS jitter caused by cryocoolers and momentum wheels are analyzed, and the time-related factors such as the jitter spectrum, the detector integration time, the frame period, and the temporal differencing background suppression algorithm are considered comprehensively; they are combined into a background-independent jitter-equivalent angle model. A jitter-caused clutter model in the form of multiplying the background radiation intensity gradient statistics by the jitter-equivalent angle is established. This model has good versatility and high efficiency and is suitable for the quantitative evaluation of clutter and the iterative optimization of sensor design. Based on satellite ground vibration experiments and on-orbit measured image sequences, the jitter-caused clutter and drift-caused clutter models are verified. The relative deviation between the model calculation and the actual measurement results is less than 20%.

Dynamic Calibration Method of Blast Pressure Pencil Probes Based on Adjustable Shock Tube

Dynamic calibration is one of the important ways to obtain the dynamic performance parameters of pressure sensors, and shock tube calibration is a common method for dynamic calibration of pressure sensors. To study the dynamic calibration method of the shock tube of the blast pressure pencil probes, such as free-field pressure sensors and dynamic pressure sensors, a convenient and adjustable PVC-U (unplasticized polyvinyl chloride) shock tube is developed. Taking the dynamic pressure sensor as the research object, the feasibility of dynamic calibration of shock tube based on CFD (Computational Fluid Dynamics) techniques is discussed. Finally, the dynamic calibration experiment based on shock tube is carried out, and the time-domain and frequency-domain signals are analyzed. The results are in good agreement with the theoretical analysis. The results show that the shock tube can effectively excite the natural frequency of the dynamic pressure sensor, and the developed shock tube can indeed achieve the dynamic calibration and dynamic experiments of pencil probes.

Real-Time Sensor Data Profile-Based Deep Learning Method Applied to Open Raceway Pond Microalgal Productivity Prediction.

Microalgal biotechnology holds the potential for renewable biofuels, bioproducts, and carbon capture applications due to unparalleled photosynthetic efficiency and diversity. Outdoor open raceway pond (ORP) cultivation enables utilization of sunlight and atmospheric carbon dioxide to drive microalgal biomass synthesis for production of bioproducts including biofuels; however, environmental conditions are highly dynamic and fluctuate both diurnally and seasonally, making ORP productivity prediction challenging without time-intensive physical measurements and location-specific calibrations. Here, for the first time, we present an image-based deep learning method for the prediction of ORP productivity. Our method is based on parameter profile plot images of sensor parameters, including pH, dissolved oxygen, temperature, photosynthetically active radiation, and total dissolved solids. These parameters can be remotely monitored without physical interaction with ORPs. We apply the model to data we generated during the Unified Field Studies of the Algae Testbed Public-Private-Partnership (ATP3 UFS), the largest publicly available ORP data set to date, which includes millions of sensor records and 598 productivities from 32 ORPs operated in 5 states in the United States. We demonstrate that this approach significantly outperforms an average value based traditional machine learning method (R2 = 0.77 ≫ R2 = 0.39) without considering bioprocess parameters (e.g., biomass density, hydraulic retention time, and nutrient concentrations). We then evaluate the sensitivity of image and monitoring data resolutions and input parameter variations. Our results demonstrate ORP productivity can be effectively predicted from remote monitoring data, providing an inexpensive tool for microalgal production and operational forecasting.

Cross-scene target detection based on feature adaptation and uncertainty-aware pseudo-label learning for high resolution SAR images

The characteristics of synthetic aperture radar (SAR) images are easily affected by factors such as sensor parameters, imaging scenes, etc., which may lead to data distributional discrepancies between the ideal training phase and the practical application phase, resulting in generalization performance degradation of deep learning based detectors in practice. In this paper, an accurate and fast domain adaptive detection method is proposed to address the performance degradation problem of cross-scene detection without requiring additional manual annotations. The cross-scene detection performance is gradually improved by confidence-induced unsupervised feature adaptation (CUFA) and uncertainty-aware adaptive pseudo-label learning (UAPL). In CUFA, image-level and instance-level feature adaptation are progressively implemented to bridge the distribution discrepancies between the source and the target domain in the latent feature space. Then, UAPL is performed to adaptively introduce accurate and stable supervisions of target domain according to the learning status of the detector, avoiding error accumulations and further improving the cross-scene target detection performance. The proposed cross-scene detection method is validated on airborne and spaceborne SAR image datasets of different imaging scenes. The experimental results show that by combining both CUFA and UAPL, compared with the second-best comparison method, the proposed method significantly improves the cross-scene target detection performance in SAR images (at least + 6.5% AP50 and F1 in miniSAR and FARAD cross-scene tasks, and at least + 7.5% AP50 and F1 in Gaofen-3 and Sentinel-1 cross-scene tasks), which facilitates application of the deep learning based detectors in the realistic scenarios.

Innovative hybrid analysis for underwater MEMS sensor imitating veterinary cilia optimum structure

Veterinary species cilia sense mechanical vibrations and movements in air as well as in water. They allow fish to locate predators and find prey. Cilium participates in detection of sound vibration phonons and determines underwater pathways. In this paper a hybrid software tool packages are used to study, analyze, and determine optimum dimensions of MEMS sensor imitating fish cilia.The availability of powerful Finite-element software such as COMSOL and the new software generations of computer make it possible to investigate the performance of different types of sensor structures and to propose new ones. MEMS Piezoresistive Vector Hydrophone (MPVH) is one of the recent generations of underwater vibration detection and surveillance devices. In this paper basic design structure of MPVH that is found in many research works is modeled and investigated from points of view of sensor parameters, namely sensor sensitivity, frequency response and linearity. A hybrid optimization technique is deduced to attain sensor's best element dimension and performance.

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provides more accurate predictions than recursive least squares (RLSs) and a single RNN. In order to reduce the influence of sensor parameter errors and prediction model errors, the adaptive threshold is mentioned as a method for analyzing prediction errors. In the meantime, the results of the threshold analysis were combined with the qualitative force analysis to determine the rudder system’s fault diagnosis and location. Experiments conducted at sea demonstrate the feasibility and effectiveness of the proposed method.

Modelling and parameter quantification of plasmonic sensor based on external sensing for analysis of body liquids

This work introduces a new plasmonic refractive index (RI) sensor to detect and analyze human body liquids. Human body liquids consist of several fluids present in the human body. These liquids are water, blood, urine, sweat, breast milk, saliva, semen, etc. A RI sensor based on the RI variation is presented in this work to analyze these human body liquids. The RI sensor performance is investigated using traditional sensor parameters like confinement loss (CL), amplitude sensitivity (AS), wavelength sensitivity (WS), sensor resolution (SR), full wave half maximum (FWHM) and figure of merit (FOM). Dual mode investigation is performed in this work for the sensor model. The sensor model has presented WS more than 12000nm/RIU for the x−pol. and y−pol. respectively. AS reported for both polarization modes is more than 10000RIU−1. The SR evaluated is in the order of 10−6. The FOM of the sensor model is 91.60RIU−1 and 87.54RIU−1 for x−pol. and y−pol. respectively. Thus the sensor model presented in this work has shown sensitive parameters for detecting human body liquids.

Highly Sensitive Hydrogen Sulfide Sensor Based on GaN/GaInN Heterostructure

Accurate detection of gases such as hydrogen sulfide in the exhaled human breath is of great interest for medical professionals as it can possibly help in the early detection of organ malfunction and other diseases. GaInN heterostructure sensors are sensitive to the changes in the surface potential caused by the adsorption of gas molecules. A quantum well (QW) placed close to the surface experiences a change in the quantum‐confined Stark effect and as a result shifts its photoluminescence signal. Several parameters of the GaInN sensors grown by metal organic vapor phase epitaxy are optimized such as the GaN cap layer, QW thickness, and doping concentration. Moreover, how various metal functionalization layers can improve its sensitivity and selectivity is investigated. Gold (Au) and Silver (Ag) shows sensitivity to hydrogen sulfide in the 10–100 parts per billion (ppb) range. Ammonia gas is also detected in the 5–10 range (ppm) with a sensor structure covered with a thin gold layer.

Microminiature Pressure Sensors Based on Diode Structures

The paper analyzes modern microminiature pressure sensors made on various diode structures, in particular on organic light-emitting diodes, field-effect transistors, photovoltaic elements and multi-circuit piezoresistive sensors. The possible areas of application of such sensors, their main advantages and disadvantages are shown. The study of 4 groups of samples of diode heterostructures based on CdS / ZnS / CuS / CdTe was carried out and the perspective of using pressure sensors based on these materials as an analogue of existing semiconductor devices was shown. On the basis of experimental studies with the application of pressure, twisting and illumination, it is substantiated that these structures are piezoelectric. The complete technological process of the step-by-step creation of these structures is presented. The obtained structures were analyzed: structural diagrams, current-voltage and piezoelectric characteristics in comparison with the characteristics of other piezoelectric materials are given. Possible areas of application of such structures are described. The provided design schemes and parameters of the obtained diode structures may be of interest to a wide range of specialists in the field of sensor technology and automation of various technological processes of microelectronic equipment manufacturing. It is shown that by changing the sensor manufacturing technologies and the concentration of chemical elements in the obtained films, it is possible to change the sensitivity of the sensor and the dynamic range of its operation, adapting the sensor parameters to the field of its application in the relevant measuring electronic equipment and pressure control systems.

Assessing Impedance Analyzer Data Quality by Fractional Order Calculus: A QCM Sensor Case Study

The paper presents the theoretical, simulation, and experimental results on the QCM sensor based on the Butterworth van Dyke (BVD) model with lumped reactive motional circuit elements of fractional order. The equation of the fractional order BVD model of the QCM sensor has been derived based on Caputo definitions and its behavior around the resonant frequencies has been simulated. The simulations confirm the ability of fractional order calculus to cover a wide range of behaviors beyond those found in experimental practice. The fractional order BVD model of the QCM sensor is considered from the perspective of impedance spectroscopy to give an idea of the advantages that fractional order calculus brings to its modeling. For the true values of the electrical parameters of the QCM sensor based on the standard BVD model, the experimental investigations confirm the equivalence of the measurements after the standard compensation of the virtual impedance analyzer (VIA) and the measurements without compensation by fitting with the fractional order BVD model. From an experimental point of view, using fractional order calculus brings a new dimension to impedance analyzer compensation procedures, as well as a new method for validating the compensation.

Recent Advances in Polymer Composites for Flexible Pressure Sensors.

Pressure sensors show significant potential applications in health monitoring, bio-sensing, electronic skin, and tactile perception. Consequently, tremendous research interest has been devoted to the development of high-performance pressure sensors. In this paper, recent progress on the polymer composite-based flexible pressure sensor is reviewed. The parameters of pressure sensors, including sensitivity, linear response range, detection limit, response speed, and reliability, are first introduced. Secondly, representative types of pressure sensors and relevant working principles are introduced and discussed. After that, the applications in human physiology monitoring, health monitoring, artificial skin, and self-powered smart system are listed and discussed in detail. Finally, the remaining challenges and outlook of polymer composite-based flexible sensors are summarized at the end of this review paper. This work should have some impact on the development of high-performance flexible pressure sensors.

Application of circuit analog optimization method in fast optimization of dynamically tunable terahertz metamaterial sensor

The simulation design of terahertz metamaterial sensors with dynamically tunable parameters typically relies on manual parameter tuning for structural optimization. However, this method is often prone to subjective factors and suffer from issues such as frequent reconstruction of simulations, high computational costs, long processing times, and suboptimal optimization results. In this paper, we propose a circuit analog optimization method (CAOM), which constructs equivalent RLC parameters to achieve a highly fitted terahertz transmission spectrum frequency obtained from CST full-wave numerical simulation. To validate the effectiveness of the proposed model, we use a typical periodic structure unit, a double-nested split ring resonator (DSRR) terahertz metamaterial sensor, as the simulation object. Both the inner and outer open resonant rings of the sensor are made of graphene, as a result, the opening size and Fermi level of the resonant rings are dynamically tunable. The results of the validation demonstrate that the adjustments of the sensor parameters can be effectively mapped by the changes of the equivalent RLC parameters. And the proposed equivalent circuit model has parameter substitutability in the simulation modeling of split ring resonator type sensors. The proposed equivalent circuit model exhibits parameter substitution in the simulation modeling of open resonant ring-type sensors. To achieve optimal sensing performance for the electromagnetically induced transparency (EIT)-like resonant peak (with a resonant frequency of f 2) of the sensor under constrained conditions, we introduce the genetic algorithm (GA) into the equivalent circuit model to enable fast optimization of the opening sizes of the inner and outer resonant rings, as well as the Fermi level of the sensor. Moreover, the accuracy of the optimization results is verified by CST simulations. Finally, the optimization results show that the optimal FOM of the EIT-like resonant peak within the given parameter range is 0.712, which is greater than that of any randomly combined parameters. This numerical result demonstrates the effectiveness of the proposed CAOM. The proposed model and optimization method have potentials to inspire further research in device design, performance optimization, theoretical modeling, etc.

A Wearable Pressure Sensor Based on Ultrathin Ionic Nanopaper for Wide-Range Human Signal Detection

Power consumption is a crucial parameter for wearable sensors to approach practical applications. As an ideal candidate for low consumption device, capacitive pressure sensor can make up for the defects of low capacitance and sensitivity by introducing the iontronic sensing mechanism, and can further improve the sensing performance through the construction of micro-nano structures at electrode-electrolyte interface. To explore a facile-effective microstructure construction method for promoting the mass manufacturing and cost reduction, in this study, an easily prepared ionic cellulose nanopaper (CNP) is served as dielectric layer, and sandwiched between two graphite paper electrodes. The combination of the CNP and graphite paper provides a typical microstructure interface, and the device is endowed with the characteristics of ultra-light and ultra-thin. The iontronic paper-based pressure (IPBP) sensor exhibits a high sensitivity of 67.5 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and an excellent linearity above 0.99 over a broad pressure range of 0-30 kPa, and a low detection limit of 0.5 Pa, together with good mechanical stability for over 3000 loading-unloading cycles. Finally, the applications of the IPBP sensor for monitoring of human motion and physiological signals such as joint bending, walking state, radial artery and breathing are demonstrated, which prove the great potential in wearable electronics, human health monitoring and human-computer interface.

Thermal conductivity measurement system for molecules-based compounds available in a wide temperature region

The construction of a thermal conductivity measurement system designed for tiny molecules-based compounds is reported. We introduce complementary usage of chip-type RuO2 thermometers and E-type thermocouples in the sample part by using thin (ϕ 13μm) constantan and chromel wires. Two pairs of the constantan and chromel wires are used as lead wires for the four-terminal measurement of the resistance of RuO2 thermometers in the low-temperature region below about 20 K. Also, in the higher temperature region above 10 K up to room temperature with the overlapping range of 10-20 K, they are used as thermocouples for detecting temperature differences from that of the heat sink. We also compare a kind of resolution parameter of several sensors as a function of temperature to discuss the rational reason to select suitable sensors depending on the temperature region. Using the constructed apparatus, we report temperature dependences of the thermal conductivity of deuterated κ-(d8:BEDT-TTF)2Cu[N(CN)2]Br in a wide temperature range between 2 and 250 K. The result provides convincing evidence for the validity of the newly developed system for the thermal measurements of molecular crystals.

Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback

Many speech processing systems struggle in conditions with low signal-to-noise ratios and in changing acoustic environments. Adaptation at the transduction level with integrated signal processing could help to address this; in human hearing, transduction and signal processing are integrated and can be adaptively tuned for noisy conditions. Here we report a microelectromechanical cochlea as a bio-inspired acoustic sensor with integrated signal processing functionality. Real-time feedback is used to tune the sensing and processing properties, and dynamic switching between linear and nonlinear characteristics improves the detection of signals in noisy conditions, increases the sensor dynamic range and enables adaptation to changing acoustic environments. The transition to nonlinear behaviour is attributed to a Hopf bifurcation and we experimentally validate its dependence on sensor and feedback parameters. We also show that output-signal coupling between two coupled sensors can increase the frequency coverage.