Output Signal Of Sensor Research Articles

A Spinning Magnetometer Based Approach to Solving the Roll Attitude of Aerial Vehicle

Abstract The accurate attitude estimation is the foundation of autonomous flight control for unmanned aerial vehicle (UAV). However, the existing inertia/magnetic field-based roll attitude estimation presents challenges, including the significant computational burden and high cumulative error. To address these issues, this paper proposes a novel roll attitude estimation approach for UAV based on spinning single-axis magnetometer. Firstly, a distinctive rotating measuring mechanism is structured by incorporating the encoder and geomagnetic sensor. Then, the rotating angle of the sensor in relation to the UAV is ascertained with the specific output signal of the geomagnetic sensor with the rotating mechanism. Based on the above, a roll attitude estimation model to be established with the relative angle relationship. Finally, comparative simulations are conducted to demonstrate the feasibility of the presented method. The suggested design method provides a rapid and cost-effective way for roll attitude estimation.

Optimizing Low-Cost Gas Analysis with a 3D Printed Column and MiCS-6814 Sensor for Volatile Compound Detection.

This paper explores an application of 3D printing technology on the food industry. Since its inception in the 1980s, 3D printing has experienced a huge rise in popularity. This study uses cost-effective, flexible, and sustainable components that enable specific features of certain gas chromatography. This study aims to optimize the process of gas detection using a 3D printed separation column and the MiCS-6814 sensor. The principle of the entire device is based on the idea of utilizing a simple capillary chromatographic column manufactured by 3D printing for the separation of samples into components prior to their measurement using inexpensive chemiresistive sensors. An optimization of a system with a 3D printed PLA block containing a capillary, a mixing chamber, and a measuring chamber with a MiCS-6814 sensor was performed. The optimization distributed the sensor output signal in the time domain so that it was possible to distinguish the peak for the two most common alcohols, ethanol and methanol. The paper further describes some optimization types and their possibilities.

Dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for nonlinear multi-agent systems

This paper is concerned with the problem of dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for multi-agent systems subject to actuator saturation, in which state variables are unmeasurable and nonlinear functions are totally unknown. A fuzzy state observer is constructed to estimate unmeasurable states. Meanwhile, fuzzy logic systems are used to approximate unknown nonlinear functions. To effectively save the usage of communication resources, this paper designs both sensor output signal and control signal triggering mechanisms, respectively. Unfortunately, the output triggering can lead to a problem that virtual control laws are non-differentiable. To solve this problem, we first utilize observer output signals to construct virtual control laws to ensure the first-order differentiation of virtual control laws. Then, a dynamic filtering technology is introduced to avoid the repeated differentiation of virtual control laws. Furthermore, an improved first-order auxiliary system is designed to compensate for the impact of actuator saturation. It is shown that the designed controller can guarantee tracking errors steer to a preset accuracy within a prescribed settling time, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results verify the effectiveness of the developed control scheme.

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

Piezoelectric Behaviour in Biodegradable Carrageenan and Iron (III) Oxide Based Sensor.

This paper is dedicated to the research of phenomena noticed during tests of biodegradable carrageenan-based force and pressure sensors. Peculiar voltage characteristics were noticed during the impact tests. Therefore, the sensors' responses to impact were researched more thoroughly, defining time-dependent sensor output signals from calibrated energy impact. The research was performed using experimental methods when a free-falling steel ball impacted the sensor material to create relatively definable impact energy. The sensor's output signal, which is analogue voltage, was registered using an oscilloscope and transmitted to the PC for further analysis. The obtained results showed a very interesting outcome, where the sensor, which was intended to be piezoresistive, demonstrated a combination of behaviour typical for galvanic cells and piezoelectric material. It provides a stable DC output that is sensitive to the applied statical pressure, and in case of a sudden impact, like a hit, it demonstrates piezoelectric behaviour with some particular effects, which are described in the paper as proton transfer in the sensor-sensitive material. Such phenomena and sensor design are a matter of further development and research.

Healing Function for Abraded Fingerprint Ridges in Tactile Texture Sensors.

Tactile texture sensors are designed to evaluate the sensations felt when a human touches an object. Prior studies have demonstrated the necessity for these sensors to have compliant ridges on their surfaces that mimic human fingerprints. These features enable the simulation of contact phenomena, especially friction and vibration, between human fingertips and objects, enhancing the tactile sensation evaluation. However, the ridges on tactile sensors are susceptible to abrasion damage from repeated use. To date, the healing function of abraded ridges has not been proposed, and its effectiveness needs to be demonstrated. In this study, we investigated whether the signal detection capabilities of a sensor with abraded epidermal ridges could be restored by healing the ridges using polyvinyl chloride plastisol as the sensor material. We developed a prototype tactile sensor with an embedded strain gauge, which was used to repeatedly scan roughness specimens. After more than 1000 measurements, we observed significant deterioration in the sensor's output signal level. The ridges were then reshaped using a mold with a heating function, allowing the sensor to partially regain its original signal levels. This method shows potential for extending the operational lifespan of tactile texture sensors with compliant ridges.

Studying Different Mechanisms of Seismo-to-Acoustic Coupling Using Ground Motion Local to Seismoacoustic Sensors

ABSTRACT The correlation between seismic and acoustic signals can be used to quantify the influence of earthquake ground motion on the output signals of infrasound sensors and thus evaluate the performance of infrasound sensors under field conditions. However, there have been relatively few studies, and earthquakes considered in those studies are large magnitude events. There remains a need to quantify correlation mechanisms at high frequencies using small earthquakes. In this study, we utilize a collocated seismoacoustic station BJT and a six-element infrasound array DQS to study different coupling mechanisms for infrasound sensors. From 2019 to 2021, 11 and 37 small magnitude earthquakes were detected by BJT and DQS, respectively. Seismoacoustic signals from these small magnitude earthquakes are used to compute the seismoacoustic spectral ratio and the observed spectral ratios are then compared with theoretical estimates. For local seismic-to-acoustic coupling (local infrasound), the highly local pressure field induced by high-frequency ground motion and the separation between seismoacoustic sensors reduce seismic and acoustic signal correlation, which prevents the use of small earthquakes in current studies. The seismic sensitivity of the infrasound sensor, MB3a is shown to be 30f0.75 for ground acceleration or 60πf1.75 for ground velocity and may be an important source of high-frequency noise for an infrasound station using this sensor. Furthermore, an empirical relation is developed to constrain the characteristics of earthquakes that can generate coupled signals on the infrasound sensor. Our study complements previous work and provides insight into the improved interpretation of infrasound signals and methods of seismoacoustic station calibration.

Evaluating head impact intensities and accelerations using flexible wearable sensors for traumatic brain injury assessment

In response to the significant health threat posed by Traumatic Brain Injury (TBI) resulting from sudden collisions on the head, there is an urgent need for timely assessment and evaluation. With the wide applications of wearable devices in health monitoring, here we report a flexible and wearable smart headband based on a polyvinylidene fluoride (PVDF) sensor to evaluate the impact force and acceleration for TBI assessment. Based on the electromechanical coupling characteristics of PVDF, a lumped parameters model of an equivalent circuit was obtained by a system identification method, which accurately transforms the output signal of the PVDF sensor to the impact force for TBI assessment of focal brain damage. Besides, a drop tower was used to impose different impact intensity levels on a dummy head wearing the smart headband; the results (MSE< 0.018) of transformed impact force show the universal applicability of the model. Moreover, the correlation analysis results show that the impact force has a positive correlation (R>85 %) with impact acceleration at the head center of gravity, which enables the TBI assessment of diffuse brain damage. This smart headband can be used for real-time monitoring of human head impact and has excellent potential for application in medical and sports devices.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Research on Vibration Accumulation Self-Powered Downhole Sensor Based on Triboelectric Nanogenerators.

In drilling operations, measuring vibration parameters is crucial for enhancing drilling efficiency and ensuring safety. Nevertheless, the conventional vibration measurement sensor significantly extends the drilling cycle due to its dependence on an external power source. Therefore, we propose a vibration-accumulation-type self-powered sensor in this research, aiming to address these needs. By leveraging vibration accumulation and electromagnetic power generation to accelerate charging, the sensor's output performance is enhanced through a complementary charging mode. The experimental results regarding sensing performance demonstrate that the sensor possesses a measurement range spanning from 0 to 11 Hz, with a linearity of 3.2% and a sensitivity of 1.032. Additionally, it exhibits a maximum average measurement error of less than 4%. The experimental results of output performance measurement indicate that the sensor unit and generator set exhibit a maximum output power of 0.258 μW and 25.5 mW, respectively, and eight LED lights can be lit at the same time. When the sensor unit and power generation unit output together, the maximum output power of the sensor is also 25.5 mW. Furthermore, we conducted tests on the sensor's output signal in conditions of high temperature and humidity, confirming its continued functionality in such environments. This sensor not only achieves self-powered sensing capabilities, addressing the power supply challenges faced by traditional downhole sensors, but also integrates energy accumulation with electromagnetic power generation to enhance its output performance. This innovation enables the sensor to harness downhole vibration energy for powering other micro-power devices, showcasing promising application prospects.

Improved empirical mode decomposition method based on amplitude-frequency characteristic of vortex signals.

The vortex flowmeter occupies a vital position in flow measurement with its unique advantages. It is essentially a fluid vibration instrument, and its measurement process is susceptible to interference, which seriously affects measurement accuracy. In particular, at low flow rates, it is an urgent problem to extract vortex signals from the complex noise. Among many signal processing methods, Empirical Mode Decomposition (EMD) is a time-frequency analysis method suitable for nonlinear, non-stationary signals. EMD can adaptively decompose noisy signals into noise and useful signal components arranged from high frequency to low frequency. For the above problems, an innovative, improved EMD method is proposed in this paper. The digital filter is designed according to the amplitude-frequency characteristic of vortex signals. After filtering, the vortex signal is adjusted to a fixed value, and high-frequency noise is filtered. According to the consistency of the filtered signal's amplitude, we design a decomposition stop criterion for EMD to process the output signal of the vortex sensor. This method not only maintains the characteristic of adaptive decomposition in EMD but also completes the automatic extraction of the vortex signal under complex noise. It provides a new comprehensive method for realizing high-precision and anti-interference vortex flowmeters.

Single-Component Electroactive Polymer Architectures for Non-Enzymatic Glucose Sensing.

Organic mixed ionic-electronic conductors (OMIECs) have emerged as promising materials for biological sensing, owing to their electrochemical activity, stability in an aqueous environment, and biocompatibility. Yet, OMIEC-based sensors rely predominantly on the use of composite matrices to enable stimuli-responsive functionality, which can exhibit issues with intercomponent interfacing. In this study, an approach is presented for non-enzymatic glucose detection by harnessing a newly synthesized functionalized monomer, EDOT-PBA. This monomer integrates electrically conducting and receptor moieties within a single organic component, obviating the need for complex composite preparation. By engineering the conditions for electrodeposition, two distinct polymer film architectures are developed: pristine PEDOT-PBA and molecularly imprinted PEDOT-PBA. Both architectures demonstrated proficient glucose binding and signal transduction capabilities. Notably, the molecularly imprinted polymer (MIP) architecture demonstrated faster stabilization upon glucose uptake while it also enabled a lower limit of detection, lower standard deviation, and a broader linear range in the sensor output signal compared to its non-imprinted counterpart. This material design not only provides a robust and efficient platform for glucose detection but also offers a blueprint for developing selective sensors for a diverse array of target molecules, by tuning the receptor units correspondingly.

A Computational Platform for Automatic Signal Processing for Bender Element Sensors

The small strain shear modulus is an important characteristic of geomaterials that can be measured experimentally using piezoelectric sensors (bender elements). However, most conventional signal interpretation techniques are based on the visual observation of the output signal and therefore inherently subjective. Objective techniques also exist, like the cross-correlation of the input and output signals, but they lack physical insight, as they rely on the (incorrect) assumption that input and output signals are similar. This paper presents GeoHyTE, the first objective and physically consistent toolbox for the automatic processing of the output signal of bender element sensors. GeoHyTE updates a finite element model of the experiment, iteratively searching for the small strain shear modulus that maximises the correlation between the experimental and numerical output signals. The method is objective, as the results do not depend on the experience of the user, and physically consistent, as the wave propagation process is modelled in full and signals of the same nature (output) are correlated. Moreover, GeoHyTE is nearly insensitive to grossly erroneous input by the user, both in terms of the starting point of the iterative maximisation process and refinement of the finite element model. The results obtained with GeoHyTE are validated against benchmark measurements reported in the literature and experimental data obtained by the authors. A detailed statistical analysis of the results obtained with GeoHyTE and conventional interpretation techniques is also presented.

High mechanical strength, strong adhesion, self-healing and conductive ionic hydrogels for motion detection

The development of conductive hydrogels with outstanding mechanical properties, robust adhesion, and high sensitivity is still a major challenge. This study successfully addresses this issue by a multi-functional ionic conductive hydrogel based on a highly entangled double network (DN) structure with excellent mechanical properties, strong adhesion, high sensitivity and self-healing ability. Polyacrylic acid (PAAc)/chitosan (CS) constructed the Double Network with physical entanglement as the main effective crosslinking. The sliding entanglement points made the hydrogel show a more uniform directional network structure during stretching, and the energy dissipation was greatly reduced. Active chemically reactive polydopamine (PDA), CS, and PAAc are complexed with aluminum ions (Al3+) respectively (building appropriate chemical crosslinking networks), and Al3+ acts as a “bridge” to build a hierarchical porous networks through multiple reversible coordination bond mediated interactions, alternating rigid and elastic domains, thereby improving the comprehensive performance of hydrogels. The tensile strength of PAAc/PDA/CS (ADC) was 173.4 kPa and the elongation at break was 921.9%, both of which were improved simultaneously. Moreover, ADC composite hydrogels exhibit excellent self-healing and adhesion properties, which can be attributed to the rich reversible/irreversible interactions brought about by PDA, with an impressive adhesion strength of 85.2 kPa on pig skin, further enhancing its practicality. Additionally, the ionic hydrogel demonstrates high strain sensitivity to tension, making it an ideal material for stable sensor signal output. The sensitivity coefficient (GF) reaches an impressive 11.24, highlighting the importance of this hydrogel in ensuring consistent and precise data collection. This innovative approach expands the idea and provides data support for the molecular structure design of high-performance wearable hydrogel sensors.

A decoupling calculation method of separating temperature component from the nano-scale metal film thickness measurement output by the eddy current sensor

The eddy current method has been widely applied in the field of nano-scale metal film thickness detection. The output signal of an eddy current sensor is generally tiny in practice, and it is easily affected by the ambient temperature variation, which results in a decrease in the measurement accuracy. How to separate the temperature effect on film thickness measurement for achieving a high precision is a major problem. Therefore, a coupling model of an eddy current sensor with an electromagnetic field and a temperature field is established in this study, and the influence of the film thickness and temperature on coil impedance is calculated. It is found that the inductance and resistance of the coil vary monotonically as thickness and temperature with a measure of linearity in a certain range, as well as the real part and imaginary part of the output voltage by using an AC bridge. Then a film thickness-temperature decoupling calculation method is proposed, and a group of linear calibration intervals are further divided considering the linearity and measurement accuracy. According to the calculation results, it is confirmed that the method can decouple the two, and accomplish a higher accuracy. In addition, the method is verified by a series of experiments, and the variation trend of real and imaginary parts of output voltage with thickness and temperature is consistent with simulation results. At the same time, it is feasible to realize a synchronous detection of metal film thickness and ambient temperature in a certain range.

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system

Multiparameter Remote Contact Force Sensor With Embedded Bend Sensing for Tendon-Driven Hand Robots

Integrating multiple sensors without increasing the structural complexity and their original form factor is a major concern in robotic applications, particularly in tendon-driven systems. This study proposes a multiparameter contact force sensor with bend sensing by utilizing an improved Bowden cable design. The proposed sensor consists of an end tip with an elastomer membrane, a Bowden cable consisting of a sheath and dual inner wires, and a remotely separated sensing component. The electrical wirings are decoupled from the mechanical Bowden cable system. The displacement difference between the force-sensing and bend-sensing wires is transmitted through the sheath, indicating the contact force on the end tip. The modeling and experiments verify that the contact force applied on the end tip is linearly related to the sensor output signal and is reliable after repeated measurements. The proposed sensor, utilizing the flexible Bowden cable as its sensing modality, can be integrated into a diverse range of tendon-driven robotic applications. These applications include a robotic hand with tactile and angle sensing capabilities, as well as a soft wearable robot for the hand (Exo-glove Poly II). The compact form factor of the proposed sensor enables geometric sensing without increasing structural complexity for multiparameter sensing in robotic applications.

High-frequency distortion phenomenon and solution of low frequency vibration sensor

This paper analyzes the difference between several acceleration over-limit faults, and finds out the cause of false acceleration fault of wind turbines, which is because the low-frequency vibration sensor used by the unit is interfered with the high-frequency signal generated by the friction of the yaw system and causes the sensor output signal distortion. The performance of different manufacturers’ sensors under different installation positions is reproduced on the spot. A cost-effective solution to the high frequency distortion problem (installing a physical low-pass filter) is developed, and the comparative test results before and after its application are demonstrated experimentally.

Synthesis of a measurement procedure for estimating the orientation of a small unmanned aerial vehicle under conditions of changing status of measurement results

The problem of processing measurement information with changing status of measurement results of micromechanical sensors of an intelligent on-board measuring system of a small unmanned aerial vehicle is considered. While vehicle is in the air, the measurement results status changes from confirmed to orienting, for example, due to sensor defects, degradation of the measuring channel, the appearance of false measurement output signals of micromechanical sensors due to vibrations and shocks caused by the movement of air masses. As a result, the probability of stability loss of a small unmanned aerial vehicle increases and it is necessary to raise the accuracy of estimating its orientation in conditions of changing status of measurement results. The measuring procedure of the Kalman structure is considered, the equations of which are determined with accuracy to the parameters of the transition and noise matrices of the state, as well as the perturbation vector, characterizing the measuring process. The parameter values are determined by a mathematical model for converting measuring information based on a dynamic mathematical model, which distinguishes the developed measuring procedure from a measuring procedure with a classical transition matrix. A neural network is used to find unknown parameters. A multilayer perceptron was selected as the basis of a neural network, for which an error back propagation algorithm is used to train. Based on the results of mathematical modeling and measurement experiment, it was found that the accuracy of the synthesized measuring procedure based on a dynamic mathematical model is higher than the accuracy of the measuring procedure of the Kalman structure with a classical transition matrix. The results of the study will be useful in the development of intelligent measurement procedures for on-board measurement systems operating under conditions of changing status of measurement results

Error analysis and modeling of the racetrack magnetohydrodynamic linear motion sensor

Micro-vibration measurement methods for spacecraft structures mainly include the use of an accelerometer, laser, and magnetohydrodynamic (MHD) measurement methods. The micro-vibration measurement sensor developed based on the MHD measurement method has no mechanical wear between internal components, a fast high-frequency vibration response, and strong anti-interference properties. To reduce the measurement error of the racetrack MHD linear motion sensor developed in the laboratory, this paper investigated the sensor error, analysed the error source, combined the BP neural network optimized by particle swarm optimization (PSO) with variational mode decomposition (VMD), used the VMD-PSO-BP neural network to establish the error compensation model of the racetrack MHD linear motion sensor, and combined the PSO-BP neural network with wavelet threshold de-noising (WTD). The WTD-PSO-BP and RBF neural networks were used to develop the error compensation model of the racetrack MHD linear motion sensor. Comparing the three models, the experimental results show that the VMD-PSO-BP model has the best compensation effect. The mean absolute error of the output signal of the racetrack MHD linear motion sensor compensated by the VMD-PSO-BP neural network model was 1–2 times lower than that before compensation, the signal-to-noise ratio was 10 times higher on average, and the correlation coefficient was more than 0.95.