Pressure Sensor Output Research Articles

Research on temperature compensation of MEMS pressure sensors based on optimized multi-kernel relevance vector machine

Abstract The high-temperature coefficient in MEMS pressure sensors significantly affects sensor accuracy. Software compensation effectively reduces temperature drift. However, compensation methods such as neural networks tend to overfit sparse and noisy datasets used for calibration, leading to reduced measurement accuracy. To address the above-mentioned crucial problems, this paper proposes a multi-kernel relevance vector machine (MKRVM) based on a snow ablation optimizer (SAO) to establish the temperature-voltage-pressure relationship of pressure sensors. Optimized weight distribution from this method mitigates noise impact on the model in sparse data scenarios. Experimental results show that within the range of -15°C to 60°C, the proposed temperature compensation method achieves a mean square error as low as 0.058, effectively reducing temperature influence on pressure sensor outputs and ensuring high precision performance of the sensor.

Dynamic Temperature Compensation of Pressure Sensors in Migratory Bird Biologging Applications

This article proposes an improved dynamic quantum particle swarm optimization (DQPSO) algorithm to optimize a radial basis function (RBF) neural network for temperature compensation of pressure sensors used in tracking and monitoring wild migratory birds. The algorithm incorporates a temperature-pressure fitting model that includes temperature rate of change and gradient reference terms. It also includes a loss function that considers fitting accuracy and complexity, thereby improving the robustness of the sensor for complex temperature variations. The calibration experiments revealed that after implementation, the average absolute error of the pressure sensor output during dynamic temperature changes was reduced from 145.3 Pa to 20.2 Pa. This reduction represents an 86% improvement over the commercial polynomial compensation method, and the DQPSO approach significantly outperformed traditional feedforward network models. Finally, the algorithm was deployed and verified in an embedded environment for low-power, high-precision, real-time pressure compensation during the tracking and monitoring of wild migratory birds.

A Pneumatic Low-Pass Filter for High-Fidelity Cuff-Based Pulse Waveform Acquisition.

Cuff-based pulse waveform acquisition (CBPWA) devices are reliable solutions for non-invasive cardiovascular diagnostics. However, poor signal resolution has limited clinical applications. This study aims to demonstrate the improved signal quality of CBPWA devices by implementing passive pneumatic low-pass filters (pLPF). Conventionally, pressure sensor output resolution is a percentage of the operating range. Therefore, measurement of small pressure changes in a large range must sacrifice signal resolution to accommodate for the large mean pressures. We design a pLPF to obtain the running mean pressure and combine it with a high-resolution differential pressure sensor for isolating the signal's pulsatile component. Thirty-one volunteers participated in a device proof-of-concept study at Caltech. Volunteers were measured at rest in the supine position on the left arm. The filtering behavior is mathematically modeled and experimentally verified, showing good agreement between measured and predicted cutoff frequencies. In the human study, the device successfully captured high-fidelity pulse waveform measurements for all volunteers: a blood pressure (BP) reading was followed by inflate-and-hold acquisition in diastolic BP (DBP), mean arterial pressure (MAP), and supra systolic BP (sSBP). The study demonstrated the reliability and high signal resolution of pLPF for CBPWA. Considering the widespread use of the brachial cuff, a system for high-resolution CBPWA motivates the clinical implementation of non-invasive pulse waveform analysis (PWA).

A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

With the aid of Doppler velocity logger (DVL), strapdown inertial navigation systems (SINSs) can provide continuous and accurate navigation parameters for unmanned underwater vehicles (UUVs). However, owing to the complex underwater environment, partial DVL beams may fail to be reflected by the seafloor, resulting in DVL measurement outage. In this study, a novel data-driven approach enhancing DVL/SINS integrated navigation system is proposed to improve the robustness and accuracy of UUV navigation with limited DVL beams. First, to make full use of the available beam, the velocities of DVL beams are used to assist the SINS navigation instead of the 3D velocity of DVL. Subsequently, a virtual beam predictor based on multi-output least-squares support vector regression (MLS-SVR) is built to run in parallel with the DVL/SINS integrated system. Specifically, when the DVL operates normally, all four beams can be applied for integrated navigation and stored as the training dataset. Once partial beams are missing, the available beams and the outputs of SINS and pressure sensors can train the MLS-SVR model along with the corresponding missing beams in the training set. Subsequently, the missing beams can be predicted by the trained model for integrated navigation together with the available beams. UUV sea trials indicate that the proposed system can accurately predict unavailable beams and improve the positioning accuracy of the DVL/SINS integrated navigation system.

Hardware Implementation of Edge Neural Network Computing for Sensor With Memristors Based on Single-Crystalline LiNbO3 Thin Film

With the development of artificial intelligent IoT (AIoT) and dramatic increase of the amount of data, conventional architecture, in which all the data collected by sensors are sent to the data center for processing and computing, suffered from heavy computing load in the data center, latency, and high energy consumption. Edge neural network computing at the sensor terminal is demanding. Based on the advantages of memristor in nature co-location of memory and computing, high computing parallelism, low energy consumption, and miniaturization potential, we proposed to take use of memristors to conduct edge neural network computing at sensor and realize the integration of sensing memory computing. Also, in order to solve the problems caused by resistance states variation and device-to-device variation without transistor connected in series, highly uniform memristors based on single-crystalline LiNbO3 (LN) thin film with two stable resistance states were fabricated and utilized to realize binarized neural networks computing, and the coupling between the pressure sensor output signal with the input of the memristor array has been built. The hardware implementation of memristor-based edge neural network computing on the signals of pressure sensor array has been realized. With the memristor-based edge neural network computing, recognition of three letters (“V,” “Z,” and “T”) wrote on the pressure sensor array has been realized.

Gait Analysis in Walking and Trotting Dairy Cows on Different Flooring Types with Novel Mobile Pressure Sensors and Inertial Sensors.

Simple SummaryTo study effects of housing conditions on the claw health of dairy cows, objective gait analysis methods can be useful. In this study, a novel mobile pressure sensor system, attached under the claws of the hind limbs of dairy cows, was used for the first time. Additionally, inertial measurement units (IMUs) in combination with a newly developed automatic step detection algorithm were used. Gait analysis was performed in ten dairy cows, walking and trotting on concrete and rubber mats. The results showed the applicability of the objective gait analysis methods in dairy cows. Analysis of pressure under the claws revealed a significantly higher load in cows moving on concrete compared to rubber mats. The development of objective methods should be used to gain knowledge about factors that impair claw health. Sensor-based research should be applied to improve animal welfare by evaluating the housing environment objectively and better adapt it to the cows’ needs.Mechanical overburdening is a major risk factor that provokes non-infectious claw diseases. Moreover, lameness-causing lesions often remain undetected and untreated. Therefore, prevention of claw tissue overburdening is of interest, especially by analyzing harmful effects within dairy cows’ housing environment. However, objective “on-cow” methods for bovine gait analysis are underdeveloped. The purpose of the study was to apply an innovative mobile pressure sensor system attached at the claws to perform pedobarometric gait analysis. A further goal was the supplementation with accelerative data, generated simultaneously by use of two inertial measurement units (IMUs), attached at metatarsal level. IMU data were analyzed with an automatic step detection algorithm. Gait analysis was performed in ten dairy cows, walking and trotting on concrete flooring and rubber mats. In addition to the basic applicability of the sensor systems and with the aid of the automatic step detection algorithm for gait analysis in cows, we were able to determine the impact of the gait and flooring type on kinematic and kinetic parameters. For pressure sensor output, concrete was associated with significantly (p < 0.001) higher maximum and average pressure values and a significantly smaller contact area, compared to rubber mats. In contrast to walking, trotting led to a significantly higher force, especially under the medial claw. Further, IMU-derived parameters were significantly influenced by the gait. The described sensor systems are useful tools for detailed gait analysis in dairy cows. They allow the investigation of factors which may affect claw health negatively.

Design of ECU (Electronic Control Unit) as Biogas Pressure Control to Micro Genset

Biogas fueled generator has a unique character because the converter kit on the generator has the characteristic of absorbing the biogas supply. Therefore, in this study, biogas is accommodated in a plastic container equipped with a valve that can adjust the amount of biogas supply pressure. Combustion in the Genset combustion chamber will occur if the ratio of air supply and supply of biogas (AFR = Air to Fuel Ratio) meets the stoichiometric number. So a ratio control is needed on air supply and biogas supply, which can follow changes in biogas conditions in plastic bags and generator loads. For this reason, this paper presents an ECU (Electronic Control Unit) that can regulate the ratio of air supply to biogas supply by using a servo motor valve, which is commanded by a control signal from the ECU. The resulting ECU prototype is equipped with a multi-input connector (to receive the signal output of CH 4 methane gas sensor and the biogas supply pressure sensor), multi-output (control signal to the servo motor valve), and multi regulators to adjust the Set Point value for the methane gas content CH4, the value of the set point of the biogas supply pressure and to tune the value of the proportional gain control (KP). The results of the ECU performance test by trial and error by giving a dummy input signal (analogy to the signal output of methane gas sensor and the analog of the pressure sensor output signal in the form of an electric voltage generated from the signal generator) can drive the servo motor, valve air supply, and servo motor, valve supply biogas. Accordance with the gain control input from the regulator.

Commissioning of a respiratory gating system involving a pressure sensor in carbon-ion scanning radiotherapy.

This study reports the commissioning methodology and results of a respiratory gating system [AZ – 733 V/733 VI (Anzai Medical Co., Japan)] using a pressure sensor in carbon‐ion scanning radiotherapy. Commissioning includes choosing a location and method for pressure sensor installation, delay time measurement of the system, and the final flow test. Additionally, we proposed a methodology for the determination of a threshold level of generating an on/off gate for the beam to the respiratory waveform, which is important for clinical application. Regarding the location and method for installation of the pressure sensor, the actual person's abdomen, back of the body position, and supine/prone positioning were checked. By comparing the motion between the pressure sensor output and the reference LED sensor motion, the chest rear surface was shown to be unsuitable for the sensor installation, due to noise in the signal caused by the cardiac beat. Regarding delay time measurement of the system, measurements were performed for the following four steps: (a). Actual motion to wave signal generation; (b). Wave signal to gate signal generation; (c). Gate signal to beam on/off signal generation; (d). Beam on/off signal to the beam irradiation. The total delay time measured was 46 ms (beam on)/33 ms (beam off); these were within the prescribed tolerance time (<100 ms). Regarding the final flow test, an end‐to‐end test was performed with a patient verification system using an actual carbon‐ion beam; the respiratory gating irradiation was successfully performed, in accordance with the intended timing. Finally, regarding the method for determining the threshold level of the gate generation of the respiration waveform, the target motion obtained from 4D‐CT was assumed to be correlated with the waveform obtained from the pressure sensor; it was used to determine the threshold value in amplitude direction.

Fabrication and Characterization of Bending- Independent Capacitive CMOS Pressure Sensor Stacks

Abstract Artificial limbs, equipped with miniaturized tactile sensors, can handle objects with more dexterousness. Next to detecting forces, the sensor devices are also able to measure temperature. With this additional information, the touched objects can be better characterized. As such sensors, active CMOS-based capacitive pressure sensors are used in this work. The Sensors are thinned to 20-30 μm target thickness to make them bendable. One challenge of such thin sensors is the strong dependence of the output signal upon bending. To compensate this dependency, two sensors were mounted back to back. This allows a numerical adjustment of the two characteristic sensor output signals to mechanical stress curves. After electrically contacting of the stacks with a 15 μm thin polyimide foil substrate, the bending dependence of the stacks was characterized with a four-point bending procedure. By this characterization the dependency of the pressure sensor output signal on the height of mechanical stress was determined. Both sensor output signals show an inverted behavior under the same mechanical stress which confirmed prior simulation results with the same setup. Based on this information, a numerical method for compensating the bending dependence was successfully proven.

Development of a pressure sensor system for unobtrusive monitoring of abdominal muscle activities

Surface electromyogram (sEMG) is often used by to objectively measure muscular activity during rehabilitation exercises. sEMG is accurate, but it is unsuitable for uses outside the clinic, and patients can benefit from an unobtrusive device which can be readily used to ubiquitously measure abdominal muscle activation. In this study, we present a pressure sensor system which can be latched onto a belt to measure abdominal muscle activation. sEMG and pressure sensor output were measured in 15 healthy young males during isometric trunk flexion exercise (public trials registration number, KCT0002351), and the results were highly correlated (median R > 0.939). As initial contact force can change the pressure sensor sensitivity, the experiment was performed at two different levels of belt tightness, but the correlations did not significantly improve after tightening the belt, suggesting that the system can be used to ubiquitously and unobtrusively monitor abdominal muscle activity with minimal discomfort.

Methods in Rodent Chemosensory Cognition.

Olfactory information processing and learning are highly developed computational abilities of rodents. These attributes can be exploited to ask questions at several levels of complexity, from aspects of odorant binding by olfactory receptors to higher order learning about the predictive consequences of odorant stimulus presentation. Quantitative understanding of rodent odorant sampling patterns, both baseline nasal breathing and odorant-stimulated sniffing, is critical to elucidating mechanisms of olfactory information processing, from primary olfactory receptors to cortical centers that synthesize olfactory percepts from preprocessed multimodal inputs. This chapter outlines an innovative new method for measuring breathing and sniffing rates in unrestrained mice while the mice are performing odor-guided tasks in a computer controlled olfactometer.The method described here involves implantation of a wireless pressure sensor in the mouse that reports on thoracic pressure transients caused by breathing and sniffing. Recordings of pressure sensor outputs are made simultaneously with optically-sensed nose pokes by the mouse into an odor delivery port or a water delivery port. Odorant delivery timing and water reward delivery are also recorded simultaneously. This method allows for breathing and sniffing dependent thoracic pressure transients to be recorded with high temporal precision before, during, and after the mouse approaches an odor delivery port, samples the delivered odor, and obtains a water reward contingent on the identity of the odor that was presented and sampled.

Thigh-calf contact parameters for six high knee flexion postures: Onset, maximum angle, total force, contact area, and center of force

In high knee flexion, contact between the posterior thigh and calf is expected to decrease forces on tibiofemoral contact surfaces, therefore, thigh-calf contact needs to be thoroughly characterized to model its effect. This study measured knee angles and intersegmental contact parameters in fifty-eight young healthy participants for six common high flexion postures using motion tracking and a pressure sensor attached to the right thigh. Additionally, we introduced and assessed the reliability of a method for reducing noise in pressure sensor output. Five repetitions of two squatting, two kneeling, and two unilateral kneeling movements were completed. Interactions of posture by sex occurred for thigh-calf and heel-gluteal center of force, and thigh-calf contact area. Center of force in thigh-calf regions was farther from the knee joint center in females, compared to males, during unilateral kneeling (82 and 67 mm respectively) with an inverted relationship in the heel-gluteal region (331 and 345 mm respectively), although caution is advised when generalizing these findings from a young, relatively fit sample to a population level. Contact area was larger in females when compared to males (mean of 155.61 and 137.33 cm2 across postures). A posture main effect was observed in contact force and sex main effects were present in onset and max angle. Males had earlier onset (121.0°) and lower max angle (147.4°) with onset and max angles having a range between movements of 8° and 3° respectively. There was a substantial total force difference of 139 N between the largest and smallest activity means. Force parameters measured in this study suggest that knee joint contact models need to incorporate activity-specific parameters when estimating loading.

水晶振動子プリアンプの恒温化と圧力センサ出力の安定性との相関

水晶振動子プリアンプの恒温化と圧力センサ出力の安定性との相関

A High-Temperature Piezoresistive Pressure Sensor with an Integrated Signal-Conditioning Circuit.

This paper focuses on the design and fabrication of a high-temperature piezoresistive pressure sensor with an integrated signal-conditioning circuit, which consists of an encapsulated pressure-sensitive chip, a temperature compensation circuit and a signal-conditioning circuit. A silicon on insulation (SOI) material and a standard MEMS process are used in the pressure-sensitive chip fabrication, and high-temperature electronic components are adopted in the temperature-compensation and signal-conditioning circuits. The entire pressure sensor achieves a hermetic seal and can be operated long-term in the range of −50 °C to 220 °C. Unlike traditional pressure sensor output voltage ranges (in the dozens to hundreds of millivolts), the output voltage of this sensor is from 0 V to 5 V, which can significantly improve the signal-to-noise ratio and measurement accuracy in practical applications of long-term transmission based on experimental verification. Furthermore, because this flexible sensor’s output voltage is adjustable, general follow-up pressure transmitter devices for voltage converters need not be used, which greatly reduces the cost of the test system. Thus, the proposed high-temperature piezoresistive pressure sensor with an integrated signal-conditioning circuit is expected to be highly applicable to pressure measurements in harsh environments.

Earth Pressure Measurements Using Tactile Pressure Sensors in a Saturated Sand During Static and Dynamic Centrifuge Testing

The performance of tactile pressure sensors used in centrifuge testing involving pressure measurements of laterally-spreading soils against a large, rigid, foundation element is evaluated. The tactile pressure sensor measurements were consistent with hydrostatic pressures measured by pressure transducers under most testing conditions, and agreed with at-rest geostatic pressures provided that the measurements were made against a rigid surface (i.e., no relative soil-structure movement). However, measured pressures decreased substantially under some conditions when shearing forces were transmitted to the pressure sensors. Dynamic pressures (minima and maxima pressure spikes) measured by the tactile pressure sensors differed from pressures measured by pore pressure transducers and, when uncorrected, they were considered unreliable. A dynamic pressure correction was developed to address this issue. Lastly, a comprehensive verification testing program was recommended for each unique centrifuge test configuration to improve interpretation of pressure sensor output.

PRESSURE MONITORING OF MICRO-PIPETTING BASED ON ABSOLUTE DEGREE OF GREY COEFFICIENTS AND DOUBLE EXTREMES METHOD

Toward improving the real-time property and stability of the micro-pipetting process of an automatic enzyme-linked immunoassay analysis system, the fault detection of the process was studied by monitoring the pressure. A new method that combines the absolute degree of grey coefficients (ADGC) with the double extremes method (DEM) for monitoring pressure is proposed. The ADGC is used to demonstrate the variation of the pressure signal. In particular, the sectioning used is more flexible and is not constrained by the key timings of the micro-pipetting process. The data for a single point can be calculated in real time to obtain the double extremes (the extremes of the pressure and the first derivative) using the DEM. The DEM can therefore be performed without the need to reduce the sampling frequency, thereby guarantying the information integrity. By comparing the double extremes with the preset threshold, the fault types such as tip blockage, presence of air bubbles, and sample shortage can be distinguished. Moreover, a grey filtering technique based on the GM (1, 1) model is used to improve the stability of the pressure sensor output and significantly reduce the real-time computation requirements. Experiments were performed to verify the feasibility of the combined pressure monitoring method, and it was confirmed that it could be used to ensure real-time integrity of the information of the pressure curve. It can thus be confidently concluded that the method can be used to improve the accuracy and stability of monitoring the pressure of micro-pipetting.

The Temperature and Pressure Information Fusion Based on BP Network

The output of pressure sensor is affected by non-objection parameters in its application, its defect is the sensitivity to temperature. The method of the data fusion from the two sensors based on BP network can eliminate the side effect of temperatures and improve the accuracy and reliability for the pressure sensors. This method based on Levenberg-Marquardt algorithm of BP network has not only a simple network structure, but also a quick learning rate, showing a better prospect.

Simulation of Temperature Compensation of Pressure Sensor Based on PCA and Improved BP Neural Network

Considering the effects of temperature on output of silicon pressure sensor, this paper proposed a novel method for analyzing temperature compensation of pressure sensor by using the combination of Principal Component Analysis (PCA) and improved BP neural networks. By using PCA to extract the prime information of temperature compensation, the multi-dimensional problem is simplified, the noise error data is eliminated, the neura1 network is improved and the fault-tolerance capability is enhanced. The results indicate that this method can restrain the effects of temperature on pressure sensors effectively and enhance their stability and accuracy.

On-board monitoring of air path for automotive IC engines

Fault detection and isolation have become one of the most important aspects of automobile design. A new fault detection and isolation (FDI) scheme is developed for automotive engines in this paper. The method uses a radial basis function (RBF) neural network to model engine dynamics, and the modelling errors are used to form the basis for residual calculation. Furthermore, another RBF network is used as a fault classifier to isolate different faults from the modelling errors. The method is developed and the performance assessed using the engine benchmark, the mean value engine model (MVEM) with MATLAB/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10%–20% change superimposed on the outputs of manifold pressure, temperature and crankshaft speed sensors; one component fault considered is air leakage in intake manifold and exhaust gas recycle (EGR); the actuator fault considered is the malfunction of fuel injector. The simulation results showed that all the simulated faults can be clearly detected and isolated in the dynamic condition throughout the operating range.

Tekscan pressure sensor output changes in the presence of liquid exposure

The purpose of the study was to evaluate the load output of a pressure sensor in the presence of liquid saturation in a controlled environment. We hypothesized that a calibrated pressure sensor would provide diminishing load outputs over time in controlled environments of both humidified air and while submerged in saline and the sensors would reach a steady state output once saturated. A consistent compressive load was repeatedly applied to pressure sensors over time (Model 4000, Tekscan, Inc., South Boston, MA) with a tensile testing machine (Instron ElectroPuls E10000, Norwood, MA). All sensors were initially calibrated in a dry environment and were tested in three groups: humid air, submerged in 0.9% saline solution, and dry. Linear regression of load output over time for the pressure sensors exposed to humidity and submerged showed a 4.6% and 4.7% decline in load output each hour for the initial 6h, respectively (β=−0.046, 95% CI: [−0.053 to −0.039]; p<0.001) (β=−0.047, 95% CI: [−0.053 to −0.042; p<0.001). Tests after 72h of exposure had linear regression decline in load output over time of 0.40% and 0.47% per hour for humidified and submerged sensors, respectively (β=−0.004, 95% CI: [−0.006 to −0.003]; p<0.001) (β=−0.047, 95% CI: [−0.053 to −0.042]; p<0.001). Because outcomes in biomedical research can affect clinical practices and treatments, the diminishing load output of the sensor in the presence of liquids should be accounted for. We recommend soaking sensors for more than 48h prior to testing in a moist environment.