Performance Evaluation Of Sensors Research Articles

Evaluation of WIMU sensor performance in estimating running stride time and vertical stiffness: a comparison with smart pressure insoles

Evaluation of WIMU sensor performance in estimating running stride time and vertical stiffness: a comparison with smart pressure insoles

Structural simulation and performance evaluation of self-priming electrostatic diesel vehicle emission particle sensor

Here is reported a self-priming electrostatic particulate matter (PM) sensor for real-time monitoring of diesel exhaust under different operating conditions. Initially, a multi-physics coupling model for the sensor was established. By varying the exhaust flow and temperature, the emissions of diesel vehicles were simulated to evaluate the sensor. The results showed that within the typical velocities (5 to 60 m/s) and temperature ranges (100 to 500 °C) of diesel exhaust, the sensor outlet pressure was consistently below the inlet pressure, with a maximum differential pressure of 259 Pa, indicating the capability for self-priming sampling of exhaust under different operating conditions. Additionally, the calibration platform was constructed using a miniature inverted soot generator which produced a series of samples to calibrate the relationship between current and mass concentration with a correlation coefficient of 0.99. From 0 to 100 mg/m3, the PM sensor exhibited a relative error between −6.00% and 6.00% with good stability and consistency for samples of different sizes and concentrations, with correlation coefficients exceeding 0.98. The results between the self-developed sensor and a commercial instrument revealed that the former provided high-precision real-time measurement of diesel exhaust.

In chamber calibration and performance evaluation of air quality low-cost sensors

Assessing individual exposure to PM2.5 (particulate matter of aerodynamic diameter lesser than 2.5 μm) requires precise monitoring of PM2.5 concentrations at specific geographical and temporal scales. This demand is met globally by low-cost particulate matter sensors, although calibrating them is difficult. In this study, four low-cost PM sensors, Sharp GP2Y1010AU0F, Honeywell HPMA115S0-XXX, Plantower PMSA003-A, and Sensirion SPS30, were calibrated and tested using various aerosols. The calibration method has three steps: individual (considering each sensor independently to a single aerosol type; n = 1), combined (all sensors for a specific model together for a specific aerosol type; n = 4), and generic (all sensors for a given model together to all aerosols; n = 16). Sensor responses are processed using linear, quadratic, power-law, and artificial neural network (ANN) algorithms in each calibration stage. Performance metrics, including coefficient of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage coefficient of variation (% CV), were utilized for assessment. Amongst all the four tested sensors, the Sensirion SPS30 sensors gave the best performance with a minimum R2 value of 0.911 when calibrated with a generic ANN calibration algorithm. Also, the MAPE was less than 10 %, and the RMSE was less than 7 % when exposed to different particles. Sensirion SPS30 showed the lowest inter-sensor variability with % CV less than 6 %. Sensors identified monodisperse polystyrene latex (PSL) particle size in the investigation. Regardless of exposure to 0.3, 0.46, 0.60, or 1.0 μm PSL, the reported number size distribution for the PMSA003 sensor remained consistent and did not align with the results from Grimm. As the PSL size rose, the SPS30 size distribution changed towards larger particle sizes, although it did not always match Grimm data. As the PSL size increased, the sensor's PM1, PM2.5, and PM10 mass proportions altered.

Room temperature NO2 gas sensor based mesoporous Zn/Al-layered double hydroxide: The effect of molar ratio

This study elucidates the fabrication and performance evaluation of mesoporous Zn/Al-layered double hydroxide (LDH) for nitrogen dioxide (NO2) efficient room temperature (RT) gas sensor through two-steps solution processed. The correlation between the microstructural analysis and gas sensing performance evaluation was clearly investigated. In detail, average porosity and surface area enhancement from 19.4 to 56.6 nm and 27.50 to 47.85 (m2/g) were perceived for molar ratios of 3:1 and 7:1 of Zn2+ to Al3+, respectively. This in turn resulted in gas sensor response enhancement from 10.21 to 16.41 % for the optimum device (molar ratio of 7:1) under gas concentration of 100 ppm. Continuously, the fabricated devices responded linearly to the applied gas concentration alteration wherein R2 average value of 0.995 was attained. In conjunction, the time-resolved characteristics revealed considerably fast response time along comparatively longer recovery window wherein the average rise/fall periods were estimated to be 2.98 and 19.3 sec., respectively.

Recognizing salat activity using deep learning models via smartwatch sensors

In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying.

Field Performance Evaluation of Low-Cost Soil Moisture Sensors in Irrigated Orchard

Field Performance Evaluation of Low-Cost Soil Moisture Sensors in Irrigated Orchard

Removing spikes in TianQin-1 inertial sensor data using machine learning method

Abstract The spikes in the inertial sensor data have been found to impact on the retrieval of the non-gravitational signals and the evaluation of the inertial sensor performance. Removing the spikes in the inertial sensor is critical for studies of gravitational reference sensors in space-based gravitational wave detection missions and accelerometers in gravity satellite missions. Thanks to a long period of inertial sensor data without thruster spikes, we can conduct machine learning based on this data to remove spikes. In this paper, a machine learning model called bi-directional long short-term memory (Bi-LSTM) neural network was built based on the inertial sensor data of TianQin-1 (TQ-1) mission. We use the machine learning method to remove the spikes in the inertial sensor data. After removing the spikes in the inertial sensor data, acceleration noise is suppressed form 2.0×10-7ms-2Hz-1/2 to the 2.8×10-10ms-2 Hz-1/2 at 0.1 Hz, which is far better than the existing methods, including the linear interpolation, data substitution and mean value of adjacent data.

Preliminary design and performance evaluation of optical fiber-based load sensor for aerospace systems

Preliminary design and performance evaluation of optical fiber-based load sensor for aerospace systems

9. Performance evaluation of commercial ammonia concentration sensors in poultry houses

9. Performance evaluation of commercial ammonia concentration sensors in poultry houses

Optically accessible gas exposure apparatus for testing and characterization of holographic gas sensors

Holographic gas sensors are of great interest due to their widespread applicability and potential for high sensitivity, fast response, immunity to electromagnetic interference, and compact and lightweight nature. For effective design and development of holographic gas sensors, it is essential to have a reliable and safe gas exposure system allowing for optical access for testing purposes. Here, the design and operation of a custom gas exposure apparatus for the performance evaluation of holographic grating-based gas sensors within a research laboratory setting is presented. The apparatus enables the real-time measurement of analyte-induced changes in key holographic grating parameters: grating diffraction efficiency and reconstruction wavelength. A demonstration of the capabilities of the optically accessible apparatus to evaluate sensor response time, sensitivity to different volatile organic compound analytes, and response to cyclical gas exposure is presented. The AutoCAD designs, as well as the material and equipment specifications for the custom apparatus, are provided to facilitate reproduction of the gas development and gas exposure setup.

Performance evaluation of QCM dew point sensors with different wettability electrode

For accurate identification characteristics of dew point sensors based on quartz crystal microbalance (QCM), the performance of QCM with different wettability electrodes was evaluated. With the materials of 1 H, 1 H, 2 H, 2 H-perfluorodecyltrimethoxysilane (PFOTMS) and 3-(methacryloyloxy) propyltrimethoxysilane (MPTMS), and the cleaning methods of plasma and ultrasound, QCM electrodes with different wettability were prepared. The influence of wettability on dew point identification characteristics was analyzed by observing differences in the static contact angle and surface structure of each QCM, and experiments were carried out to study the frequency characteristics of each QCM. The results demonstrated that when the wettability of the QCM electrodes was lower, the dew was close to spherical and its distribution was dense, which was beneficial for accurate identification. The dew point temperature was identified by detecting the temperature of the point when the QCM frequency variation slop changed to 0, and the QCM treated with plasma-PFOTMS had excellent measurement performance as its maximum measurement error was less than 0.38 ℃DP at the dew point range of 0–14 ℃DP in five days. This study provided a new approach for optimizing high-precision QCM dew point sensors.

Performance evaluation of MeteoTracker mobile sensor for outdoor applications

Abstract. The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring.

Biosynthesized (SnO2/ZnO/TiO2) Composite for Gas Sensor Application: Structural and Sensing Performance Evaluation

Biosynthesized (SnO2/ZnO/TiO2) Composite for Gas Sensor Application: Structural and Sensing Performance Evaluation

Development of a Flexible Sensor-Integrated Tissue Patch to Monitor Early Organ Rejection Processes Using Impedance Spectroscopy.

Heart failure represents a primary cause of hospitalization and mortality in both developed and developing countries, often necessitating heart transplantation as the only viable recovery path. Despite advances in transplantation medicine, organ rejection remains a significant post-operative challenge, traditionally monitored through invasive endomyocardial biopsies (EMB). This study introduces a rapid prototyping approach to organ rejection monitoring via a sensor-integrated flexible patch, employing electrical impedance spectroscopy (EIS) for the non-invasive, continuous assessment of resistive and capacitive changes indicative of tissue rejection processes. Utilizing titanium-dioxide-coated electrodes for contactless impedance sensing, this method aims to mitigate the limitations associated with EMB, including procedural risks and the psychological burden on patients. The biosensor's design features, including electrode passivation and three-dimensional microelectrode protrusions, facilitate effective monitoring of cardiac rejection by aligning with the heart's curvature and responding to muscle contractions. Evaluation of sensor performance utilized SPICE simulations, scanning electron microscopy, and cyclic voltammetry, alongside experimental validation using chicken heart tissue to simulate healthy and rejected states. The study highlights the potential of EIS in reducing the need for invasive biopsy procedures and offering a promising avenue for early detection and monitoring of organ rejection, with implications for patient care and healthcare resource utilization.

Impact of Rainfall on the Detection Performance of Non-Contact Safety Sensors for UAVs/UGVs.

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios.

Nanowire sensor calibration and performance evaluation in microfluidic flow velocity monitoring

A microfluidic chip, integrated with a contact flow velocity sensor, was fabricated. A single gold nanowire prepared by nanoskiving was positioned across the bottom of a microfluidic channel. Real-time monitoring of the microfluidic flow velocity is achieved by leveraging the principle of forced heat exchange between the fluid and the solid nanowire. To harmonize theoretical calculations and experiments, leading to the derivation of a conversion formula between the flow equivalence factor and the flow velocity. Analysis of experimental data, specifically curves of resistance versus time, yields two characteristic parameters for the nanowire sensor: the step size of the flow equivalence factor and the resistivity variation. The performance of nanowire sensor was further analyzed by examining the patterns exhibited in the relationship between characteristic parameters (resolution and response characteristics) and both voltage and inlet flow rate. This work has potential applications in biomedical sensing, microfluidic mixing, and other areas where flow velocity control is required.

Performance Evaluation of Micro Automatic Pressure Measurement Sensor for Enhanced Accuracy

The major objective of this research is to design sensitive components, conversion components, and various sensor circuits to achieve the miniaturization design for more accurate measurements. This article conducts performance testing on the designed miniaturized pressure sensor to determine whether it meets the qualified standards. The response time of designed sensor results increases with the increase of pressure under experimental conditions of different pressure application values (5MPa to 50MPa). The detection accuracy of the micro automatic pressure measurement sensor designed in this paper can reach 0.0452\%; the average pressure is 0.00364%, and the insulation resistance is 68.44 megohms, which meets reliability requirements. The sensitivity is 0.0582%; the nonlinearity is 0.0741%; the hysteresis is 0.0266%; the repeatability of 0.0625% meets the qualification standard for this instrument. Still, compared with traditional sensors, the sensor reduces the response time of results by about 60%. However, the author conducted the detection in an ideal environment. The actual working environment of sensors is relatively good. Therefore, the detection results obtained in this article may have some errors compared to the actual situation, and further analysis and testing are needed to optimize the performance of the designed sensor.

Anodized porous silicon based humidity sensor: evaluation of material characteristics and sensor performance of AU/PSIO2/AU

Anodized porous silicon based humidity sensor: evaluation of material characteristics and sensor performance of AU/PSIO2/AU

Environmental Chamber Characterization of an Ice Detection Sensor for Aviation Using Graphene and PEDOT:PSS.

In the context of improving aircraft safety, this work focuses on creating and testing a graphene-based ice detection system in an environmental chamber. This research is driven by the need for more accurate and efficient ice detection methods, which are crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors' heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors tested under varied and controlled atmospheric conditions exhibit a remarkable potential to enhance ice detection systems for aircraft. Their lightweight, efficient, and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

Design and Performance Evaluation of a Deep Ultraviolet LED-Based Ozone Sensor for Semiconductor Industry Applications.

Ozone (O3) is a critical gas in various industrial applications, particularly in semiconductor manufacturing, where it is used for wafer cleaning and oxidation processes. Accurate and reliable detection of ozone concentration is essential for process control, ensuring product quality, and safeguarding workplace safety. By studying the UV absorption characteristics of O3 and combining the specific operational needs of semiconductor process gas analysis, a pressure-insensitive ozone gas sensor has been developed. In its optical structure, a straight-through design without corners was adopted, achieving a coupling efficiency of 52% in the gas chamber. This device can operate reliably in a temperature range from 0 °C to 50 °C, with only ±0.3% full-scale error across the entire temperature range. The sensor consists of a deep ultraviolet light-emitting diode in a narrow spectrum centered at 254 nm, a photodetector, and a gas chamber, with dimensions of 85 mm × 25 mm × 35 mm. The performance of the sensor has been meticulously evaluated through simulation and experimental analysis. The sensor's gas detection accuracy is 750 ppb, with a rapid response time (t90) of 7 s, and a limit of detection of 2.26 ppm. It has the potential to be applied in various fields for ozone monitoring, including the semiconductor industry, water treatment facilities, and environmental research.