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As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area. [ABSTRACT FROM AUTHOR]

Rotary wing drones stand out among Unmanned Aerial Vehicles with their vertical landing and take-off feature and are used in many industrial applications and different sectors. Ensuring the stability of motion in these vehicles is crucial. Errors in the motor assembly can disrupt the stability of the motion in rotary wing drones. Therefore, it is essential to detect these errors during the assembly phase. In this study, we propose a cost-effective method based on deep learning to detect assembly failure of brushless direct current motors, which are widely used in rotary wing drones. A test setup representing the motor assembly defects is created and vibration data for three different speeds of the motor are obtained through a low-cost vibration sensor. The combined one- and two-dimensional deep convolutional neural network (WDD-CNN), used to classify these data was trained with the Case Western Reserve University (CWRU) dataset and the data collected in this study. The hyper-parameter settings of the network were determined using the CWRU data set and the data obtained from the experimental setup described in the paper. The network parameters of the WDD-CNN network were transferred to the Raspberry Pi micro-controller with specialized software, and the classification process was performed there. The fact that the proposed method runs on a micro-controller reduces its cost. Because there is already a micro-controller card in drones. In addition, the selected sensor is cost-effective. Thanks to these features, the proposed method is cost effective. In this classification process performed on Raspberry Pi 5, assembly errors were detected with 97–100% accuracy. [ABSTRACT FROM AUTHOR]

The widespread use of automobiles has revolutionized transportation and attracted a large population owing to their convenience and effectiveness. However, this widespread adoption has resulted in a significant increase in road traffic accidents. The alarming road fatalities suggest that medical responders are overwhelmed by the need to save lives in a timely manner. This is due to a lack of affordable autonomous detection and notification mechanisms. Prior work in this domain includes the use of vehicular ad hoc networks, Arduinos, and Raspberry Pis; machine-learning approaches for predictions; and smart devices using integrated sensors. These methods are either expensive to acquire, human-reliant, or require vehicular modifications. Therefore, the aim of this study is to suggest a cheap prototype that can work with smartphones. The prototype should have embedded micro-electromechanical system (MEMS) sensors that measure g-force to find car accidents and global system for mobile communications-long term evolution (GSM-LTE) to call the closest medical responders, which would be found using GPS. A prototype was developed using the.NET Multi-Platform App UI (MAUI) framework. This study applied the design science research methodology (DSRM) to produce a socially acceptable, low-cost artifact similar to existing in-vehicle systems to save lives on the road during a road traffic accident. The FEDS evaluation of the results indicated that smartphones can perform such complex tasks with reasonable accuracy compared with expensive in-vehicle systems. The prototype can be adopted by lower- to middle-class individuals as it is a cheaper alternative. This study makes a practical contribution to the society by utilizing artifacts to ensure road safety. [ABSTRACT FROM AUTHOR]

Many people die from not wearing helmets. They also die from delayed treatment. Remote accidents are hard for emergency services to detect. Helmets save lives. Early treatment prevents 60% of accident deaths. This scheme will ensure riders wear helmets and call 911, if they crash. Bike accidents are rising as our country’s bikers. Many deaths occur due to not wearing helmets and not receiving prompt medical attention. The project protects bikers from traffic accidents. The primary aim of this work is to detect smart helmets and report accidents. The system uses sensors, Wi-Fi processors, and cloud computing. The processor checks accelerometer values from the accident detection system for irregularities. Cloud-based services send emergency contacts accident details. GPS locates vehicles. A smart helmet ‘Konnect’ guarantees real-time, verified accident information. Thus, a smart helmet for accident detection uses smart city’s ubiquitous connectivity. [ABSTRACT FROM AUTHOR]

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

Landslides in northern India are a frequently occurring risk during the rainy season resulting in human, animal, and property losses as well as obstructing transportation facilities. Usually numerical and analytical approaches are applied towards the prediction and monitoring of landslide but the unpredictable nature of rainfall induced landslide limits these methods. Sensor based monitoring proved to be an accurate and reliable method in real time and it also collect accurate and site-specific required data for further investigation with numerical and analytical approach. In this study, a self-developed low-cost slope monitoring system including MEMS-based tilt and moisture sensors were used to monitor variation in tilt deformation and water content. A physical slope model was also prepared to test the monitoring system in a real scenario. A landslide occurred at Kotrupi village in Mandi district of Himachal Pradesh, India, was chosen for the modelling to investigate the failure mechanism. Further numerical modelling was conducted to investigate the slope's failure mechanism and for validation purposes, allowing for the development of feasible strategies for the future study of various landslides and the mitigation of their effects. The results show that the developed system is very effective in monitoring rainfall-induced landslides as it monitors the gradual and sudden movement effectively. The tilt angle records the deviation in terms of angle with a least count of 0.01 degree and the moisture content was recorded in terms of percentage with a least count of 1. Numerical analysis highlighted the factor of safety before rainfall was 1.045 and after rainfall it decreased to 0.670 validating that the rainfall was the triggering cause of the slope failure. This study explains the mechanism behind the landslide and it can be helpful in monitoring the slope in order to enable the implementation of preventative actions that will mitigate its impact. [ABSTRACT FROM AUTHOR]

We aim to develop new portable desktop tissue analysers (DTAs) to provide fast, low-cost, and precise test results for fast nanomechanical profiling of tumours. This paper will explain the reasoning for choosing indentation-type atomic force microscopy (IT-AFM) to reveal the functional details of cancer. Determining the subtype, cancer stage, and prognosis will be possible, which aids in choosing the best treatment. DTAs are based on fast IT-AFM at the size of a small box that can be made for a low budget compared to other clinical imaging tools. The DTAs can work in remote areas and all parts of the world. There are a number of direct benefits: First, it is no longer needed to wait a week for the pathology report as the test will only take 10 min. Second, it avoids the complicated steps of making histopathology slides and saves costs of labour. Third, computers and robots are more consistent, more reliable, and more economical than human workers which may result in fewer diagnostic errors. Fourth, the IT-AFM analysis is capable of distinguishing between various cancer subtypes. Fifth, the IT-AFM analysis could reveal new insights about why immunotherapy fails. Sixth, IT-AFM may provide new insights into the neoadjuvant treatment response. Seventh, the healthcare system saves money by reducing diagnostic backlogs. Eighth, the results are stored on a central server and can be accessed to develop strategies to prevent cancer. To bring the IT-AFM technology from the bench to the operation theatre, a fast IT-AFM sensor needs to be developed and integrated into the DTAs. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the development of a flexible temperature sensor array using multi-layer ceramic capacitors. By integrating the capacitors into a 5 × 5 array on a polydimethylsiloxane (PDMS) substrate, we exploit the principle of changing dielectric constant with temperature, which results in a change in capacitance. Our sensor array demonstrates a consistent decrease in capacitance with increasing temperature, with a sensitivity ranging from 1.42 to 1.62 pF/°C. This sensitivity range is maintained even when measurements are taken using a capacitance-to-voltage conversion circuit, with a sensitivity of 1.1 to 1.5 mV/°C. The repeatability and hysteresis of the sensors were also investigated, with the latter revealing a maximum error of 12.7%. Our findings provide valuable insights for the development of efficient, flexible, and reliable temperature sensor arrays using ceramic capacitors.

This paper presents the development of a flexible temperature sensor array using multi-layer ceramic capacitors. By integrating the capacitors into a 5 × 5 array on a polydimethylsiloxane (PDMS) substrate, we exploit the principle of changing dielectric constant with temperature, which results in a change in capacitance. Our sensor array demonstrates a consistent decrease in capacitance with increasing temperature, with a sensitivity ranging from 1.42 to 1.62 pF/°C. This sensitivity range is maintained even when measurements are taken using a capacitance-to-voltage conversion circuit, with a sensitivity of 1.1 to 1.5 mV/°C. The repeatability and hysteresis of the sensors were also investigated, with the latter revealing a maximum error of 12.7%. Our findings provide valuable insights for the development of efficient, flexible, and reliable temperature sensor arrays using ceramic capacitors. [ABSTRACT FROM AUTHOR]

Micro-electro-mechanical system (MEMS) pressure sensors play a significant role in pulse wave acquisition. However, existing MEMS pulse pressure sensors bound with a flexible substrate by gold wire are vulnerable to crush fractures, leading to sensor failure. Additionally, establishing an effective mapping between the array sensor signal and pulse width remains a challenge. To solve the above problems, we propose a 24-channel pulse signal acquisition system based on a novel MEMS pressure sensor with a through-silicon-via (TSV) structure, which connects directly to a flexible substrate without gold wire bonding. Firstly, based on the MEMS sensor, we designed a 24-channel pressure sensor flexible array to collect the pulse waves and static pressure. Secondly, we developed a customized pulse preprocessing chip to process the signals. Finally, we built an algorithm to reconstruct the three-dimensional pulse wave from the array signal and calculate the pulse width. The experiments verify the high sensitivity and effectiveness of the sensor array. In particular, the measurement results of pulse width are highly positively correlated with those obtained via infrared images. The small-size sensor and custom-designed acquisition chip meet the needs of wearability and portability, meaning that it has significant research value and commercial prospects. [ABSTRACT FROM AUTHOR]

The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean's environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean. [ABSTRACT FROM AUTHOR]

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm's effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3–1.7 m. [ABSTRACT FROM AUTHOR]

Landslides in northern India are a frequently occurring risk during the rainy season resulting in human, animal, and property losses as well as obstructing transportation facilities. Usually, numerical and analytical approaches are applied to predicting and monitoring landslides, but the unpredictable nature of rainfall-induced landslides limits these methods. Sensor-based monitoring is an accurate and reliable method, and it also collects accurate and site-specific required data for further investigation with a numerical and analytical approach. This study developed a low-cost tilt-based rainfall-induced landslide monitoring system using the economical and precise MEMS sensor to record displacement and volumetric water content. A self-developed direct shear-based testing setup was used to check the system's operational performance. A physical slope model was also prepared to test the monitoring system in real scenarios. A debris failure occurred at Kotrupi village in the Mandi district of Himachal Pradesh, India, which was chosen for the modelling to investigate the failure mechanism. A rainfall generator was developed to simulate the rainfall, equipped with a flow sensor for better simulation and data recording. The tilt angle records the deviation in terms of angle with a least count of 0.01 degrees, and the moisture content was recorded in terms of percentage with a least count of 1. The results show that the developed system is working properly and is very effective in monitoring the rainfall-induced landslide as it monitors the gradual and sudden movement effectively. This study explains the mechanism behind the landslide, and it can be helpful in monitoring the slope to enable the implementation of preventative actions that will mitigate its impact. [ABSTRACT FROM AUTHOR]

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU's accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU's performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro's error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro's ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of Xb, Yb and Zb were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope. [ABSTRACT FROM AUTHOR]

For measuring the structural health of buildings, high-performance vibration detection devices are used in a structural health monitoring (SHM) system, which consists of a sensor and a data logger. Those devices are seismographs or devices with high-performance sensors which are expensive. Recently, smartphones are being used as seismographs to accumulate big data of earthquake wave detection because they have accelerometers of microelectromechanical systems. Since a smartphone has the functions of a detection sensor and a data logger, a low-cost SHM system can be developed by using a low-cost smartphone. In this paper, smartphones were used to confirm the possibility of the development of a low-cost SHM system. To evaluate the vibration detection performance from small displacement and large displacement, smartphones were installed in a specimen of a large shaking table test. The specimen is a scale model of a two-story non-reinforced masonry-filled reinforce concrete (RC) frame building. The natural period and interstory drift ratio were used as the evaluation criteria. The natural period estimated by the smartphone data agreed with that found by the piezoelectric accelerometer data. For estimating the building deformation, which is related to building stability, the measurement performance for large deformation using smartphones was evaluated. The smartphones have 90% or higher accuracies for the estimation of the maximum acceleration and displacement. [ABSTRACT FROM AUTHOR]

Ship noise observation is of great significance to marine environment research and national defense security. Acoustic stealth technology makes a variety of ship noise significantly reduced, which is a new challenge for marine noise monitoring. However, there are few high spatial gain detection methods for low-noise ship monitoring. Therefore, a high Signal-to-Noise Ratio (SNR) MEMS noise listener for ship noise detection is developed in this paper. The listener achieves considerable gain by suppressing isotropic noise in the ocean. The working principle and posterior end signal processing method of the listener are introduced in detail. A gain of 10 dB over the sound pressure detector is obtained by detecting the standard sound source. In addition, the traffic vessel noise monitoring experiment verifies that the listener can detect the ship noise. The results show that the listener has a very broad application prospect in the field of low-noise ship observation. [ABSTRACT FROM AUTHOR]

Since pressure while drilling (PWD) has the disadvantages of single-point measurement and high cost of application, a micro-measurer based on MEMS (micro-electromechanical systems) sensor technology, which can measure downhole temperature, pressure, magnetic field, and dynamic signal, has been developed to achieve real-time, efficient, and accurate measurement of multiple parameters in the well. The kernel circuit system is the core of measurement and control, and the shell plays the role in protecting the kernel circuit. The shell of the micro-measurer is made of preferably selected materials with high-temperature and high-pressure resistance, corrosion resistance, small size, and low density, which can adapt to working in drilling fluid for a long time. The micro-measurer uses integrated interfaces on the shell to enable communication between the host computer and measuring machine. Based on the field test, both the functional integrity and data measurement accuracy of the micro-measurer are verified. Through analysis of the measured data, the profile of the downhole temperature field is constructed. The physical phenomena reflected by the measured magnetic field signal and dynamic signal are consistent with the actual working conditions observed in the test. Hence, as a new microchip measuring device, the micro-measurer can better serve the drilling engineering field and provide technical support for real-time measurement of downhole parameters in the future.

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance. [ABSTRACT FROM AUTHOR]

Traditional motion posture monitoring system has some problems such as time-consuming monitoring, low monitoring accuracy and poor monitoring effect. Therefore, this paper designs a real-time motion posture monitoring system based on sensor data flow. In the aspect of hardware, the whole structure of human motion posture monitoring system is designed. The mPU-92/65 six-axis motion processing sensor is selected to collect motion posture, and the module of human motion status detection is designed by MEMS sensor. In software, according to the amplitude fusion information of sensor output and correlation mining, fuzzy fusion model of motion posture data acquisition is established by using correlation component detection method, and real-time monitoring of motion posture is realized by combining sensor data flow technology. The experimental results show that when the number of iterations is 300, the motion posture monitoring accuracy of the designed system can reach 99.57%, the motion posture monitoring time is only 31 ms, and the motion posture monitoring recall rate is as high as 98.65%, indicating that the designed system can improve the motion posture monitoring effect. [ABSTRACT FROM AUTHOR]

Abstract: In view of the problems of large volume, high energy consumption and difficult maintenance of electric field measurement sensors in existing power systems, non-contact miniature electric field sensors have become a hot topic in current research. In this paper, a MEMS miniature electric field measurement sensor model based on the principle of piezoelectric-piezoresistive coupling is constructed, and the sensor structure is optimized by analyzing the steady-state characteristics of the piezoelectric material and semiconductor membrane of the sensor. The input and output characteristics of the sensor were tested. The test results show that the sensor has excellent mechanical strain capacity, and the output voltage of the sensor has a linear relationship with the electric field strength, thus verifying the feasibility of the sensor measurement in the electric field. The research results will provide some reference for the development of contactless coupled sensors.

Vacuum packaging is used extensively in MEMS sensors for improving performance. However, the vacuum in the MEMS chamber gradually degenerates over time, which adversely affects the long-term performance of the MEMS sensor. A mathematical model for vacuum degradation is presented in this article for evaluating the degradation of vacuum packaged MEMS sensors, and a temperature-accelerated test of MEMS gyroscope with different vacuums is performed. A mathematical degradation model is developed to fit the parameters of the degradation of Q-factor over time at three different temperatures. The results indicate that the outgassing rate at 85 °C is the highest, which is 0.0531 cm2/s; the outgassing rate at 105 °C is the lowest, which is 0.0109 cm2/s; and the outgassing rate at 125 °C is in the middle, which is 0.0373 cm2/s. Due to the different mechanisms by which gas was released, the rate of degradation did not follow this rule. It will also be possible to predict the long-term reliability of vacuum packaged MEMS sensors at room temperature based on this model. [ABSTRACT FROM AUTHOR]

Humidity sensors with comprehensive performance are of great interest for industrial and environmental applications. Most sensors, however, have to compromise on at least one characteristic such as sensitivity, response speed, and linearity. This paper reports a gravimetric humidity sensor based on a capacitive micromachined ultrasonic transducer (CMUT) with exceptional all-around performance, and presents a side-by-side comparative investigation of two types of gravimetric humidity sensors for a better understanding of their characteristics and sensing mechanisms. For these purposes, a circular CMUT and a quartz crystal microbalance (QCM) with a resonance frequency of 10 MHz were designed and fabricated. Poly(vinyl alcohol) (PVA) was employed as the humidity sensing layer for its hydrophilicity and ease of film formation. The electrical properties of the sensors, including the electrical input impedances and quality factors, were characterized by a vector network analyzer. The relative humidity (RH) sensing performance of the sensors was evaluated and compared from RH levels of 11% to 97%. Both sensors exhibited good repeatability and low hysteresis. The unique microscale resonant structure of the CMUT humidity sensor contributed to a high sensitivity of 2.01 kHz/%RH, short response and recovery times of 8 s and 3 s, respectively, and excellent linearity (R2 = 0.973), which were far superior to their QCM counterparts. The underlying mechanism was revealed and discussed. [ABSTRACT FROM AUTHOR]

This paper addresses physical and chemical resistance evaluation of tactile sensors. We have developed cantilevertype MEMS tactile sensors embedded in the elastomer. In this work, we used a combination of silicone elastomer with excellent mechanical properties and fluoroelastomer with excellent chemical resistance. As a new embedding method for the sensor, we devised a method of embedding with PDMS with low creep and coating with a fluoroelastomer for surface protection. We further performed three evaluations to demonstrate the physical and chemical resistance of sensors. Consequently, using the devised method, we have demonstrated that the sensor with both physical and chemical resistance are feasible. [ABSTRACT FROM AUTHOR]

In this paper, we report on the design and characterization of a microelectromechanical systems (MEMS) directional sensor inspired by the tympana configuration of the parasitic fly Ormia ochracea. The sensor is meant to be operated at resonance and act as a natural filter for the undesirable frequency bands. By means of breaking the symmetry of a pair of coupled bridged membranes, two independent bending vibrational modes can be excited. The electronic output, obtained by the transduction of the vibration to differential capacitance and then voltage through charge amplifiers, can be manipulated to tailor the frequency response of the sensor. Four different frequency characteristics were demonstrated. The sensor exhibits, at resonance, mechanical sensitivity around 6 μm/Pa and electrical sensitivity around 13 V/Pa. The noise was thoroughly characterized, and it was found that the sensor die, rather than the fundamental vibration, induces the predominant part of the noise. The computed average signal-to-noise (SNR) ratio in the pass band is about 91 dB. This result, in combination with an accurate dipole-like directional response, indicates that this type of directional sensor can be designed to exhibit high SNR and selectable frequency responses demanded by different applications. [ABSTRACT FROM AUTHOR]

As an important green energy source for the future, deep-sea natural gas hydrate has attracted worldwide attention in recent years, and several trial exploitations have been carried out. Hydrates are prone to decomposition leading to terrain subsidence; hence, there is an urgent need to monitor terrain change during the exploration. In this study, a monitoring device based on six-axis Micro-Electro-Mechanical System array is developed to monitor the terrain subsidence during production of gas hydrate. The liability of the device has been tested both by lab experiments and a sea trial in the "Shenhu" area of the South China Sea with water-depth of 1,203 m. The device performed in-situ monitoring for 193 consecutive days; the deformation of the seafloor terrain has been successfully measured, and the seafloor topography has been obtained and reconstructed, showing that the overall average uplift of the seafloor terrain is 0.82 cm, with a maximum uplift of 5.98 cm and a maximum subsidence of 3.21 cm. The result shows that the geological conditions in the "Shenhu" area are stable, which provide a reference for the development of hydrates. [ABSTRACT FROM AUTHOR]

The current monitoring methods for tunnel structure deformation mainly focus on laser distance measurement, fiber Bragg grating, photogrammetry, electronic total station, hydrostatic leveling and so on. Compared with traditional monitoring methods, MEMS sensors have the advantages of small size, low cost, low energy consumption and high accuracy. In this paper, MEMS sensors are used for the continuous real-time intelligent monitoring of model tunnels, and the multi-point deployment of MEMS sensors is set up for the tunnel structure monitoring with the indicators of acceleration and inclination. The results demonstrated that β-sample interpolation of the angles of the MEMS measurement points, and then integration of the overall displacements can better reflect the form of uneven settlement of the tunnel. For tunnel models with uneven settlement as the main deformation, the angle interpolation method allows the MEMS sensor to measure the vertical displacement more accurately and to determine the load mode to a certain extent. However, for tunnel models with global settlement as the main deformation, the results vary considerably from reality, as only the uneven part of the settlement can be measured using the angular interpolation method. [ABSTRACT FROM AUTHOR]

Abstract The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm’s effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3–1.7 m.

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to −1.2646 × 10−7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10−5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals. [ABSTRACT FROM AUTHOR]

The usage of unmanned aerial vehicles (UAVs) is rapidly increasing in the current era as these devices are capable enough in providing unique solutions in applications such as inspection of environment, identification of disaster, rescue operations, and defense systems. For the governance of the flight missions in a complex defense environment, the usage of these systems necessitated a sound command over data mining process. The large volume of data is generated by UAVs and processing of this data is a challenging issue. The existing data tracking and management systems are expensive and complex. For defense systems, smarter solutions are needed to process the large volume of data at low cost and with high accuracy. Therefore, a technique of tracking the data generated by UAV and automatic measurement of trajectory based on micro-electro-mechanical systems sensor in UAV has been proposed in this paper to provide inexpensive solutions to overcome the problems of the existing data tracking and processing systems. An iterative learning control algorithm is utilized in UAV to ascertain the disturbance and modeling errors. The finest characteristics of the Kalman filter technique are used for estimations of UAV trajectory. The quadratic performance function is introduced in discrete equation to solve the model error disturbance. Then on the basis of gyroscope data, the quaternion differential equation is formulated. The gradient descent process is also used to speed up the processing of UAV data. The results depict that the proposed technique has the lowest data tracking error of the UAV trajectory (0.09%) and has good measurement accuracy of 92%. The proposed method also reduces time complexity and searches the solution space in a faster manner. [ABSTRACT FROM AUTHOR]

The normal force exerted on a propulsive element is estimated based on acceleration measurements of an articulate-flexible propulsion mechanism in a subcarangiform swimming robotic fish. The propulsion mechanism is an articulating torso followed by a flexible caudal fin to provide thrust. The trunk is an assemblage of five ABS-plastic vertebrae driven by an actuator through a pair of wires, whereas the caudal fin is a silicone-rubber lunate-shaped tail coupled to the last vertebra. MEMS 3-axis sensors measured the linear acceleration experienced by the rigid head, articulated trunk, and compliant caudal fin at different undulation frequencies with the robotic fish prototype suspended in still water. The transverse acceleration measured was approximated as the reaction force exerted by the water on a propulsive element that accelerates the surrounding water. Subsequently, the caudal fin midline motion was analyzed by video processing to compare with the subcarangiform swimming kinematics model and to depict the normal force vectors in an undulation excursion. This study provides a feasible alternative to quantify the normal force generated by propulsive elements in bio-inspired propulsion mechanisms by using low-cost MEMS sensors to complement other well-suited techniques. [ABSTRACT FROM AUTHOR]

For measuring the structural health of buildings, high-performance vibration detection devices are used in a structural health monitoring (SHM) system, which consists of a sensor and a data logger. Those devices are seismographs or devices with high-performance sensors which are expensive. Recently, smartphones are being used as seismographs to accumulate big data of earthquake wave detection because they have accelerometers of microelectromechanical systems. Since a smartphone has the functions of a detection sensor and a data logger, a low-cost SHM system can be developed by using a low-cost smartphone. In this paper, smartphones were used to confirm the possibility of the development of a low-cost SHM system. To evaluate the vibration detection performance from small displacement and large displacement, smartphones were installed in a specimen of a large shaking table test. The specimen is a scale model of a two-story non-reinforced masonry-filled reinforce concrete (RC) frame building. The natural period and interstory drift ratio were used as the evaluation criteria. The natural period estimated by the smartphone data agreed with that found by the piezoelectric accelerometer data. For estimating the building deformation, which is related to building stability, the measurement performance for large deformation using smartphones was evaluated. The smartphones have 90% or higher accuracies for the estimation of the maximum acceleration and displacement.

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU’s accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU’s performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro’s error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro’s ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of Xb, Yb and Zb were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

Pd-modified metal sulfide gas sensors exhibit excellent hydrogen (H 2 ) sensing activity through spillover effects. However, the emulative oxygen adsorption often occupies an exposed Pd surface and thus limits the effective Pd-H interaction, impeding the H 2 sensing performance in air. Herein, we develop an edge-rich Pt-shell/Pd-core structure to adjust the selective adsorption between oxygen and hydrogen for effective H 2 sensing in an air atmosphere. Detailedly, through accurately regulating the rate of Pt deposition onto the icosahedron Pd surface, an edge-rich Pt-shell/Pd-core structure can be first achieved. It has been found that marginal Pt aggregations can segregate the oxygen molecules around the Pt species and induce easier Pt-O bonding, further guiding accessible Pd surfaces for effective Pd-H interactions, which can be verified by 1 H ssNMR, in-situ Raman, ex-situ XPS, and density functional theory analyses. The final ZnS/PdPt sensor exhibits an ultrasensitive response (8608 to 4% H 2 ) and a wide detected range (0.5 ppm-4%) in air, exceeding most reported hydrogen sensors.

Gait is a basic characteristic of human motion. Different gaits are usually associated with different body functions. Gait recognition has wide applications in clinical medicine, rehabilitation training, posture recognition, and other fields. At present, many wearable systems based on MEMS pressure and inertial sensors have been developed and used for gait recognition. However, there has been a lack of a comprehensive summary and comparison of these systems from the perspectives of their hardware compositions, working principles, algorithm models, and applications. This review aims to promote the development of wearable gait recognition devices. First, sensor technologies for gait measurement are summarized from the perspectives of their working principles and characteristics. Then, these technologies are compared in terms of the performance of algorithms for data preprocessing, cycle and phase segmentation, and gait recognition. Next, the applications of MEMS sensor-based gait recognition systems in various fields are summarized. Finally, some limitations of existing wearable gait recognition systems and their future directions are discussed. [ABSTRACT FROM AUTHOR]

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MEMS sensor is a new type of sensor manufactured by micro electronics and micro machining technology. Compared with traditional sensors, it has the characteristics of small size, light weight, low cost, low power consumption, high reliability, suitable for batch production, easy integration and intelligent realization. This project adopts a multi in one sensor based on MEMS principle. The sensor is a wireless passive three-axis sensor that integrates acceleration, speed, displacement, temperature, inclination and other parameters. It is very convenient to install and disassemble, and can realize the cycle monitoring of the whole wind field.

Recently, research has established a screening system comprising a combination of two technologies, the MEMS sensor and the field and laboratory emission cell (FLEC), to perform experiments. The aim of our study was to establish MEMS sensor field emission cell technology (MS-FECT) to measure the changes in the pollutant concentration of field emission cells. A building material emission database was created via an Internet of Things (IoT) in order to develop a MEMS sensor field emission modeling platform. On the basis of information of the building material emission database and decay models, the MS-FECT, IoT, indoor positioning system (Beacon), and cloud database are integrated in this system. The source of pollution was determined by sensing and data analysis to create a multidimensional map of pollution sources to determine air quality in order to monitor the location of pollutants and flow conditions in the long term to change the indoor air quality efficiently. Moreover, the results of this study will be helpful in house interior design, the maintenance of residents' health, and the reduction of carcinogenic threats. [ABSTRACT FROM AUTHOR]

With the wide applications of smart devices and mobile computing, smart home becomes a hot issue in the household appliance industry. The controlling and interaction approach plays a key role in users' experience and turns into one of the most important selling points for profit growth. Considering the robustness and privacy protection, wearable devices equipped with MEMS, e.g., smartphones, smartwatches, or smart wristbands, are thought of one of the most feasible commercial solutions for interaction. However, the low-cost built-in MEMS sensors do not perform well in capturing finely grained human activity directly. In this paper, we propose a method that leverages the arm constraint and historical information recorded by MEMS sensors to estimate the maximum likelihood action in a two-phases model. First, in the arm posture estimation phase, we leverage the kinematics model to analyze the maximum likelihood position of users' arms. Second, in the trajectory recognition phase, we leverage the gesture estimation model to identify the key actions and output the instructions to devices by SVM. Our substantial experiments show that the proposed solution can recognize eight kinds of postures defined for man-machine interaction in the smart home application scene, and the solution implements efficient and effective interaction using low-cost smartwatches, and the interaction accuracy is >87%. The experiments also show that the algorithm proposed in this paper can be well applied to the perceptual control of smart household appliances, and has high practical value for the application design of the perceptual interaction function of household appliances.

Vacuum packaging is used extensively in MEMS sensors for improving performance. However, the vacuum in the MEMS chamber gradually degenerates over time, which adversely affects the long-term performance of the MEMS sensor. A mathematical model for vacuum degradation is presented in this article for evaluating the degradation of vacuum packaged MEMS sensors, and a temperature-accelerated test of MEMS gyroscope with different vacuums is performed. A mathematical degradation model is developed to fit the parameters of the degradation of Q-factor over time at three different temperatures. The results indicate that the outgassing rate at 85 °C is the highest, which is 0.0531 cm2/s; the outgassing rate at 105 °C is the lowest, which is 0.0109 cm2/s; and the outgassing rate at 125 °C is in the middle, which is 0.0373 cm2/s. Due to the different mechanisms by which gas was released, the rate of degradation did not follow this rule. It will also be possible to predict the long-term reliability of vacuum packaged MEMS sensors at room temperature based on this model.

Humidity sensors with comprehensive performance are of great interest for industrial and environmental applications. Most sensors, however, have to compromise on at least one characteristic such as sensitivity, response speed, and linearity. This paper reports a gravimetric humidity sensor based on a capacitive micromachined ultrasonic transducer (CMUT) with exceptional all-around performance, and presents a side-by-side comparative investigation of two types of gravimetric humidity sensors for a better understanding of their characteristics and sensing mechanisms. For these purposes, a circular CMUT and a quartz crystal microbalance (QCM) with a resonance frequency of 10 MHz were designed and fabricated. Poly(vinyl alcohol) (PVA) was employed as the humidity sensing layer for its hydrophilicity and ease of film formation. The electrical properties of the sensors, including the electrical input impedances and quality factors, were characterized by a vector network analyzer. The relative humidity (RH) sensing performance of the sensors was evaluated and compared from RH levels of 11% to 97%. Both sensors exhibited good repeatability and low hysteresis. The unique microscale resonant structure of the CMUT humidity sensor contributed to a high sensitivity of 2.01 kHz/%RH, short response and recovery times of 8 s and 3 s, respectively, and excellent linearity (R2 = 0.973), which were far superior to their QCM counterparts. The underlying mechanism was revealed and discussed.

The current monitoring methods for tunnel structure deformation mainly focus on laser distance measurement, fiber Bragg grating, photogrammetry, electronic total station, hydrostatic leveling and so on. Compared with traditional monitoring methods, MEMS sensors have the advantages of small size, low cost, low energy consumption and high accuracy. In this paper, MEMS sensors are used for the continuous real-time intelligent monitoring of model tunnels, and the multi-point deployment of MEMS sensors is set up for the tunnel structure monitoring with the indicators of acceleration and inclination. The results demonstrated that β-sample interpolation of the angles of the MEMS measurement points, and then integration of the overall displacements can better reflect the form of uneven settlement of the tunnel. For tunnel models with uneven settlement as the main deformation, the angle interpolation method allows the MEMS sensor to measure the vertical displacement more accurately and to determine the load mode to a certain extent. However, for tunnel models with global settlement as the main deformation, the results vary considerably from reality, as only the uneven part of the settlement can be measured using the angular interpolation method.

The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node's limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy. [ABSTRACT FROM AUTHOR]

An acoustic quench detection method utilizing MEMS (Micro Electro-Mechanical System) acoustic sensors is proposed. To investigate this method, a commercially available MEMS piezoelectric microphone, the Vesper VM1000, and two types of second stage amplifiers, using either an OPA344 or a LMH6629 based amplifier circuit, were characterized at cryogenic temperatures in helium gas. The MEMS microphones were in their original package with an integrated preamplifier. The tests were performed inside a two-stage Gifford-McMahon cryocooler from room temperature down to 60 K, at static pressures between 1.2 and 1.4 bar in gaseous helium, over the frequency band from 100 Hz to 10 kHz. Second stage amplifiers were needed to achieve signal to noise ratios approaching the manufacturer specified operating levels. The OPA344 based amplifier reduced in gain by >55 dB below 230 K, while the LMH6629 based amplifier performed well down to 60 K. The MEMS microphones appear to perform acoustic measurements down to 165 K but with some reduction in sensitivity down to 60 K. An acoustic model of the cryocooler plane wave tube calibration setup is developed and used to calibrate the microphone despite the presence of a significant thermal gradients down the plane wave tube. [ABSTRACT FROM AUTHOR]

We designed a highly selective sensor to monitor 2,4-dinitrotoluene vapor. Sensor includes a microelectromechanical system (MEMS) based- polyvinyl alcohol nanofibers containing nanoparticles of polypyrrole and molecularly imprinted molecule that fabricated via lift-off process. Molecularly imprinted polymer nanoparticles were synthesized by polyvinyl alcohol as a functional polymer, glutaraldehyde as cross-linker and 2,4-dinitrotoluene as the template molecule. SEM, FT-IR and XRD techniques found properties of molecular template nanoparticles and synthesized polypyrrole. Results showed that 56 and 45 nm, respectively, were the typical particle size of synthesized polypyrrole and molecularly imprinted polymer nanoparticles. Sensor surface was deposited via the electrospinning technique by composite nanofibers like polyvinyl alcohol/polypyrrole/molecularly imprinted polymer. AFM analysis on MEMS surface showed that sensor surface area increased from approximately 300 nm (coating nanofiber) to approximately $3.5~\mu \text{m}$ (coating nano mat). Sensor was characterized by a dynamic set-up designed for the production of explosive vapors at a wide range of concentrations of the explosive vapor (10-1000 ppb). The limit of detection for the sensor was projected as 65 ppb. The developed MEMS sensor was measured at a steady 2,4-dinitrotoluene concentration in presence of vapor from other organic compounds. The developed sensor was highly sensitive and was highly selective against 2,4-dinitrotoluene. [ABSTRACT FROM AUTHOR]

In this article, novel thermal gas sensors with newly designed diffusion gas channels are proposed to reduce the flow-rate disturbance. Simulation studies suggest that by lowering the gas flow velocity near the hot film, the maximum normalized temperature changes caused by flow-rate variations in the two new designs (Type-H and Type-U) are decreased to only 1.22% and 0.02%, which is much smaller than in the traditional straight design (Type-I) of 20.16%. Experiment results are in agreement with the simulations that the maximum normalized flow-rate interferences in Type-H and Type-U are only 1.51% and 1.65%, compared to 24.91% in Type-I. As the introduced CO2 flow varied from 1 to 20 sccm, the normalized output deviations in Type-H and Type-U are 0.38% and 0.02%, respectively, which are 2 and 3 orders of magnitude lower than in Type-I of 10.20%. In addition, the recovery time is almost the same in all these sensors. These results indicate that the principle of decreasing the flow velocity near the hot film caused by the two novel diffusion designs can enhance the flow-rate independence and improve the accuracy of the thermal conductivity as well as the gas detection.