Cost-Efficient Oceanographic Instrument with Microfabricated Sensors for Measuring Conductivity, Temperature and Depth of Seawater.

The design, fabrication and characterization of a cost-efficient oceanographic instrument with microfabricated sensors for measuring conductivity, temperature and depth of seawater are presented. Conductivity and temperature sensors were fabricated using MEMS technology, which allows for customized small footprints and low production costs. Dedicated electronics for reading, processing and storing acquired sensor data are described. The developed instrument enables the measurement of seawater conductivity in a range from 4 mS/cm to 70 mS/cm. The conductivity measurement is temperature-compensated in the range from 2 °C to 40 °C, with an accuracy of ±0.1 mS/cm. The temperature sensor's stability is 0.025 °C. The depth/pressure measurement range is up to 2000 m/200 bar, with a resolution of 0.1 bar. Temperature and conductivity sensor performance was assessed using laboratory equipment and designed electronics. The conductivity sensor was temperature-compensated to 0.01 mS/cm. The conductivity sensor electrode corrosion effect is presented below and was eliminated through adaptation of a signal acquisition circuit. Custom software was developed for monitoring critical conductivity sensor parameters (currents, voltages). A variation of 0.4% between cell conductance currents and voltages was established as a criterion for stable conductivity sensor operation.

Low cost MEMS accelerometer and microphone based condition monitoring sensor, with LoRa and Bluetooth Low Energy radio.

Vibration-based Condition Monitoring (CM) is an essential tool for identifying potential defects in industrial machinery. However, the implementation of an efficient CM system often necessitates the use of high-cost accelerometers with a large bandwidth. To address this challenge, this study introduces a low-cost CM sensor composed of an ultrasonic MEMS microphone - SPH0641LU and an ADXL1002 MEMS accelerometer. The combination of these two sensor types allows for comparative analysis of the captured data. The SPH641LU microphone is capable of detecting audible vibration signals with a frequency range up to 80 kHz, while the ADXL1002 accelerometer can measure vibrations up to 21 kHz. In addition a three axis ultra low power accelerometer is included, allowing measurement of unbalance or rotating speed, below 200Hz. Moreover, the sensor is designed to operate on battery power and provides the capability for raw data transmission via Bluetooth Low Energy (BLE) or the transmission of pre-processed features from the raw data using LoRaWAN.

Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

Assessment of Noise of MEMS IMU Sensors of Different Grades for GNSS/IMU Navigation.

Inertial measurement units (IMUs) are key components of various applications including navigation, robotics, aerospace, and automotive systems. IMU sensor characteristics have a significant impact on the accuracy and reliability of these applications. In particular, noise characteristics and bias stability are critical for proper filter settings to perform a combined GNSS/IMU solution. This paper presents an analysis based on the Allan deviation of different IMU sensors that correspond to different grades of micro-electromechanical systems (MEMS)-type IMUs in order to evaluate their accuracy and stability over time. The study covers three IMU sensors of different grades (ascending order): Rokubun Argonaut navigator sensor (InvenSense TDK MPU9250), Samsung Galaxy Note10 phone sensor (STMicroelectronics LSM6DSR), and NovAtel PwrPak7 sensor (Epson EG320N). The noise components of the sensors are computed using overlapped Allan deviation analysis on data collected over the course of a week in a static position. The focus of the analysis is to characterize the random walk noise and bias stability, which are the most critical for combined GNSS/IMU navigation and may differ or may not be listed in manufacturers' specifications. Noise characteristics are calculated for the studied sensors and examples of their use in loosely coupled GNSS/IMU processing are assessed. This work proposes a structured and reproducible approach for working with sensors for their use in navigation tasks in combination with GNSS, and can be used for sensors of different levels to supplement missing or incorrect sensor manufacturers' data.

Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy.

This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.

Comprehensive assessment of fine motor movement and cognitive function among older adults in China: a cross-sectional study.

BACKGROUND: Fine motor skills are closely related to cognitive function. However, there is currently no comprehensive assessment of fine motor movement and how it corresponds with cognitive function. To conduct a complete assessment of fine motor and clarify the relationship between various dimensions of fine motor and cognitive function. METHODS: We conducted a cross-sectional study with 267 community-based participants aged ≥ 60 years in Beijing, China. We assessed four tests performance and gathered detailed fine motor indicators using Micro-Electro-Mechanical System (MEMS) motion capture technology. The wearable MEMS device provided us with precise fine motion metrics, while Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess cognitive function. We adopted logistic regression to analyze the relationship between fine motor movement and cognitive function. RESULTS: 129 (48.3%) of the participants had cognitive impairment. The vast majority of fine motor movements have independent linear correlations with MoCA-BJ scores. According to logistic regression analysis, completion time in the Same-pattern tapping test (OR = 1.033, 95%CI = 1.003-1.063), Completion time of non-dominant hand in the Pieces flipping test (OR = 1.006, 95%CI = 1.000-1.011), and trajectory distance of dominant hand in the Pegboard test (OR = 1.044, 95%CI = 1.010-1.068), which represents dexterity, are related to cognitive impairment. Coordination, represented by lag time between hands in the Same-pattern tapping (OR = 1.663, 95%CI = 1.131-2.444), is correlated with cognitive impairment. Coverage in the Dual-hand drawing test as an important indicator of stability is negatively correlated with cognitive function (OR = 0.709, 95%CI = 0.6501-0.959). Based on the above 5-feature model showed consistently high accuracy and sensitivity at the MoCA-BJ score (ACU = 0.80-0.87). CONCLUSIONS: The results of a comprehensive fine-motor assessment that integrates dexterity, coordination, and stability are closely related to cognitive functioning. Fine motor movement has the potential to be a reliable predictor of cognitive impairment.

Design, Optimization and Performance Assessment of Single Port Film Bulk Acoustic Resonator through Finite Element Simulation.

In this paper, the study is supported by design, FEA simulation, and practical RF measurements on fabricated single-port-cavity-based acoustic resonator for gas sensing applications. In the FEA simulation, frequency domain analysis was performed to enhance the performance of the acoustic resonator. The structural and surface morphologies of the deposited ZnO as a piezoelectric layer have been studied using XRD and AFM. The XRD pattern of deposited bulk ZnO film indicates the perfect single crystalline nature of the film with dominant phase (002) at 2θ = 34.58°. The AFM micrograph indicates that deposited piezoelectric film has a very smooth surface and small grain size. In the fabrication process, use of bulk micro machined oxide (SiO2) for the production of a thin membrane as a support layer is adopted. A vector network analyzer (Model MS2028C, Anritsu) was used to measure the radio frequency response of the resonators from 1 GHz to 2.5 GHz. As a result, we have successfully fabricated an acoustic resonator operating at 1.84 GHz with a quality factor Q of 214 and an effective electromechanical coupling coefficient of 10.57%.

The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems.

In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models' performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters.

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors.

The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors' calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precision of the estimation. The weight of the sensor is adaptively adjusted according to the RMSE concerning the weighted average of all sensors. The estimated angular velocity was compared with a reference (ground truth) value obtained using a tactical-grade fiber-optic gyroscope. We have experimented with low-cost MEMS gyroscopes, but the proposed method can be applied to basically any sensor array.

Decoupling Transmission and Transduction for Improved Durability of Highly Stretchable, Soft Strain Sensing: Applications in Human Health Monitoring.

This work presents a modular approach to the development of strain sensors for large deformations. The proposed method separates the extension and signal transduction mechanisms using a soft, elastomeric transmission and a high-sensitivity microelectromechanical system (MEMS) transducer. By separating the transmission and transduction, they can be optimized independently for application-specific mechanical and electrical performance. This work investigates the potential of this approach for human health monitoring as an implantable cardiac strain sensor for measuring global longitudinal strain (GLS). The durability of the sensor was evaluated by conducting cyclic loading tests over one million cycles, and the results showed negligible drift. To account for hysteresis and frequency-dependent effects, a lumped-parameter model was developed to represent the viscoelastic behavior of the sensor. Multiple model orders were considered and compared using validation and test data sets that mimic physiologically relevant dynamics. Results support the choice of a second-order model, which reduces error by 73% compared to a linear calibration. In addition, we evaluated the suitability of this sensor for the proposed application by demonstrating its ability to operate on compliant, curved surfaces. The effects of friction and boundary conditions are also empirically assessed and discussed.

A High-Performance MEMS Accelerometer with an Improved TGV Process of Low Cost.

High-performance MEMS accelerometers usually use a pendulum structure with a larger mass. Although the performance of the device is guaranteed, the manufacturing cost is high. This paper proposes a method of fabricating high-performance MEMS accelerometers with a TGV process, which can reduce the manufacturing cost and ensure the low-noise characteristics of the device. The TGV processing relies on laser drilling, the metal filling in the hole is based on the casting mold and CMP, and the packaging adopts the three-layer anodic bonding process. Moreover, for the first time, the casting mold process is introduced to the preparation of MEMS devices. In terms of structural design, the stopper uses distributed comb electrodes for overload displacement suppression, and the gas released by the packaging method provides excellent mechanical damping characteristics. The prepared accelerometer has an anti-overload capability of 10,000 g, the noise density is less than 0.001°/√Hz, and it has ultra-high performance in tilt measurement.

MEMS Vibrometer for Structural Health Monitoring Using Guided Ultrasonic Waves.

Structural health monitoring of lightweight constructions made of composite materials can be performed using guided ultrasonic waves. If modern fiber metal laminates are used, this requires integrated sensors that can record the inner displacement oscillations caused by the propagating guided ultrasonic waves. Therefore, we developed a robust MEMS vibrometer that can be integrated while maintaining the structural and functional compliance of the laminate. This vibrometer is directly sensitive to the high-frequency displacements from structure-borne ultrasound when excited in a frequency range between its first and second eigenfrequency. The vibrometer is mostly realized by processes earlier developed for a pressure sensor but with additional femtosecond laser ablation and encapsulation. The piezoresistive transducer, made from silicon, is encapsulated between top and bottom glass lids. The eigenfrequencies are experimentally determined using an optical micro vibrometer setup. The MEMS vibrometer functionality and usability for structural health monitoring are demonstrated on a customized test rig by recording application-relevant guided ultrasonic wave packages with a central frequency of 100 kHz at a distance of 0.2 m from the exciting ultrasound transducer.

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures.

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.

A hybrid learning-based stochastic noise eliminating method with attention-Conv-LSTM network for low-cost MEMS gyroscope.

Low-cost inertial measurement units (IMUs) based on microelectromechanical system (MEMS) have been widely used in self-localization for autonomous robots due to their small size and low power consumption. However, the low-cost MEMS IMUs often suffer from complex, non-linear, time-varying noise and errors. In order to improve the low-cost MEMS IMU gyroscope performance, a data-driven denoising method is proposed in this paper to reduce stochastic errors. Specifically, an attention-based learning architecture of convolutional neural network (CNN) and long short-term memory (LSTM) is employed to extract the local features and learn the temporal correlation from the MEMS IMU gyroscope raw signals. The attention mechanism is appropriately designed to distinguish the importance of the features at different times by automatically assigning different weights. Numerical real field, datasets and ablation experiments are performed to evaluate the effectiveness of the proposed algorithm. Compared to the raw gyroscope data, the experimental results demonstrate that the average errors of bias instability and angle random walk are reduced by 57.1 and 66.7%.

Improved Resolution and Cost Performance of Low-Cost MEMS Seismic Sensor through Parallel Acquisition.

In earthquake monitoring, an important aspect of the operational effect of earthquake intensity rapid reporting and earthquake early warning networks depends on the density and performance of the deployed seismic sensors. To improve the resolution of seismic sensors as much as possible while keeping costs low, in this article the use of multiple low-cost and low-resolution digital MEMS accelerometers is proposed to increase the resolution through the correlation average method. In addition, a cost-effective MEMS seismic sensor is developed. With ARM and Linux embedded computer technology, this instrument can cyclically store the continuous collected data on a built-in large-capacity SD card for approximately 12 months. With its real-time seismic data processing algorithm, this instrument is able to automatically identify seismic events and calculate ground motion parameters. Moreover, the instrument is easy to install in a variety of ground or building conditions. The results show that the RMS noise of the instrument is reduced from 0.096 cm/s2 with a single MEMS accelerometer to 0.034 cm/s2 in a bandwidth of 0.1-20 Hz by using the correlation average method of eight low-cost MEMS accelerometers. The dynamic range reaches more than 90 dB, the amplitude-frequency response of its input and output within -3 dB is DC -80 Hz, and the linearity is better than 0.47%. In the records from our instrument, earthquakes with magnitudes between M2.2 and M5.1 and distances from the epicenter shorter than 200 km have a relatively high SNR, and are more visible than they were prior to the joint averaging.

Additive Manufacturing of Micro-Electro-Mechanical Systems (MEMS).

Recently, additive manufacturing (AM) processes applied to the micrometer range are subjected to intense development motivated by the influence of the consolidated methods for the macroscale and by the attraction for digital design and freeform fabrication. The integration of AM with the other steps of conventional micro-electro-mechanical systems (MEMS) fabrication processes is still in progress and, furthermore, the development of dedicated design methods for this field is under development. The large variety of AM processes and materials is leading to an abundance of documentation about process attempts, setup details, and case studies. However, the fast and multi-technological development of AM methods for microstructures will require organized analysis of the specific and comparative advantages, constraints, and limitations of the processes. The goal of this paper is to provide an up-to-date overall view on the AM processes at the microscale and also to organize and disambiguate the related performances, capabilities, and resolutions.

A Review on the Development of Earthquake Warning System Using Low-Cost Sensors in Taiwan.

Seismic instrumentation for earthquake early warnings (EEWs) has improved significantly in the last few years, considering the station coverage, data quality, and the related applications. The official EEW system in Taiwan is operated by the Central Weather Bureau (CWB) and is responsible for issuing the regional warning for moderate-to-large earthquakes occurring in and around Taiwan. The low-cost micro-electro-mechanical system (MEMS)-based P-Alert EEW system is operational in Taiwan for on-site warnings and for producing shakemaps. Since 2010, this P-Alert system, installed by the National Taiwan University (NTU), has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for on-site warning. Using real-time seismic signals, each P-Alert system can provide a 2-8 s-long warning time for the locations situated in the blind zone of the CWB regional warning system. The shakemaps plotted using this instrumentation help to assess the damage pattern and rupture directivity, a key feature in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Earlier, the network provided only peak ground acceleration (PGA) shakemaps, but has now been updated to include peak ground velocity (PGV), spectral acceleration (Sa) at different periods, and CWB intensity maps. The PGA and PGV shakemaps plotted using this network have proven helpful in establishing the fact that PGV is a better indicator of damage detection than PGA. This instrumentation is also useful in structural health-monitoring and estimating co-seismic deformations. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries.

Study of Building Safety Monitoring by Using Cost-Effective MEMS Accelerometers for Rapid After-Earthquake Assessment with Missing Data.

Suffering from structural deterioration and natural disasters, the resilience of civil structures in the face of extreme loadings inevitably drops, which may lead to catastrophic structural failure and presents great threats to public safety. Earthquake-induced extreme loading is one of the major reasons behind the structural failure of buildings. However, many buildings in earthquake-prone areas of China lack safety monitoring, and prevalent structural health monitoring systems are generally very expensive and complicated for extensive applications. To facilitate cost-effective building-safety monitoring, this study investigates a method using cost-effective MEMS accelerometers for buildings' rapid after-earthquake assessment. First, a parameter analysis of a cost-effective MEMS sensor is conducted to confirm its suitability for building-safety monitoring. Second, different from the existing investigations that tend to use a simplified building model or small-scaled frame structure excited by strong motions in laboratories, this study selects an in-service public building located in a typical earthquake-prone area after an analysis of earthquake risk in China. The building is instrumented with the selected cost-effective MEMS accelerometers, characterized by a low noise level and the capability to capture low-frequency small-amplitude dynamic responses. Furthermore, a rapid after-earthquake assessment scheme is proposed, which systematically includes fast missing data reconstruction, displacement response estimation based on an acceleration response integral, and safety assessment based on the maximum displacement and maximum inter-story drift ratio. Finally, the proposed method is successfully applied to a building-safety assessment by using earthquake-induced building responses suffering from missing data. This study is conducive to the extensive engineering application of MEMS-based cost-effective building monitoring and rapid after-earthquake assessment.

Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis.

One of the most popular options in the Structural Health Monitoring field is the tracking of the modal parameters, which are estimated through the frequency response functions of the structure, usually in the form of accelerances, which are computed as the ratio between the measured accelerations and the applied forces. This requires the use of devices capable of synchronously recording accelerations at several points of the structure at high sampling rates and the subsequent computational analysis using the recorded data. To this end, this work presents and validates a new scalable acquisition system based on multiple myRIO devices and digital MEMS (Micro-Electro-Mechanical System) accelerometers, intended for modal analysis of large structures. A simple form of this system was presented by the authors in a previous work, showing that a single board with some accelerometers connected to it got to obtain high quality measurements in both time and frequency domains. Now, a larger system composed by several slave boards connected and synchronized to a master one is presented. Delays lower than 100 ns are found between the synchronised channels of the proposed system. For validation purposes, a case study is presented where the devices are deployed on a timber platform to estimate its modal properties, which are compared with the ones provided by a commercial system, based on analog accelerometers, to show that similar results are obtained at a significantly lower cost.

Design and Fabrication of a Low-Cost Thermopile Infrared Detector.

In this paper, we design and optimize a low-cost, closed-film structure of a microelectromechanical systems (MEMS) thermopile infrared detector. By optimizing the circular arrangement of thermocouple strips and the thermal isolation design of the cold end to pursue a higher temperature difference, in addition to eliminating the absorption region, silicon nitride is deposited on the whole device surface as a passivated absorption layer. This reduces the cost while maintaining the voltage response and is suitable for mass production. The optimized detector had a 22.6% improvement in the response rate to 34.2 V/W, a detection rate of 1.02 × 108 cm·Hz1/2/W, and a response time of 26.9 ms. The design optimization of this detector provides a reference for further development of IR detectors.