A Novel Packaging of the MEMS Gas Sensors Used for Harsh Outdoor and Human Exhale Sampling Applications.

Dust or condensed water present in harsh outdoor or high-humidity human breath samples are one of the key sources that cause false detection in Micro Electro-Mechanical System (MEMS) gas sensors. This paper proposes a novel packaging mechanism for MEMS gas sensors that utilizes a self-anchoring mechanism to embed a hydrophobic polytetrafluoroethylene (PTFE) filter into the upper cover of the gas sensor packaging. This approach is distinct from the current method of external pasting. The proposed packaging mechanism is successfully demonstrated in this study. The test results indicate that the innovative packaging with the PTFE filter reduced the average response value of the sensor to the humidity range of 75~95% RH by 60.6% compared to the packaging without the PTFE filter. Additionally, the packaging passed the High-Accelerated Temperature and Humidity Stress (HAST) reliability test. With a similar sensing mechanism, the proposed packaging embedded with a PTFE filter can be further employed for the application of exhalation-related, such as coronavirus disease 2019 (COVID-19), breath screening.

A Smart Active Phase-Change Micropump Based on CMOS-MEMS Technology.

The rational integration of many microfluidic chips and micropumps remains challenging. Due to the integration of the control system and sensors in active micropumps, they have unique advantages over passive micropumps when integrated into microfluidic chips. An active phase-change micropump based on complementary metal-oxide-semiconductor-microelectromechanical system (CMOS-MEMS) technology was fabricated and studied theoretically and experimentally. The micropump structure is simple and consists of a microchannel, a series of heater elements along the microchannel, an on-chip control system, and sensors. A simplified model was established to analyze the pumping effect of the traveling phase transition in the microchannel. The relationship between pumping conditions and flow rate was examined. Based on the experimental results, the maximum flow rate of the active phase-change micropump at room temperature is 22 µL/min, and long-term stable operation can be achieved by optimizing heating conditions.

A stochastic parabolic model of MEMS driven by fractional Brownian motion.

In this paper, we study a stochastic parabolic problem that emerges in the modeling and control of an electrically actuated MEMS (micro-electro-mechanical system) device. The dynamics under consideration are driven by an one dimensional fractional Brownian motion with Hurst index [Formula: see text]. We derive conditions under which the resulting SPDE has a global in time solution, and we provide analytic estimates for certain statistics of interest, such as quenching times and the corresponding quenching probabilities. Our results demonstrate the non-trivial impact of the fractional noise on the dynamics of the system. Given the significance of MEMS devices in biomedical applications, such as drug delivery and diagnostics, our results provide valuable insights into the reliability of these devices in the presence of positively correlated noise.

A Novel Wireless Low-Cost Inclinometer Made from Combining the Measurements of Multiple MEMS Gyroscopes and Accelerometers.

Structural damage detection using inclinometers is getting wide attention from researchers. However, the high price of inclinometers limits this system to unique structures with a relatively high structural health monitoring (SHM) budget. This paper presents a novel low-cost inclinometer, the low-cost adaptable reliable angle-meter (LARA), which combines five gyroscopes and five accelerometers to measure inclination. LARA incorporates Internet of Things (IoT)-based microcontroller technology enabling wireless data streaming and free commercial software for data acquisition. This paper investigates the accuracy, resolution, Allan variance and standard deviation of LARA produced with a different number of combined circuits, including an accelerometer and a gyroscope. To validate the accuracy and resolution of the developed device, its results are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC) in laboratory conditions. The results of a load test experiment on a simple beam model show the high accuracy of LARA (0.003 degrees). The affordability and high accuracy of LARA make it applicable for structural damage detection on bridges using inclinometers.

Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors.

This article proposes a novel initial bias estimation method using a trajectory generator (TG). The accuracy of attitude and position estimation in navigation after using the inertial navigation system/Doppler velocity log (INS/DVL) and INS/DVL/gyrocompass (IDG) for 1 h were evaluated, and the results were compared to those obtained using the conventional Kalman filter (KF) estimation method. The probability of a horizontal position error < 1852 m (1 nautical mile) with a bias interval > 400 s was 100% and 9% for the TG and KF, respectively. In addition, the IDG average horizontal position errors over 1 h were 493 m and 507 m for the TG and KF, respectively. Moreover, the amount of variation was 2 m and 27 m for the TG and the KF, respectively. Thus, the proposed method is effective for initial bias estimation of INS/DVL and IDG using micro-electro-mechanical system sensors on a constantly moving vessel.

Tilt Sensor with Recalibration Feature Based on MEMS Accelerometer.

The main errors of MEMS accelerometers are misalignments of their sensitivity axes, thermal and long-term drifts, imprecise factory calibration, and aging phenomena. In order to reduce these errors, a two-axial tilt sensor comprising a triaxial MEMS accelerometer, an aligning unit, and solid cubic housing was built. By means of the aligning unit it was possible to align the orientation of the accelerometer sensitive axes with respect to the housing with an accuracy of 0.03°. Owing to the housing, the sensor could be easily and quickly recalibrated, and thus errors such as thermal and long-term drifts as well as effects of aging were eliminated. Moreover, errors due to local and temporal variations of the gravitational acceleration can be compensated for. Procedures for calibrating and aligning the accelerometer are described. Values of thermal and long-term drifts of the tested sensor, resulting in tilt errors of even 0.4°, are presented. Application of the sensor for monitoring elevated loads is discussed.

A Machine Learning Approach for an Improved Inertial Navigation System Solution.

The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.

Microelectromechanical System Measurement of Platelet Contraction: Direct Interrogation of Myosin Light Chain Phosphorylation.

Myosin Light Chain (MLC) regulates platelet contraction through its phosphorylation by Myosin Light Chain Kinase (MLCK) or dephosphorylation by Myosin Light Chain Phosphatase (MLCP). The correlation between platelet contraction force and levels of MLC phosphorylation is unknown. We investigate the relationship between platelet contraction force and MLC phosphorylation using a novel microelectromechanical (MEMS) based clot contraction sensor (CCS). The MLCK and MLCP pair were interrogated by inhibitors and activators of platelet function. The CCS was fabricated from silicon using photolithography techniques and force was validated over a range of deflection for different chip spring constants. The force of platelet contraction measured by the clot contraction sensor (CCS) was compared to the degree of MLC phosphorylation by Western Blotting (WB) and ELISA. Stimulators of MLC phosphorylation produced higher contraction force, higher phosphorylated MLC signal in ELISA and higher intensity bands in WB. Inhibitors of MLC phosphorylation produced the opposite. Contraction force is linearly related to levels of phosphorylated MLC. Direct measurements of clot contractile force are possible using a MEMS sensor platform and correlate linearly with the degree of MLC phosphorylation during coagulation. Measured force represents the mechanical output of the actin/myosin motor in platelets regulated by myosin light chain phosphorylation.

Imperceptible energy harvesting device and biomedical sensor based on ultraflexible ferroelectric transducers and organic diodes.

Energy autonomy and conformability are essential elements in the next generation of wearable and flexible electronics for healthcare, robotics and cyber-physical systems. This study presents ferroelectric polymer transducers and organic diodes for imperceptible sensing and energy harvesting systems, which are integrated on ultrathin (1-µm) substrates, thus imparting them with excellent flexibility. Simulations show that the sensitivity of ultraflexible ferroelectric polymer transducers is strongly enhanced by using an ultrathin substrate, which allows the mounting on 3D-shaped objects and the stacking in multiple layers. Indeed, ultraflexible ferroelectric polymer transducers have improved sensitivity to strain and pressure, fast response and excellent mechanical stability, thus forming imperceptible wireless e-health patches for precise pulse and blood pressure monitoring. For harvesting biomechanical energy, the transducers are combined with rectifiers based on ultraflexible organic diodes thus comprising an imperceptible, 2.5-µm thin, energy harvesting device with an excellent peak power density of 3 mW·cm-3.

3D high-density microelectrode array with optical stimulation and drug delivery for investigating neural circuit dynamics.

Investigation of neural circuit dynamics is crucial for deciphering the functional connections among regions of the brain and understanding the mechanism of brain dysfunction. Despite the advancements of neural circuit models in vitro, technologies for both precisely monitoring and modulating neural activities within three-dimensional (3D) neural circuit models have yet to be developed. Specifically, no existing 3D microelectrode arrays (MEAs) have integrated capabilities to stimulate surrounding neurons and to monitor the temporal evolution of the formation of a neural network in real time. Herein, we present a 3D high-density multifunctional MEA with optical stimulation and drug delivery for investigating neural circuit dynamics within engineered 3D neural tissues. We demonstrate precise measurements of synaptic latencies in 3D neural networks. We expect our 3D multifunctional MEA to open up opportunities for studies of neural circuits through precise, in vitro investigations of neural circuit dynamics with 3D brain models.

Effect of running speed on temporal and frequency indicators from wearable MEMS accelerometers.

Amplified by the development of new technologies, the interest in personal performance has been growing over the last years. Acceleration has proved to be an easy variable to collect, and was addressed in several works. However, few of them evaluate the effect of running speed on relevant indicators. The influence of the sensors location on the measurement is rarely studied as well. This study is dedicated to investigating the effect of running speed on acceleration measured at three different positions on 18 volunteers. All participants were equipped with three inertial measurement units: on the dorsal surface of the right foot (Fo), at the centre of gravity of the tibia (Ti), at the L4-L5 lumbar (Lu). The test was performed on a treadmill at nine randomised speeds between 8 and 18 km/h. Ten accelerometric variables were calculated. Linear regressions were used to calculate speed from the indicators calculated on (Lu), (Ti), (Fo). Indicators associated to signal energy were highly correlated with speed (r2>0.90). Median frequency appears to be affected by the frequency resolution. Finally, the measurement points closest to the impact zone result in the most correlated indicators.

Analysis of the technical feasibility of using artificial intelligence for smoothing active power in a photovoltaic system connected to the power system

Abstract Recent technological advances and increased participation of energy systems based on photovoltaic solar energy place this renewable energy source in a prominent position in the current scenario. With the increase in the share of solar photovoltaic systems, the impact of power fluctuations in these sources has worsened, which can affect the quality of electrical energy and the reliability of the electrical power system. Therefore, with the use of energy storage together with control algorithms based on artificial intelligence, it is possible to control and perform power smoothing. In this context, the study presents a technical feasibility study on the use of artificial neural network (ANN) to perform the power smoothing of the photovoltaic system connected to the network. Being studied the performance of a real photovoltaic system operating in conjunction with an ideal energy storage for comparative analysis of the performance of the artificial neural network when the numbers of neurons and layers are modified for different real operating conditions considered as temperature variation, humidity, irradiation, pressure and wind speed, which are considered to be ANN input data. The results obtained point to the feasibility of using ANN, with acceptable precision, for power smoothing. According to the analyzes carried out, it is clear that ANN's with few neurons, the smoothing profile tends to be more accurate when compared to larger amounts of neurons. In the current state of the study, it was not possible to determine a relationship between the variations in the number of neurons with the most accurate results, it is important to note that the development of the curve pointed by the neural network can be influenced by the database. It should be noted that, when ANN exceeds or does not reach the optimal smoothing curve, the storage system compensates for the lack or excess of power, and there is a need for other mechanisms to optimize power smoothing.

Real-Time and Sensitive Immunosensor for Label-Free Detection of Specific Antigen with a Comb of Microchannel Long-Period Fiber Grating.

This study aimed to develop a comb of microchannel and immunosensor based on long-period fiber grating using the process of Lithographie Galvanoformung Abformung-like micro-electromechanical systems (LIGA-like MEMS) for real-time and label-free detection of specific antigen. The coupling between propagating core and cladding modes was conducted from the comb of microchannel long-period fiber grating (CM-LPFG). The CM-LPFG-based immunosensor consisted of a microchannel structure through photoresist stacking processes and was sandwiched with an optical fiber to obtain a long-period structure. Specific immunoglobulin against protein antigen was immobilized onto an optical fiber surface and produced a real-time resonance effect on sensing specific protein antigen from the extracted protein mixtures of the cancer cell lines. The variable transmission loss was -14.07 dB, and the resonant wavelength shift was 11.239 nm. The low limit of detection for total protein concentration was 1.363 ng/µL. Our results revealed that the CM-LPFG-based immnosensor for real-time detection of label-free protein antigen is feasible and sensitive based on the diversification of a transmission loss and achieves specific immunosensing purposes for lab-on-fiber technology.

Extraction of Cell-free Dna from An Embryo-culture Medium Using Micro-scale Bio-reagents on Ewod.

As scientific and technical knowledge advances, research on biomedical micro-electromechanical systems (bio-MEMS) is also developing towards lab-on-a-chip (LOC) devices. A digital microfluidic (DMF) system specialized for an electrowetting- on-dielectric (EWOD) mechanism is a promising technique for such point-of-care systems. EWOD microfluidic biochemical analytical systems provide applications over a broad range in the lab-on-a-chip field. In this report, we treated extraction of cell-free DNA (cf-DNA) at a small concentration from a mouse embryo culture medium (2.5 days & 3.5 days) with electro-wetting on a dielectric (EWOD) platform using bio-reagents of micro-scale quantity. For such extraction, we modified a conventional method of genomic-DNA (g-DNA) extraction using magnetic beads (MB). To prove that extraction of cf-DNA with EWOD was accomplished, as trials we extracted designed-DNA (obtained from Chang Gung Memorial Hospital (CGMH), Taiwan which shows properties similar to that of cf-DNA). Using that designed DNA, extraction with both conventional and EWOD methods has been performed; the mean percentage of extraction with both methods was calculated for a comparison. From the cycle threshold (Ct) results with a quantitative polymerase chain reaction (q-PCR), the mean extraction percentages were obtained as 14.8 percent according to the conventional method and 23 percent with EWOD. These results show that DNA extraction with EWOD appears promising. The EWOD extraction involved voltage 100 V and frequency 2 kHz. From this analysis, we generated a protocol for an improved extraction percentage on a EWOD chip and performed cf-DNA extraction from an embryo-culture medium (KSOM medium) at 3.5 and 2.5 days. The mean weight obtained for EWOD-extracted cf-DNA is 0.33 fg from the 3.5-day sample and 31.95 fg from the 2.5-day sample. All these results will pave a new path towards a renowned lab-on-a-chip concept.

Real-time Lissajous imaging with a low-voltage 2-axis MEMS scanner based on electrothermal actuation.

Laser scanning based on Micro-Electro-Mechanical Systems (MEMS) scanners has become very attractive for biomedical endoscopic imaging, such as confocal microscopy or Optical Coherence Tomography (OCT). These scanners are required to be fast to achieve real-time image reconstruction while working at low actuation voltage to comply with medical standards. In this context, we report a 2-axis Micro-Electro-Mechanical Systems (MEMS) electrothermal micro-scannercapable of imaging large fields of view at high frame rates, e.g. from 10 to 80 frames per second. For this purpose, Lissajous scan parameters are chosen to provide the optimal image quality within the scanner capabilities and the sampling rate limit, resulting from the limited A-scan rate of typical swept-sources used for OCT. Images of 233 px × 203 px and 53 px × 53 px at 10 fps and 61 fps, respectively, are experimentally obtained and demonstrate the potential of this micro-scannerfor high definition and high frame rate endoscopic Lissajous imaging.

Quantum simulation of particle creation in curved space-time.

Conversion of vacuum fluctuations into real particles was first predicted by L. Parker considering an expanding universe, followed in S. Hawking's work on black hole radiation. Since their experimental observation is challenging, analogue systems have gained attention in the verification of this concept. Here we propose an experimental set-up consisting of two adjacent piezoelectric semiconducting layers, one of them carrying dynamic quantum dots (DQDs), and the other being p-doped with an attached gate on top, which introduces a space-dependent layer conductivity. The propagation of surface acoustic waves (SAWs) on the latter layer is governed by a wave equation with an effective metric. In the frame of the DQDs, this space- and time-dependent metric possesses a sonic horizon for SAWs and resembles that of a two dimensional non-rotating and uncharged black hole to some extent. The non-thermal steady state of the DQD spin indicates particle creation in form of piezophonons.

MEMS-Based Ionization Gas Sensors for VOCs with Array of Nanostructured Silicon Needles.

Although volatile organic compound samples can be detected by gas nanosensors in adsorption principles, extreme concentrations of target gases imply the excessive adsorption, which would lead to a long recovery time and even a shortened lifetime. Herein, we report the observations of the ionization current sensing behavior on the volatile organic compounds in an ionization gas sensor with silicon-based nanostructures. The micro ionization gas sensor consists of a pair of silicon microneedle array electrodes covered by nanolayer structures and a microdischarge gas gap. The dynamic response behaviors of the sensors to the exposure of ethanol, acetone, and 2-chloroethyl ethyl sulfide have been carefully scrutinized. The sensor exhibits sound performances to the high-concentration volatile organic compounds with a fast-recovery property and could generate effective responses well at 36 V, namely, the safety operation voltages. It could be well understood by the Jesse effect where small proportion of impurities in gases could lead to an intensive increase in the overall ionization probability. Besides, the reproducibility, recovery time, sensitivity, and selectivity properties have been systematically characterized.

Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for Pedestrian Navigation.

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions.

The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms.

Feasibility of Discriminating UAV Propellers Noise from Distress Signals to Locate People in Enclosed Environments Using MEMS Microphone Arrays.

Detecting and finding people are complex tasks when visibility is reduced. This happens, for example, if a fire occurs. In these situations, heat sources and large amounts of smoke are generated. Under these circumstances, locating survivors using thermal or conventional cameras is not possible and it is necessary to use alternative techniques. The challenge of this work was to analyze if it is feasible the integration of an acoustic camera, developed at the University of Valladolid, on an unmanned aerial vehicle (UAV) to locate, by sound, people who are calling for help, in enclosed environments with reduced visibility. The acoustic array, based on MEMS (micro-electro-mechanical system) microphones, locates acoustic sources in space, and the UAV navigates autonomously by closed enclosures. This paper presents the first experimental results locating the angles of arrival of multiple sound sources, including the cries for help of a person, in an enclosed environment. The results are promising, as the system proves able to discriminate the noise generated by the propellers of the UAV, at the same time it identifies the angles of arrival of the direct sound signal and its first echoes reflected on the reflective surfaces.