Search Results (17)

To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. To enhance predictive accuracy of water trace, spectral data were preprocessed using smoothing combined with first-derivative processing, and optimal selection of absorption wavelength feature was performed using interval Partial Least Squares (iPLS). Cross-batch external validation demonstrated a Limit of Detection (LOD) of 0.026% with 95% confidence which satisfies the rapid screening requirements for water exceedances (>0.1%) in industrial applications. These findings provide a robust technical foundation for developing handheld, in situ water detection devices. Full article

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials. Full article

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM—micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds—namely melamine, urea, taurine, and glycine—resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R²P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders. Full article

Swept-source optical coherence tomography (SS-OCT), benefiting from its high sensitivity, relatively large penetration depth, and non-contact and non-invasive imaging capability, is ideal for human skin imaging. However, limited by the size and performance of the reported optical galvanometer scanners, existing portable/handheld OCT probes are still bulky, which makes continuously handheld imaging difficult. Here, we reported a miniaturized electrothermal-MEMS-based SS-OCT microscope that only weighs about 25 g and has a cylinder with a diameter of 15 mm and a length of 40 mm. This MEMS-based handheld imaging probe can achieve a lateral resolution of 25 μm, a 3D imaging time of 5 s, a penetration depth of up to 3.3 mm, and an effective imaging field of view (FOV) of 3 × 3 mm². We have carried out both calibration plate and biological tissue imaging experiments to test the imaging performance of this microscope. OCT imaging of leaves, dragonfly, and human skin has been successfully obtained, showing the imaging performance and potential applications of this probe on human skin in the future. Full article

Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R² metric was 0.49. Full article

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (R_pred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way. Full article

Microelectromechanical (MEMS) piezoelectric accelerometers are diversely used in consumer electronics and handheld devices due to their low power consumption as well as simple reading circuit and good dynamic performance. In this paper, a tri-axial piezoelectric accelerometer with folded beams is presented. The four beam suspensions are located at two sides of the mass aligned with edges of the mass, and the thickness of the beams is the same as the thickness of the mass block. In order to realize the multi-axis detection, a total of 16 sensing elements are distributed at the end of the folded beams. The structural deformations, stress distribution, and output characteristics due to the acceleration in x-, y-, and z-axis directions are theoretically analyzed and simulated. The proposed accelerometer is fabricated by MEMS processes to form Mo/AlN/ScAlN/Mo piezoelectric stacks as the sensing layer. Experiments show that the charge sensitivity along the x-, y-, and z-axes could reach up to ~1.07 pC/g, ~0.66 pC/g, and ~3.35 pC/g. The new structure can provide inspiration for the design of tri-axial piezoelectric accelerometers with great sensitivity and linearity. Full article

In this paper, a detail design procedure of the real-time trajectory tracking for the nonholonomic wheeled mobile robot (NWMR) is proposed. A 9-axis micro electro-mechanical systems (MEMS) inertial measurement unit (IMU) sensor is used to measure the posture of the NWMR, the position information of NWMR and the hand-held device are acquired by global positioning system (GPS) and then transmit via radio frequency (RF) module. In addition, in order to avoid the gimbal lock produced by the posture computation from Euler angles, the quaternion is utilized to compute the posture of the NWMR. Furthermore, the Kalman filter is used to filter out the readout noise of the GPS and calculate the position of NWMR and then track the object. The simulation results show the posture error between the NWMR and the hand-held device can converge to zero after 3.928 seconds for the dynamic tracking. Lastly, the experimental results show the validation and feasibility of the proposed results. Full article

This paper presents a novel approach for hand-held low-cost MEMS (micro-electro-mechanical system) gyroscope in-self calibration. This method does not need the support of external high-precision equipment compared with traditional calibration scheme and can be accomplished by user hand rotation. In this approach, Kalman filter is designed to perform the calibration procedure and estimate gyroscope bias error, scale factor error and non-orthogonal error. The system observability is analyzed and the dynamic rotating conditions under which the sensor errors become observable are derived. The design principles of optimal calibration procedure are provided as well. Both simulated and practical experiments are carried out to test the validation of the proposed calibration algorithm. The achieved results demonstrate that the introduced approach can provide promising calibration scheme for the low-cost MEMS gyroscope. Full article

In this paper, a self-out-readable, miniaturized cantilever resonator for highly sensitive
airborne nanoparticle (NP) detection is presented. The cantilever, which is operated in the
fundamental in-plane resonance mode, is used as a microbalance with femtogram resolution. To
achieve a maximum measurement signal of the piezo resistive Wheatstone half-bridge, the
geometric parameters of the sensor design were optimized by finite element modelling (FEM).
Struts at the sides of the cantilever resonator act as piezo resistors and enable an electrical read-out
of the phase information of the cantilever movement whereby they do not contribute to the
resonators rest mass. For the optimized design, a resonator mass of 0.93 ng, a resonance frequency
of ~440 kHz, and thus a theoretical sensitivity of 4.23 fg/Hz can be achieved. A μ-channel guiding a
particle-laden air flow towards the cantilever is integrated into the sensor chip. Electrically charged
NPs will be collected by an electrostatic field between the cantilever and a counter-electrode at the
edges of the μ-channel. Such μ-channels will also be used to accomplish particle separation for sizeselective
NP detection. Throughout, the presented airborne NP sensor is expected to demonstrate
significant improvements in the field of handheld, MEMS-based NP monitoring devices. Full article

Bad breath or halitosis affects a majority of the population from time to time, causing personal discomfort and social embarrassment. Here, we report on a miniaturized, microelectromechanical systems (MEMS)-based, amperometric hydrogen sulfide (H₂S) sensor that potentially allows bad breath quantification through a small handheld device. The sensor is designed to detect H₂S gas in the order of parts-per-billion (ppb) and has a measured sensitivity of 0.65 nA/ppb with a response time of 21 s. The sensor was found to be selective to NO and NH₃ gases, which are normally present in the oral breath of adults. The ppb-level detection capability of the integrated sensor, combined with its relatively fast response and high sensitivity to H₂S, makes the sensor potentially applicable for oral breath monitoring. Full article

Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone. Full article

This paper introduces a new method which facilitate the use of smartphones as a handheld low-cost mobile mapping system (MMS). Smartphones are becoming more sophisticated and smarter and are quickly closing the gap between computers and portable tablet devices. The current generation of smartphones are equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite System (GNSS), accumulated drift, and high signal noise. These issues affect the accuracy of the initial Exterior Orientation Parameters (EOPs) that are inputted into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. This paper proposes new methodologies for increasing the accuracy of direct geo-referencing of smartphones using relative orientation and smartphone motion sensor measurements as well as integrating geometric scene constraints into free network bundle adjustment. The new methodologies incorporate fusing the relative orientations of the captured images and their corresponding motion sensor measurements to improve the initial EOPs. Then, the geometric features (e.g., horizontal and vertical linear lines) visible in each image are extracted and used as constraints in the bundle adjustment procedure which correct the relative position and orientation of the 3D mapping solution. Full article

Over the last decade, optical fiber-based forms of microscopy and endoscopy have extended the realm of applicability for many imaging modalities. Optical fiber-based imaging modalities permit the use of remote illumination sources and enable flexible forms supporting the creation of portable and hand-held imaging instrumentations to interrogate within hollow tissue cavities. A common challenge in the development of such devices is the design and integration of miniaturized optical and mechanical components. Until recently, microelectromechanical systems (MEMS) sensors and actuators have been playing a key role in shaping the miniaturization of these components. This is due to the precision mechanics of MEMS, microfabrication techniques, and optical functionality enabling a wide variety of movable and tunable mirrors, lenses, filters, and other optical structures. Many promising results from MEMS based optical fiber endoscopy have demonstrated great potentials for clinical translation. In this article, reviews of MEMS sensors and actuators for various fiber-optical endoscopy such as fluorescence, optical coherence tomography, confocal, photo-acoustic, and two-photon imaging modalities will be discussed. This advanced MEMS based optical fiber endoscopy can provide cellular and molecular features with deep tissue penetration enabling guided resections and early cancer assessment to better treatment outcomes. Full article

The wealth of information concealed in a single human breath has been of interest for many years, promising not only disease detection, but also the monitoring of our general well-being. Recent developments in the fields of nano-sensor arrays and MEMS have enabled once bulky artificial olfactory sensor systems, or so-called “electronic noses”, to become smaller, lower power and portable devices. At the same time, wearable health monitoring devices are now available, although reliable breath sensing equipment is somewhat missing from the market of physical, rather than chemical sensor gadgets. In this article, we report on the unprecedented rise in healthcare problems caused by an increasingly overweight population. We first review recently-developed electronic noses for the detection of diseases by the analysis of basic volatile organic compounds (VOCs). Then, we discuss the primary cause of obesity from over eating and the high calorific content of food. We present the need to measure our individual energy expenditure from our exhaled breath. Finally, we consider the future for handheld or wearable devices to measure energy expenditure; and the potential of these devices to revolutionize healthcare, both at home and in hospitals. Full article