Development of a novel MEMS gas flowmeter with a temperature difference suspension structure

Abstract
Micro-electro-mechanical systems (MEMS) gas flowmeters are innovative devices that use microfabrication technology to measure gas flow with high precision and sensitivity. With MEMS technology, flow measurement can now be performed more accurately and compactly than ever before, using low-power, compact, and highly accurate sensors. MEMS gas flowmeters utilize various principles to measure gas flow, including thermal, Coriolis, and pressure differential methods.A micro flowmeter was designed using a MEMS sensor and a weak signal acquisition technique. The MEMS sensor used a thermal resistor-suspended VO2 structure to provide high heat isolation and sensitivity. Since SU-8 gum was used for the flow channel, the technology was simple and affordable, making it suitable for batch production.
To acquire high-resolution, low-noise data, the following device used a super low bias current operational amplifier, aided by Guard ring protection, and a 24-bit high-resolution ADC. The sensor and data acquisition combination shows that the flow meter has favorable linearity and sensitivity between 0 and 50 mL/min at a specific offset voltage. The flow meter should have advantages such as high sensitivity and stability, low cost, and so on, to satisfy the applications in the fields of biochemical detection and medicine.

The present work presents an innovative marine sensing robotics device based on piezoelectric cantilever-integrated micro-electro-mechanical systems (MEMS) modeled on approach of fish lateral lines. The device comprises 12 cantilevers of different sizes and shapes in a cross-shaped configuration, embedded between molybdenum (Mo) as electrodes in a piezoelectric thin film (PbTiO3, GaPO4). It has the advantage of directional response due to the unique design of the circular cantilevers. In COMSOL software, we designed, modeled, and simulated a piezoelectric device based on a comparative study of these piezoelectric materials. Simulations were performed on cantilever microstructures ranging in length from 100 um to 500 um. The results show that Lead Titanate (PbTiO3) performs best with these materials. The maximum potential voltage was 4.9 mV using the PbTiO3 material cantilever with 37 um displacement. To investigate the first resonance frequency mode and displacement measurements, a Laser Doppler Vibrometer was used, and good agreement between simulations and experimental results was achieved. Its performance and compactness make us envision its employment in underwater acoustics for monitoring marine cetaceans and ultrasound communications. In conclusion, MEMS piezoelectric transducers can be used as hydrophones to sense both the amplitude and directionality of underwater acoustics pulses.

The interconnection of measurement devices in the paradigm of the Internet of Things has led to a significant increase in the amount of shared data but, at the same time, has increased system’s vulnerability to cyber-attacks aimed at stealing confidential information and/or damaging the system. Countermeasures have been proposed to mitigate these security risks, such as authentication procedures and cryptographic algorithms. In both cases, however, passwords and cryptographic keys are stored in a non-volatile memory (NVM), that can be hacked to disclose the confidential information.

Physical unclonable functions (PUFs) can be adopted as an alternative to NVMs for the generation of secure codes, exploiting the device parameters variations induced by the production process. Among the different PUF architectures, ring oscillator (RO) PUFs are very popular, since they feature a simple structure and can be easily implemented on FPGAs. In their standard implementation, RO PUFs output code is generated by comparing the oscillation frequencies of the periodic signals of different ROs. However, alternate solutions have been proposed in the literature, such as those comparing the duty-cycle of the periodic signals of different ROs. This solution improves the PUF reliability, but the number of generated output codes remains the same.

In this paper, the feasibility of using both the oscillation frequency and the duty-cycle of the periodic signals of different ROs to generate the PUF output code is demonstrated. The correlation between the oscillation frequency and the duty-cycle of a RO PUF is evaluated by means of Spice simulations, as well as by experimental measurements. The results have shown that the oscillation frequency and the duty-cycle of a RO PUF feature a weak correlation and, therefore, can be used to increase the number of output codes of the PUF device, with benefits in terms of increased security.

Current line-follower-robot simulation tools often lack the ability to accurately replicate real-world conditions, making them ineffective for testing and optimizing algorithms and inaccessible for educational use. In this scenario, the present study introduces Robosim, a comprehensive line follower robot simulator that mimics physical dynamics, allowing users to develop, test, and refine control algorithms in a virtual environment. The developed RoboSim simulator accepts input in the form of a floor map, which represents the environment in which the robot operates. The map is provided in a structured text format, such as a matrix or grid. Each element in the matrix corresponds to a specific type of terrain or path, with different characters or numbers representing different features. The Robosim simulator comprises key modules, including Map Reader for loading the floor map into a 2D array, Robot Initialization for setting start coordinates, Sensor Simulation for environmental sensing, and Robot Movement for executing control algorithms by adjusting coordinates. Users can implement custom algorithms for testing, with the Position Display module showing the robot's location on the map. The proposed software is a very lightweight simulator designed in C++, which makes it easy to use in computationally restricted environments. Moreover, the computer program, along with sample map files, is freely available for download at https://github.com/Samarth-Godara/Robosim_v1 . The simulator is a versatile tool that can be utilized in a variety of settings. It can be used in educational institutions for teaching robotics and control systems, in research labs for developing and testing advanced robotic algorithms, and in the robotics industry for prototyping and refining line follower robots before physical deployment. Additionally, its user-friendly interface and comprehensive features make it an intriguing tool for hobbyists and enthusiasts to explore and learn about robotic systems.

Fourier Transform Infrared (FT-IR) spectroscopy is a powerful analytical technique to obtain molecular fingerprints of various biological samples. Several studies have indicated the potential use of this technique for distinguishing between healthy and cancerous cells [1,2] and among different cell lines [3,4]. This study aims to compare the FT-IR spectra of three distinct cell lines, SH-SY5Y (neuroblastoma), HepG2 (hepatocellular carcinoma), and MCF-10A (epithelial mammary), to identify characteristic features in their spectra that can be used for this purpose. The cells were grown on MirrIR substrates, and the spectra were acquired in transflection mode using the microscopic stage of a Perkin Elmer Spectrum One Spectrometer equipped with a mercury cadmium telluride (MCT) detector. The FTIR spectra revealed significant protein, lipid, and nucleic acid content variations among the cell lines. The differences mentioned above reflect each cell type's unique biochemical environment and metabolic states. This distinction can help identify different cell lines. Understanding these spectral differences can provide insights into the molecular basis of cellular functions and aid in the development of cell-specific therapeutic strategies.

References

[1] M. Lasalvia, V. Capozzi, G. Classification of healthy and cancerous colon cells by Fourier transform infrared spectroscopy Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 321 (2024) 124683

[2] W.M. Elshemey, A.M. Ismail, N.S. Elbialy, Molecular-level characterization of normal, benign, and malignant breast tissues using FTIR spectroscopy, J. Med. Biol. Eng. 36 (2016) 369–378.

[3] Zendehdel R, H Shirazi F. Discrimination of Human Cell Lines by Infrared Spectroscopy and Mathematical Modeling. Iran J Pharm Res. 2015 Summer;14(3):803-10.

[4] Yadi Wang, Wentao Dai, Zhixiao Liu, Jixiang Liu, Jie Cheng, Yuanyuan Li, Xueling Li, Jun Hu, and Junhong Lü Single-Cell Infrared Microspectroscopy Quantifies Dynamic Heterogeneity of Mesenchymal Stem Cells during Adipogenic Differentiation Analytical Chemistry 2021 93 (2), 671-676.

In the long term we have been focused on mapping the influence of examination using magnetic resonance imaging (MRI) technique on the mental and physiological state of a tested person. The stress effect during this examination manifests in a current state of a human cardiovascular system including changes in the arterial stiffness and the heart rate and can be monitored by wearable optical sensors based on photoplethysmography (PPG) principle.

It is well known that the actual state of the skin surface including a color, a temperature, and other factors have an influence on properties of the sensed PPG signals. We have developed the wearable PPG sensor with a contact thermometer to carry out a measurement of the skin temperature. The precision of the determined PPG wave features depends also on the position of the optical sensor and the contact pressure exerted in the place of sensor. Force-sensitive resistors (FSR) can be successfully used to measure the localized physical pressure.

Motivation of our work is to analyze influence of the adjusted contact pressure on the quality and temporal features of sensed PPG waves. This paper describes realization of a wearable PPG sensor including also an integrated contact thermometer and an FSR element for contact pressure measurement. To enable application of this sensor in the weak magnetic field environment, all parts of sensor are shielded by aluminum boxes. The FSR sensor film is mounted on surface of the cover box near hole for the light source and the photodetector. For different contact forces on a finger/wrist, we will realize measurement of PPG signals to collect a database of PPG wave records. Then, we will statistically analyze the determined PPG wave features to make practical recommendation about setting the contact force which is important for long-time measurement experiments inside the MRI device.

The reliable prediction of future system behavior using sensor data is often hindered by inherent uncertainties, especially in cases where the data undergo gradual changes over time. These uncertainties typically arise from environmental factors or system degradation, posing significant challenges to accurate prognosis and decision-making. In this study, we propose a solution to address this issue by employing confidence intervals to quantify uncertainty in prognosis based on progressively drifted sensor data. Our approach aims to establish a robust framework for evaluating the uncertainty associated with predictions derived from sensor data affected by gradual changes. To illustrate the importance of our proposed method, we mathematically model an exponentially growing sinusoidal pattern with additive noise and outliers, a pattern commonly observed in vibration signals from rotating machinery. Through various deep learning models, well-trained and optimized under hyperparameter optimizations and validation, our empirical validation and analysis demonstrate the effectiveness of our approach in enhancing the reliability and accuracy of prognosis models in dynamic sensor data environments. Thus, we draw important conclusions about the trustworthiness of predictions. This research contributes to advancing the understanding and application of statistical techniques in managing uncertainty within sensor-based prognostic systems, thereby improving their effectiveness across diverse real-world applications.

Data-driven methods have emerged as indispensable tools for wind turbine prognosis, offering unparalleled insights into system health and performance monitoring. However, harnessing the full potential of these methods poses significant challenges, specifically when it comes to data complexity due to harsh conditions. This absolutely necessitates innovative approaches and less computationally intensive methods to simply and effectively navigate the inherent complexities in wind turbine data analysis. Accordingly, this study presents a novel approach to wind turbine state-of-health prognosis for maintenance purposes using a realistic high-speed shaft wind turbine dataset capturing vibration run-to-failure data. Leveraging this dataset, we employ an Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) as a lightweight neural network algorithm for predictive modeling. The optimization process focuses on identifying optimal hyperparameters, including neurons, activation functions, and regularization parameters, aiming to minimize uncertainty in predictions and enhance generalization performance. To quantify uncertainty, we employ a confidence interval-based approach, computing multiple confidence interval features to provide a comprehensive numerical evaluation of uncertainty. The neural network's performance is further evaluated using a diverse set of error metrics, including the coefficient of determination. Despite the massive scale of the dataset, our proposed methodology proves to be simple and computationally efficient, yielding impressive approximation and generalization results. Compared to advanced deep learning methods, this approach offers practical utility by leveraging existing computational resources, minimizing costs, and enabling fast validation without prolonged wait times.

The main objective of the POREM (LIFE17 ENV/IT/000333) project consisted in demonstrating the applicability of the treated poultry manure for the soil restoration or bioremediation. To perform the research activities planned for the project, a considerable amount of poultry manure was stored in a large depot located in a rural, remote, and unattended area. The use of the manure implied the emissions of odors and gases that required a continous and real-time monitoring. This task could not be accomplished by placing expensive instrumentation in such remote and unattended location, therefore, we have investigated the use of low-cost gas sensors for monitoring such poultry manure emissions. A portable monitoring unit mainly based on chemoresistive gas sensors was used to provide indications about the concentrations of NH3, CH4, H2S, and CO2. One of these devices was deployed in the manure storage depot, while the second one was deployed far from the storage site to compare the data related to the background environment with the measures coming out from the manure. Both the monitors were wirelessly linked with internet, even though the radio signal was weak and swinging in that location. This situation gave us the opportunity to test a particular protocol to remotely control the devices based on sending and receiving e-mails containing commands for the remote machines. This experiment proved the feasibility of the use of the low-cost devices in such particular environments, and data gathered seem to indicate that, if properly stored, gases and odors emitted by poultry manure have a limited impact on the air quality of the surrounding environment.

A sophisticated precision irrigation system to precisely determine the water requirements of crops and implement effective irrigation control strategies for automated, real-time, and targeted crop irrigation in the semi-arid regions of India. This system incorporates ZigBee Technology, Wireless Sensor Networks, and Fuzzy Logic based control methodologies. This system discussed by the author actively gathers data for the most prominent parameters of the targeted area such as soil water potential and meteorological conditions, encompassing ambient temperature, humidity, solar radiation, and wind speed. This data obtained from the sensors then processed with the fuzzy logic based algorithms gets utilized to transmit precise irrigation control instructions to the system. Moreover, this proposed system employs the Priestley and Taylor Model (PTM) so as to calculate farmland evapotranspiration. This algorithm has been chosen instead of Penman & Monteith Model (PMM) because of its better accuracy and simple calculations. Both field evapotranspiration and soil water potential serve as crucial inputs for the suggested fuzzy controller based system. A comprehensive multi-factor control rule library is establish, facilitating the implementation of fuzzy-control mechanisms for regulating crop irrigation water requirements with enhanced performance. The testing results obtained from this proposed system demonstrate the system's economic viability and practicality, underscoring its reliability in communication, high control accuracy, and suitability for precision irrigation in semi-arid regions in India that in turn enhances the crop yield.