Measurement of static and total pressure is widely used to determine the flight conditions of aircrafts. Results of pressure measurements are used to monitor flight attitude, equivalent speed, Mach number, vertical velocity, etc. A variety of systems for monitoring of aircraft flight parameters based on different working principles exists today. The main objective of the study was to develop an improved design of an optical pressure sensor and algorithm for data acquisition and processing. The working principle of the developed optical pressure sensor is based on determination of the light spots position on the surface of photodetector array. The light spots are formed by radiation from light emitting diodes passing through the slotted shutter. The shutter is fixed to the elastic membrane which is displaced by pressure change. The obtained data is then processed by the microcontroller. The developed data acquisition and processing algorithm is based on the centroid-method. It allows to obtain the averaged result from n data points in one sequential poll of the photodetector array (where n is the number of slots in the shutter) increasing measurement accuracy. If the maximum measurement speed is required, the developed algorithm allows measuring the position of only a certain light spot thus increasing the measurement speed. The improved design together with the developed data processing algorithm increase speed and accuracy of measurements.

Laccase is an enzyme belonging to the oxidoreductase class and has copper atoms in the catalytic centre. The catalytic property of this enzyme consists of the enzymatic oxidation of the phenolic compounds, in the corresponding quinones, with the concomitant reduction of molecular oxygen to water. There is growing interest in developing innovative sensing methods for detecting phenolic substrates, such as catechol. Preliminary absorption and fluorescence measurements were carried out in the UV-Visible range to evaluate the possibility of using the variations produced in the spectra of laccase and/or catechol to monitor the presence of this substrate. The absorption and fluorescence emission increase upon UV excitation is detected. By monitoring the time course of the fluorescence signal, an evident increase in the signal detected in the UV range is observed until a saturation level is reached. The observed variations in the spectra, in the presence of the catechol, are discussed also in terms of the interactions between the enzyme and the phenolic compound. The results are very promising for the design of new optical detection methods for polyphenols pollutants.

Sensor networks using the Internet of things (IoT) are gaining momentum for real-time monitoring of the environment. Increased use of natural resources due to a rise in agriculture production, manufacturing, and civil infrastructure, poses a challenge to sustainable growth and development of the global economy. For sustainable use of natural resources (including air, soil, and water), data-driven modeling is needed to understand and simulate contaminant transport and proliferation. Different logging devices are specifically designed to integrate with environmental sensors that send real-time data to the cloud using IoT systems for monitoring. The IoT systems use an LTE network or WiFi to transmit air, water, and soil quality data to the cloud networks. This seamless integration between the logging devices and IoT sensors creates an autonomous monitoring system that can observe environmental parameters in real-time. Various federal organizations and industries have implemented the IoT-based sensor network to monitor real-time air quality parameters (particulate matter, gaseous pollutants), water quality parameters (turbidity, pH, temperature, and TDS), and soil parameters (moisture content, soil nutrients). Although several organizations have used IoT systems to monitor environmental parameters, a proper framework to make the monitoring systems reliable and cost-efficient was not explored. The main objective of this study is to present a framework that combines a sensing layer, a network layer, and a visualization layer, allowing modelers and other stakeholders to observe a progressive trend in environmental data while being cost-efficient. This efficient real-time monitoring framework with IoT systems helps in developing robust statistical and mathematical models. Sustainable development of smart cities while maintaining public health requires reliable environmental monitoring data that can be possible by the proposed IoT framework.

Environmental noise and air pollution as well as the poor green infrastructure quality is a major concern for the European population due to its impact of citizens health, especially for those citizens living in urban environments, which is materialized in a rising number of complaints to public administration. This issue is further stressed for urban areas located close to aggressive pollutants such as airports, railways, highways, or leisure areas. To attend this situation from the citizen everyday life, this paper proposes a hybrid methodology of a collective campaign, where citizens, especially from those environments that have a stronger impact on them, and scientists collect high quality acoustic, chemical and biodiversity data. The campaign consists in a conscious walk that considers both the acoustic measurements conducted by experts but also by citizens, as well as air quality measurements and biodiversity descriptions. The final goal of the method is to obtain subjective and objective data coming from soundscape, air quality and biodiversity to evaluate a pre-designed walk in an urban location, in the surroundings of Parc de la Ciutadella, in Barcelona.

Temperature sensors are widely employed in control systems that maintain required temperature in a vessel or container irrespective of the temperature changes in the outer environment. However, limited power of the heater/cooler (the plant of the control system) might lead to uncomfortable or even inacceptable deviations from the required temperature. This behaviour can be mitigated if the control system can have access not only to the present temperature in the vessel but also to the forecasted environmental temperature.

This situation occurs, among others, at industrial vessels that require elevated temperatures during their operation but shut down out of hours. To start heating these to the required temperature at the beginning of a working shift wastes processing time until the required temperature is reached. It is more productive to turn on heating in advance in order to get the vessel ready on time.

In order to achieve fully autonomous automatic operation, the sensor should have some intelligence and access to the temperature forecast, which can be provided over the internet. Both these requirements can be met by employing a WiFi enabled microcontroller.

We present development of a predictive IoT temperature sensor based on the ESP32 microcontroller, which uses internet service to get time and weather forecast, and upload temperature logs to a cloud server for convenient remote access and storage.

Early and accurate detection of bearing fault is most essential for the safe and reliable working of industrial machinery units. The main problem of the traditional fault diagnosis method is manually extracting the features which requires the much experimenter’s experience; expert knowledge. Therefore, the shallow diagnostic model's classification rate does not produce good results. To address this issue, this research proposes a novel approach to detect and classify bearing fault using a convolutional neural network (CNN), which is capable of automating the extraction of features and remove the impact of expert expertise on the feature extraction process. A time-moving segmentation window is used to segment the vibration raw signal and the segmented signals are decomposed up to two levels using DWT. After that, decomposed signals are converted into gray scale images to train and test the proposed CNN model. To verify the performance of the model, CWRU bearing dataset and MFPT dataset are used. The proposed CNN model achieves the highest accuracy in terms of performance both under different load conditions as well as under noisy situations with varying SNR values. The experimental findings show that the proposed system is effective and extremely dependable in detecting bearing faults.

Ultrasonic oscillating temperature sensors (UOTSes) allow sensing aggregate temperatures across, for example, a complete room, and react at the temperature changes within milliseconds [1]. Their output frequency is to be measured with relatively high accuracy (standard crystal oscillators might be insufficient) and resolution (down to 0.01%) though. For this reason, wide adoption of these sensors requires development of a robust, inexpensive and convenient way of measuring their output frequency.

In our previous experiments we used chained electronic counters implemented on proprietary Programmable Systems-on-Chip parts (Cypress Semiconductors). However, these parts are increasingly becoming sparse and phasing out, and there is a clear need to enable UOTS frequency measurement using mainstream microcontrollers. After briefly trying some 8 bits Arduino libraries for frequency measurement that were found not sufficiently good for the purpose, the STM32 parts were selected for their abundance of built in timers and vast variety of configuration and operation options available. The standard for most microcontrollers timer Capture mode along with chained, interrupted and direct memory access (DMA) operations were considered.

Utilising the DMA allows recording measurements for every half period of the incoming frequency. Despite every individual measurement is inaccurate on its own, moving average of these allows achieving arbitrary accuracy (at the expense of measurement latency) along with providing frequencies after every half period of the UOTS output pulses. This capability not only exceeds the needs of, say, room temperature measurement, but also gives an opportunity to study short term variations in aggregate temperatures that can be useful for studying non-stationary heat distributions and flows.

[1] Elyounsi, A.; Kalashnikov, A.N. Ultrasonic Oscillating Temperature Sensor for Operation in Air. Eng. Proc. 2021, 10, 62.

The current revolution in the field of electromagnetic vibration energy harvester where it is desired that both wireless sensor nodes and relevant power sources are cost and size optimized. It is also crucial to ensure that during the design/fabrication of the sensors power sources in the energy harvester, the power deliverable to the sensors is maximum while maintaining an optimized size. A much deliberate efforts has been reported on the dependency of the flux density on the nature of the coupling material placed in the spaces between the transduction magnets of an electromagnetic vibration energy harvester. However, limited research is reported on the minimization of the geometrical magnet dimension including the outer coupling steel which prevent flux leakage affects, the flux density and voltage/power harvested in an electromagnetic vibration energy harvester. This paper presented a premium works to realize an electromagnetic transduction mechanism which optimize the overall geometrical size while maximizing the magnetic flux density simultaneously. The approach adopted justifiably verify the flux variation with the magnet dimension/outer couplings on a Finite Element Magnetic Method Software (FEMM). The associated flux density on the permanent magnet (NdFeB N52) separated at different distances were predicted on the FEMM software. The coupling associated with each flux level were obtained and correlated with the geometry of the transduction magnet. The results will be analyzed and discussed. Empirical formula will be predicted and to be used in the future to produce a miniature energy harvester for wireless sensor nodes application.

Microelectromechanical Systems (MEMS) vibratory gyroscopes are one of the integral inertial sensors of the inertial measurement unit (IMU). The usage of MEMS vibratory gyroscopes as inertial sensors have been risen enormously in many applications, from household to automotive, smartphones to space applications, smart gadgets to military applications, and so on. This paper presents the mathematical modeling and initial development of the starfish structure with C-shaped springs for MEMS vibratory ring gyroscope (VRG). The symmetric design methodology of VRGs corroborates higher sensitivity, mode-matching, good thermal stability, better resolution, and shock resistance in extreme conditions. The proposed VRG has been designed and investigated by using ANSYS^TM software. This novel design incorporates two rings structure inner and outer with 16 C-shaped springs. The outer ring radius is 1000 um and the whole VRG structure is supported by outer 8 small square pillars. The gyroscope structure’s wine glass mode driving and sensing resonant frequencies recorded at 51.5 kHz and 52.20 kHz. The mode mismatch between driving and sensing resonant frequency is measured at 0.7 kHz, which is quite low as compared to the other structures of vibratory gyroscopes. The proposed design provides high shock absorption with higher sensitivity for space applications to the controlling and maneuvers of the mini-satellites for space applications.

Stone Inscriptions and archaeological structures are an asset to humankind, which contains the history of the past. Estampage is the traditional method used to obtain the replica of the Inscriptions which were primarily used to decrypt the text and for documentation purposes. Presently, Close-Range Photogrammetry (CRP) is a useful remote sensing technique to digitalize these Inscriptions for study as well as preservation. The current study focuses on the creation of a 3-D model of the Hero-Stone using digital camera technology. The photographs were acquired using a Sony Alpha7 III camera with a 35mm full-frame CMOS sensor. 261 images/frames were acquired from different heights above ground and with various positions and angles about the stone inscription to cover it all around. The data acquired from the mirrorless camera was processed in a series of steps which includes image matching, dense point cloud generation, mesh reconstruction and texturing of the model. As the sensor is non-metric, two markers acquired from the field were added to the scene for scaling it accurately. The processed model has 10,915,514 facets (TIN) and 8000 x 8000 x 4 textures providing a realistic appearance. The recent developments in computer vision using the Structure from Motion (SfM) approach enables the reconstruction of the Hero-Stone accurately with realistic textures and details useful for preservation works.