Prototype Monitoring Research Articles

IoT-Based Agricultural Compost Monitoring System: Prototype Development and Sensor Technology Evaluation

Smart agriculture refers to the application of advanced technologies in the field of agriculture, such as the Internet of Things, big data, edge/cloud computing, among others. These technologies facilitate the monitoring, tracking, recording, visualization, automation, and analysis of various agricultural operations. One critical process in this domain is composting, an organic waste disposal method that enriches soil with nutrients and encourages sustainable farming practices. However, it requires constant monitoring to ensure high-quality compost production at an optimal rate, without the development of harmful toxins. In this context, we propose a novel IoT-based agricultural compost monitoring system. The system is composed of an IoT sensor probe, edge/cloud platforms, and integrated solutions for networking, monitoring, logging, reporting, and visualization. This paper provides a comprehensive physical implementation of the probe, including detailed prototype components, testing of pertinent sensor technologies, and analysis of experiments conducted on a compost site using several probe variants. The main contributions of this paper are twofold: (1) the development of a novel IoT system prototype for efficient compost monitoring, and (2) the identification of the optimal sensor technology for measuring compost temperature. Our findings indicate that infrared sensing technology delivers the fastest and most accurate readings.

Smartphone based structural health monitoring using deep neural networks

In order to create a low-cost and easy-to-implement structural health monitoring method, this paper uses smartphones to collect data on the movement of a building to determine whether it has suffered any damage. Smartphones have easy handling, sensors embedded, and data processing properties. The smartphone-based structural health monitoring system have many advantage over the classical methods. Recurrent fuzzy neural network of long- and short-term memory is applied to determine the damage of the structure. Comparisons with classical neural networks and fuzzy systems are given to show the proposed deep neural networks have better performances. Finally, several experiments are applied to evaluate the proposed methods for the determination of structural damage. • A smartphone-based building structural health monitoring system is developed. • This system uses fuzzy long and short term memory (LSTM) to deal the data obtained from the smartphone. • This system is implemented in a prototype of structural health monitoring.

Performance Evaluation of an Imaging Radiation Portal Monitor System

An organic scintillator-based radiation portal monitor (RPM) prototype system with imaging capabilities has been developed based on the neutron–gamma emission tomography technique. The technique enables rapid detection and precise location of small amounts of special nuclear materials, such as plutonium, using time and energy correlations between fast neutrons and gamma rays from spontaneous fission with low false-alarm rates. These capabilities, in addition to state-of-the-art detection of various gamma-emitting sources, enables the novel imaging RPM concept to efficiently address global security threats from terrorism and the proliferation of nuclear weapons. The detector approach is simple and versatile and can easily be adapted for different applications in nuclear security, public safety, nuclear emergency response, and radiological surveying. In this work, basic performance parameters of the imaging RPM prototype system developed at KTH have been evaluated.

Wave height forecast method with multi-step training set extension LSTM neural network

As the main loading parameter in offshore facilities operations, the accurate prediction of wave height is of great importance. In order to solve the problem of wave height prediction with complex and changeable characteristics, the present paper establishes a wave height forecast model based on prototype monitoring data and multi-step training set extension Long-Short-Term Memory (LSTM) neural network. Firstly, the correlation analysis of wind speeds and wave heights is carried out based on prototype monitoring data. Then, a one-step-ahead wave height forecast method is established based on LSTM neural network. Different prediction models of different time intervals (t = 0.5h, 1h, 3h, 6h, 12h) are built to verify the accuracy. Prediction results indicate that with the increase in time intervals (such as 6h and 12h), the accuracy of the one-step-ahead wave height prediction method decreases rapidly. Hence, aiming at the low prediction accuracy for the large time interval, a multi-step training set extension LSTM model for wave height prediction is presented. Multiple sub-step wave height prediction data are used as the known quantities to improve the accuracy at the final prediction time. The influence of different sub-step step sizes is considered in the presented model. Simulated results show that the 6h prediction error is reduced by 45.83% compared with the one-step model and the 12h prediction error decreases by 52.83%.

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

BackgroundDiabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring.ObjectiveOur study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy.MethodsOur system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy.ResultsWhen using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement.ConclusionsOur pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

Evaluation of low-cost capacitive moisture sensors in three types of soils in the Cerrado, Brazil

Soil moisture sensors enabled new automation and computerization solutions in agriculture. However, its use still requires prior calibrations to attest its accuracy to estimate soil moisture. Thus, in this study a prototype for soil moisture monitoring was developed using the capacitive sensor SKU:SEN0193 integrated with the Arduino microcontroller. The prototype was calibrated based on the relationship between volumetric moisture and sensor output voltage in deformed samples of Red-yellow Latosol (RYL), Regolitic Neosol (RN), and Red Latosol (RL) of the Cerrado region, Federal District, Brazil. The calibration of sensors resulted in a second-degree polynomial model with R² between 0.93 and 0.96 for RYL, 0.89 and 0.92 for RN, and 0.86 and 0.88 for RL. Root mean square errors (RMSE) of volumetric moisture were 0.08, 0.12, and 0.15 cm3.cm-3 for RYL, RN, and RL, respectively. Sensors at operating voltage of 5.00 V showed less variability of readings, with coefficient of variation (CV) between 0.21 and 1.54%, while sensors at 3.30 V showed CVs between 3.27 and 17.84%. It is recommended to calibrate the SKU:SEN0193 for each soil type in order to obtain reliable estimates of water content for RYL, RN, and RL.

LoRaWAN Monitoring System for Emergency Vital Signs in Pusu River

Sungai Pusu is the river that passes through the IIUM Gombak campus. The river has been having a cloudy appearance for years hence it is needed to quantify the vital signs such as pH, temperature, and turbidity because these are the vital signs indicating the health of a river. Currently, there a lot of monitoring systems however the available monitoring systems do not support long-range communication and consume a lot of power. The data need to be transmitted at a long-range as it is being monitored remotely at a long distance. Therefore, a river pollution monitoring system must be developed to track the emergency vital signs (EVSs) of the river water The EVs include pH, temperature as well as turbidity. This project capitalizes on the long-range communication and low power consumption LoRaWAN system. The prototype monitoring station design can read the important EVSs of the river such as temperature, pH level, and turbidity. The sensors are connected to a microcontroller board. The readings of the EVSs are transmitted by the LoRa gateway which is forwarding the data to The Things Network Server. A graphical representation of the data is displayed on Ubidots. The results attained quantify the contribution of the IIUM populace to Sungai Pusu Pollution and raise awareness. It is also important to discuss the possibilities to the pollution of the river to see how one’s action could contribute to it. A statistical data of the results is as important so that an overall result can be deduced. Based on the results, the pH is decreased 1.32 pH, the temperature is increased by 2.29°C, and turbidity is decreased by 0.44 NTU throughout the monitoring period. In the future, six more monitoring stations will be added to accommodate KL River of Life (RoL) EVSs too.

Monitoring of an Electromechanical Prototype Material for Environmental Parameters Using IoT

The Internet of Things (IoT) is a phenomenon where everything is connected to everything, where the development of sensors and microprocessors helped in the rise of embedded technologies, such as Wi-Fi and Bluetooth, and the evolution of bandwidth available on the Internet. This phenomenon will continue unabated due to sensor technology, programmable chips, and embedded technologies. In recent years, the Internet of Things (IoT) has become one of the most prominent technology niches. IoT is the network of interconnected devices or “things” that exchange information or send data with each other over the Internet. The article describes a computer prototype for monitoring and controlling the environmental parameters of a communication equipment room. A technological tool, such as the one obtained, can be implemented in many applications, such as weather stations, food chains, smart home systems, measuring the heart rate of humans, and fruit dehydrators. The article’s research was focused on an IoT electronic device and a web platform. A major feature of this electronic device is that its Internet connectivity is achieved using the TCP/IP protocol, an integrated feature of the Shield Ethernet board stacked on top of the Arduino mega board.

An Overview of Structural Design, Analysis and Common Monitoring Technologies for Floating Platform and Flexible Cable and Riser

Offshore oil and gas development plays an important part in the global energy sector. Offshore platforms and flexible pipes are the key equipments in the whole offshore oil and gas development system. Because of the randomness and uncertainty of wave and current loads in the ocean environment, the structural design and mechanical analysis of the marine equipment can be highly complicated. Therefore, this paper reviews the recent works of the theoretical model, numerical simulation, and experimental test in three research areas: hydrodynamic analysis of offshore platforms, structural mechanics analysis of flexible pipe and cable, and monitoring technology of offshore floating structures under marine loads. By analyzing their main research methods and key technical difficulties, this paper provides theoretical basis and technical support for the reliability engineering application of offshore platforms and flexible pipelines. Also, China is relatively backward in the design of marine floating platform, the design, analysis and testing of flexible pipeline and cable, as well as the marine equipment prototype monitoring technology research. Calling for breakthroughs at the earliest possible stage in the above fields, prime research should be focused on and strategic planning should be made to deal with “key areas and stranglehold problems”. It is of great significance for the development of China’s deep-sea energy and resource development of independent technology and on time to achieve the “carbon peak” national strategic objectives.

Synergistic strain engineering of perovskite single crystals for highly stable and sensitive X-ray detectors with low-bias imaging and monitoring

Although three-dimensional metal halide perovskite (ABX3) single crystals are promising next-generation materials for radiation detection, state-of-the-art perovskite X-ray detectors include methylammonium as A-site cations, limiting the operational stability. Previous efforts to improve the stability using formamidinium–caesium-alloyed A-site cations usually sacrifice the detection performance because of high trap densities. Here we successfully solve this trade-off between stability and detection performance by synergistic composition engineering, where we include A-site alloys to decrease the trap density and B-site dopants to release the microstrain induced by A-site alloying. As such, we develop high-performance perovskite X-ray detectors with excellent stability. Our X-ray detectors exhibit high sensitivity of (2.6 ± 0.1) × 104 μC Gyair−1 cm−2 under 1 V cm−1 and ultralow limit of detection of 7.09 nGyair s−1. In addition, they feature long-term operational stability over half a year and impressive thermal stability up to 125 °C. We further demonstrate the promise of our perovskite X-ray detectors for low-bias portable applications with high-quality X-ray imaging and monitoring prototypes.

Open-source low-cost design of a buoy for remote water quality monitoring in fish farming.

In this paper we present the design of an open-source and low-cost buoy prototype for remote monitoring of water quality variables in fish farming. The designed battery-powered system periodically measures temperature, pH and dissolved oxygen, transmitting the information locally through a low-power wide-area network protocol to a gateway connected to a cloud service for data storage and visualization. We provide a novel buoy design that can be easily constructed with off-the-shelf materials, delivering a stable anchored float for the IoT device and the probes immersed in the water pond. The prototype was tested at an operating fish farm, showing promising results for a low-cost remote monitoring tool that enables automatic data acquisition and storage in fish farming scenarios. All the elements of this design, including hardware and software designs, are freely available under permissive licenses as an open-source project.

6-DOF Motion Assessment of A Hydrodynamic Numerical Simulation of A Semisubmersible Platform Using Prototype Monitoring Data

6-DOF Motion Assessment of A Hydrodynamic Numerical Simulation of A Semisubmersible Platform Using Prototype Monitoring Data

Prototype of Monitoring and Controlling System on Gondola Cage Security Based Android Application

Gondola is one of the facilities used in high-rise buildings that are used to access the outside of the building. Gondola has a maximum load limit that is safe to transport it. Gondola is also often damaged in the cage caused by collisions on the side of the building. Until now, the gondola control still uses buttons which also often experience problems. This study aims to provide monitoring of the cage load and provide information about the distance of the cage to surrounding objects. Gondola control through an android application also includes this research in order to provide an alternative if the buttons on the gondola control experience problems. The short-term goal of this research is to carry out an innovation in the gondola field by conducting experiments through prototypes. The medium term goal is to apply the results of this research into a gondola. The results of this study control and monitoring can function properly even though the results produced are not yet accurate due to the imperfect quality of components, materials and assembly.

Development of Internet of Things-Based Rumen Health Monitoring Prototype with Fuzzy Logic Controllers for Cattle

Development of Internet of Things-Based Rumen Health Monitoring Prototype with Fuzzy Logic Controllers for Cattle

A Prototype for Remote Monitoring of Ocean Heat Content Anomalies

Abstract To overcome challenges with observing ocean heat content (OHC) over the entire ocean, we propose a novel approach that exploits the abundance of satellite data, including data from modern satellite geomagnetic surveys such as Swarm. The method considers a novel combination of conventional in situ (temperature and pressure) as well as satellite (altimetry and gravimetry) data with estimates of ocean electrical conductance (depth-integrated conductivity), which can potentially be obtained from magnetic observations (by satellite, land, seafloor, ocean, and airborne magnetometers). To demonstrate the potential benefit of the proposed method, we sample model output of an ocean state estimate to reflect existing observations and train a machine learning algorithm [Generalized Additive Model (GAM)] on these samples. We then calculate OHC everywhere using information potentially derivable from various global satellite coverage—including magnetic observations—to gauge the GAM’s goodness of fit on a global scale. Inclusion of in situ observations of OHC in the upper 2000 m from Argo-like floats and conductance data each reduce the root-mean-square error by an order of magnitude. Retraining the GAM with recent ship-based hydrographic data attains a smaller RMSE in polar oceans than training the GAM only once on all available historical ship-based hydrographic data; the opposite is true elsewhere. The GAM more accurately calculates OHC anomalies throughout the water column than below 2000 m and can detect global OHC anomalies over multiyear time scales, even when considering hypothetical measurement errors. Our method could complement existing methods and its accuracy could be improved through careful ship-based campaign planning. Significance Statement The purpose of this manuscript is to demonstrate the potential for practical implementation of a remote monitoring method for ocean heat content (OHC) anomalies. To do this, we sample data from a reanalysis product primarily because of the dearth of observations below 2000 m depth that can be used for validation and the fact that full-depth-integrated electrical seawater conductivity data products derived from satellite magnetometry are not yet available. We evaluate multiple factors related to the accuracy of OHC anomaly estimation and find that, even with hypothetical measurement errors, our method can be used to monitor OHC anomalies on multiyear time scales.

Data Transmission in Wearable Sensor Network for Human Activity Monitoring Using Embedded Classifier Technique

Abstract: The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies. Keywords: wireless sensor network; wearable sensors; activity recognition; lifetime; energy con- sumption; transmission suppression; embedded machine learning

Experimental verification of real-time gamma-ray energy spectrum and dose monitor

The purpose of this study is to develop a portable monitor that can measure the energy spectrum and dose of gamma-rays simultaneously in real time for the benefit of medical staff who must work in clinical radiation environments. For this purpose, we have developed a prototype monitor using a CsI (Tl) scintillator combined with a multi-pixel photon counter (MPPC). For real-time measurement, we employed an improved sequential Bayesian estimation (k-α method) to convert the measured pulse height spectrum into an energy spectrum. Then we confirmed that reconstruction of the energy spectrum and dose estimation could simultaneously be carried out in real time by the k-α method in a radiation field composed of mixed standard gamma-ray sources. In this study, we carried out measurements in a background gamma-ray field to confirm applicability of the prototype monitor to the weakest type of radiation field. In addition, we conducted measurements in front of a nuclear fuel storage room (∼2 μSv/h) in the authors’ laboratory to evaluate practicality of the monitor for measuring fields with a complex energy spectrum. As a result, it was found that the dose could be estimated in about 20 s after start of measurements even in the background field. For the energy spectrum, it was instantly reconstructed within 60 s in front of the fuel storage room. On the other hand, it could successfully be estimated within 10 min in the background gamma-ray field. Currently, the convergence of the energy spectrum is determined visually from time dependent change of the spectrum and dose. As a next step, we will attempt to develop a more quantitative procedure for determining the convergence.

Development of a Low-cost Arduino-based Patient Monitoring System for Heartrate, Oxygen Saturation and Body Temperature Parameters

The implementation of Patient Monitor (PM) in ICU, ICCU, PICU and NICU currently imposes a significantly high cost of investment. According to studies conducted, there are three main parameters required to be measured, which are heartrate (BPM), oxygen saturation (SPO2) and body temperature (T). These are highly needed by the medical personnel to monitor in order to determine patient’s vital state. The low-cost patient monitor prototype (named Patient Monitor 3 Parameters or PM3P) was developed using Arduino platform. In order to measure the parameters, two types of sensor modules were chosen, namely MAX30100 for gathering heartrate and oxygen saturation data and DS18B20 for gathering the body temperature data. The prototyping process also included fixation and finalization of electrical circuit on a printed circuit board (PCB). The PM3P underwent extensive validation process by comparing it to the real industrial PM made by healthcare equipment industry. The comparison was done over data gathered by both system that were conducting the same measurement runs in parallel. Results of validation indicates that the low-cost patient monitor prototype has a slight error of 0,31% for heartrate and 1,59% for oxygen saturation compared to the industrial one. Further improvements for the Arduino based PM3P are also proposed in this paper in order to enhance its reading accuracy, namely calibration.

Data Transmission in Wearable Sensor Network for Human Activity Monitoring using Embedded Classifier technique

The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies.

A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring

Self-powered RF passive sensors have potential application in temperature measurements of patients with health problems. Herein, this work presents the design and implementation of a self-powered UHF passive tag prototype for biomedical temperature monitoring. The proposed battery-free sensor is composed of three basic building blocks: a high-frequency section, a micro-power management stage, and a temperature sensor. This passive temperature sensor uses an 860 MHz to 960 MHz RF carrier and a 1 W Effective Isotropic Radiated Power (EIRP) to harvest energy for its operation, showing a read range of 9.5 m with a 13.75 µW power consumption, and an overall power consumption efficiency of 10.92% was achieved. The proposed device can measure temperature variations between 0 °C and 60 °C with a sensitivity of 823.29 Hz/°C and a standard error of 13.67 Hz/°C over linear regression. Circuit functionality was validated by means of post-layout simulations, characterization, and measurements of the manufactured prototype. The chip prototype was fabricated using a 0.18 µm CMOS standard technology with a silicon area consumption of 1065 µm × 560 µm. The overall size of the self-powered passive tag is 8 cm × 2 cm, including both chip and antenna. The self-powered tag prototype could be employed for human body temperature monitoring.