Prototype Monitoring Research Articles

ATEX-Certified, FPGA-Based Three-Channel Quantum Cascade Laser Sensor for Sulfur Species Detection in Petrochemical Process Streams

In this work, a highly sensitive, selective, and industrially compatible gas sensor prototype is presented. The sensor utilizes three distributed-feedback quantum cascade lasers (DFB-QCLs), employing wavelength modulation spectroscopy (WMS) for the detection of hydrogen sulfide (H2S), methane (CH4), methyl mercaptan (CH3SH), and carbonyl sulfide (COS) in the spectral regions of 8.0 µm, 7.5 µm, and 4.9 µm, respectively. In addition, field-programmable gate array (FPGA) hardware is used for real-time signal generation, laser driving, signal processing, and handling industrial communication protocols. To comply with on-site safety standards, the QCL sensor prototype is housed in an industrial-grade enclosure and equipped with the necessary safety features to ensure certified operation under ATEX/IECEx regulations for hazardous and explosive environments. The system integrates an automated gas sampling and conditioning module, alongside a purge and pressurization system, with intrinsic safety electronic components, thereby enabling reliable explosion prevention and malfunction protection. Detection limits of approximately 0.3 ppmv for H2S, 60 ppbv for CH3SH, and 5 ppbv for COS are demonstrated. Noise-equivalent absorption sensitivity (NEAS) levels for H2S, CH3SH, and COS were determined to be 5.93 × 10−9, 4.65 × 10−9, and 5.24 × 10−10 cm−1 Hz−1/2. The suitability of the sensor prototype for simultaneous sulfur species monitoring is demonstrated in process streams of a hydrodesulphurization (HDS) and fluid catalytic cracking (FCC) unit at the project’s industrial partner, OMV AG.

Development of an early warning system to reduce the impact of floods related to glacial lake outburst floods (SAGAZ)

Abstract. The retreat of glaciers has led to increasing in natural hazards due, for example, by emptying of lakes dammed by unconsolidated glacial deposits (moraines) or ice, which are susceptible to catastrophic erosion, generating rapid floods, phenomena known as “Glacial Lake Outburst Floods” (GLOFs). Current systems that warn of the occurrence of a GLOF are activated when the emptying begins, which leaves little time to act, so they should be considered early alarm systems. The challenge is to predict the onset of the flood allowing the generation of an early warning system. To address this problem, SAGAZ ultimately aims to develop a system capable of identifying periods of increased GLOF risk using a predictive model fed by weather forecasts and monitoring station data. This system identifies a period of higher risk, which allows informing authorities several days in advance. This paper presents the results of the first phase of SAGAZ implementation, which aimed to (1) develop and validate a prototype monitoring station and deploy a network of stations on glacial lakes across southern Patagonia, (2) collect the necessary data for the development, training and validation of predictive models and (3) begin the implementation and testing of the predictive model. As a result, a network of 10 monitoring stations was installed in the Aysén and Magallanes regions of Chile and 1 in the Province of Santa Cruz in Argentina, of which 6 are currently operational and transmitting data in real time. The rest went off due to power failures and icebergs damaging sensors. The measures we have taken to avoid station’s failures are described, as well as some characteristics of the implemented prototype, the installed networks and the data obtained so far.

Reconstruction of motor voltage control signal in industrial applications using IoT

The IIoT allows to have an efficient automation system that monitors specific parts of a system in real time. This paper implements a prototype of speed control and monitoring in a motor belonging to an industrial process with PLC, where the current regulation is related to the data acquisition system and the proposed control algorithm in an IIoT architecture. Finally, the characterization of the starting signal is modeled by means of the angular velocity, the disturbances present in the voltage change and stabilization will be processed using the numerical integration theory and the Matlab tool, to obtain a polynomial approximation of order n

Validation of a Wearable Sensor Prototype for Measuring Heart Rate to Prescribe Physical Activity: Cross-Sectional Exploratory Study.

Wearable sensors are rapidly evolving, particularly in health care, due to their ability to facilitate continuous or on-demand physiological monitoring. This study aimed to design and validate a wearable sensor prototype incorporating photoplethysmography (PPG) and long-range wide area network technology for heart rate (HR) measurement during a functional test. We conducted a transversal exploratory study involving 20 healthy participants aged between 20 and 30 years without contraindications for physical exercise. Initially, our laboratory developed a pulse wearable sensor prototype for HR monitoring. Following this, the participants were instructed to perform the Incremental Shuttle Walk Test while wearing the Polar H10 HR chest strap sensor (the reference for HR measurement) and the wearable sensor. This test allowed for real-time comparison of HR responses between the 2 devices. Agreement between these measurements was determined using the intraclass correlation coefficient (ICC3.1) and Lin concordance correlation coefficient. The mean absolute percentage error was calculated to evaluate reliability or validity. Cohen d was used to calculate the agreement's effect size. The mean differences between the Polar H10 and the wearable sensor during the test were -2.6 (95% CI -3.5 to -1.8) for rest HR, -4.1 (95% CI -5.3 to -3) for maximum HR, -2.4 (95% CI -3.5 to -1.4) for mean test HR, and -2.5 (95% CI -3.6 to -1.5) for mean recovery HR. The mean absolute percentage errors were -3% for rest HR, -2.2% for maximum HR, -1.8% for mean test HR, and -1.6% for recovery HR. Excellent agreement was observed between the Polar H10 and the wearable sensor for rest HR (ICC3.1=0.96), mean test HR (ICC3.1=0.92), and mean recovery HR (ICC3.1=0.96). The agreement for maximum HR (ICC3.1=0.78) was considered good. By the Lin concordance correlation coefficient, the agreement was found to be substantial for rest HR (rc=0.96) and recovery HR (rc=0.96), moderate for mean test HR (rc=0.92), and poor for maximum HR (rc=0.78). The power of agreement between the Polar H10 and the wearable sensor prototype was large for baseline HR (Cohen d=0.97), maximum HR (Cohen d=1.18), and mean recovery HR (Cohen d=0.8) and medium for mean test HR (Cohen d= 0.76). The pulse-wearable sensor prototype tested in this study proves to be a valid tool for monitoring HR at rest, during functional tests, and during recovery compared with the Polar H10 reference device used in the laboratory setting.

Real-time prediction of structural responses in deep-water jacket platforms using Ada-STLNet: A comprehensive analysis with prototype monitoring data

Real-time prediction of the mechanical behaviors based on prototype monitoring data is crucial for ensuring the integrity and safety of deep-water jacket platforms. As complex nonlinear systems, these platforms’ response exhibit varying temporal trends, dynamic spatial correlations, and load dependencies. This makes accurate prediction of future axial force and bending moment responses a significant challenge. In this paper, a novel deep learning method called Adaptive Spatio-Temporal Load Network (Ada-STLNet) is proposed aiming to address the issue of multi-node axial force and bending moment response prediction of deep-water jacket platform. Our model utilizes an enhanced graph convolution (EGCN) and self-attention mechanism for efficient spatial correlation modeling in multi-node response, LSTM for long sequence temporal feature capture. It integrates spatial and temporal, along with load-aware modules to capture intricate patterns in structural responses. Additionally, a multi-gated coupling mechanism with two independent gating modules is designed to adaptively represent the complex dependencies of structural responses, ensuring model stability and robustness. Extensive experiments conducted on prototype structural monitoring data from a deep-water jacket platform in the South China Sea demonstrate that Ada-STLNet outperforms six traditional models, including HA, SVR, KNN, MLP, LSTM, and GRU, across various prediction steps. The proposed model exhibits superior accuracy, particularly in short-term predictions, achieving up to 62.80% improvement in MAE and 48.11% in RMSE for axial force predictions compared to the second-best model. Ablation studies confirm the effectiveness of each component within Ada-STLNet. This research highlights the potential of Ada-STLNet for reliable and accurate prediction of structural responses, contributing significantly to the field of marine engineering.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating.

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Perancangan Prototype Monitoring dan Akuisi Data Pengujian Noise Trafo Berbasis ESP32

The noise level on the transformer is very important to be considered, because it can cause problems if it does not meet the standards. Excessive noise can violate regulations set by the Ministry of Environment regarding noise limit thresholds, In addition, excessive noise also indicates an indication of a bad transformer construction and will result in the performance of the transformer itself. PT Trafoindo Prima Perkasa, as one of the largest and first transformer manufacturers in Indonesia, tests every transformer produced, including its noise level, The testing is conducted in the Quality Check division. However, testing that is still done manually often causes queue buildup in the production line, along with the increasing demand, especially from PLN, a faster testing process is needed but still meets the set standards. Therefore, a faster, safer, and more efficient testing method is needed to support a continuous production. Based on this requirement, the author designs a prototype monitoring system and data acquisition of ESP32-based transformer noise testing to help speed up and simplify the testing process, so that the finished product can be sent immediately without reducing the quality and standards that have been set.

Surface-tension-directed gate functionalization in organic electrochemical transistor for wearable sweat lactate monitoring

Wearable sweat lactate detection holds significant potential for applications in healthcare and exercise performance evaluation. Organic electrochemical transistors (OECTs) is emerging as an efficient platform in wearable lactate sensing by integrating an electron transfer interface between the electrode and the bio-catalytic layer. In this study, we demonstrated a wearable OECT designed for dynamic lactate monitoring in sweat through a one-step deposition of gold nanoparticles (Au NPs) on the gate electrode. Utilizing the simple surface-tension-directed deposition strategy, this method ensures a uniform distribution of Au NPs with high catalytic activity, enabling rapid and sensitive lactate signal transition. Herein, the OECT device exhibits a linear detection range for sweat lactate from tens of micromolars to hundreds of millimolars (r² > 0.97), along with high selectivity and long-term stability in human sweat. Furthermore, we successfully employed this flexible OECT sensor for real-time lactate monitoring system during various physical activities, highlighting its potential for non-invasive evaluation of fatigue and exercise performance in athletes. This work provides an easy-to-use prototype for continuous physiological monitoring, enhancing personalized healthcare and training management.

Prototype Design IoT Based Air Quality Monitoring Tool for Urban Environment

In rapidly urban environments, air quality has become an important issue that affects public health and the environment. This research aims to design and implement a prototype of an IoT-based air quality monitoring device that can be used to monitor air conditions in urban environments. This prototype uses the DHT11 sensor for measuring temperature and humidity, the MQ135 sensor to detect the concentration of air pollutants such as CO, CO2, ALCOHOL, TOLUENE, NH4, and ACETONE, and an OLED LCD for displaying information. This system uses the NodeMCU ESP8266 as a microcontroller that connects devices to the internet and sends data to the Blynk application platform. The Blynk application serves as a user interface that allows for real-time monitoring of the device through a smartphone. With this system, users can obtain real-time air quality data to protect health and the environment in urban areas. The prototype evaluation shows that this system is effective in monitoring and displaying air quality information, as well as providing easy access to data through the Blynk application.

Prototype for Multi-UAV Monitoring–Control System Using WebRTC

Most unmanned aerial vehicle (UAV) ground control station (GCS) solutions today are either web-based or native applications, primarily designed to support a single UAV. In this paper, our research aims to provide an open, universal framework intended for rapid prototyping, addressing these objectives by developing a Web Real-Time Communication (WebRTC)-based multi-UAV monitoring and control system for applications such as automated power line inspection (APOLI). The APOLI project focuses on identifying damage and faults in power line insulators through real-time image processing, video streaming, and flight data monitoring. The implementation is divided into three main parts. First, we configure UAVs for hardware-accelerated streaming using the GStreamer framework on the NVIDIA Jetson Nano companion board. Second, we develop the server-side application to receive hardware-encoded video feeds from the UAVs by utilizing a WebRTC media server. Lastly, we develop a web application that facilitates communication between clients and the server, allowing users with different authorization levels to access video feeds and control the UAVs. The system supports three user types: pilot/admin, inspector, and customer. Our research aims to leverage the WebRTC media server framework to develop a web-based GCS solution capable of managing multiple UAVs with low latency. The proposed solution enables real-time video streaming and flight data collection from multiple UAVs to a server, which is displayed in a web application interface hosted on the GCS. This approach ensures efficient inspection for applications like APOLI while prioritizing UAV safety during critical scenarios. Another advantage of the solution is its integration compatibility with platforms such as cloud services and native applications, as well as the modularity of the plugin-based architecture offered by the Janus WebRTC server for future development.

Empowering energy conservation: A prototype of household energy consumption monitoring based on Internet of Things

Abstract One of the obstacles to control household energy consumption is unavailability electricity instruments/tools to monitor electricity energy consumed by appliances causing a lack of awareness of household energy saving. Energy meter installed on buildings only show electricity consumption in total. This study aims to design and create a prototype system that can monitor and control the electrical appliances usage utilizing the Internet of Things (IoT)-based electronic devices where electricity usage data is stored in Firebase real-time database. The methods carried out are arranged in several stages, starting from data collection, design, configuration, and implementation. The tools used in this research will utilize the ESP8266 as the microcontroller, the PZEM-004T as current sensor and the Blynk as the web server. The tool will send current voltage (Volt (V)) and capacity (Watt (W)) data and then converted to energy and also the costs incurred from electricity usage based on the basic electricity tariff of National Electricity Company (PLN) and the results can be seen directly through IoT (i.e. smartphone, PC, laptop). The accuracy of power measurement by the system is 97.4% compared to measurement used by watt-checker. The results of this study show that an electrical power monitoring system makes people easier to monitor electrical power consumption by appliances used. The pilot measurement shows that a unit system can monitor electricity usage for four units of electronic equipment i.e. water pump, Television, fridge, electrical iron, rice cooker, etc. precisely and succeeded to control and increase energy saving awareness to reduce electricity consumption at the end.

Development of Pre-paid Energy Meter using Internet of Things

This paper presents an implementation of IoT to develop a pre-paid energy meter system using an IoT prototype for monitoring and controlling energy and power consumption. This system is used for the remote or on-site management of energy and power consumption. The system provides a user interface using Global System for Mobile Communications (GSM) so the user can pay the required price for the desired unit. The system provides a web user interface to monitor energy and power consumption, including the number of units and price. The ultimate goal is to promote a sense of optimization of power usage by letting the user pay before they have access to electricity. The system consists of an energy meter box that is connected to individual load connections. This energy meter box consists of a microcontroller on Arduino, a GSM module, a voltage sensor, and current sensor, and an LCD. The LCDs are the energy and power consumed, the price remaining, and the number of units consumed.

Clinical Validation of a Prototype Smart Non-Invasive Pregnancy Glucose Monitor

The aim of this study is to evaluate the effectiveness of a smart non-invasive blood glucose monitor prototype during pregnancy through clinical validation. The monitor utilizes near-infrared spectroscopy combined with AI big data analysis of photoelectric volumetric pulse wave data to achieve non-invasive monitoring of blood glucose in women during pregnancy. The research team developed a monitor that employs a sensing chip, effectively overcoming the problems of weak signals and individual differences in non-invasive blood glucose monitoring. The user experience is enhanced by visualizing the test results on the accompanying cell phone APP (application) of the smart non-invasive pregnancy blood glucose monitor. Clinical validation revealed that the non-invasive monitoring data for pregnant women aged 20~30 years significantly differed from those obtained via traditional blood glucose measurement methods, whereas no significant difference ( P<0.05) was observed for pregnant women aged 31~42 years. The study concluded that further calibration of the monitor and an expansion of the sample size are necessary to enhance consistency with invasive glucose monitoring results.

Prototipe Sistem Monitoring, Proteksi, dan Kontrol Putaran pada Mesin Emergency Generator Menggunakan Lora untuk Mencegah Kerusakan pada Sistem di Kapal

In ship operations, the existence of a reliable emergency generator system is a critical factor to ensure continuity of electricity supply in emergency situations. Damage to the generator engine can cause significant disruption and even safety risks. To overcome this problem, a prototype monitoring, protection and rotation control system for emergency generator machines was developed using LoRa (Long Range) technology. This system is designed to monitor critical engine parameters, including speed, temperature and other operational conditions in real-time. Data collected from sensors is sent over a LoRa network that has wide coverage and low power consumption, enabling remote and continuous monitoring of Blynk's Internet of Think (IoT). This system is equipped with a protection algorithm that can detect anomalies and automatically activate corrective action or provide early warning to engineers and electricians. By implementing this prototype, it is hoped that it can reduce the risk of damage to the emergency generator engine and increase the reliability of the ship's electrical system, thereby supporting safer and more efficient operations.

Using Hardware Events to Detect Side-Channel Attacks

The article discusses the monitoring system being developed by authors for the detection of Side-Channel Attacks, SCA. The theoretical justification of the chosen method of detecting SCA attacks is given: analysis of hardware events of the target system. The architecture of the monitoring system is described, including low-level data collection processes and an expert system that analyzes the collected data. The results of the proof-of-concept on various classes of SCA-attacks are presented. The paper considers a class of attacks on side-channels that use the processor cache to obtain secret information. A method of countering attacks of this class based on the use of a hardware event mechanism is proposed. As a result of the analysis of existing hardware events, predicates have been built for the Intel architecture, allowing one to identify the suspicious behavior of programs in the Linux OS environment. To test the proposed method, a prototype monitoring system was implemented that successfully coped with the detection of simulated SCA-attacks. The advantages and disadvantages of this method are considered, and the direction of further research is indicated.

A novel low-cost sensors system for real-time multipollutant indoor air quality monitoring – Development and performance

Indoor air pollution poses a significant health risk, thus demanding effective indoor air quality (IAQ) monitoring strategies. Low-cost sensors (LCS) have gained attraction for IAQ monitoring, but their data accuracy and robustness remain key challenges. This study addresses gaps in the literature by developing and evaluating a novel low-cost monitor prototype for IAQ monitoring. A detailed design methodology based on requirements analysis was used to create two identical low-cost monitors (LCM) prototypes for multipollutant monitoring, including PM2.5, CO2, CO, O3, NO2, temperature and relative humidity. The LCM were deployed in an in-field monitoring campaign alongside research-grade instruments. The uncorrected sensor signals showed linear response compared to research-grade instruments with high Pearson Correlation Coefficients for 1-min mean: PM2.5 (0.97), CO2 (0.81–0.89), CO (0.95–0.98), and O3 (0.80–0.85). The concentration range of pollutants was observed to play a crucial role in the response and accuracy of the LCS. Kalman filter was implemented with optimised parameters to reduce noise from the signal of ozone sensors. Univariate and multivariate linear models were used to field calibrate the sensors. Linear regression models, with high coefficients of determination (>0.8) and low error values, imply that the developed LCM prototypes can be reliably used for indicative monitoring. In most of the cases, univariate linear models using only the sensor response of LCS as explanatory variables showed significant improvement. Future work will involve longer monitoring campaigns in different microenvironments, evaluation of calibration drift and to assess failure episodes in long-term use.

Development of a subsurface monitoring system for land subsidence hazard in the Bandung basin: a laboratory prototype

Abstract Land subsidence is a global environmental hazard posed by urban area worldwide, causing degradation in environmental quality and leads to further disasters such as increased flood risk, contamination risk, and infrastructure damages. Anthropogenic causes, such as groundwater extraction and building weight, cause high land subsidence rates. The behaviour of the subsurface under the influence of anthropogenic causes must be well understood to mitigate this problem. Conventional subsurface monitoring is robust and costly. In this case, we aim to first develop a versatile and low-cost subsurface monitoring prototype for land subsidence at a laboratory scale using a model box. Methods employed were constructing an experiment model box, developing the monitoring system, and trial experiments in the laboratory. The sensors are linear vertical displacement transducer, potentiometer, water level and pressure sensors. The monitoring system uses Arduino microcontroller to convert sensors inputs to desirable outputs and a logger to record the real-time data and transmit it to personal computer in the laboratory. Land subsidence in the Gedebage area in Bandung basin was taken as a model case. The model box was filled with soil from Gedebage, Bandung. A scenario of exploitation of confined groundwater and weight of building was applied for the model case. The results show that the laboratory prototype can monitor the subsurface changes due to the applied anthropogenic forces. In the case of Bandung soil, groundwater exploitation did not immediately cause subsidence instantaneously. There is a time delay between the drawdown of groundwater level and the subsequent vertical deformation. The rate of subsidence varies spatially inside the tank, the largest is close to the pump well and surface loads. A maximum of 2.88 mm subsidence over 663 days was recorded following 1 m groundwater drawdown. This information asserts the importance of long-time monitoring in subsidence areas.

Prototype of Solar Fuel Monitoring And Security System in Dump Truck Tank Based on SMS Gatway

The process of security and monitoring of fuel oil is one of the activities that is often overlooked. Fuel oil is a driver of industry on a large scale. The use of fuel is less controlled, so many people abuse the use of diesel fuel which has a fairly high selling price among the public. , so this research was built by utilizing an Arduino microcontroller, proximity sensor, ultrasonic sensor, LCD and utilizing a cellular network with a GSM module 900 device which can send messages. Through this research a system was obtained that can be used for security and monitoring of diesel fuel in tanks. truck to provide information on the condition of the amount of fuel in the tank whether it is suitable for use or not.

Evaluate Prototype Performance of Battery Pack Monitoring for PT XYZ E-Bus Maintenance

One of the primary issues with electric vehicles is battery longevity. This study uses Kodular as an application developer for mobile app monitoring. Furthermore, using the monitoring data, PT XYZ may increase the operational efficiency of electric buses, lengthen battery life, and lower the risk of damage and maintenance costs. The following parameters are used: power voltage, SoC, SoH, system insulation resistance, positive insulation, negative insulation, pack voltage, and current. Inspectors can perform maintenance, diagnose faults, and improve overall system performance by keeping track of these parameters. It also contributes to the prevention of equipment failure, the avoidance of safety hazards, and the reliable operation of electrical and electronic systems. The goal of this research is to examine the performance of prototype monitoring with battery pack measurement parameters on a mobile app using prototype monitoring of battery packs on electric bus maintenance at PT XYZ. In this trial, interval changes happened every 30 minutes from 20:10 to 21:40. This research was carried out four times. The output parameters are pack voltage (545.9 V), current (-67.7 A), power voltage (26676), SoC (81.4%), SoH (100%), system insulation resistance (1233 KΩ), positive insulation (2952 KΩ), and negative insulation (1233 KΩ).

Real-time gamma-ray energy spectrum / dose monitor with k-α method based on sequential bayesian estimation

Medical applications of radiation have been widely spread until now. However, the exposure of medical staff is sometimes overlooked, because treatment of patients is the first priority. The purpose of this study is to develop a small and light monitor that can measure the energy spectrum and dose of gamma-rays at the same time in real-time for medical applications. Using the monitor, the medical staff could be guided to be more aware ofthe risk of radiation, and finally the exposure to them could be substantially suppressed. So far, a CsI scintillator has been chosen as a detection device of gamma-rays and combined with a Multi-Pixel Photon Counter (MPPC) to develop a prototype monitor. Then we confirmed its basic performance with standard gamma-ray sources. To achieve the real-time measurement, α method (sequential Bayesian estimation) was adopted and improved to propose a new unfolding process, named k-α method, with which the convergence speed could really be accelerated to realize real-time measurement. Also, gamma-ray measurements with a mixed source of 133Ba, 137Cs and 60Co were carried out to confirm the validity of the present monitor. As a result, it was found that gamma-ray energy spectrum could be estimated successfully in several-tens seconds in the field of around 6 μSv/h. For the dose estimation, the correct values could be estimated just after starting measurement.