Prototype Monitoring Research Articles

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.

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.

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.

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.

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.

Exploring needs and requirements for a prototype device measuring physical activity in pediatric physical therapy: A qualitative study.

To analyze needs and requirements of Pediatric Physical Therapists (PPTs), parents, children and adolescents with and without developmental disabilities in the future use of an activity monitor prototype (AM-p) in everyday clinical practice. Qualitative exploratory study with a thematic analysis approach, based on Braun and Clarke's six steps. Codes derived from the analysis and central themes were collated, based on Fleuren et al.'s groupings of determinants. We interviewed 25 PPTs, 12 parents, and 12 children and adolescents. Within four groupings of determinants, we found nine themes: 1) development of information materials; 2) application: output visualization and ease of use; 3) design; 4) relevance and acceptance; 5) shared decision-making; 6) compatibility in daily living; 7) finances, 8) time, and 9) legislation and regulations. End-users have similar basic needs, with individual fine-tuning to be addressed during further development of the AM-p. A child-friendly design, information material, and an easy-to-use application to read and interpret results, need to be developed. Efficient training for PPTs is important for the use of the AM-p and analysis of results. Communication between PPTs and children as well as parents enhances shared decision-making. We recommend involving diverse end-users to enable maximum customization of the AM-p.

IMPLEMENTATION OF FUZZY ADAPTIVE NEURONAL CONTROL APPLIED TO TRAJECTORY TRACKING OF AN AUTONOMOUS UNMANNED SURFACE VEHICLE

ABSTRACT: This article presents the hardware implementation of the fuzzy adaptive neural control law for trajectory tracking based on a simplified model of the navigation system of an autonomous unmanned surface vehicle prototype for water quality monitoring. The parameters of the simplified model are estimated. Trajectory tracking is carried out in-line by fuzzy adaptive neurons for tuning the gains of a Proportional Integral Derivative controller (PID). The stability analysis is developed. Simulations were obtained at Matlab®/Simulink for circular trajectories and the implementation in Arduino Mega boards, very good precision is obtained for the experimental results in a diving pit. Keywords: Autonomous unmanned surface vehicle, fuzzy adaptive neuronal control, trajectory tracking, implementation of a Proportional Integral Derivative controller, Arduino Mega.

Monitoring the bogie lateral dynamics of a high-speed train through wireless sensor nodes

The condition monitoring of railway vehicles and of the infrastructure (track and overhead equipment) through the use of vehicle-based sensors is becoming a major trend for the railway industry and, to this aim, new rolling stock generations are often natively fitted with numerous sensors. However, older vehicles are not equipped with sensors and monitoring systems so that, considering the long service life of the rolling stock, advanced monitoring and condition-based maintenance techniques can only be applied to a relatively minor portion of the in-service railway fleets. This paper presents results from a research programme aimed at designing and testing an innovative monitoring system based on wireless sensor nodes, suitable for the retrofitting of older rolling stock generations, not natively equipped with advanced sensing and monitoring capabilities. The prototype monitoring system was used to detect the incipient bogie instability, which can be related to degradation in some suspension components or to increase of the equivalent conicity at wheel/rail contact. Different vehicle conditions and the influence of the track were considered in the study. Experimental tests have shown that is possible to identify not only how the vehicle condition is evolving in time but also the effect that maintenance operations like wheel reprofiling may have on the bogie lateral dynamics.

The Effect of Phonomyography Prototype for Intraoperative Neuromuscular Monitoring: A Preliminary Study.

Quantitative neuromuscular monitoring, as extolled by clinical guidelines, is advocated to circumvent the complications associated with neuromuscular blockers (NMBs), such as residual neuromuscular block (rNMB). Nonetheless, the worldwide utilization of such methods remains undesirable. Phonomyography (PMG) boasts the advantages of convenience, stability, and multi-muscle recording which may be a promising monitoring method. The purpose of this preliminary study is conducting a feasibility analysis and an effectiveness evaluation of a PMG prototype under general anesthesia. A prospective observational preliminary study was conducted. Twenty-five adults who had undergone none-cardiac elective surgery were enrolled. The PMG prototype and TOF-Watch SX simultaneously recorded the pharmacodynamic properties of single bolus rocuronium at the ipsilateral adductor pollicis for each patient. For the primary outcome, the time duration to 0.9 TOF ratio of the two devices reached no statistical significance (p > 0.05). For secondary outcomes, the multi-temporal neuromuscular-monitoring measurements between the two devices also reached no statistical significance (p > 0.05). What is more, both the Spearman's and Pearson's correlation tests revealed a strong correlation across all monitoring periods between the PMG prototype and TOF-Watch SX. Additionally, Bland-Altman plots demonstrated a good agreement between the two devices. Thus, the PMG prototype was a feasible, secure, and effective neuromuscular-monitoring technique during general anesthesia and was interchangeable with TOF-Watch SX.

GIS Constructed Water Monitoring Network System Via IOT

Monitoring the quality of water plays a vital role in the advancement towards intelligent and smart agriculture. It also facilitates the seamless transition to automated monitoring of essential components of human daily needs, as new technologies continue to be developed and adopted in both agricultural and human daily life, particularly in relation to water. Assessing water stress requires accurate measurement of water usage [1]. The objective of this study is to optimize the distribution strategy and achieve more efficient and rapid distribution, while also making better use of technology to address the urgent need for water conservation. One of the criteria for the second filtering phase, which utilized a realtime data collection system, focused on the implementation of water quality monitoring and characterization methods. Water measurement sensors and the Internet of Things (IoT) can provide immediate information on water flow and pressure rates. This information is crucial for the analysis and surveillance of water [2]. At the RCEW campus, online smart water monitoring is imperative to ensure a more efficient and sustainable use of water. By combining the Internet of Things and geographic information systems (GIS), an online smart water monitoring prototype was developed specifically for RCEW [3]. This cutting-edge technology has proven to be highly successful in providing real-time information on water parameters, saving both time and money, while enabling the continuous study of water consumption patterns. This innovative technology seamlessly integrates IoT and GIS for effective water surveillance [4,5].

Application of the participatory design in the testing of a baropodometric insole prototype for weight-bearing asymmetry after a stroke: A qualitative study.

Currently studies indicate the need to incorporate the user`s perspective in the testing of new assistive technologies. The objective of this paper is to test a baropodometric insole prototype for monitoring and treatment weight-bearing asymmetry, according to the Participatory Design. We used a qualitative case study approach during the testing phase of the baropodometric insole prototype. The focus group approach addressed topics related to the experience and accessibility of the potential user in conjunction with professionals, researchers, and physiotherapy students. Facilitators, barriers, and requirements for the device were collected through audio recordings of the discussions during and after prototype testing. Key steps in the prototype testing process were divided into (1) Test of the prototype according to the Participatory Design, divided into Who, When, How, and Why the potential user was involved in the study; and (2) Facilitators, barriers and requirements to improve the prototype. The baropodometric insole prototype can be seen as a promising device for monitoring and treating weight-bearing asymmetry.

Dynamic risk evaluation of supersaturated dissolved gas at a confluence with unsteady flow conditions

The confluence of two rivers gives rise to a distinctive hydrodynamic structure where numerous nutrients can be accumulated, making the confluence area a pivotal biological habitat. The threat of supersaturated dissolved gas in upstream water caused by dam discharging is indispensable for the confluence areas. Therefore, it is imperative to study and assess the risks faced by fish in these areas, particularly when the merging of main and tributary streams leads to varying degrees of supersaturation and non-uniform distribution of dissolved gases. The spatial and temporal distribution of supersaturated dissolved gas at the natural confluence in the Yangtze River utilizing continuous prototype monitoring and unsteady flow numerical simulation was investigated, and a dynamic evaluation method that classifies risk into three levels (high, medium, low) was proposed. The confluence area exhibited evident non-uniform distribution characteristics, wherein the primary factors influencing this distribution were identified as the dissolved gas saturation of inlet flow, and flow ratio. The presence of non-continuous discharge intensity resulted in an increase in medium-risk areas within the river, thereby mitigating the risk to fish from the supersaturation of the discharge. A velocity barrier was formed at the confluence, with the velocity range being 1.8 ∼ 3.4 m/s, exceeding the fish’s burst speed of 1.7 m/s. Because of the barrier, there was no direct access to the low-saturation areas, and potential avoidance routes are primarily located in the medium-risk zones downstream. The paper offers a useful method to assess risk distribution in critical ecological regions characterized by non-constant characteristics. Additionally, it recommends dam discharging strategies to expand the scope of fish risk avoidance.

Development and experimental validation of a prototype system for Machine Learning based SHM in composite aerostructures

The development and experimental validation of a Machine Learning (ML) vibration based Structural Health Monitoring (SHM) prototype system for composite aerostructures operating under uncertainty is presented. Following initial training, it is intended to operate autonomously, in almost real-time, without interrupting normal operation, relying on a limited number of acceleration and strain measurements from properly selected locations on the aerostructure. It is equipped with a multi-level information fusion methodology comprising multiple sensor technologies and diagnostic algorithms for optimized SHM performance in terms of damage detection and characterization. Through its flexible software, the system provides multiple options of ML based SHM methods targeting wide applicability to various types of structures. The system is validated based on numerous experiments with healthy and damaged full-scale composite bonded spars operating under multiple uncertainty factors. Its diagnostic performance indicates that the current progress in hardware and robust vibration-based Machine Learning algorithms may lead to compact, effective, and low cost SHM systems for composite aerostructures under normal operating conditions.