Arduino Research Articles

Design and PIL Implementation of Fuzzy Logic-based MPPT Control for Symmetrical Multilevel Boost Converter

This paper aims to introduce a fuzzy logic adaptive MPPT controller to control a symmetrical multilevel converter in a standalone PV system. The system is evaluated under fixed and variable solar radiation with 15 V as the input voltage 60 V as the required output voltage and 50 kHz as the switching frequency. Results prove that by using the controller, the system successfully tracks the MPPT point for constant and variable radiation without oscillation around the maximum power point. In addition, the overshoot and time response is reduced while the voltage ripples are eliminated. The proposed controller is verified through practical implementation in Arduino mega board to test the accuracy of results. The practical finding via a processor in the loop test validates the simulation results.

Enhancing Surveillance Systems with Cost-Effective Ultrasonic Radar for Object Detection and Tracking

Object detection is critical in surveillance systems because it enables timely identification of unauthorized objects, which is essential for maintaining security and mitigating risks. Existing approaches to surveillance typically suffer from limited coverage, variable detection range, moderate accuracy, high cost, and complex setup requirements. This article presents an approach to object detection using microcontroller-based ultrasonic sensors. Our approach leverages four distinct processing phases to create a radar-like system capable of real-time detection and autonomous decision-making. In this study, we conducted experiments by integrating more than six peripheral modules with the Arduino platform. By evaluating 17 diverse test cases in various scenarios and environments, the approach demonstrated enhanced object tracking and robustness compared to existing methods. The system effectively detects objects within the targeted area, providing precise distance measurements (around 50 cm) and position information (0 to 360 degrees). Moreover, our approach enabled the identification of three risk zones—medium, high-risk, and mild danger—within the critical region, contributing to improved security and risk management. The results reveal the system’s effectiveness in real-time detection, accurate distance estimation, and comprehensive risk assessment. Overall, our study significantly contributes to the advancement of object detection technology by effectively addressing the limitations of existing methods and offering a cost-effective and practical solution. Our research has the potential to monitor objects in surveillance systems across various domains, with significant implications for enhancing security, risk mitigation, and monitoring in industries reliant on effective object detection systems. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 157−171

Improving Elementary Students’ Computational Thinking Skills through an Educational Robot Intervention: A Quasi-Experimental Study

Globally, many educational institutions recognize the importance of computational thinking and have begun incorporating it into primary education. Educators cultivate students’ computational thinking skills through block-based programming; however, a lack of digital learning tools that offer real interaction may negatively impact students’ computational thinking learning performances. This study proposes a cost-effective, block-based, programmable computational thinking educational robot developed using the open-source Arduino platform, combined with Android application development. This robot is specifically designed for use in elementary computational thinking education. To assess the impact of the proposed approach on elementary students’ learning achievement, motivation, and attitudes towards computational thinking education, a quasi-experimental design with control and experimental groups was implemented in the computational thinking curriculum at an elementary school. The experiment was conducted over a period of three weeks and involved two classes of students and one teacher. The control group engaged in computational thinking learning activities using computers, while the experimental group completed computational thinking learning activities using the computational thinking educational robot and application developed in this study. Data were collected through prior knowledge tests of computational thinking, learning motivation and attitude questionnaires, and computational thinking achievement tests completed by the students. The results indicate that the experimental group outperformed the control group in learning achievement, motivation, and attitudes, demonstrating that physical interaction in learning can effectively enhance learning performances.

Development of an integrated device based on the gain/phase detector and Arduino platform for measuring magnetoelastic resonance

This study presents the development of an integrated device for measuring magnetoelastic resonance, utilizing a gain/phase detector and the Arduino platform. The device stands out for its simplicity, low cost, and efficiency. It employs the Arduino’s ATmega328P microcontroller, coupled with the AD8302, and an intuitive graphical interface developed in Python, which facilitates the measurement of magnetoelastic signals in sensors. The device’s validation is carried out through measurements of mass deposits on magnetoelastic sensors, confirming its accuracy and functionality. This advancement offers a practical and portable solution for detecting magnetoelastic resonances, promoting the use of these technologies in field environments and in diverse scientific applications. The integration of components into a compact, robust, and low-cost electronic circuit, along with an easy-to-operate graphical interface, significantly simplifies the complexity of the components, making the device complete and readily operational.

Development of an Automatic Portable Calibrator for Tipping Bucket Rain Gauge (TBRG) Using a Load Cell and Simple Water Sensor

The tipping bucket rain gauge is an automatic rain gauge that can measure rain intensity in mm/hour. Accurate measurement of rain intensity requires calibration of the rain gauge to ensure traceability. Calibration of a tipping bucket rain gauge is related to the volume of water as a calibration medium and time so as to produce a discharge that can be converted into rain intensity. Technological developments can make tipping bucket rain gauge calibration automatic by utilizing an Arduino Mega2560 microcontroller, a load cell and a simple water sensor based on the LM393 comparator IC, and combined with a compact design printed using a three-dimensional printer so that it can be used for ex-situ calibration. The data produced by this tool can be downloaded via the application that was built and produces a file in Excel form.

Hardware implementation of Firefly algorithm-based maximum power point tracking on Arduino

In this paper, we present the design and implementation of a Firefly Algorithm-based Maximum Power Point Tracking (MPPT) system on an Arduino platform, aimed at optimizing the performance of photovoltaic systems under variable environmental conditions, particularly partial shading. The Firefly Algorithm was chosen for its ability to efficiently locate the global maximum power point in the presence of multiple local maxima caused by non-uniform irradiance. The experimental setup utilized a Boost converter controlled by the Arduino, dynamically adjusting the duty cycle to maintain the PV system at its optimal operating point. Results demonstrated the system's effectiveness in responding to changes in solar irradiance, ensuring consistent maximum power output. Building on this, our research highlights the use of low-cost hardware like Arduino for implementing advanced MPPT algorithms, bridging the gap between theoretical research and practical applications. The study emphasizes that even with limited resources, significant gains in efficiency can be achieved in renewable energy systems. The findings suggest that using such a platform not only reduces costs but also enhances the accessibility of advanced MPPT technologies in real-world applications, making them a viable option for broader adoption.

An ultraviolet light data logger for studies of organismal ecology and physiology

Abstract Monitoring environmental conditions over time is essential for understanding how species regulate those conditions to satisfy their physiological needs. Variation in ultraviolet (UV) light exposure, for instance, is crucial for numerous taxa, influencing behaviour as well as essential physiological processes like vitamin D3 synthesis. Yet, despite these considerations, no commercially available data loggers exist to record UV exposures over time, preventing inquiry into this important but neglected fitness demand. To overcome this challenge, I designed the first UV data logger, custom built on the open‐source Arduino platform. This device logs UV exposures (in voltage or converted to UV index) and date‐time information for each UV reading, at user‐defined intervals. The integration of a low‐power timer into the assembly, and its weather‐resistant housing, enables long‐term use (at least several months). The cost of one device (including housing) is under £45/$55 USD. Calibration and performance tests reveal that this device is reliable under various weather conditions and produces accurate UV readings under both artificial lighting and sunlight. Both the device and its customised housing can be easily replicated and expanded upon (e.g. inclusion of additional sensors). Overall, this device represents a crucial first step in our ability to better characterise UV exposure patterns in habitats for physiological ecology studies. Exposure to UV light is adaptive for many types of organisms, both in the wild and in captivity (e.g. zoos and private hobbyists), and so I expect a broad array of scientific and public users alike to benefit from using this device.

Design and implementation of an intelligent building security system using Arduino GIGA R1 Wi-Fi

In the face of evolving security challenges, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has become essential for modern building security systems. This paper explores the design and implementation of an intelligent building security system utilizing the Arduino GIGA R1 Wi-Fi, OV7670 camera, and Giga Display Shield, enhanced by Convolutional Neural Networks (CNNs). The system is designed to detect and respond to security threats in real-time, offering a proactive approach to building security. Leveraging the processing power of the Arduino platform and the image recognition capabilities of CNNs, the proposed system distinguishes between authorized and unauthorized access with a demonstrated accuracy of 92%. The study also identifies challenges, including performance in low-light conditions and communication delays, which affect the system's efficiency. Future work will focus on overcoming these limitations and further enhancing the system's functionality. The results indicate that the proposed solution is a promising step toward more intelligent and responsive building security systems, providing a foundation for further innovation in the field.

Automatic Nutrient Control System Base on Internet of Things for Hydroponics Crops

The objectives of this research are to examine the growth rate of plants, the yield, and the nutrient application rate. The research team has developed an automatic nutrient control system which consisted of an Arduino MEGA 2560 R3 as the main processor to receive data from the sensor, such as pH sensor as the sensor to measure the acidity, EC sensor as the sensor used to measure the internal current of a liquid. The Arduino MEGA 2560 R3 will transfer the signal to control the opening and closing of the pump of each nutrient to get the nutrient solution as determined; in addition, the data is send to the cloud via a NodeMCU ESP8266 board connected to the Arduino MEGA 2560 R3, which users can track the data through the application. In the experiment, the developed system was used to grow hydroponic lettuce in a greenhouse and compared to growing hydroponic lettuce by human care under the same environment. The result from experiments revealed that the proposed system can maintain the pH and EC values more stable than the cultivation of hydroponic lettuce by human care, and the growth rate was 99.04%, while the use of caregivers was 95.38%; its harvest of yields was more compared to the use of caregivers up to 7.35%, and the nutrient application rate was less than the care of caregivers at 22.05%.

A Next-Generation Iot-Integrated Arduino System for Intelligent Home Environment Monitoring

To stay informed about home living conditions, a home environment monitoring device was developed using an Arduino platform. Utilizing the Arduino UNO hardware and an array of sensors, the device captures real-time data on indoor temperature, humidity, light levels, and the presence of combustible gases. This information is then uploaded to a monitoring platform, allowing for adjustments to the surrounding environment based on the collected data. Testing has demonstrated that the design effectively gathers and reliably uploads home environment data, meeting the requirements for remote monitoring of living conditions. The device offers several advantages, including low cost, minimal power consumption, a simple structure, and easy installation. DOI: https://doi.org/10.52783/pst.797

USING THE ARDUINO PLATFORM IN THE RESEARCH OF INTERNAL COMBUSTION ENGINES

Modern measuring systems used in the study of internal combustion engines require significant investments. The universal Arduino platform offers ready-made powerful hardware modules for data collection (shields) and control, which work in a wide range of frequencies and signal amplitudes, provide their analysis and processing, allow to implement equipment management, and have a relatively low cost and free software with standard libraries. These facts testify to the high relevance of the use of the Arduino platform in the study of internal combustion engines. The purpose of this work is to determine the possibilities and features of using the Arduino hardware and software platform in the study of processes occurring in energy power plants with internal combustion engines, early prototyping of their control and diagnostic systems. The paper considers the possibility of hardware and software connection of sensors for measuring basic physical quantities to the Arduino platform: crankshaft rotation frequency (based on induction sensors and Hall sensors), pressure, air and fuel flow, temperatures (resistance sensors and thermocouples). In addition, the characteristics (dependence of the measurement parameter on the output value) of some popular sensors used in the study of internal combustion engines are given. It is important to consider that the equipment used for the study of processes in the ICE must meet the certification requirements. In the case of Arduino, this certification may not be available. However, the recommendations for the use of various sensors, shields and Arduino boards can be applied if the measured quantity is not the determining factor. It is important to consider these limitations and use Arduino with an understanding of the capabilities and limitations of the platform. The use of Arduino in the educational process in the training of specialists in the field of power engineering and in the research of energy installations of vehicles will allow students to apply the knowledge, skills and abilities acquired in the learning process at the hardware and software levels, which will create additional motivation for further study devices and principles of operation of automated and automatic vehicle control systems.

Optimizing Electric Vehicle Performance: Advances in Battery Management Systems for Enhanced Efficiency and Longevity

The management of electric car batteries has taken front stage in automotive research and development as more people choose electric vehicles (EVs). Optimal performance, safety, and lifetime of electric vehicles depend on effective battery management systems (BMS). To address this, we developed software for system functionality testing and a BMS prototype with an AVR ATmega family micro-controller. The originality of this work is the practical application of Arduino platform for BMS development, offering a scalable and reasonably priced solution that goes beyond past efforts by including the most recent technological developments and addressing modern challenges in the field. Conclusions drawn show that overcoming present battery performance constraints by developing BMS technology will open the path for more general EV acceptance.

Arduino-based investigation of transmission lines and impedance matching

Abstract Transmission lines play a crucial role in radio-frequency energy transmission between source and load. These systems connect essential physical phenomena like the formation of standing waves and the attenuation of electromagnetic waves in material media with practical considerations such as impedance matching and signal integrity. Understanding these systems requires knowledge of fundamental concepts like inductance, capacitance, and impedance. The absence of impedance matching between the line and load leads to a modulated amplitude in the resulting signal owing to interference between the injected and reflected signals. Here, we present an experiment utilizing an Arduino platform as a signal generator and a data acquisition interface to analyze a radio frequency transmission line. By employing a few-meter-long coaxial cable as the transmission line and a digital oscilloscope with a high sampling rate (several giga-samples per second), we demonstrate how to determine the reflection coefficient as a function of the load impedance. The reflection coefficient enables determination of the return loss, underscoring the significance of impedance matching for optimal performance among the source, transmission line, and the load.

Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car

Laboratory Evaluation of Soil Moisture Sensors for Precision Irrigation in Agriculture

Water is essential for agricultural production and food security, making efficient water use critical. Accurate measurement of soil moisture is vital for scheduling irrigation, ensuring that crops receive the right amount of water at the right time. This prevents both under- and over-irrigation, conserving water and maximizing crop production. Currently, digital soil moisture sensors are used for their accuracy and instant measurement capabilities. The soil moisture sensor's functionality was evaluated through observations of four Capacitive soil moisture sensors 1.2 (SMS A, B, C, D) at various moisture levels, with gravimetric method verification. The calibrated linear equations demonstrated a strong linear relationship for each sensor. The coefficient of determination (R2) were also found to be 0.92, 0.93, 0.91 and 0.93 respectively. These R² values indicate a strong linear relationship for each sensor. An automated drip irrigation module was developed by integrating these soil moisture sensors with the Arduino platform. The system activates the irrigation motor when soil moisture content falls below the field capacity or a desired set value, and turns it off once the required moisture level is reached. This sensor-based automated drip irrigation module enables farmers to irrigate their fields precisely and efficiently, delivering the right quantity of water at the right time. The system is also suitable for deficit irrigation practices, improving crop yield and water use efficiency.

Comparative analysis of 12 water lily plastid genomes reveals genomic divergence and evolutionary relationships in early flowering plants

The aquatic plant Nymphaea, a model genus of the early flowering plant lineage Nymphaeales and family Nymphaeaceae, has been extensively studied. However, the availability of chloroplast genome data for this genus is incomplete, and phylogenetic relationships within the order Nymphaeales remain controversial. In this study, 12 chloroplast genomes of Nymphaea were assembled and analyzed for the first time. These genomes were 158,290–160,042 bp in size and contained 113 non-repeat genes, including 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. We also report on codon usage, RNA editing sites, microsatellite structures, and new repetitive sequences in this genus. Comparative genomics revealed that expansion and contraction of IR regions can lead to changes in the gene numbers. Additionally, it was observed that the highly variable regions of the chloroplast genome were mainly located in intergenic regions. Furthermore, the phylogenetic tree showed the order Nymphaeales was divided into three families, and the genus Nymphaea can be divided into five (or three) subgenera, with the subgenus Nymphaea being the oldest. The divergence times of nymphaealean taxa were analyzed, with origins of the order Nymphaeales and family Nymphaeaceae being about 194 and 131 million years, respectively. The results of the phylogenetic analysis and estimated divergence times will be useful for future evolutionary studies of basal angiosperm lineages.

Dependability Assessment of a Dual-Axis Solar Tracking Prototype Using a Maintenance-Oriented Metric System

This study presents a numerical method for evaluating the maintainability of a dual-axis solar tracking system that can be deployed in residential areas for improved energy production. The purpose of this research manuscript is threefold. It targets the following objectives: (i) First, we present the construction of a self-sufficient dual-axis solar tracking system based on a low-power electronic schematic that requires only one motor driver to control the azimuth and elevation angles of the photovoltaic (PV) panel. The automated system’s main electronic equipment comprises 1 × Arduino Mega2560 microcontroller unit (MCU), 1 × TB6560 stepper driver module, 2 × stepper motors, 2 × relay modules, 1 × solar charge controller, 1 × accumulator, and 1 × voltage convertor. Additional hardware components such as photoresistors, mechanical limit switches, rotary encoders, voltage, and current sensors are also included to complete the automation cycle of the solar tracking system. (ii) Second, the Arduino Mega 2560 prototyping board is replaced by a custom-made and low-cost application-specific printed circuit board (ASPCB) based on the AVR controller. The MCU’s possible fault domain is then further defined by examining the risks of the poor manufacturing process, which can lead to stuck-at-0 (Sa0) and stuck-at-1 (Sa1) defects. Besides these issues, other challenges such as component modularity, installation accessibility, and hardware failures can affect the automated system’s serviceability. (iii) Third, we propose a novel set of maintenance-oriented metrics that combine the previously identified variables to provide a maintainability index (MI), which serves as a valuable tool for evaluating, optimizing, and maintaining complex systems such as solar tracking devices. The experimental data show that the computed MI improves the system’s maintainability and enhances repair operations, increasing uptime.

Smart and cost-effective water quality monitoring system: University of Baghdad canal case study

Polluted water causes diseases, affecting the ecosystem. Water quality is crucial due to high demand. Regular monitoring is essential for a consistent supply of fresh water. Internet of Things (IoT) advancements enable real-time monitoring and timely intervention for water quality issues. The current investigation evaluated a proposed smart, cost effective, high-efficacy IoT application of a water quality detection device that real-time monitors six measurement points along the University of Baghdad canal for five water quality parameters. With the Arduino platform and specific sensor properties, a low-cost monitoring system has been built. The data is shown on an LCD monitoring screen and presented on a Wi-Fi module before being transferred to the cloud and presented on a smartphone and internet platform. The results from the monitoring system for both winter and spring water quality are within the permissible limit according to standard limitations were average pH 7.687, total dissolved solid 406.75 mg/l, turbidity 25.883 NTU, and electrical conductivity 630.8 µS/cm. This framework may successfully offer an environment where there is enough water for agricultural purposes while also keeping a careful check on the contamination of water resources. On the other hand, this permits a well-managed water quality standard.

Hyposense: An Integrated Sensor Device for Hydroponics Farm Monitoring

In recent years, the monitoring of hydroponics farms has undergone a significant transformation due to the integration of sensors. However, the conventional method of manually observing measurements from multiple sensors has proven to be excessively time-consuming and costly. Additionally, the lack of automatic data recording options in most sensors has been a limiting factor. To address these challenges, we conducted an experimental study to introduce a novel integrated sensor device named “HypoSense”, designed to monitor essential parameters such as temperature, humidity, pH, light intensity, Total Dissolved Solids (TDS), and Electrical Conductivity (EC) in hydroponic systems. HypoSense consists of three microcontroller units (MCUs) incorporated with two controlling circuits based on the Arduino platform: the Arduino UNO, ESP32, and the ESP8266 (NodeMCU) microcontrollers. The device is designed using high-precision sensors, including the DHT11 for temperature and humidity monitoring, an Analog pH sensor kit for pH measurement, BH1750 for light intensity, and an Analog TDS sensor kit for TDS and electrical conductivity measurements. These sensors were chosen for their reliability, accuracy, and compatibility with the Arduino platform, ensuring that HypoSense delivers precise and consistent readings. HypoSense is a cost-effective, portable sensor device specially designed for monitoring growth parameters simultaneously in hydroponic farming, saving growers time and effort. The evaluation was carried out in two phases. Firstly, field sensors were employed to calibrate HypoSense device sensors. Next, a secondary evaluation was conducted to confirm the practicality and user-friendliness of the HypoSense device. To facilitate the evaluation of HypoSense, we set up an indoor hydroponic system for growing tomatoes and lettuce. The results of these evaluations, focusing on the performance and applicability of the HypoSense device, will provide valuable insights for hydroponic growers and future researchers in precision agriculture.

Modelling of Gas Leakage Detector with Safety System

Abstract: Leaking of gas is the major problem with industrial sector, domestic areas and vehicles run by LPG gas. Many accidents occur in day-to-day life like explosion because of gas leakage. If gas leakage is not detected earlier then major harm is caused. Due to leakage of LPG gas, it produces hazardous and toxic impact for living things.[1] LPG and natural gas is highly combustible and can burn even at some distance from the source of outflow. The main aim of this project is to present the detection of leakage, alert the danger to people and on the exhaust fan to throw out the gas to prevent from explosion. The gas will be detected by using MQ5 gas sensor. It will immediately turn on the buzzer and exhaust fan and also off the all of the lights, fans of the house for safety, when the sensor detect any gas leakage. This system is controlled by Arduino platform