Arduino Uno Microcontroller Research Articles

Design of an E-Nose Detector for Contaminated Gas in Cow Farming Waste

Cattle farm waste is organic waste produced by the livestock industry in the form of feces and can emit gases that cause air pollution. Cow dung contamination can cause environmental problems, disturbing humans and livestock. Hazardous gases produced from the decomposition of livestock manure, namely methane (CH4), sulfur dioxide (SO2), ammonia (NH3), and hydrogen sulfide (H2S), can cause respiratory diseases, deplete ozone and contribute to the greenhouse effect. Based on these problems, measuring gas levels using an Electronic Nose (E-Nose) tool capable of detecting harmful gases in livestock pens is necessary. The E-nose is a device that mimics the human nose and consists of several gas sensors that can detect environmental odors. The design of the detection and measurement of toxic gases using an electronic nose in this study adopts the Internet of Things (IoT) concept using the Arduino Uno and ESP8266 microcontrollers and MQ-4, MQ-136, and MQ-137 gas sensors to read levels of pollutant gas in units of ppm with an average of 46.25 ppm for the MQ-4 sensor, 1.86 ppm for the MQ-136 sensor, and 4.97 ppm for the MQ-137. The sensors were subjected to functional tests consisting of validity testing, resulting in a calculated r value of 1, meaning that each gas sensor performs its measurement function correctly or validly. The reliability test of the MQ-4 sensor is 0.00, the MQ-136 sensor is 2.442× 10-15, and the MQ-137 sensor is 1.554× 10-15, which means that the data reading from each gas sensor is unstable, less accurate, or unreliable compared to secondary data. However, the designed E-Nose tool can work well and improve farmers' performance in their daily work.

Identifikasi Kerusakan Dini Otomatis Komponen Elektronika Berbasis Arus Dengan Mikrokontrol Arduino Uno

Electric current is the flow of electricity in an electronic circuit where the amount flows through the circuit. The greater the current flowing in the circuit, the greater the value in amperes. The components in electronic circuits always have an electric current flowing through them. The electric current in this component is the basis for knowing whether the component is in good condition or not. On this basis, researchers use current sensors to detect damage to electronic components. In designing DC Current Based Early Damage Identification of Electronic Components with Text Notifications on the LCD using the Arduino UNO microcontroller, the current sensor is used, namely the ACS 712 current sensor. This Electronic Component Early Damage Identification Tool works automatically, the system will provide automatic error information via notifications on the LCD and the system will stop itself if one of the components is damaged. With this tool, Early Damage Identification of Electronic Components Based on DC Current with Text Notification on the LCD using the Arduino UNO microcontroller will make repairs easier and faster if one of the components is damaged. Because the notification on the LCD will notify you of damage to the damaged component

C-Remor: UVC-Room Sterilization Mobile Robot

Mobile robots can be used in a variety of Internet of Things projects. The sterilization robot is created in the form of a mobile robot by combining two microcontrollers. The Arduino Uno microcontroller provides a sensor acquisition device, while the ESP32-cam provides the actuator controller, which is linked to the Wi-Fi network displayed on the web server. The millis function has been used in the Arduino module programming process because it requires doing tasks at the same time. This modeling system has an accuracy of about 80% for Arduino module settings as sensor controllers and 80%-86% for ESP32-cam modules as user interfaces and actuator controllers. This is due to the sensor's limited range and the Wi-Fi network used.

Design a Realtime Datalogger on a Simple House KWH Meter with a Capacity of 1300 VA

At this time, monitoring tools that directly monitor electricity usage in the home are very necessary, because in this era of global warming, it requires us to be a little careful in order to use electricity effectively. Where if we do not control electricity usage, the costs charged per month could increase and become burdensome in the payment process. This research designs a KWH meter monitoring tool with output estimates of usage costs so that the tool created can minimize costs in the field of electricity usage. Design a circuit where the tool can display the values ​​of voltage, current, frequency and cos phi flowing through the KWH, then also display the usage costs for a week which is indicated by a buzzer sound if usage has exceeded normal limits. The next step is the validation or calibration process for the designed tool which is planned to be able to test the tool that has been made. The results obtained from designing a realtime datalogger tool on this KWH meter can monitor household electrical energy usage with a capacity of 1300 VA where the monitoring aspects that can be displayed include voltage, current, power factor, usage power and usage costs while this tool is installed. The main system of this tool is driven by an Arduino UNO microcontroller which is programmed so that it can display voltage, current, power factor, usage power and usage costs. In the tool system that has been created, the error rate of voltage measurements is <1%. The error rate in reading current and power factor (PF) is <3%. Meanwhile, the error rate in reading power and usage costs is <9%. Keywords: Monitoring, KWH, Data Logger

Real-Time Air Density Measurement with IoT Integration

This paper presents the design and implementation of an IoT-based air density measurement system that integrates BMP180 and DHT22 sensors with an Arduino Uno microcontroller and ESP8266 Wi-Fi module. The system measures temperature, humidity, and atmospheric pressure to calculate air density in real-time, displaying the results on an LCD screen and transmitting the data to a smartphone app (Blynk) for remote monitoring. The goal of this research is to create a reliable, automated system capable of providing continuous air density measurements without the need for manual intervention. To evaluate the system’s accuracy, data were collected and compared with reference values from the Korea Meteorological Administration (KMA) for August 2023. The comparison revealed that the system produced air density measurements with an average error of less than 0.2%, demonstrating its high level of accuracy and reliability. The system is particularly suited for laboratory environments, where real-time and accurate air density measurements are essential. The use of IoT technology allows for remote data access and continuous monitoring, making the system convenient for various applications, including environmental monitoring and industrial settings where air density plays a crucial role. Future improvements could include sensor calibration enhancements and the integration of additional environmental parameters, such as CO2 levels or particulate matter, to broaden the system’s functionality. Overall, the IoT-based air density measurement system offers a cost-effective and scalable solution for real-time environmental data monitoring.

Design and Implementation of an Incubator with PI Controller for Optimizing the Chicken Egg Incubation Process

This paper describes the design and development of a chicken egg incubator with a structure made of medium-density fiberboard (MDF). The incubator features a temperature control system that integrates a proportional-integral (PI) controller, an Arduino Uno microcontroller, and a regulated light bulb. A display is also included to provide real-time information on temperature, humidity, and the setpoint value, which is set at 37 degrees Celsius to ensure optimal development of the chicks. The Proteus software was utilized for the electrical stimulation of components such as the dimmer and optocouplers. For the mathematical modeling of the incubator, a black box modeling approach was employed to derive the system’s transfer function. Pole cancellation techniques were applied to tune the PI controller, and the controller constants were optimized using Matlab to achieve an optimal system response.

Otomatisasi Sistem Bagasi Terminal Internasional Di Bandar Udara Jenderal Ahmad Yani Semarang

The increasing number of flights proves that the public believes air transportation is one of the most desirable. Of course, the operation of the claim baggage conveyor will increase along with the number of existing passenger luggage. For this reason, the operation of the baggage claim conveyor needs to be done with the help of technology. This research was conducted to assist the work of airline porters, including time and energy efficiency through technology. This tool uses the Arduino Uno microcontroller as the program processing centre. This system will work when the Infrared sensor detects baggage after an x-ray, so the conveyor will be activated automatically. After the conveyor runs, another Infrared sensor will see the baggage on the conveyor. The conveyor will stop automatically when the Infrared sensor does not detect any baggage for a particular time. This tool is equipped with a Push Button so that the baggage claim conveyor can be operated manually and an LCD to explain the instrument's condition and the amount of baggage available. The amount of luggage can be monitored periodically via a smartphone using the ESP8266 WiFi module.

Machine learning driven portable Vis-SWNIR spectrophotometer for non-destructive classification of raw tomatoes based on lycopene content

Most of the research on intact fruit spectroscopy is derivative in nature as it primarily showcase application of existing spectroscopy devices which are often proprietary in nature. The regression models developed by researchers to predict physicochemical attributes using spectra remain theoretical due to lack of mechanism to integrate the developed models back into proprietary devices. This poses challenge for commercial adaptation of this technology in commercial food quality supply chain. The present study addresses this research gap by presenting first of its kind innovative approach to classify tomatoes based on lycopene content using chemometrics-machine learning framework driven portable short-wave near infra-red (SWNIR) spectrophotometer developed by integration of open-source hardware (AS7265x multispectral chipset having wavelength range 410–940 nanometre (nm), Arduino Uno microcontroller) and software (R platform), housed in ergonomically designed and 3-dimension printed cabinet ensuring noise-free spectra acquisition. The lycopene content was observed to have strong negative correlation with wavelengths (nm) 485, 560 and 585 at ρ = – 0.65, – 0.70, – 0.70, whereas strong positive correlation with 760 nm at ρ = +0.64. Similar associations were qualitatively observed using principal component analysis. Atypical of literature, feature selection was performed based on analysis of variance and 14 wavelengths which exhibited statistically significant difference with respect to 15-days storage study (p ≤ 0.05) were selected for model development. Chemometrics-machine learning framework was used for development of optimised probabilistic and non-probabilistic models including logistic regression, Linear Discriminant Analysis (LDA), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models using 10-fold cross validation subjected to 80–20% train-test split of the dataset. In agreement with literature, 500–750 nm wavelength range dominated the classification of lycopene content. Notably, specific wavelengths for logistic regression (560 nm), LDA (730 nm, 645 nm, 560 nm, 535 nm), RF (760 nm, 585 nm, 560 nm, 645 nm), and ANN (585 nm, 560 nm) significantly influenced outcome instances across classifiers. Accuracy obtained from confusion matrix on test dataset was used as performance metric to compare different models. Logistic regression and RF showcased accuracy of 80%, LDA and SVM at 90% while ANN outperformed all models with accuracy of 95%. This study successfully augmented technological advancement in field of spectroscopy for non-invasive quality assessment of fruit. It is recommended to conduct similar studies on other climacteric fruits for wider adoption of this technology.

Development of graphical user interface for boost converter employing visual studio

In power electronics, the boost converter is a typical circuit that raises the voltage from the input to the desired output voltage. For learning how a boost converter works, you need an easy-to-use GUI that lets you change the input voltage, output voltage, and load resistance. In this study, we look at how to use Visual Studio to make a boost converter’s simple user interface (GUI). Details are given about how the software was programmed and how the interface was made, as well as how the graphical user interface was made. This circuit has a total of four devices, two for current and two for voltage. Voltage sensors can measure both the input and output voltage, while current sensors can measure both the input and output current. The Arduino Uno microcontroller is used as a processor for getting data and as a link between sensor devices and personal computers. The results show that the GUI we made works well for analyzing the features of the boost converter circuit.

Design of a Web-based Cold Storage Temperature Monitor with Arduino Uno for Fish Quality Maintenance: Sensor-based Methodology and Innovative Contribution

The development of a web-based cold storage temperature and humidity monitoring device using Arduino Uno aims to monitor room conditions, particularly in cold storage spaces, through a website. This device utilizes DHT11 and DS18B20 sensors, along with the Wemos D1 Mini module, integrated with the Arduino Uno microcontroller. Arduino configuration is performed through an application to send commands to other components. The research was conducted at PT. Indu Manis Gresik, East Java. Data collection and testing were carried out at the specified location. The test results demonstrate the device's efficient operation, transmitting testing data to the associated website. The accuracy of the DS18B20 temperature sensor testing reached 98.8%, while the DS18B20 humidity sensor achieved 99.35%.

EXPERIMENTAL STUDY OF SAMPLES MANUFACTURED FROM CRUSHED POLYMER WASTE ON A 3D PRINTER WITH A SCREW EXTRUDER

The article conducts an experimental study of samples made from crushed polymer waste on a 3D printer with a screw extruder. General information about the experimental study is given. An analysis of literary sources on the study of mechanical properties of products manufactured by FDM technology on 3D printers was carried out. Two 3D printers were chosen for the production of experimental prototypes, one of which prints with filament, the other with granules or crushed polymer waste. The equipment for determining the accuracy of printed samples has been selected. The measuring part of the tearing machine for taking tensile diagrams of polymer samples has been improved. A strain gauge for force measurement and an optical encoder for displacement measurement are installed in it. A digital electronic system for data collection from sensors based on the Arduino Uno microcontroller was developed. Software for experimental data processing was created. The general view of the experimental setup is given. It is proposed to use uniaxial tension to evaluate the mechanical characteristics of the test samples printed on a 3D printer. Current international standards for tensile testing of plastics are described. To determine the mechanical tensile characteristics, samples in the shape of a blade were made from polymer materials ABS and TPU. The parameters for printing test samples made from these materials are set. With the use of the developed installation, the failure diagrams of samples (blades) made of crushed polymer waste and filament of ABS and TPU plastics with different amounts of internal filling were obtained. In the form of tables, the values of the mechanical characteristics of the samples produced by the 3D printing method from ABS and TPU polymers with different internal filling obtained during the uniaxial tensile test are presented. A photo of the places of breakage of ABS plastic blades, taken using a digital camera, is given. An analysis of the structure of the samples was carried out on them. Experimental test samples were made using two 3D printers to determine the accuracy of 3D printing. Measurements were made in different planes of the test cube using a digital electronic caliper. The obtained results were compared with the created model. Ways to improve print accuracy are described.

Implemantation of firefly algorithm on Arduino Uno

Not only getting the optimal solution of a problem, embedding the algorithm on the microcontroller is also expected to work optimally without burdening the system and fast response. Getting a microcontroller specification that matches the complexity of an algorithm is necessary so that the system can execute the algorithm perfectly. Values for the basic parameters of optimization algorithms inspired by nature such as the firefly algorithm (FFA) which are interpreted into variables greatly affect the performance of the microcontroller in obtaining the expected optimal solution. The observed performance of the Arduino Uno microcontroller in running the FFA includes execution time and memory capacity required to obtain optimal values based on changes in absorption coefficient, random parameters, iterations, and population. Changes in the absorption coefficient and random parameters affect the optimal value but do not significantly affect the execution time and memory capacity of Arduino Uno. Iteration changes greatly affect execution time and population changes most affect the performance of Arduino Uno. With a dynamic memory capacity of 2 Kb, the FFA can be run with a maximum range of 50 populations and up to 20 iterations.

Shopping Redefined: An Innovative System with Barcode Scanning and Bill Display

This paper presents the development of automated shopping cart for supermarkets. This automated shopping cart reduces the shopping time of customer. This project works on Arduino UNO microcontroller, bar code scanner for scanning the products and LCD to display the billing. Hence it works with low cost, low power consumption. So that customers can enjoy shopping without pushing trolley themselves.

Strain Detecting Smart Chair

Abstract: Prolonged sitting with poor posture can lead to discomfort and health issues. This paper presents the design and implementation of a posture-detecting smart chair aimed at promoting ergonomic awareness and preventing discomfort associated with improper sitting positions. The proposed system utilizes an Arduino Uno microcontroller and ultrasonic sensors to monitor the user's posture in real-time. The system consists of an Arduino Uno microcontroller, an ultrasonic sensor (HCSR04), power supply, connectivity. The ultrasonic sensor is strategically positioned to measure the distance between the chair and the user's back. The Arduino Uno processes the sensor data and notify the user to encourage a correct sitting posture when improper positioning is detected. The algorithm involves continuous monitoring of the ultrasonic sensor's readings. When the distance between the user and the chair falls below a predefined threshold, indicative of poor posture, the system notify user to change the posture. Additionally, feedback mechanisms such as LEDs or buzzers provide real-time alerts to the user, enhancing their awareness of their sitting habits. This low-cost, Arduino-based solution offers a practical and accessible means to address the increasingly prevalent issue of poor posture. The proposed smart chair contributes to the growing field of smart furniture, leveraging simple yet effective technology to promote health-conscious behaviors in daily life

Women Safety Device with Shock Taser and Location Tracking

Abstract: This abstract provides an overview of a Women Safety Device. The safety and security of women continue to be a significant concern in modern society. To address this issue, we present a comprehensive Women Safety Device equipped with a Shock Taser and Location Tracking, implemented using the Arduino Uno microcontroller. The core functionalities of the proposed device include a shock taser for self- defense and a location tracking system to ensure quick response in case of emergencies. The shock taser is designed to deliver a non-lethal electric shock, acting as a deterrent against potential threats. The device utilizes high-voltage, low-current pulses generated by the Arduino Uno to create a safe and effective self-defense mechanism. Furthermore, the location tracking feature utilizes GPS and GSM technologies to continuously monitor the user's real-time position. The Arduino Uno gathers GPS data and transmits it via GSM to the predefined contacts. This allows authorized contacts, such as family members or authorities, to track the user's location and respond promptly to distress situations. By combining a shock taser for self-defense and a real-time location tracking system, this device contributes to a safer environment for women and provides peace of mind for both users and their loved ones.

Parking Management System

Abstract: Parking management systems play a crucial role in optimizing parking space utilization and improving user experience. This research presents a novel approach to parking management using infrared (IR) sensors and an Arduino Uno microcontroller. The system employs a network of IR sensors strategically placed at the entrance and parking slots to monitor occupancy status in real-time. The data collected by the sensors is processed by the Arduino Uno, which then displays the availability of each parking slot on an LCD display. This paper discusses the design, implementation, and evaluation of the proposed parking management system, highlighting its effectiveness in enhancing parking space utilization and user convenience.

Machine Learning Models for Predicting Air Pollution

Abstract: The air quality monitoring system collects data of pollutants from different location to maintain optimum air quality. In the current situation, it is the critical concern,. The introduction of hazardous gases into the atmosphere from industrial sources, vehicle emissions, etc. pollutes the air. Today, the amount of air pollution in large cities has surpassed the governmentset air quality index value and reached dangerous levels. It has a significant effect on a human health. The prediction of air pollution can be done by the Machine Learning (ML) algorithms. Machine Learning (ML) combines statistics and computer science to maximize the prediction power. ML is used in order to calculate the Air Quality Index. Various sensors and an Arduino Uno microcontroller are utilized to collect the dataset. Then by using K- Nearest Neighbor (KNN) algorithm, the air quality is predicted.

Smart Voice Controlled Wheelchair for Physically Disabled People

Abstract: In this groundbreaking study, we introduce a state-of-the-art, user-friendly smart voice-controlled wheelchair designed specifically for individuals with physical disabilities. Leveraging advanced technologies, including the powerful Arduino Uno microcontroller, precise ultrasonic sensors for obstacle detection, and seamless Bluetooth connectivity, our wheelchair system redefines the paradigm of mobility solutions. An intuitive mobile application, available for download on popular platforms such as the Play Store, complements the hardware, enabling effortless control and customization through natural voice commands. This paper meticulously details the design, implementation, and evaluation of our smart voice-controlled wheelchair, emphasizing its innovative features and discussing compelling avenues for future improvements.

Intelligent Traffic Management System with Adaptive Signal Control and Emergency Vehicle Prioritization

Abstract: Efficient traffic management is essential for ensuring smooth flow and minimizing congestion on roadways. In this project, we propose an Arduino-based solution that utilizes IR sensors for real-time traffic monitoring and signal timing adjustment, coupled with a Bluetooth module to prioritize emergency vehicles during critical situations. The IR sensors are deployed at intersections to monitor vehicle presence, enabling real-time traffic monitoring. The Arduino Uno microcontroller processes the sensor data and dynamically adjusts signal timings to ensure smooth traffic flow. Additionally, a Bluetooth module facilitates communication between the traffic management unit and ambulance vehicles, triggering an alert signal to clear the way for emergencies. The system's adaptability allows for easy integration into existing infrastructure. Results indicate improved traffic efficiency, reduced congestion, and enhanced emergency vehicle response times

Application note—A novel, low-cost pH-controlled solenoid-based CO2 dosing device for microalgal and cyanobacterial cultivation systems

This application note proposes a novel, low-cost, pH-controlled solenoid-based CO2 dosing device for microalgal and cyanobacterial cultivation systems. The device consists of an Arduino-Uno microcontroller, a (ph-4502c) module, a pH probe, a micro-SD card for data logging, and a relay-controlled solenoid valve. C functions are used to control, and data log the pH and operate the solenoid valve for CO2 dosing in microalgal cultivation systems. With open-source programming, the device can be used for a wide range of applications in both industry and academia. The proposed device is flexible, customizable, and upgradable. This study aims to develop a low-cost pH-controlled, solenoid-based CO2 dosing and data logging device and utilize it for pH control and CO2 dosing in a 200 L outdoor raceway tanks utilized in cultivating and studying the growth of marine cyanobacterium Geitlerinema sp. and Spirulina sp.