Raspberry Pi Research Articles

Coin Insertion Based EV Car Charging System Using Raspberry PI PICO

The coin insertion-based Electric Vehicle (EV) charging system represents a novel approach to enhance accessibility and convenience in EV charging infrastructure. This system integrates a coin based payment mechanism with Raspberry Pi technology to provide users with an alternative and straightforward payment option. Utilizing a Raspberry Pi as the central control unit, the system interfaces with EV charging hardware, a coin acceptor, and sensors to facilitate the charging process. Users can effortlessly charge their EVs by inserting coins, addressing the needs of individuals who prefer cash transactions or lack access to digital payment methods. The system ensures safety compliance, calculates charging duration based on coins inserted, and offers a user friendly interface displaying charging status and payment information. This abstract presents an innovative solution to broaden the accessibility of EV charging services by incorporating a familiar and efficient payment method in tandem with modern technology

MIRROR MAGIC: REFLECTING INNOVATION FOR EVERYDAY CONVENIENCE USING RASPBERRY PI

Smart is not an adjective anymore, Now it is an used for anything that improves functioning in multi-device environment, by simplifying our daily tasks by speeding up the information flow and allowing us to do incredible tasks in the twinkling of an eye. A smart mirror is a two-way with an electronic display behind the glass. The display can show the viewer different kinds of information in the form of widgets, such as weather, time and date and news updates. This project would be useful for busy individuals that want to multitask and stay informed while on the go.The basic design of smart mirror starts with two-way glass is the recommended types as it lets the graphics on the display come through clearer. Upon uploading the code to the device from android studio, all the prototypes and design that we have installed in the design module will be displayed. The electrical component to our project was a simple 19mm Computer monitor mounted to the back of the see through glass. In addition to, installation of PIR on and off sensor to different motion atmospheres is added. And an LED strip around the smart mirror comes with multiple luminosity levels from cool to warm light. The Google voice assistant feature has been implemented to control device with spoken commands. All of this works with Raspberry pi 3 A+ model which is a single board computer. To install the raspbian OS a micro SD card is needed prepared with an OS image all that will be running on the mirror will be coded on the same device at least a keyboard, mouse are required. One mode of interaction with smart is through the microphones. All the coding are written in python which most popular in the Raspberry pi community and it has all the libraries Mirror OS is the software we created for the smart mirror’s interface and it runs on the Raspbian (LINUX) ontop if the electron. The device must be connected to the Wi-fi in order to gather the data through online services.When every device such as phone, watch, TV, speaker has their smart versions, it doesn’t come as a surprise that mirrors they become smart too. KEYWORDS: IOT, PIR, RASPBERRY-PI, RASPBIAN OS, LINUX

Flood early warning system at Jakarta dam using internet of things (IoT)-based real-time fishbone method to support industrial revolution 4.0

This research aims to develop an effective flood early warning system to provide timely information to the public and support the government in disaster management. The Raspberry Pi mini-computer functions as the central control, collecting data from the Water Level Sensor to measure water height, the Ultrasonic Sensor for further monitoring, the DHT11 Sensor to monitor temperature and humidity, and a Buzzer as an audible warning device. The research method involves review of the literature and data acquisition from related journals. These data are utilized to design an Internet of Things (IoT)-based flood detection tool with the Raspberry Pi minicomputer as the main controller. The system can be implemented in vulnerable locations such as reservoirs, sluice gates, and rivers, as part of the Smart City and Smart Environment concepts. The test results indicate that the developed early warning system, integrating the Raspberry Pi minicomputer, the Water Level Sensor, the the Ultrasonic Sensor DHT11 Sensor, and Buzzer, approaches perfection. Real-time information is transmitted through the Twitter social media platform, which is shown to be more effective than manual notifications. The system can provide accurate early warnings, reduce flood-related damages, and positively contribute to flood prevention and disaster management efforts. This research is expected to make a significant contribution to improving the community and government preparedness for future flood disasters.

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images

Background and objective:Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. Methods:Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. Results:The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. Conclusion:The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.

Navigating Abnormal Environmental Conditions: Traffic Signal and Obstacle Detection for Autonomous Vehicles

Autonomous cars are the future smart cars anticipated to be efficient and crash avoiding ideal urban car of the future. The purpose of the project is to build up a car which can move with and without any driver. It just need a command from the user and then it will start moving towards the destination. The project aims to build a autonomous car prototype using Raspberry Pi processor. Sensor interfacing is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like traffic light detection, obstacle detection and speed control are combined together to provide the necessary control to the car. The technology that autonomous cars and “connected vehicles” use would allow the vehicles to communicate with roadside infrastructure like traffic lights and road congestion, and then use this information to curtail fuel consumption and emissions significantly.

Mixed-Input Residual Network for Air Temperature Forecasting by Low-Power Embedded Devices

Accuracy and model compactness are essential requirements for weather forecasting models designed for operation on low-power embedded devices. This study developed Mixed-Input Residual Network (MIRNet), a compact temperature-forecasting deep neural network model. MIRNet integrates stacked bidirectional long short-term memory layers using concatenated 1-dimensional and 2-dimensional convolutional layers to improve model accuracy. MIRNet was trained and tested on two datasets: one, IfeData, comprising historical weather data from Ile-Ife, Nigeria and the other a standard weather forecasting dataset called the Jena dataset. Training was carried out using 100 epochs of data partitioned in the standard 80:20 ratio, with an adaptable learning rate strategy. The model was tested for Nth-hour-ahead prediction for 1N24; where N are natural numbers, and performance quantified using metrics such as mean absolute percentage error (MAPE) and mean square error (MSE). The model was also implemented on a Raspberry Pi 4 device with a 1.8 GHz 64-bit quad-core ARM Cortex-A72 processor. The model achieved a MSE of 1.00 x 10-3 on the IfeData dataset, and 1.23x10-4 on the Jena dataset for 1-hour ahead forecasting. This is currently the best verifiable result achieved on the Jena dataset by any prediction model globally. For Nth hour ahead forecasting, MIRNet achieved an MSE generally below 2.0x10-3 for all values of N on the standard Jena dataset. The MSE of MIRNet for N-sequential 1-hour ahead and single Nth hour predictions using the Jena dataset reveal quadratic and linear relationships with N respectively. The model compares favourably with existing models for multi-hour predictions. The developed model is compact and has good forecasting properties.

Development design of an IoT-based smart home monitoring system with security features

<div style="text-align: start;" align="center"><p>A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry.</p></div>

Reducing Road Mishaps: A CNN Model for Driver Fatigue Detection

Over the last few decades, there have been substantial developments in a variety of domains, including computer science, artificial intelligence, and machine learning, which has accelerated the evolution and application of intelligent systems in specific areas such as transportation. One way these systems are used in transportation is through fatigue detection, and to enable this technology, Convolutional Neural Networks serve as the foundation on which this system is built. CNNs are frequently employed in computer vision applications because they can automatically extract pertinent features from input data without the need for manual feature engineering. This CNN-based fatigue detection system was built in-order to address the repercussions of fatigued driving by monitoring the driver’s facial features in real-time so as to predict the fatigue levels and send out an alert to restore the driver’s alertness level so as to reduce road mishap. The system relies on visual input from a camera, which sends this input to a fatigue detection program installed on a Raspberry Pi microprocessor and sends out via alerts via a display and audio alerts via a piezoelectric buzzer. The model shows excellent performance, consistently achieving high accuracy, peaking at 97.34% on the 64th epoch, and consistently maintaining high validation accuracy, reaching 96.02% in the 67th epoch, indicating its ability to function effectively.

Gammatone-Frequency Cepstral Coefficients Based Fear Emotion Level Recognition System

Emotions represent affective states that induce alterations in behavior and interactions within one's environment. An avenue for discerning human emotions lies in the realm of speech analysis. Empirical evidence indicates that 1.6 million Indonesian teenagers grapple with mental anxiety disorders, characterized by sensations of fear or ambiguous vigilance. This work endeavors to devise a tool for discerning an individual's emotional state through voice processing, focusing particularly on fear emotions stratified into three levels of intensity: low, medium, and high. The proposed system employs Gammatone-Frequency Cepstral Coefficients (GFCC) for feature extraction, leveraging the efficacy of its gamma filter in reducing noise. Furthermore, a Random Forest (RF) Classifier is integrated to facilitate the recognition of fear's emotional intensity in speech signals. The system is deployed on a Raspberry Pi 4B and establishes a Bluetooth connection using the RFCOMM communication protocol to an Android application, presenting the classification results. The outcomes reveal that the Signal-to-Noise Reduction achieved through GFCC extraction surpasses that of Mel-Frequency Cepstral Coefficients (MFCC). In terms of accuracy, the implemented recognition system for fear emotion levels, employing GFCC extraction and Random Forest Classifier, attains a commendable accuracy of 73.33 %.

Car Washing System using raspberry Pi Pico as PLC

Abstract: Using the Raspberry Pi Pico microcontroller, we have designed and implemented an automatic vehicle wash system in this work. By automating steps including scrubbing, rinsing, drying, and applying soap, the system seeks to expedite the automobile washing process. We go over the Raspberry Pi Pico, actuators, and sensor hardware configuration in addition to the software architecture that controls the system. We also emphasize the advantages of utilizing Raspberry Pi Pico for this application, such as its affordability, portability, and versatility. The efficacy and dependability of the suggested automatic car wash system are shown by the experimental results.

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

A Deep Learning Based Framework for Accurate Prediction of Disease of Crops and Nutrients in Smart Agriculture

Abstract: The health and production of crops have a major impact on societal well-being, and agriculture is essential to maintaining global food security. Crop output and quality are greatly impacted by nutrient shortages and crop diseases. We offer a unique approach to this problem that makes use of the Raspberry Pi, an inexpensive, small, and energy-efficient single-board computer, to forecast crop illnesses and identify nutrient deficits in real time. Our solution provides farmers and other agricultural stakeholders with fast and accurate information for efficient crop management by combining computer vision, machine learning, and sensor technologies. The Raspberry Pi has many sensors to assess ambient and soil conditions in addition to a high-resolution camera for taking pictures. Using a machine learning model trained on a variety of datasets pertaining to agricultural illnesses and nutritional deficits, the system takes pictures of crop leaves and soil conditions. Preprocessing images, feature extraction, and convolutional neural network (CNN)-based categorization are important parts of our approach. The system can recognize evidence of nutrient deficits, such as nitrogen, phosphorus, and other deficiencies, as well as symptoms of a variety of crop illnesses, including fungal infections, bacterial blights, and viral diseases.

Automated Water Meter Reading through Image Recognition

Abstract: In this paper, we propose a solution for reading an analog water meter automatically by digitalizing the meter. Since digital water meters are expensive, we retrofit the meter using Raspberry Pi, a camera, and an LED. Though the structure of the analog water meter is simple, consumes less power, and has high durability, it has some disadvantages. Primarily, it requires human effort for reading, which is time-consuming and causes inconvenience to users. To address this challenge, there is a need for an automated system. Thus, the project is implemented using image recognition techniques, specifically Optical Character Recognition (OCR), and Internet of Things (IoT). This method accurately detects the numerical values present on the meter, and this data is transmitted to ThingSpeak, an IoT platform. Overall, this project offers an effective solution for reading water meters and enhance the comfort and convenience of users.

AI-Powered Disease Detection and Growth Monitoring System Using IoT for Improved Tomato Yield

Abstract: This project introduces an advanced AI- powered disease detection and growth monitoring system designed specifically for enhancing tomato yield. With Internet of Things (IoT) technology, the system integrates Raspberry Pi as the central processing unit with temperature and humidity sensors for environmental monitoring. AI algorithms are used to predict tomato ripening stages and detect diseases, to ensure proactive management of plant health. If a disease is identified, the system provides the solutions to control its impact, thereby enhancing yield and quality. The captured data is analysed and results are displayed on a user-friendly webpage accessible to farmers, enabling informed decision-making and improved agricultural practices. This innovative approach combines technology with agricultural expertise, offering a solution to the challenges faced by tomato farmers and contributing to sustainable tomato production. Keywords: Image Recognition, Disease Detection

Smart Automated Irrigation System using Wireless Technology

Abstract: The need for agricultural output has surged due to the exponential rise of the world population, resulting in a strain on water supplies and a need for more effective irrigation systems. The goal of this project is to optimize water utilization in agricultural settings by presenting an automated and intelligent irrigation system. Based on current soil moisture levels, the system monitors and regulates irrigation using soil moisture sensors, an Arduino Nano, and a Raspberry Pi 3B+. By converting analog signals from the soil moisture sensor into digital data, the Arduino Nano serves as an analog-to-digital converter. This digital data is sent to the Raspberry Pi 3B+, which acts as the central control unit. A MOSFET is then triggered to activate a relay that is coupled to a 5V DC water pump when the moisture levels drop below a certain threshold. The system also has wireless connection, which enables remote control and monitoring via an Android application. Users have the ability to manually override the automatic system if needed, monitor the moisture content of the soil, and turn on or off the water pump. This feature improves the usability and flexibility of the system.

Design and Development of IIOT Based Prototype Model by Using Machine Learning Techniques for Workflow Management in Industry 4.0

Manufacturing is undergoing a revolutionary shift known as Industry 4.0 (I4.0), which unifies production and business operations through data-driven End-to-End digital integration. I4.0 goes beyond the digital ecosystem of the Third Industrial Revolution to include autonomous tech-human convergence and real-time data interchange. The Industrial Internet of Things (IIoT) and machine learning are driving breakthroughs in cyber-physical systems, which form the basis of this. In order to advance I4.0, technology and human talents must be balanced. It is essential to upskill the workforce and engage in coordinated transdisciplinary activities. A holistic approach, integrating human factors and tech advancements, ensures smooth automation transition. The proposed study introduces a cost-effective liquid filling system based on a Raspberry Pi, addressing automation challenges for smaller firms.

Determining the Quality of Measuring the Level of Lower Extremity Joint Movement in Inclusive Physical Education Using Electronic IT Resources

Objectives. The study aimed to investigate the quality characteristics of the tool developed based on electronic IT resources for measuring the level of movements in the joints of the lower limbs of students with disabilities caused by injuries. Material and methods. The experimental study involved 32 first-year students who had sustained lower limb injuries as a result of the war and were in remission. The methods used included analysis, synthesis, systematization, generalization, technical modeling, pedagogical experiments, and mathematical statistics. Results. A means of measuring movements in the joints of the lower limbs has been developed using electronic IT resources. The basis of the tool is a measuring line consisting of a printed circuit board on which is placed a design of capacitive sensors, a switching line, and a signal converter that measures the signal received by the sensors. The measurement results are transmitted to the controller and then to the PC via an interface implemented based on Bluetooth wireless technology. To implement the measuring tool, a controller is used, which has a board built on the synthesis of the Arduino electronic hardware platform and the Raspberry Pi minicomputer. The test results are displayed on the PC monitor screen. The determination of the qualitative characteristics of the test, in the case of recording the results of a tool developed on the basis of electronic IT resources for measuring the level of movements in the joints of the lower limbs of students with disabilities, established that the level of reliability of the test is above 0.90 (“excellent”) validity – from 0, 6 (“high”). Empirical data collected directly from the experiment have a low level of reliability: qualitative indicators of reliability in the range are below 0.70 (“may have limited applicability”), validity – < 0.3 (“low validity”). Conclusions. Developed based on electronic IT resources, the tool for measuring the level of movements in the joints of the lower limbs of students with disabilities due to limb injuries has significant advantages, such as the availability of functions, ease of use and efficiency. Ensuring high efficiency and objectivity of control contributes to performing control operations in real-time. By using assessment tools with a high level of reliability and validity, we ensure the detection of reliable changes in the state of the joints of the lower limbs of students with disabilities, thus eliminating the influence of errors in making managerial decisions in the planning process of their PE.

Traffix : Efficient Traffic Control using IoT

This paper aims to alleviate traffic congestion brought on by antiquated, ineffective traffic management systems that are based on a predefined countdown. Long red light delays are the result of these traditional systems, which have a predefined countdown regardless of the actual traffic on a given road. Our system makes sure that time set for the traffic lights reflects the traffic density in real time, which ensures efficient use of time. In order to do this, we first compute the traffic density, which is ascertained by combining image processing methods along with the use of ultrasonic sensors. The Raspberry Pi then processes this data and then manages the time set for the traffic light. Furthermore, the use of SQL Alchemy ensures that this processed information is stored to the cloud, where it may be utilized in events of sensor failure or system malfunction. Index Terms—Image Processing, Raspberry Pi, Traffic Congestion, Ultrasonic sensors

Low-cost Raspberry Pi star sensor for small satellites. II: StarberrySense flight and in-orbit performance

Low-cost Raspberry Pi star sensor for small satellites. II: StarberrySense flight and in-orbit performance

IoT Based Smart Trolley for Auto Billing

This paper proposes the development of a Smart Trolley system aimed at enhancing the shopping experience by automating the billing process. The system integrates Raspberry Pi, a low-cost, credit-card-sized computer, and RFID (Radio-Frequency Identification) technology to create an efficient and seamless checkout process for customers in retail environments. The Smart Trolley employs RFID tags attached to products and an RFID reader mounted on the trolley to identify items as they are placed inside. The Raspberry Pi processes the RFID data and maintains a running total of the items in the trolley. Additionally, it communicates with the store's database to retrieve product information and pricing. Through this integration, the Smart Trolley eliminates the need for manual scanning of each item at checkout, reducing waiting times and enhancing overall efficiency. Moreover, the system provides real-time updates on the total bill, allowing customers to track their expenses as they shop. This paper outlines the design, implementation, and testing of the Smart Trolley system, demonstrating its effectiveness in automating the billing process while improving the shopping experience for customers. The proposed system holds promise for revolutionizing retail operations, offering a glimpse into the future of smart, technology-driven shopping solutions. Key Words: Autobilling, Rasberry Pi, RFID(EM-18), LCD, Smart Shopping, IoT.