Raspberry Pi Board Research Articles

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

AbstractEngineering education requires both a theoretical and practical basis; some aspects are better understood through practical examples. Providing practical experience in biomedical engineering is of great importance in terms of understanding concepts and assisting the retention of learning. Hands‐on experience can be particularly beneficial in supporting the theoretical explanation of the acquisition, processing, and displaying of biomedical signals. This study has developed an expansion board that can be plugged into Raspberry Pi boards to capture electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG) biomedical signals with a single‐channel amplifier circuit. A toolbox has been written in the Python programming language to; capture, process, filter, apply fast fourier transform, and display these signals. The device and laboratory applications were introduced in the curriculum of a final year module, “Introduction to Biomedical Engineering” in the Department of Electrical and Electronics Engineering at Gazi University. The pedagogical value of the application was assessed by administering a questionnaire to the students. Results show the use of measurement and display of biomedical signals increased the curiosity and motivation of the students in Biomedical Engineering.

IOT based Flood Monitoring and Alerting System

Abstract: Flooding is considered one of the most destructive natural disasters in the world. In countries like India with climatic conditions occurrence of heavy rain fall and subsequent discharge of water leads to Flood. Flooding creates major damages to life, their habitats and the economy By installing of flood alerting systems near major waterways vital information can be provide so that lives and property can be protected. Normal Weather monitoring and alerting systems are not quick and accurate enough to predict floods in time to prevent personal or environmental damages. The government has to spend tons of money in flood mitigation plans to help the victims and also to reduce the number in the long run damages that can occur after flooding. Since Most of the flood alerting systems involves high cost they are deployed on select locations based on priority. In this project we make use of a cost effective system using raspberry pi board and sensors, to measure rise of water level in rivers and water bodies and alert government authorities and people instantly by transmitting information using IOT. In the present work we have used thingspeak-IOT platform and GSM module. The data can be accessed from android smart phones using things View mobile application at anytime from anywhere in the world where GSM module is used to send the alert message to the people.

IoT-Galvanized pandemic special E-Toilet for generation of sanitized environment

The entire world is enmeshing the pandemic COVID-19, which affected the public health and it is spread all over the world. Every country’s authority is making more and more efforts towards maintaining the public health as well as mental health and are developing the vaccination to control the COVID-19. As COVID-19 is an infected disease for which a sanitized environment must be chosen at top most priority to prevent infection. Most of the countries are facing the problem of incrementing the infection rate of COVID due to griminess problems. Most healthcare industries are also working to create a sanitized environment with incorporation of the Internet of Things (IoT) which is resulting in the smart health care appliances for generating the sanitized environment. In this paper the researchers have developed an IoT- Galvanized Pandemic Special E-Toilet for generation of sanitized environment which resolves the issues of griminess problem. The proposed architecture has been comprising with three levels of working methodology as Occupancy Check, Auto Flush and Auto control of Appliances using Mobile Application. It is implemented using a raspberry pi board, Arduino Uno as a core unit of the system with interfacing the peripherals: IR sensor, Ultrasonic sensor and HC05 Bluetooth module, servo motor, Relay module, CFL lamp and LED. The proposed architecture has been simulated individually using single piece of sensor, then later assembled all the peripheral together and simulated. The proposed architecture can be used in field of education, industries, corporate offices, multiplexes, hospitals etc.

Design Laparoscopy Camera Vision System by Using Raspberry PI

AIM: Design a laparoscopy camera system by using a small electronic board raspberry pi and including it with the appropriate programs to improve the doctor’s image or video while using the device, in addition to the ease of carrying the device and transporting it in the workplace or training and with an efficiency that is no less than the traditional device available. Methods: key component in this project is the 3-in-1 camera laparoscopy compatibility lens ,The light source with camera in front of the scope makes the training platform more realistic for the trainee .The design involves considering several important criteria, including the compromise with conventional one an Having enough workspace in the cameras field of view, easy Setup for users, making it more user-friendly and cost-effective for individual use by the trainee. Results: evaluation of the overall system vision qualities , measurement , color of the video and image in hospital . The paper introduces an innovative viewing system that can be combined with a traditional laparoscope and provides the surgeon with a general view of the abdominal cavity. Conclusion: Laparoscopy as a surgical technique has continue grow. The improvements to our video imaging laparoscopy system that is inexpensive, portable, and camera compact (Laparoscopy with Camera and Light (LDS) did not differ significantly from those obtained with conventional video laparoscopic. Keywords: laparoscopic camera system , image vision ,raspberry pi .

Surface Mounted Active Vibration Cancellation Device Using Raspberry Pi

Active vibration cancellation device detects vibrations on a host surface using a piezoelectric vibration sensor, amplifies them using a signal amplifier, and sends them to a Raspberry Pi board implemented with PID control, responsible for signal processing, which further actuates Piezoelectric actuators vibrating at a phase difference of 180° to the vibrating surface (destructive interference) and reduces the vibrations over the surface. The device presented in this paper suggests an application that tries to make it accessible to a large group of people.

Role of Embedded System for X-Ray comparison of lungs to detect a COVID-19 affected person at the entry point of Aerodromes, Railway Stations and Metro Stations in perspective of India.

Nowadays, the biggest threat to mankind is COVID-19. This is a disease caused by severe acute respiratory syndrome coronavirus 2, SARS-Co V-2. It is must to control and stop the spreading of this disease. This paper is proposed to control the spread of this pandemic at the entry point of railway stations, metro stations, and aerodromes. The testing of COVID-19 is the most significant remedy to control, contain and reduce the COVID-19 pandemic. This paper proposes an embedded system design, which can be used at the entry point of aerodromes, railway stations, and metro stations, especially from an Indian perspective. This system can be used to analyze x-ray results of the lungs of the passenger who is entering the railway stations, aerodromes, and metro stations. The proposed system comprises a portable X-ray machine and an embedded system which consists of a Raspberry Pi Board ( Model B), that will be interfaced with a data input module, SD card slot, display monitor at HDMI port, LEDs, and buzzers. The system compares the passenger's X-ray image at the entry point of railway stations, aerodromes, and metro stations with the X-ray image of a COVID-19 patient, which is already stored in the SD card. If the similarity is found between the X-ray image taken at entry and the X-ray of a COVID-19 patient, at that moment, buzzers and LEDs will be activated. This system can be very much useful to restrict the spreading of infection of COVID-19.

Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions.

Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.

Access control and surveillance in a smart home

In the past years smart homes solutions have become more and more popular with the introduction of a high number of both Internet of Things (IoT) applications and smart devices. Home automation and security systems market is in a continuous growth, traditional security systems are evolving fast, and more and more people choose Smart home solutions. In this paper, we propose two IoT based systems in the context of Smart homes: qToggle for multiple home automation, and MotionEyeOS, a video surveillance OS for single-board computers. Most qToggle devices are based on ESP8266/ESP8285 chips or on Raspberry Pi boards and smart sensors, while MotionEye uses Raspberry Pi boards.

Aplikasi IoT Pada Sistem Monitoring Cairan Infus Berbasis Raspberry PI

In the medical world, the role of intravenous fluids is very important because it can support the patient's treatment process. However, at this time, the comparison of medical personnel with the number of patients often finds that the installed infusion fluids are not properly monitored by the officers, thus affecting the healing process and satisfaction of patients and their families. On the other hand, rapid technological advances can be used for all fields including medical, therefore the researchers made a smart device that utilizes a photodiode [1] as a sensor to detect intravenous fluids and can provide information to medical staff in real time via smartphone devices. [2]. This device utilizes the Raspberry PI board as a data processor and executor of the data received from the sensor. Raspberry PI itself is a smart device that can process data to data execution because it is equipped with a GPIO pin that can be connected to other external devices [3]. With the design of this device, officers get information on the installed infusion fluids starting from the drip, the discharge to the drip rate. In addition, if the infusion fluid is about to run out, the officer's smartphone will give a notification in the form of vibration and ringing so that the officer can act quickly and precisely

A Portable Device for the Measurement of Venous Pulse Wave Velocity

Pulse wave velocity in veins (vPWV) has recently been reconsidered as a potential index of vascular filling, which may be valuable in the clinic for fluid therapy. The measurement requires that an exogenous pressure pulse is generated in the venous blood stream by external pneumatic compression. To obtain optimal measure repeatability, the compression is delivered synchronously with the heart and respiratory activity. We present a portable prototype for the assessment of vPWV based on the PC board Raspberry Pi and equipped with an A/D board. It acquires respiratory and ECG signals, and the Doppler shift from the ultrasound monitoring of blood velocity from the relevant vein, drives the pneumatic cuff inflation, and returns multiple measurements of vPWV. The device was tested on four healthy volunteers (2 males, 2 females, age 33±13 years), subjected to the passive leg raising (PLR) manoeuvre simulating a transient increase in blood volume. Measurement of vPWV in the basilic vein exhibited a low coefficient of variation (3.6±1.1%), a significant increase during PLR in all subjects, which is consistent with previous findings. This device allows for carrying out investigations in hospital wards on different patient populations as necessary to assess the actual clinical potential of vPWV.

Implementation of BIOBOT System for COVID Patient and Caretakers Assistant using IOT

Everything is now connected because to the Internet of Things and medical technology advancements. Numerous applications of the Internet of Things have been found, including in daily life. However, the primary impact of IoT on healthcare is astounding. The project, which uses these sensors and a Raspberry Pi board to track patient needs and metrics like temperature and heart rate before uploading the data to the cloud, makes the suggestion of a health monitoring system. The mobile app will promptly notify the care team and the treating doctor if there is a problem. Security must come first when developing a successful remote monitoring system.

Power System Frequency and Phasor Estimation for a Low-Cost Synchrophasor Device Using the Nonlinear Least-Square Method

Wide area monitoring, protection, and control has now become an essential infrastructure to run an interconnected power system. To accomplish this by using synchrophasor technology, a wide range of phasor measurement unit (PMU) designs are available in the literature, but most of them are very costly because of the requirement of a high-speed processor to run complex algorithms. In this article, an algorithm for the synchrophasor and frequency estimation is proposed for processing platforms with low computational resources and is delineated extensively in respect of both accuracy and processing time. The proposed solution harnesses the main advantages of two well-known algorithms, viz. the zero-crossing detection and nonlinear least-squares method. Both algorithms are incorporated in a computationally efficient manner to reduce the processing time up to a lower extent while maintaining a good accuracy as per the standard IEEE testing conditions. The performance of the proposed algorithm has been evaluated through simulations as well as experimentally on the Raspberry Pi board. The consistency of the test results and minimum processing time provides clear evidence that the implemented design is suitable for PMU prototyping and is compliant with the mandatory IEEE standard C37.118.1-2011 and its amendment IEEE C37.118.1a-2014.

Cloud Computing and Networking for SmartFarm AgriTech

In this paper, the implementation of SmartFarm AgriTech is done using IoT and cloud computing. The current world population is 7.9 billion and supposed to reach 12 billion by 2050, and it is difficult to feed such population in the future. So, for feeding the entire population, the agriculture sector should be embedded with the latest technologies. People living in urban city will be covered with their work and day to day activities which makes it really difficult to travel the village and monitor their cultivation regularly. Without proper maintenance of farms, it is hard to get the desired results, so using the cloud computing, IOT, networking, and many other technologies, one can easily maintain and monitor the crops, weather, water, and spraying fertilizers whenever needed. This SmartFarm AgriTech System is designed using Raspberry Pi and Arduino as the main microcontrollers to control the various sensors, relay‐switch, and motor. AWS and ThingSpeak are used to create server and APIs to collect and store the data through Internet or via a Local Area Network (LAN). In addition to that, a GUI (Graphical User Interface) application is also created to control and monitor the data coming from Raspberry Pi and Arduino board.

In-Classroom Faculty Attendance Monitoring System based on Ultra High Frequency (UHF) Radio Frequency Identification (RFID) with Captured Image Cross-verification Mechanism

Objectives: Faculty attendance in schools are monitored based on Daily Time Record (DTR) which captures a daily time-in and time-out of teachers in and out of the school campus using various technologies and methods, however there is a scarcity of studies in monitoring faculty attendance in the actual bounds of the classroom. Hence, this study aimed to develop an In-Classroom Faculty Attendance Monitoring System based on Ultra High Frequency (UHF) Radio Frequency Identification (RFID) with captured image cross-verification mechanism. Methods: The system comprises two modules, namely the Teacher Attendance Module and Attendance Monitoring Web Application module. The Teacher Attendance Module, it is composed of a UHF-RFID reader and a camera module that is interfaced to the Raspberry Pi board. Passive tags are embedded on the teachers’ ID cards for them to be uniquely identified by the UHF-RFID reader when they are within the 2- meter read distance. Meanwhile, the Attendance Monitoring Web Application module is developed using a PHP framework connected to a MySQL database. It aggregates the attendance data collected from the Teacher Attendance Module for monitoring purposes which is accessible to both the monitoring staff and teachers via a web browser. Findings: Based on the evaluation results, the proposed system received positive feedback from the end users in terms of its functionality and usability and was found to be more favorable over existing attendance monitoring systems. Novelty: The system provides a non-obtrusive way of checking teachers’ attendance by utilizing the UHF-RFID technology which has a longer read range than other RFID categories used in previous studies. Moreover, unlike existing solutions, the system checks for attendance multiple times, not only during entrance and exit which then calculates thetotal stay-in time of the teacher during a class session. Most importantly, the system can prevent fraudulent activities through a camera module. Keywords: UHFRFID; Attendance; Faculty; Raspberry Pi; Camera

An embedded intelligence engine for driver drowsiness detection

Motor vehicle crashes involving drowsy driving are huge in number all over the world. Many studies revealed that 10%–30% of crashes are due to drowsy driving. Fatigue has costly effects on the safety, health, and quality of life. This drowsiness of drivers can be detected using various methods, for example, algorithms based on behavioural gestures, physiological signals and vitals. Also, few of them are vehicle based. Drowsiness of drivers was detected based on steering wheel movement and lane change patterns. A pattern is derived based on slow drifting and fast corrective steering movement. A prototype that detects the drowsiness of an automobile driver using artificial intelligence techniques, precisely using open-source tools like TensorFlow Lite on a Raspberry Pi development board, is developed. The TensorFlow model is trained on images captured from the video with the help of object detection using cascade classifier. In order to have a better accuracy, an Inception v3 architecture is used in pre-training the model with the image dataset. The final model is created and trained using long short-term memory and then the final TensorFlow model is converted to TensorFlow Lite model and this Lite model is used on Raspberry Pi board to detect the drowsiness of drivers. The results are comparable with desktop-based results in the literature.

Enhanced Algorithm Implementation for Low Powered IoT Devices Using Authenticator to Improve Data Integrity

Internet of Things enables many industries to connect to end customers and provide seamless products and services delivery. Due to easy access to network, availability of devices, penetration of IoT services exponentially Growing. Meanwhile, Ensuring the Data Security and Integrity of devices connected to network is paramount. In this work, we bring the efficient way of implementing Secure Algorithm for low powered devices and enhancing the encryption and decryption process. In addition to the data security, to enhance node integrity with less power, Authenticator and intermediate network manager introduced which will acts as a firewall and manager of data flow. To demonstrate the approach, same is implemented using low cost Arduino Uno, Raspberry Pi boards. Arduino Uno used to demonstrate low powered encryption process using EDIA Algorithm and raspberry pi used as nodal manager to manage the integrity of nodes in a low-powered environment. Data Security and Integrity is ensured by the way of enhanced Algorithm and Integrity through BlockChain and results are provided and discussed. Finally result and future enhancement are explained.

Monitoring rocket (Eruca sativa) growth parameters using the Internet of Things under supplemental LEDs lighting

Internet of Things (IoT) was employed for monitoring the growth parameters of rocket (Eruca sativa) plants cultivated in a greenhouse under supplemental LEDs lighting. Red (R), blue (B) and the combination red and blue in different ratios (75R:25B, 50R:50B, 25R:75B), were employed, as well as white LEDs. The LED lamps were automatically turned on at 18:00 and off at 24:00. Thus, the plants were grown in an 18 h photoperiod until the harvest time under daylight supplementary lighting (i.e. 12 h sunlight and 6 h LED). Yield, biomass, leaf area (LA), leaf area index (LAI), leaf area rate (LAR), leaf weight rate (LWR), specific leaf area (SLA), relative growth rate (RGR), crop growth rate (CGR) and net assimilation rate (NAR) of rocket plants were investigated, through sensors of temperature, humidity and light intensity via a Raspberry Pi board by the Message Queuing Telemetry Transport (MQTT) communication protocol. ANOVA results demonstrated that 50R:50B LEDs lighting produce higher yield, total biomass, LA, CGR, LAI, LWR, as well as lower SLA as compared to other treatments. Reliability analysis showed that IoT monitoring system is stable and reliable for controlling of environmental conditions (i.e., temperature, humidity) of the greenhouse.

DESIGN AND IMPLEMENTATION OF ANTI-TANK GUIDED-MISSILE (ATGM) CONTROL SYSTEM USING SEMI-AUTOMATIC COMMAND LINE OF SIGHT (SACLOS) METHOD BASED ON DIGITAL IMAGE PROCESSING

<div><p class="Els-history-head">The Anti-Tank Guided-Missile (ATGM) system has a very important role in the modern battlefield. This system proved its effectiveness in many modern conflicts such as the Syrian Civil War and Nagorno-Karabakh War. The ATGM system has a very simple electronic and mechanism but it has a very high level of accuracy and precision. One of the control methods used in ATGM is SACLOS method. This method tracks missile position by detecting an infrared lamp that is placed on the missile tail. The tracking system sends control signals to the missile as a result of the correction of the missile position when flying. The infrared tracking system in this research was made using a modified OV5647 camera with the addition of a 940 nm narrow bandpass filter. There are 3 cameras with 1x, 8x, and 16x magnifications which are accessed using 3 Raspberry Pi boards. X and y coordinate data of the infrared lamp is sent to the airframe using wireless telemetry. Atmega328 microcontroller process x and y coordinate data into input proportional control. The result of this research is the prototype of an anti-tank missile control system with an infrared tracking instrument capable track a series of 88 infrared LEDs as far as 997.16 meters with a tracking speed of 90.11 FPS. The threshold parameters of image processing using luminance of YUV color space has a range of 240-255. The control parameter Kp=7 is used in wind tunnel testing with airspeed 20 m/s capable of directing airframe motion to the telescope's crosshairs.</p></div>

Cloud based Vehicle Tracking System

Vehicle tracking is beneficial for a variety of purposes, including personal vehicle security, public transit systems, fleet management, and more. Furthermore, the number of automobiles on the road is predicted to increase considerably internationally. As a result, a cloud-based vehicle tracking system based on the Global Positioning System (GPS), Global System for Mobile Communications (GSM) modem, and the Raspberry Pi processor board is being developed with the goal of allowing customers to easily and conveniently find their automobiles. The GPS and GSM modules in the tracking system are used to readily find the user's vehicle. The GPS module is used to track the vehicle's location using information such as latitude and longitude. These values are sent to the user through SMS using a GSM modem. Alcohol consumption is detected with the MQ-3 Alcohol sensor. The user can monitor the sensor data via the thingspeak channel. The latitude and longitude coordinates, as well as a webpage to plot the exact position of the vehicle, will be supplied to the user by e-mail. The Raspberry Pi processor board receives the data and outputs the result. The hardware prototype for a cloud-based vehicle tracking system is shown in this study. The GPS receiver module, GSM module, I2C Protocol, Raspberry Pi board, camera, and MQ-3 alcohol sensor are the system's primary hardware devices.

Real-Time Performance Comparison of Vision-Based Autonomous Landing of Quadcopter on a Ground Moving Target

The tremendous applications of unmanned aerial vehicles (UAVs), such as inspection in complex environments, search, and rescue missions, have established this area of research. The domain of UAVs autonomous navigation and landing has received considerable amount of interest of robotics researchers in the past decades. Nevertheless, the limited capability of UAVs for autonomous landing severely hampers the use of aerial vehicles specially in GPS-denied areas. Fortunately, with the rapid development of computer vision, vision-based techniques have become an important and cheap tool to be used in UAVs for moving target detection and landing. In this paper, four major vision-based techniques namely PID controller, fuzzy logic, sliding mode control and model predictive control have been investigated and their landing performance is compared to each other. These landing techniques are implemented on a quadcopter for the purpose of ground moving target detection and then landing on it. The Raspberry Pi board with two Pi cameras for 3D scene construction and depth estimation along with ultrasonic sensor have been used in the quadcopter. A wheeled mobile robot with landing platform is taken as the target. The landing performance of different techniques has been observed and compared in terms of landing displacement error, time taken and the distance travelled to finish the operation in an open and obstacle-free environment. The simulation results are further verified by the hardware experiments.