Raspberry Pi Development Board Research Articles

Smart Home Intelligent System for Elder People Using Raspberry Pi

Nowadays, the care of elderly peoples is a significant problem and needs more time and attention from the caregivers.In order to provide home intelligent control function for the elderly more efficiently and conveniently, this paper designed a smart home system controller based on raspberry PI with simple hardware structure and low development cost. The design is implemented by raspberry PI development board and python language, and the fatigue detection algorithm is implemented by OpenCV visual library, Dlib library and EAR algorithm. Through temperature and humidity sensor, infrared extended version, LCD display screen and other hardware intelligent control indoor environment. The test results show that the fatigue state can be accurately detected and the electrical control function can be realized. This design enhanced the intelligence of household control, and met the life demand of energy-saving convenience.

Underground Garage Patrol Based on Road Marking Recognition by Keras and Tensorflow

The purpose of this study was to design an unmanned patrol service in combination with artificial intelligence technology to solve the problem of underground vehicle patrol. This design used the Raspberry Pi development board, L298N driver chip, Raspberry Pi camera, and other major hardware equipment to transform the remote control car. This design used Python as the programming language. By writing Python code, the car could be driven under the control of the computer keyboard and the camera was turned on for data collection. The Keras neural network library was used to quickly build a neural network model, the collected data was used to train the model, and the model was finally generated. The model was placed in the TensorFlow system for processing, and the car could travel in a preset track for unmanned driving.

SMART HOME INTELLIGENT SYSTEM FOR ELDER PEOPLE USING RASPBERRY PI

In order to provide home intelligent control function for the elderly more efficiently and conveniently, this paper designed a smart home system controller based on raspberry PI with simple hardware structure and low development cost. The design is implemented by raspberry PI development board and python language, and the fatigue detection algorithm is implemented by OpenCV visual library, Dlib library and EAR algorithm. Through temperature and humidity sensor, infrared extended version, LCD display screen and other hardware intelligent control indoor environment. The test results show that the fatigue state can be accurately detected and the electrical control function can be realized. This design enhanced the intelligence of household control, and met the life demand of energy-saving convenience.

SMART HOME INTELLIGENT SYSTEM FOR ELDER PEOPLE USING RASPBERRY PI

In order to provide home intelligent control function for the elderly more efficiently and conveniently, this paper designed a smart home system controller based on raspberry PI with simple hardware structure and low development cost. The design is implemented by raspberry PI development board and python language, and the fatigue detection algorithm is implemented by OpenCV visual library, Dlib library and EAR algorithm. Through temperature and humidity sensor, infrared extended version, LCD display screen and other hardware intelligent control indoor environment. The test results show that the fatigue state can be accurately detected and the electrical control function can be realized. This design enhanced the intelligence of household control, and met the life demand of energy-saving convenience.

MULTILAYER AUTHENTICATION SYSTEM TO ACCESS VEHICLE USING FINGERPRINT AND FACE IDENTIFICATION WITH RASPBERRY PI

Vehicle security is a big issue worldwide. Vehicle manufacturing company provides ac-tive/passive RFID based remote key for door entry-level, and same is used engine ignition. Us-ing the master RFID signal reader device can read the active/passive signal remotely which are available in open market, this situation is very vulnerable to vehicle safety. The advantage of a multilayer authentication system is, it provides protection at entry level, So, if a door is not opened, neither engine turning on nor vehicle movement is possible, goods and personal be-longiness inside a vehicle remain safe. A smart authentication system is developed with a Rasp-berry pi development board, which is interfaced with fingerprint biometric, and camera mod-ules. This multilayer vehicle door security system provides an option for the use of anyone or both authentication level in logically ANDing mode, such as a driver can be utilizing security system for smart key, finger biometric, and face recognition. Multilayer authentication will re-strict unauthorized entry into the vehicle. The owner of the vehicle can set and use a multilayer authentication system as per the requirement. Once this system is installed in a vehicle, it will provide maximal level security at the preliminary stage as restricted entry from the door. In a laboratory environment, system implementation and testing were very successfully carried out and provided satisfying results. Multilayer authentication for Vehicle Door Access using Open-source devices will reduce the number of crimes significantly related to vehicle theft and unau-thorized access of the vehicle.

Adopting MQTT for a multi protocols IoMT system

<span>Coronavirus disease (COVID-19) altered the way of caregiving and the new pandemic forced the health systems to adopt new treatment protocols in which remote follow-up is essential. This paper introduces a proposed system to link a remote healthcare unit as it is inside the hospital. Two different network protocols; a global system for mobile communication (GSM) and Wi-Fi were used to simulate the heath data transfer from the two different geographical locations, using Raspberry Pi development board and Microcontroller units. Message queuing telemetry transport (MQTT) protocol was employed to transfer the measured data from the healthcare unit to the hospital’s Gateway. The gateway is used to route the aggregated health data from healthcare units to the hospital server, doctors’ dashboards, and the further processing. The system was successfully implemented and tested, where the experimental tests show that the remote healthcare units using a GSM network consumed about 900 mWh. A high percentage of success data packets transfer was recorded within the network framework as it reaches 99.89% with an average round trip time (RTT) of 7.5 milliseconds and a data transfer rate up to 12.3 kbps.</span>

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.

Development of Detection Equipment for a Polymerase Chain Reaction with a Loop-Mediated Isothermal Amplification Reaction

In this research, low-cost detection equipment intended to carry out a polymerase chain reaction (PCR) through a loop-mediated isothermal amplification (LAMP) reaction is presented. We designed the internal structure with SolidWorks and AutoCAD. The equipment comprised a Raspberry Pi development board, a temperature control module, and a fluorescent optical detection module. The main program, temperature control, florescent signal processing, signal analysis, and screen display were programmed with Java. We applied the digital temperature controller module to obtain precise temperature control of the equipment. The experimental results showed that the heating rate of the testing equipment could reach 65°C within 4 minutes and could be accurately controlled to within 1°C. The duration of the LAMP PCR experiment was found to be significantly shorter than that of the conventional PCR. The results also revealed that with LAMP PCR, the temperature could be accurately controlled within a specific range, and the designed heating tasks could be completed within 15 minutes to one hour, depending on the specimen. The equipment could also correctly read both the positive and negative reactions with fluorescent signals. Thus, the proposed LAMP PCR detection equipment is more sensitive, more stable, and more cost-effective than other conventional alternatives and can be used in numerous clinical applications.

Computerized pragmatic assessment of Prakriti Dosha using tongue images- Pilot study

Objective: To design an intelligent system to identify Prakriti of a person based on tongue image analysis and machine learning algorithms. Method: Tongue images were captured using webcam and processing was done using Raspberry Pi development board. The algorithms were developed using OpenCV python libraries. Thirteen geometry features, two non-geometry features, and two texture attributes were extracted from each tongue image. These features were used to identify prakriti Vata, Pitta and Kapha. Performance of three classifiers namely, KNN, Neural network and Decision tree was verified for the precision in identifying class of the test image. Findings: KNN provided sensitivity of 42.85% for Vata, for Pitta and Kapha prakriti it was 55% and 45% respectively. With Neural network sensitivity was improved to 62.5% for Vata and Pitta and for Kapha Prakriti it was 60%. Decision tree exhibited better sensitivity of 83.33% for Vata, for Kapha and Pitta prakriti it was 75% and 71.42% respectively. During blind validation to identify prakriti, each physician was told to analyse images for said classes. This procedure resulted in sensitivity of 81.25% and 84.61% respectively. Keywords: Prakriti dosha; tongue image analysis; machine learning

Controlling the autonomous vehicle using computer vision and cloud server

Implementing the unmanned cars has been given much importance from the recent past years. These cars have been designed by the automobile industries and are in the stage to test their rides on the roads. With being implemented with automated cars, the problem arises when there is any technical issue of how the backup works. So, in this paper, we are presenting the implementation of the miniature model of the automated robotic vehicle using the Raspberry Pi and how the data can be sent to the cloud. So that a person can be able to observe and maintain the Speed, Distance Covered and the Movement of the Vehicle on the Roads of the city. This model is designed on the applications of the safety measures to be taken for these unmanned vehicles that are almost a reality in the coming days. The Raspberry Pi development board is used as the core processor, camera module for the identification of the traffic signals and the Ultrasonic sensor for the identification of any of the vehicle nearby, and if so the direction of the vehicle changes from which it is moving and all the remaining data regarding the data posted in the cloud is well described through the paper.

Internet of Things–based smart home system using a virtualized cloud server and mobile phone app

This article proposes an Internet of Things–based smart home system composed of a virtualized cloud server and a mobile phone app. The smart Internet of Things–based system includes a sensing network, which is developed with the ZigBee wireless communication protocol, a message queuing telemetry transport, a virtualized cloud server and a mobile phone app. A Raspberry Pi development board is used to receive packet information from the terminal sensors using ZigBee wireless communication. Then, the message queuing telemetry transport broker not only completes transmission of the message but also publishes it to the virtualized cloud server. The transmission can then be viewed through the website using a mobile phone. The designed app combines the application of the virtualized cloud server, client sensors and the database. Verification experiments revealed the measured average response time and throughput of approximately 4.0 s and 6069 requests per second, respectively, for the virtualized web server and approximately 0.144 s and 8866 packets per second, respectively, for the message queuing telemetry transport broker. The designed functions of the mobile phone app are a global positioning system home monitoring, family memo, medical care and near-field communication key. Both interlinkage and handler methods are proposed to facilitate a powerful function without delay in displaying information. The proposed system integrates with software and hardware to complete the data analysis and information management quickly and correctly. It can cater to user needs with superior ease and convenience.

IoT Based Multitasking Games and Entertainment Arcade Station using Raspberry-Pi

The world lives in a technology revolution that started from invention the computer system, the Internet and now in the Internet of Things (IoT) or Internet of every things (IoE). This revolution impacted the human life strongly, by depending on this technology to enhance our life and inventing new products depending on information technology like social media, mobile applications, video games and so on. This research will present the simplest, yet efficient way to build a classic Arcade Machine by using a Raspberry Pi development board, also this research will highlight how to build and install all related components, such as body structure, buttons and joysticks, programming the Raspberry Pi and adding the video game files. Later, the research will explain how to connect the Arcade Machine to the Internet for remote operation by using MCU ESP8266, relays, and the Wi-Fi function on the Raspberry Pi itself. Beside the video games, the machine can behave as an ordinary computer for the Internet browsing, music listening, image processing, documenting or doing some programing tasks, as the Raspbian operating system has python programming apps and it is possible to install any other programing language needed to execute tasks on Raspberry Pi microcontroller.

IoT BASED HOME AUTOMATION USING RASPBERRY PI

This paper presents the design of the low cost home automation system using the IoT (Internet of Things) technology along with the feature of speech recognition. The Internet of things (IoT) is the inter-networking of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. In this project IoT technology is used to control the home appliances wirelessly over the internet. The computing module used is a Raspberry pi development board. The project also aims to provide a speech control interface to the users to control the appliances. Speech recognition is provided using an online Speech-To-Text platform called wit. The home automation system listens for the user’s speech and whenever a defined phrase is identified it triggers corresponding action to switch appliances on or off. With speech recognition physically challenged people can control appliances with much more ease.

Design and Deployment of UAV-Aided Post-Disaster Emergency Network

Designing a reliable, resilient, and quickly deployable emergency communication network is a key challenge for post-disaster management. In this paper, a UAV-assisted emergency Wi-Fi network is proposed to expedite the rescue operations by guiding the survivors to the nearest rescue camp location. Here, the Raspberry PI (RPI) development board, mounted on UAV is considered to form a Wi-Fi chain network over the disaster region. During network set-up, the proposed solutions for the design challenges like UAV synchronization, avoid communication disruption and surveillance data management are the key contributions of this paper. The designed UAV network is capable of doing on-site surveillance and transmitting the data to the relief center for better rescue planning. One major challenge is to alert a survivor about the emergency network, which is addressed by designing a captive portal. Furthermore, to extend the Wi-Fi network, an Android-based application is developed by which each smartphone acts as a relay for its neighbor. Three types of field experiment are carried out to evaluate the performance of the designed prototype. It is found from the field results; the Wi-Fi access point mode and user datagram protocol are more suitable for network design as compared to Ad-Hoc mode and transmission control protocol, respectively. It is also observed from the experiment that the maximum hop distance for the prototype is 280 meters and 290 meters for a Wi-Fi configuration following IEEE 802.11n and IEEE 802.11ac protocol, respectively.

Protocol Development for Indoor Navigation using Bluetooth Device

Background: GPS based location measurement is not precise for indoor environment because satellite signals are getting attenuated and scattered by roof and wall. This can be achieved by a Bluetooth protocol developed for indoor navigation. Methods: In this paper an indoor navigation system using Bluetooth is proposed. Two Bluetooth devices used as positioning devices measure the Received Signal Strength (RSSI) of surrounding Bluetooth devices. Both the devices measure Received Signal Strength (RSSI) of a selected user device. These RSSI values are given to a raspberry Pi development board which acts as a processing unit in this system. In processing unit further processing is done by grid following method. There are three grids and nine points (spacing 1 m each) of grid following method finalized by performing repeated number of experiment. Findings: Raspberry Pi gives the location of surrounding device by comparing RSSI value obtained from grid following method during training phase and real time RSSI values obtained from two Bluetooth devices. This location is further plotted using GNUPLOT in raspberry Pi terminal and sent it to the user’s smartphone. This graph file is transferred on user’s smartphone by using USB Bluetooth dongle in a format of image.

Driver Assistance System based on Raspberry Pi

Electronics sector having more demand due to day by day use of embedded system for different applications in car. Most of luxurious cars having more automatic controls like Airbags,ABS,ESP,ECU,ESP,climate control & more. Automatic Guided Vehicle (AGV) nothing but vehicle guideline provided by capturing images of the road. Intelligent driver assistance system nothing but provide the full assistance to the driver when drive driving the car on the road along with considering the traffic intensity & white lane detection. System consist of the camera module used to take continuous video streaming. This stream video store into SD card first & process this video by writing the script in python. Lane detection is done from the video by using the Hough Transform Algorithm & Hough lines. Design the system in such a way that by considering small Robot as the demo module & one is pilot car it acts as obstacle in all the direction. Detection of the obstacle in front & rear direction means it need different type of obstacle detector sensors to detect the obstacle. Ultrasonic sensor it acts as the obstacle detector to detect the any obstacle within the range of 4 meters. Full driver assistance provided by detecting the side lane by taking the video streaming by using camera mounted on the car & obstacle detection is done by using the Ultrasonic sensor module. It is possible to display the distance apart from obstacle in meter on the display. Display consist of TFT screen connected to the system to display the continuous video & distance from other car. All system implemented on the new platform Raspberry Pi Development Board having ARM1176-JFZS core & BCM Audio-Video Codec with operating frequency 700MHz.Board support TFT screen as well as the HDMI support(6).Vehicles need to be re- advancing by video transmission among vehicles for safety and cooperative deriving. The video images captured from camera could help the driver to monitor the surroundings as well as transmit the compressed images over vehicular communication network Video over wireless communication has a lot of potential applications in intelligent transportation systems (ITS). Capturing the video is done by taking the continuous Video of the road lanes.Video streaming utilizes high bandwidth data links to transmit information. The high- bandwidth systems required larger equipment, better line-of- sight, and more complex mechanism for reliable transmit ion over the network. The intended platform for the system described in this study, is to develop a software defined algorithm for automatic video compression and transmission. The proposed algorithm is able to robustly find the left and right boundary of the lane using Hough Transform method and transmit over the network. Therefore the limitations of high-bandwidth equipment become more significant in a tactical scenario. The results show that the proposed method works well on marked roads and transmission in various lighting conditions(7).

INTELLIGENT DRIVER ASSISTANCE SYSTEM FOR AUTOMOTIVE

Abstract-Automotive Electronics sector having more demand due to day by day increasing technology. Most of luxurious cars having more automatic controls for different parameters. Automatic Guided Vehicle (AGV)(2)nothing but vehicle guideline provided by capturing images of the road. Intelligent driver assistance system nothing but provide the full assistance to the driver when drive driving the car on the road along with considering the traffic intensity & white lane detection. System consist of the camera module used to take continuous video streaming. This stream video store into SD card first & process this video by writing the script in python. Lane detection is done from the video by using the Hough Transform Algorithm & Hough lines. Design the system in such a way that by considering small Robot as the demo module & one is pilot car it acts as obstacle in all the direction(5).Detection of the obstacle in front & rear direction means it need different type of obstacle detector sensors to detect the obstacle. Ultrasonic sensor it acts as the obstacle detector to detect the any obstacle within the range of 4 meters. Full driver assistance provided by detecting the side lane by taking the video streaming by using camera mounted on the car & obstacle detection is done by using the Ultrasonic sensor module. It is possible to display the distance apart from obstacle in meter on the display. Display consist of TFT screen connected to the system to display the continuous video & distance from other car. All system implemented on the new platform Raspberry Pi Development Board having ARM1176EJ-FZS core & BCM Audio-Video Codec with operating frequency 700MHz.Board support TFT screen as well as the HDMI support.

Navigation of Mobile Devices based on Embedded Image Processing with Support of GPS Systems and Sensors

A recent development in system-on-chip processors provides possibilities of integration performancedemanding tasks, which can be performed only with the help of superior range of computers before. The article introduces advanced control system with embedded image processing to improving GPS localization of autonomous mobile devices in real environment. The main control system is based on ARM (SoC.) architecture integrated in Raspberry PI development board. Presented control systems can handle real-time video capturing, power-efficient image processing operation for example (thresholding, binarization, motion detection, ...etc.) and streaming results images as video. GPS precision can provide only 3 meters precision together with WAAS (Egnos) satellite. Our solution uses GPS solution and embedded image processing to detect border of roads or sidewalks. Our algorithm detects road or sidewalk universally without detection of white line in roadside, because pathways and some roads cannot provide this feature. Control system can be used for small autonomous mobile devices for example parts transport in industrial park of heavy industries, where are long distances between production halls.