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Optimization and Development of Raspberry Pi 4 Model B for the Internet of Things
... The Raspberry Pi4 is a 64-bit embedded microprocessor developed based on the Linux operating system. Its powerful functions and computing capabilities are almost equivalent to a complete computer, capable of connecting peripherals such as keyboards, mice, and monitors, and it includes I/O pins and communication interfaces like GPIO, I2C, and UART [11][12]. A small and low-cost embedded microprocessor, Raspberry Pi, in this system not only replaces the personal computer but also establishes an IoT server. ...
... GHz IEEE 802.11b/ g/n/ac wireless LAN and Bluetooth 5.0 BLE; (6) HDMI output supporting rev. 1.3, 1.4, composite video jack supporting NTSC, PAL, and 3.5mm audio jack; (7) 2 USB 3.0 ports and 2 USB 2.0 ports; (8) 40-pin 2.54mm header providing 27 GPIO pins and power pins, including +3.3V, +5V, GND; (9) 15-pin MIPI CSI-2 camera interface; (10) Display Serial Interface (DSI) display connector; (11) micro SD card reader supporting SDIO; (12) Operating System booting from micro SD card with support for Linux and Windows. The dimensions of the device were as follows: 85mm x 56mm x 17mm. ...
Magic is typically presented live, and computer presentation is relatively rare. This article is based on mind-reading magic and presents the magic process on a computer. Twenty-one cards are given to the spectator to make choices. Through the processes of dealing, collecting and questioning, the playing cards considered are transformed through mathematical derivation. This article uses the tree table lookup method to replace the mathematical algorithm used in the original magic. The program execution results can indeed achieve good results. Finally, we considered only displaying the card that the person was thinking about on the screen, but found this too monotonous and lacking in interactivity. Thus, the magic result will be presented on a touch panel or mobile webpage, so as to set off the final climax at the end of the magic. The playing card suits and numbers generated by the computer are transmitted to the touch panel via Bluetooth. Another method is to use Raspberry Pi to set up a server and create a database form, sending the card suits and numbers to the database form through Wi-Fi. Players can then connect to the IP address through their mobile phones to display the picture of the playing cards.
The massive development of the Internet of Things to date continues to develop a lot of small devices that are integrated such as the Wireless Sensor Network, several experts have conducted research using simulation and practice of WSN, one of the powerful embedded system hardware is the Raspberry Pi Pico, which is a small-sized Raspberry Pi such as Mini Arduino or Micro Arduino. The purpose of developing research in the field of Embedded Systems is to strive in terms of power consumption and dynamism in research, which can be placed on drones and also in various research ideas such as medical for patient comfort. Raspberry Pi RP2040 microcontroller uses an ARM Cortex-M0+ dual-core processor, Raspberry Pi Pico works on a voltage of 1.8 – 5.5 V DC, and can be handled with a 3.7 V DC battery. With its small dimensions, Raspberry Pi pico can be applied to various applicative research.
The development of several AI applications is made possible by the exciting endeavour of building a Raspberry Pi AI system. The Raspberry Pi board, operating systems, programming languages like Python, and machine learning libraries like TensorFlow and PyTorch are all covered in this article as examples of the tools and methods that can be utilised to create a Raspberry Pi AI system. The use of power supply, GPIO pins, and sensors and actuators is also covered in the paper, along with the necessity of giving the Raspberry Pi a casing for protection. The article shows the Raspberry Pi's potential as a development platform for AI and offers insights into the various applications that may be created using this flexible platform. The methods and tools used to create an artificial intelligence (AI) system using a Raspberry Pi are covered in this paper. Popular single-board computers like the Raspberry Pi can be used to create a variety of applications, including artificial intelligence systems. The setup instructions for the Raspberry Pi for AI development, including the installation of well-known AI frameworks like TensorFlow and Keras, are provided in the paper. It also covers the procedures for setting up a neural network on the Raspberry Pi, training the model, and using it. Also, the article discusses the advantages and restrictions of utilising a Raspberry Pi for AI development and provides suggestions for improving performance. At the end, this paper offers a useful manual for anyone considering constructing an AI system on a Raspberry Pi.
Developing a Raspberry Pi personal cloud server that can serve as your very own personal cloud storage is the goal of the project "Personal Cloud Using Raspberry Pi." Moving your data to private cloud storage is a preferable solution because privacy protection is a major concern today. The Raspberry Pi 3 model B module that houses the cloud server is configured. The server is linked to the internet by the router. For greater data storage, the server offers an optional USB hard disc. The Raspberry Pi server's wireless network card or adapter is used to link it to Personal Cloud, an open-source cloud computing platform. The Raspberry Pi can be configured remotely from any device, such as a smartphone, using a local tunnel.Numerous services, including SSH, HTTP, and VNC, are configured on and managed by the local tunnel. Access to the Personal Cloud account is restricted to the admin. Through the local tunnel, an administrator can provide users a variety of services. You can store your data for free on our project as needed. With tight system control, this project provides you the freedom to use space efficiently.
In den letzten Jahren hat sich die Art und Weise, wie Datenzentren betrieben werden, stark verändert. Eine der wichtigsten Entwicklungen in diesem Bereich ist die zunehmende Verwendung von Virtualisierungstechnologien, die es ermöglichen, den Betrieb des Gesamtsystems und der angebotenen Dienste zu optimieren und zu vereinfachen. Ein weiteres wichtiges Thema im Zusammenhang mit Datenzentren ist die Automatisierung von Prozessen, insbesondere im Bereich des Deployments und des Monitorings.
Ein interessantes Anwendungsgebiet für diese Technologien ist der Einsatz von Raspberry Pi in Datenzentren. Raspberry Pi ist ein kostengünstiger und leistungsfähiger Computer, der in vielen Anwendungen eingesetzt werden kann, einschließlich des Aufbaus von Datenzentren. Die Einsatzmöglichkeiten von Raspberry Pi erstrecken sich von der Verwendung als einfache Web-Server bis hin zu komplexen Anwendungen wie dem Aufbau von Cloud-Computing-Umgebungen.
In diesem Zusammenhang ist es wichtig, Werkzeuge zur Verfügung zu haben, die das Monitoring und Deployment von neuen Diensten und Anwendungen in einem Raspberry Pi Datenzentrum automatisieren können. Automatisierte Deployment-Prozesse ermöglichen es, neue Dienste und Anwendungen schnell und einfach in einem Datenzentrum bereitzustellen, ohne dass manuelle Schritte erforderlich sind. Auf diese Weise kann die Zeit, die für die Bereitstellung von neuen Diensten und Anwendungen benötigt wird, erheblich reduziert werden.
Das Ziel dieser Arbeit ist es daher, ein automatisiertes Deployment-System für Raspberry Pi Datenzentren zu entwickeln, das es ermöglicht, neue Dienste und Anwendungen schnell und einfach bereitzustellen. Dabei sollen sowohl die Automatisierung der Deployment-Prozesse als auch die Möglichkeiten des Monitorings berücksichtigt werden. Durch die Verwendung von Raspberry Pi und automatisierten Deployment-Prozessen soll es möglich sein, ein kosteneffizientes und leistungsfähiges Datenzentrum aufzubauen.
The internet of things (IoT) is the communication of everything with anything else, with the primary goal of data transfer over a network. Raspberry Pi, a low-cost computer device with minimal energy consumption is employed in IoT applications designed to accomplish many of the same tasks as a normal desktop computer. Raspberry Pi is a quad-core computer with parallel processing capabilities that may be used to speed up computations and processes. The Raspberry Pi is an extremely useful and promising technology that offers portability, parallelism, low cost, and low power consumption, making it ideal for IoT applications. In this article, the authors provide an overview of IoT and Raspberry Pi and research on IoT applications using Raspberry Pi in various fields, including transportation, agriculture, and medicine. This article will outline the details of several research publications on Raspberry Pi-based IoT applications.
This study discusses the Performance of Moisture soil sensor, which is helpful as a sensor to detect soil moisture levels used as plant nutrition. The monitoring process is carried out in realtime using internet of things technology based on LoRa or Long-Range Radio Frequency (IoTLoRa). The application server used is Thingspeak. With this research, agricultural processes and monitoring can be carried out dynamically and efficiently. Therefore, when the soil conditions are
dry, or the soil moisture level is <300, it will immediately affect automatic watering by opening the valve or rotating the Servo motor. Furthermore, the watering process can be done automatically by
looking at the soil conditions on potted plants or agricultural land. The position of the sensor on the ground level is not immediately removed and moved to another place, but it is always in the same condition and location, so the Adaptive Data Rate mechanism is used for the management of Power Consumption on IoT-LoRa
This study discusses the Performance of Moisture soil sensor, which is helpful as a sensor to detect soil moisture levels used as plant nutrition. The monitoring process is carried out in real-time using internet of things technology based on LoRa or Long-Range Radio Frequency (IoT-LoRa). The application server used is Thingspeak. With this research, agricultural processes and monitoring can be carried out dynamically and efficiently. Therefore, when the soil conditions are dry, or the soil moisture level is <300, it will immediately affect automatic watering by opening the valve or rotating the Servo motor. Furthermore, the watering process can be done automatically by looking at the soil conditions on potted plants or agricultural land. The position of the sensor on the ground level is not immediately removed and moved to another place, but it is always in the same condition and location, so the Adaptive Data Rate mechanism is used for the management of Power Consumption on IoT-LoRa
One factor that leads to excellence in terms of plant quality is how to treat plants well by giving the proper water, neither abundantly nor lacking. The weakness of farmers is their lack of routine in watering plants, coupled with lousy weather factors that will damage plants and the type of water used for watering plants. Monitoring is carried out on agricultural land in watersheds utilizing an irrigation system. This study monitors and conducts water pH, soil pH, and soil moisture using an Arduino Atmega 328p microcontroller with LoRa 915 MHz. Internet-based automation was carried out on plant monitoring using soil pH sensors, water pH, and soil moisture. The device used is Long Range Radio Frequency 915 MHz. One of the plants used for the experiment is Pakcoy. Water is distinguished by two containers that have been integrated with LoRa 915 MHz, and the water is ensured to have an average pH that is flowed to agricultural land automatically. The data is displayed in real-time in the application Server the Thingspeak IoT
This research will be a test of a multi-sensor data transmission using the Wireless Sensor Network based on Bluetooth RN-42. Accordingly this research, LM35 is a type of Temperature Sensor, furthermore, this research will be used two LM35 sensors installed on the Arduino board and to be processed by Arduino Integrated Development of Environment (IDE) with C++ language. Arduino will be sending of all sensor data from LM35 temperature sensor by Slave RN-42 Bluetooth Configuration to master RN-42 Bluetooth configuration. Furthermore, the temperature data will be sending on Raspberry Pi 3 as an Internet Gateway then data will be sent to the internet and sensor data will be stored in the MySQL database. Furthermore, Sensor data can be accessed by other computers on the internet network using PuTTY with the Raspberry Pi 3 IP Address 192.168.1.145. Moreover, testing is also done by measuring the Signal power of Wireless Personal Area Network with the Receive Signal Strength Indicator variable, so the Bluetooth signal strength in sending multi-sensor data can be known appropriately. © 2018 International Journal of Advanced Computer Science and Applications.
Recently, the wireless sensor network has been widely deployed for medical care purpose. We have developed a wireless sensor network that can monitor the patients' pulse status for triage purpose, so that a medical team can monitor remotely the health condition of patient and they can treat the patient based on severity of patients' health condition. We developed an electronic triage operating as a sensor node (SN) tagged in patient's arm. The SN consists of microcontroller ATmega328P, ZigBee and pulse sensor to detect patient's pulse. Operating as an electronic triage, the pulse rate from sensor is classified into three categories of severe conditions, i.e., major, minor, and normal status by the microcontroller in SN and sent to the coordinator node (CN) through ZigBee interface. Our system can be deployed in emergency room, triage room, pre/post-op surgery in hospital as well as in disaster area. This paper aims to evaluate the performance of the ZigBee-based wireless sensor network that we developed. We evaluate the effective distance between CN and SN to deliver patients' pulse rate via ZigBee as well as the effective number of SNs that can be accommodated by single CN. The experimental results shown that the effective distance between CN and SN to deliver the pulse rate data is less than 30 meters and the maximum number of SNs can be accommodated by a single CN is 3 (three) nodes.
Radar technology for health continues to be developed, one of which is from this research, which focuses on Respiratory patients. Not only in terms of FMCW Radar analysis for medical but also in terms of flexibility in displaying data that can be integrated with smartphone devices used by many people today. One of the servers used is MQTT, which has been installed and successfully displayed realtime patient respiratory data. In the future, this research will be installed using a permanent casing that is more fixed and stable in the connectivity process between devices, involving the Raspberry Pi 4 B and OmniPreSense Radar. The data is integrated into the MQTT server with the patient’s respiratory data and produces realtime data in the form of respiratory data graphs.
Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years.
The smart mirror’s design, construction, and operation are described and implemented in this study. Every morning, we start our day by looking in the mirror at least once before leaving our homes. We use it mentally to figure out how we look and what we’re wearing, and sometimes just by instinct. One of the Raspberry Pi’s applications is smart mirror, often known as magic mirror. A computer screen is placed in a mirror that has a futuristic appearance. The Raspberry Pi remains in the background scenes and is in charge of the data displayed on the mirror. You may check numerous notifications from social media sites as well as news, weather forecast, and other things while just staring at a mirror. The Raspberry Pi is connected to the monitor through a HDMI cable and also includes a built-in Wi-Fi and bluetooth interfaces, allowing us to stream music and videos on the screen of the mirror.KeywordsSmart mirrorRaspberry PiMagic mirrorHDMIWi-FIBluetooth
All over the world, people use the Raspberry Pi to learn the coding skills, build simple projects, implement simple clusters and a little bit of Edge computing. Raspberry Pi is considered as a low-cost pocket-sized minicomputer used in various applications. In this chapter, we have focused on hands on implementation of few simple applications such as, Interfacing LEDs, Organic LED (OLED) display, Camera Interfacing to capture video/pictures, different types of motor control and lastly implementation of Bluetooth with mobile .To implement these examples, we have used Thonny Python IDE which is already integrated with Raspbian OS.KeywordsLEDPWMCamera interfacingMotor controlBluetooth
The Braille machines are expensive and as a result, are not accessible to many. In particular, there is a need for a portable text reader that is affordable and readily available to the blind community. The designed system is a portable, low-cost, reading device made for blind people. The designed system also overcomes the limitation of a conventional Braille machine by making it affordable for the common masses. This paper proposes a smart reader for visually challenged people using raspberry pi. This paper addresses the integration of a complete Text Read-out system designed for the visually challenged. The designed system consists of a webcam interfaced with raspberry pi which accepts a page of printed text. This system uses OCR technology to convert images into text and reads out the text by using Text-to-Speech conversion. This system supports audio output via Speakers as well as headphones. The OCR (Optical Character Recognition) package is installed in raspberry pi which scans it into a digital document which is then subjected to skew correction, and segmentation, before feature extraction to perform classification. Once classified, the text is read out by a text-to-speech conversion unit (TTS engine) installed in raspberry pi. The output is fed to an audio amplifier before it is read out.
We designed a portable Raspberry Pi-based spectrometer, which mainly consists of a white LED acting as the wide-spectrum source, a reflection grating for light dispersion, and a CMOS imaging chip aiming at spectral recording. All the optical elements and Raspberry Pi were integrated using 3-D printing structures with a size of 118 mm × 92 mm × 84 mm, and home-built software was also designed for spectral recording, calibration, analysis, and display implemented with a touch LCD. Additionally, the portable Raspberry Pi-based spectrometer was equipped with an internal battery, thus supporting on-site applications. Tested by a series of verifications and applications, the portable Raspberry Pi-based spectrometer could reach a spectral resolution of 0.065 nm per pixel within the visible band and provide spectral detection with high accuracy. Therefore, it can be used for on-site spectral testing in various fields.
During LoRaWAN data transmission, the Bit Rate transmitted from the transmitter to the Receiver experiences attenuation from the object being passed, so Packet Loss (Bytes) may occur due to diffraction, scattering, and reflection in the LoRa data transmission process. LoRa data that has errors is researched and further developed with a formula and method to minimize data rate errors in LoRa communication. Analysis development lies in the gateway's ability to accommodate LoRa data from end nodes. This condition requires a development method of Adaptive Data Rate, which is helpful for Battery Power Saving or power consumption of sensor nodes, especially in data communication with LoRa servers, i.e., the uplink and downlink processes that are correct, regular, and on-time. We use pulse data in the existing case examples to monitor the patient's health status. This data is applied to elderly patients and patients from victims of natural disasters. The development is the use of extra small and comfortable LoRa for all patients in the hospital. One of the mechanism developments is Blind-ADR, which is used for unpredictable Mobile LoRa data transmission, e.g., Blind-ADR is intended to monitor the movement of the patient's changing position. This research also examines the effects of increasing the number of end-nodes that cause health data collisions and the mechanisms used to avoid collisions, including the Adaptive Data Rate.
Due to recent advances in deep learning, the performance of object detection techniques has greatly increased in both speed and accuracy. This enabled highly accurate real-time object detection in modern desktop systems. This project investigates the applicability of working object detection on Raspberry Pi 3. Real-time detection of objects requires a lot of processing power, and achieving real-time speed is a difficult task in a system with limited performance. Many different methods can be used to detect objects. Two methods were implemented in the Raspberry Pi 3 B to determine if they are suitable to work with such weak hardware. An implemented target detector is considered suitable if it achieves a high enough resolution and frame rate to be useful in practical applications. The evaluation performs a number of tests on each detector and measures their performance in terms of detection accuracy, hell time, and frame rate. Raspberry Pi Model B, which is the latest and most powerful product of the Raspberry Pi series, is used as hardware. The camera used is a Raspberry Pi Camera Module v2
In this study, temperature monitoring was carried out in broiler chicken coops using LoRAWAN to transmit temperature and humidity sensor data in chicken coops to monitor and determine ideal conditions. LoRa can communicate data over long distances, transmit on the Line of Sight >= 15 km with Low Power Consumption with small BitRate. It is suitable for temperature and humidity data by considering the growth determinants of broiler chickens analyzed using Mamdani Fuzzy Logic. The growth of broiler chickens is determined at the level of ideal temperature and humidity availability, temperature, and humidity. Temperature and Humidity level is based on the age of broiler chickens. Therefore, the need for sorting according to the age of chickens, i.e., age 0-7 days, air temperature 31 o c -33 o c with optimal temperature 33 o c with 30-50% humidity, at 7-14 days old, air temperature 29 o c-31 o c with optimum temperature 30 o c and humidity 40-60%, at an age above 14 days, air temperature 26 o c-29 o c with optimum temperature 27 o c and 40-60% humidity. The age of broiler chickens to harvest is 30-35 days, with a live weight of 1.5-2.0 kg per chicken. Fuzzy logic can be obtained for the most appropriate temperature and humidity applied to broiler chicken coop to support the development of chicken seen from the chicken's weight at the time of harvesting.
