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Zeitschriftenartikel zum Thema „Raspberry Pi 4“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

Supriyono, Joko, Dody Wahjudi, and Tri Watiningsih. "Prototype Robot Manipulator 4 DOF Berbasis Raspberry Pi Zero W." Journal of Telecommunication, Electronics, and Control Engineering (JTECE) 4, no. 1 (2022): 25–38. http://dx.doi.org/10.20895/jtece.v4i1.528.

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Pemanfaatan mikrokontroler untuk menunjang pekerjaan manusia serta didunia industri menuntut adanya perkembangan dalam hal ketepatan, kecepatan serta teknologi yang terbaru. Raspberry Pi merupakan papan tunggal single board circuit (SBC) seukuran kartu kredit yang dapat digunakan untuk menjalankan program perkantoran, permainan komputer, sebagai pemutar media beresolusi tinggi serta menjadi mikrokontroler. Dengan menggunakan bahasa pemrograman PYTTHON yang mudah dipahami dan populer, Raspberry Pi dapat dengan mudah dalam pengembangannya. Salah satu penggunaan Raspberry Pi yaitu sebagai mikroko

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Tyler, Neil. "Farnell Unveils Raspberry Pi 4 Computer." New Electronics 52, no. 12 (2019): 7. http://dx.doi.org/10.12968/s0047-9624(22)61314-2.

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Mulyanto, Trio Ade, Mukhtar Habiby, Kusnadi Kusnadi, and Rinaldi Adam. "HOME AUTOMATION SYSTEM DENGAN MENGGUNAKAN RASPBERRY PI 4." Jurnal Digit 11, no. 1 (2021): 60. http://dx.doi.org/10.51920/jd.v11i1.180.

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ABSTRAK  Home automation merupakan salah satu kemajuan teknologi dan komunikasi dengan otomasi kontrol terhadap rumah. Dengan metode ini, perangkat-perangkat elektronik rumah dapat di kontrol mengunakan sistem kendali jarak jauh tanpa harus menyetuh perangkat elektronik yang ada di rumah. Raspberry Pi digunakan sebagai pemroses untuk menciptakan sebuah sistem. Raspberry Pi merupakan mini computer yang fungsi-fungsi dasarnya sama seperti sebuah personal komputer biasa dengan ukuran sebesar kartu kredit. Raspberry Pi dapat mengontrol perangkat-perangkat elektronik yang berada di ruma

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Priyambudi*, Rizky, Dian Budhi Santoso, and Lela Lela Nurpulaela. "Perancangan dan Purwarupa Raspberry PI CM4 dengan NVME SSD Menggunakan Allegro PCB Editor." JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah 8, no. 2 (2023): 723–31. http://dx.doi.org/10.24815/jimps.v8i2.24811.

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Raspberry Pi CM4 (Compute Module 4) adalah gadget kecil yang dibuat untuk memudahkan membuat barang elektronik yang terhubung ke internet. NVMe SSD (Non-Volatile Memory Express Solid State Drive), media penyimpanan yang lebih cepat dari kartu MicroSD, digunakan untuk meningkatkan kecepatan Raspberry Pi CM4. Dalam studi ini, kami menggunakan Allegro PCB Editor untuk merancang dan membuat papan sirkuit cetak (PCB) yang terintegrasi dengan Raspberry Pi CM4 dan NVMe SSD. Desain skematis, tata letak PCB, dan verifikasi koneksi antara Raspberry Pi CM4 dan NVMe SSD adalah semua bagian dari proses des

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Paulenka, D. A. "Comparative analysis of single-board computers for the development of a microarchitectural computing system for fire detection." Informatics 21, no. 2 (2024): 73–85. http://dx.doi.org/10.37661/1816-0301-2024-21-2-73-85.

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Objectives. The purpose of the work is to select the basic computing microplatform of the onboard microarchitectural computing complex for the detection of anomalous situations in the territory of the Republic of Belarus from space on the basis of artificial intelligence methods.Methods. The method of comparative analysis is used to select a computing platform. A series of performance tests and comparative analysis (benchmarking) are performed on the selected equipment. The methods of comparative and benchmarking analysis are performed in accordance with the terms of reference to the current p

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Srivastava, Ankit, Prabhat Singh, Sushil Kumar Verma, Kumar Kartikey, and Prof Shubham Shukla. "Path Planning Robot Using PI-CAM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 66–70. http://dx.doi.org/10.22214/ijraset.2022.41133.

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Abstract: Currently, the path planning robot is one among the foremost researched topics in autonomous robotics technology. Path planning has important applications in many areas, for example industrial robotics, autonomous systems, and 3D digital representation that can perform various types of simulations iteratively about product performances. The utilization of versatile robots for modern purposes like arranging, moving, and putting is at this point not an inclination however a need. Our path planning robot is using OpenCV, image processing and various hardware’s such as raspberry pi 4, Ar

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Fadilah, Muhammad Derri, Hafiz Firdaus, and Muhamad Riyyan. "Implementasi Monero Mining Pada Raspberry Pi 4 Model B." Jurnal Ilmiah Informatika 8, no. 1 (2023): 13–23. http://dx.doi.org/10.35316/jimi.v8i1.13-23.

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In the world of cryptocurrency, Monero is one of the cryptocurrencies that was created in 2014. Monero uses CryptoNight as a proof-of-work algorithm to get cryptocurrency coins. The proof-of-work algorithm requires miners to have tools with large computing power so they are competing to buy tools with large computing power, but there is a problem with the electricity generated is also large. The Raspberry Pi was tested as an alternative cryptocurrency mining tool that has economic value. In this study, we implemented the Raspberry Pi 4 Model B to calculate how much computing power was generate

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Hermawan, Indra, Defiana Arnaldy, Maria Agustin, M. Farishanif Widyono, David Nathanael, and Meutia Tri Mulyani. "Sistem Pengenalan Benih Padi menggunakan Metode Light Convolutional Neural Network pada Raspberry PI 4 B." Jurnal Teknologi Terpadu 7, no. 2 (2021): 120–26. http://dx.doi.org/10.54914/jtt.v7i2.443.

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Baru-baru ini, metode pembelajaran mendalam dengan Convolution Neural Network (CNN) telah banyak digunakan untuk tugas klasifikasi gambar. CNN memiliki keunggulan yang tak tertandingi dalam mengekstraksi fitur gambar diskriminatif. Namun, banyak metode berbasis CNN yang ada dirancang untuk lebih dalam dan lebih besar dengan lapisan yang lebih kompleks. Sehingga membuatnya sulit untuk diterapkan pada perangkat seluler atau pada perangkat waktu nyata yang menggunakan mikrokontroler seperti raspberry pi, Arduino, dan lain sebagainya. Hal tersebut diatasi dengan menggunakan Light Convolution Neura

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Mallesh, S. Jambagi, H. Basavaraj, L. Gagan, and R. Prabhu Prateek. "Smart Speaker using Raspberry Pi." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 1682–85. https://doi.org/10.31142/ijtsrd14440.

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As the software systems are getting more and more complex each day, the ability of the user to put up with this complexity is reducing. An elegant solution to deal with this issue is to build a personal assistant agent which is capable of determining users habits, preferences and intentions and helping them accordingly. This work discusses on developing a Smart Speaker using Raspberry Pi . The work aims at the development of a personal voice assistant which can assist users in performing their personal and professional tasks using speech commands and provide more interactive and user friendly

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Sutar, Ankita, Shreya Bhagat, Shruti Shinde, Apeksha Kambale, and Prof Puja Patil. "Human Image Classification and Identification." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1034–36. http://dx.doi.org/10.22214/ijraset.2022.42397.

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Abstract: Face Recognition is in recent times used as a chunk of various applications. It is used as a piece of the makes use of images processing applications. The Raspberry Pi 4 is embedded system. It is low cost, a single-board computer used to lessen the complexity of systems in actual time applications. This project is especially based on python. Raspberry pi carries Camera slot Interface (CSI) to interface the raspberry pi camera. Here, Images captured by using the Raspberry Pi camera module are improved so that it will identify the specific region of image. This is used in the real time

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Sunardi, Sunardi, Andi Suhandi, and Muslim Muslim. "Profiles of Facilities and Students’ Responses in Supporting Implementation of Raspberry Pi-Based Bifocal Modeling Physics Practicum." Jurnal Pendidikan Fisika 11, no. 1 (2023): 24–36. http://dx.doi.org/10.26618/jpf.v11i1.8892.

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This research has been conducted to seek overviews of facilities and students’ responses in supporting the implementation of Raspberry Pi-based bifocal modeling physics practicum. This research was a qualitative case study done in a senior high school in Bandung District, Indonesia. The subjects of this research were two physics teachers and seventy-nine of students enrolled in tenth, eleventh, and twelfth grades in the academic year of 2020/2021. The data collection in this research was carried out through observation, interviews, and questionnaire. Observation and interviews activities were

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Prasath kumar, S., P. Rayavel, N. Anbarasi, B. Renukadevi, and D. Maalini. "Raspberry pi based secured cloud data." Journal of Physics: Conference Series 1964, no. 4 (2021): 042101. http://dx.doi.org/10.1088/1742-6596/1964/4/042101.

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Ganesan, M., R. Hemanth., S. Gunalan., and J. Hemprasad. "Raspberry PI Based Smart Walking Stick." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042090. http://dx.doi.org/10.1088/1757-899x/981/4/042090.

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Budiyanta, Nova Eka, Catherine Olivia Sereati, and Ferry Rippun Gideon Manalu. "Processing time increasement of non-rice object detection based on YOLOv3-tiny using Movidius NCS 2 on Raspberry Pi." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 1056–61. http://dx.doi.org/10.11591/eei.v11i2.3483.

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This study aims to increase the processing time of detecting non-rice objects based on the you only look once v3-tiny (YOLOv3-tiny) model. The system was developed on the Raspberry Pi 4 embedded system with the Movidius neural compute stick 2 (NCS 2) implementation approach. Data object in the form of gravel on a bunch of rice in the video. The video data was obtained using a webcam with a resolution of 1920 x 1080 pixels with a total of 2759 frames. From the test results, frames per second (FPS) have increased by 1.27x in the Movidius NCS 2 implementation compared to processing using the cent

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Nova, Eka Budiyanta, Olivia Sereati Catherine, and Rippun Gideon Manalu Ferry. "Processing time increasement of non-rice object detection based on YOLOv3-tiny using Movidius NCS 2 on Raspberry Pi." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 1056~1061. https://doi.org/10.11591/eei.v11i2.3483.

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This study aims to increase the processing time of detecting non-rice objects based on the you only look once v3-tiny (YOLOv3-tiny) model. The system was developed on the Raspberry Pi 4 embedded system with the Movidius neural compute stick 2 (NCS 2) implementation approach. Data object in the form of gravel on a bunch of rice in the video. The video data was obtained using a webcam with a resolution of 1920 x 1080 pixels with a total of 2759 frames. From the test results, frames per second (FPS) have increased by 1.27x in the Movidius NCS 2 implementation compared to processing using the cent

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Zhang, Fan. "Test of Cartographer based on Raspberry PI." Academic Journal of Science and Technology 2, no. 2 (2022): 145–48. http://dx.doi.org/10.54097/ajst.v2i2.1303.

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The Raspberry Pi-based AI car uses a 4-wheeled Ackerman wheeled robot as a motion platform and is equipped with a high-performance lidar. In the ROS system under Ubuntu18, the test has passed the cartographer mapping algorithm. After experiments, it is found that the cartographer algorithm can also present a good and superior mapping effect under the Raspberry Pi platform.

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Goyal, Yash. "Comparative Study of Microcontoller: ARDUINO UNO, RASPBERRY PI 4, ESP 32." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 588–92. http://dx.doi.org/10.22214/ijraset.2024.63598.

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Abstract: This paper provides a comparative study of three popular microcontrollers: Arduino Uno, Raspberry Pi 4, and ESP32. Arduino Uno is known for its simplicity and low power consumption, making it ideal for educational and small-scale projects. Raspberry Pi 4 offers high computational power and extensive connectivity, suitable for IoT gateways, media centers, and industrial automation. ESP32 combines robust wireless connectivity with efficient processing, perfect for IoT applications, smart home devices, and wearable electronics. The study evaluates their technical specifications, perform

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Maragatham, T., P. Balasubramanie, and M. Vivekanandhan. "IoT Based Home Automation System using Raspberry Pi 4." IOP Conference Series: Materials Science and Engineering 1055, no. 1 (2021): 012081. http://dx.doi.org/10.1088/1757-899x/1055/1/012081.

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Khai Tran, Thien, Kha Tu Huynh, Dac-Nhuong Le, Muhammad Arif, and Hoa Minh Dinh. "A Deep Trash Classification Model on Raspberry Pi 4." Intelligent Automation & Soft Computing 35, no. 2 (2023): 2479–91. http://dx.doi.org/10.32604/iasc.2023.029078.

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Prasetya, Aldo Dwi, Muhammad Daffa Raihan Ma'arif, Shania Syaharani, Imam Halimi, and Dezetty Monika. "Smart Mirror Berbasis Raspberry Pi 4 untuk Home Automation." ELECTRICES 3, no. 1 (2021): 34–39. http://dx.doi.org/10.32722/ees.v3i1.4136.

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Smart Mirror merupakan cermin dua arah dengan tampilan elektronik di belakang kaca. Smart Mirror dapat menampilkan berbagai jenis informasi dalam bentuk widget dan akan berguna bagi individu yang ingin melakukan banyak tugas dan tetap mendapat informasi. Alat ini dapat dikontrol dengan voice command atau dengan berupa sentuhan pada IR Frame untuk mengaktifkan voice detector. Untuk mengetahui apakah alat ini berfungsi dilakukan metode pengujian dengan melihat sistem dan proses secara keseluruhan pada alat dimulai dari tampilan grafis Smart Mirror, fungsi sensor pendukung, fungsi komponen, dan f

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Vega Luna, José Ignacio, Francisco Javier Sánchez Rangel, José Francisco Cosme Aceves, Gerardo Salgado Guzmán, and Víctor Noé Tapia Vargas. "Motion detection using a Raspberry Pi 4 and OpenCV." International Journal of Latest Engineering and Management Research (IJLEMR) 8, no. 5 (2023): 109–17. http://dx.doi.org/10.56581/ijlemr.8.5.109-117.

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Hadiwandra, T. Yudi, and Feri Candra. "High Availability Server Using Raspberry Pi 4 Cluster and Docker Swarm." IT Journal Research and Development 6, no. 1 (2021): 43–51. http://dx.doi.org/10.25299/itjrd.2021.vol6(1).5806.

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In the Industrial 4.0 era, almost all activities and transactions are carried out via the internet, which basically uses web technology. For this reason, it is absolutely necessary to have a high-performance web server infrastructure capable of serving all the activities and transactions required by users without any constraints. This research aims to design a high-performance (high availability) web server infrastructure with low cost (low cost) and energy efficiency. low power) using Cluster Computing technology on the Raspberry Pi Single Board Computing and Docker Container technology. The

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ANDRIOAIA, DRAGOS-ALEXANDRU, GEORGE CULEA, and PETRU-GABRIEL PUIU. "ENVIRONMENTAL TEMPERATURE AND HUMIDITY MONITORING SYSTEM USING RASPBERRY PI 4 AND THINGSPEACK." Journal of Engineering Studies and Research 27, no. 3 (2022): 20–23. http://dx.doi.org/10.29081/jesr.v27i3.283.

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In recent years, IoT platforms have become increasingly used due to their untapped potential. This paper aims to create an IoT system to monitor temperature and humidity in an enclosure The Raspberry Pi 4 SBC (Single-Board Computer) development board and ThingSpeak cloud platform will be used to make this system. Data from the DHT11 humidity and temperature sensor will be collected by the Raspberry PI 4 SBC development board, which will transmit it via the WiFi connection to the IoT ThingSpeak platform cloud for further analysis. The IoT ThingSpeak platform provides data storage, processing an

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M. Hodgess, Erin. "High Performance Computing on the Raspberry PI." International Journal of Computational Science and Information Technology 10, no. 1 (2022): 1–5. http://dx.doi.org/10.5121/ijcsity.2022.10101.

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We considered building high performance tools on the Raspberry Pi 4. We implemented OpenMP and OpenCoarrays Fortran in conjunction with the statistical language R. We found that the OpenCoarrays is more effective when working with vectors, while OpenMP is better in the arena with large matrices in a geostatistics application. These results can be very useful for researchers with limited access to high performance tools or limited funding.

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Indra, Dolly, Tasmil Tasmil, Herman Herman, St Hajrah Mansyur, and Erick Irawadi Alwi. "DESIGN WEB-BASED ELECTRICAL CONTROL SYSTEM USING RASPBERRY PI." Journal of Information Technology and Its Utilization 2, no. 1 (2019): 1. http://dx.doi.org/10.30818/jitu.2.1.2275.

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The use of current website technology can be applied as a control and monitoring system, which is used to control electrical devices, so that the user can only control the PC or smartphone that has been connected to Wi-Fi or the Internet. In this case the control uses the Raspberry Pi Mini PC which has several advantages such as low power and is relatively easy when connected with a web server compared to a microcontroller. By utilizing the Raspberry Pi Mini PC as a web server, it can replace PC functions in general. The results in this study are the Electric Control System that has been made

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Ayşenur, ŞAHİN, ÇİL Sevcan, and İSTANBULLU Ayhan. "Raspberry Pi 4 ile Sürücü Yorgunluk Tespiti ve Uyarı Sistemi." Journal of Scientific, Technology and Engineering Research (JSTER) 1, no. 1 (2020): 13–18. https://doi.org/10.5281/zenodo.3902907.

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Şahin A., Çil S., İstanbullu A., (2020). <em>Raspberry Pi 4 ile Sürücü Yorgunluk Tespiti ve Uyarı Sistemi, </em>Bilim, Teknoloji ve Mühendislik Araştırmaları Dergisi, 1(1), 13-18.

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Podorozhniak, A., N. Liubchenko, D. Petrukovich та D. Onishchenko. "ДОСЛІДЖЕННЯ СИСТЕМИ РОЗПІЗНАВАННЯ АВТОМОБІЛЬНИХ НОМЕРІВ НА RASPBERRY PI". Системи управління, навігації та зв’язку. Збірник наукових праць 1, № 71 (2023): 134–37. http://dx.doi.org/10.26906/sunz.2023.1.134.

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Об’єктом дослідження даної роботи є засоби машинного навчання для автоматичного розпізнавання автомобільних номерів. Мета даної роботи полягає у дослідженні реалізації системи для розпізнавання автомобільних номерів засобами Raspberry PI 4. Основним завданням даного дослідження є аналіз аналогів існуючих систем автоматичного розпізнавання автомобільних номерів у середовищі апаратних продуктів Raspberry PI, а також реалізація власної системи та її дослідження. У результаті дослідження існуючих аналогів було виявлено певні проблеми та знайдено шляхи їх вирішення. Система використовує сучасні тех

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Nafiza, Nafiza. "ARMPI 4DOF VISION ROBOTIC ARM." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04583.

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Abstract - This paper presents the ArmPi 4DOF Vision Robotic Arm, a cost-effective, vision-guided robotic system capable of real-time pick-and-place tasks using Raspberry Pi and OpenCV. The system integrates a 4 Degrees of Freedom robotic arm with a Pi Camera for object detection based on shape and color. It achieves autonomous movement through Python-based control logic. Results show reliable operation in controlled environments with scope for enhancements like AI-based object recognition and closed-loop feedback. Key Words: Robotic Arm, Computer Vision, Raspberry Pi, 4DOF, OpenCV, Automation

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Jais, Ron. "Description VEHICLE PLATE DETECTION USING RASPBERRY PI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47460.

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Abstract- This project introduces a cost-effective, portable, and adaptable license plate detection and recognition system using Raspberry Pi, leveraging computer vision and machine learning technologies. Unlike traditional stationary systems relying on high-end hardware and specialized software, this solution addresses cost, flexibility, and en vironmental adaptability challenges. Featuring five modules—image acquisition, pre processing, license plate detection, Optical Character Recognition (OCR), and result output—the system utilizes OpenCV, Tesseract OCR, and Python with TensorFlow/K eras

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Tompunu, Alan Novi, Yulian Mirza, Azwardi Azwardi, and Arsia Rini. "Sistem Absensi Face Recognition Berbasis Raspberry Pi 4 dan Telegram." El Sains Jurnal Elektro 4, no. 1 (2022): 15–18. http://dx.doi.org/10.30996/elsains.v4i1.6776.

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Pengenalan wajah merupakan teknologi biometrik yang telah banyak diterapkan di berbagai bidang salah satunya adalah keamanan. Dengan menggunakan teknologi sebuah sistem dapat mengenali seseorang dengan menyimpan tekstur muka orang tersebut pada databse dan menggunakan sebuah kamera digital untuk mencocokkan tekstur wajah pada database. Raspberry PI adalah sebuah mini komputer yang cukup handal dalam menjalankan program program yang cukup komplek telah terinstal lengkap seperti komputer sudah memiliki ARM SoC (System-on-a-chip) menggunakan SD card untuk megantikan hardisk dalam penyimpanan. Tel

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Indra, Dolly, Herman Herman, and Firman Shantya Budi. "Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision." Komputika : Jurnal Sistem Komputer 12, no. 1 (2023): 53–62. http://dx.doi.org/10.34010/komputika.v12i1.9082.

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The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehic

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Swinney, Carolyn, and John Woods. "Low-Cost Raspberry-Pi-Based UAS Detection and Classification System Using Machine Learning." Aerospace 9, no. 12 (2022): 738. http://dx.doi.org/10.3390/aerospace9120738.

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Small Unmanned Aerial Systems (UAS) usage is undoubtedly increasing at a significant rate. However, alongside this expansion is a growing concern that dependable low-cost counter measures do not exist. To mitigate a threat in a restricted airspace, it must first be known that a threat is present. With airport disruption from malicious UASs occurring regularly, low-cost methods for early warning are essential. This paper considers a low-cost early warning system for UAS detection and classification consisting of a BladeRF software-defined radio (SDR), wideband antenna and a Raspberry Pi 4 produ

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Mujahidin, Syamsul, Muhammad Insan Kamil, and Riska Kurniyanto Abdullah. "Pengembangan Deteksi Objek Dalam Rumah Bagi Tunanetra Berbasis Optimasi YOLOv8 Menggunakan Metode Ghost Module dan Attention Mechanism." JURNAL FASILKOM 14, no. 3 (2024): 646–53. https://doi.org/10.37859/jf.v14i3.8179.

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Penyandang tunanetra sering menghadapi kesulitan dalam mobilitas sehari-hari karena keterbatasan alat bantu yang tersedia saat ini. Meskipun tongkat khusus dapat membantu dalam berjalan, namun masih sulit bagi mereka untuk mendeteksi objek secara real-time. Kemajuan dalam pengenalan objek berbasis citra, terutama dengan penggunaan machine learning, menawarkan solusi yang menjanjikan. Untuk mewujudkan sistem pendeteksi objek yang efektif, diperlukan menjalankan deteksi objek pada perangkat kecil seperti Raspberry Pi 4. Perangkat tersebut ringan dan kompatibel untuk kebutuhan mobilitas tinggi se

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Vlakh-Vyhrynovska, Halyna, and Bohdan Boretskyi. "IMPLEMENTATION OF AN APACHE SPARK COMPUTING CLUSTER BASED ON RASPBERRY PI MICROCOMPUTERS." Measuring Equipment and Metrology 86, no. 2 (2025): 92–97. https://doi.org/10.23939/istcmtm2025.02.092.

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The paper presents the implementation of an Apache Spark distributed computing cluster based on Raspberry Pi microcomputers. The solution consists of three Raspberry Pi 4 devices (one master node and two worker nodes), each equipped with 8 GB of RAM and a high-speed network connection. The cluster configuration was optimized by adjusting the SPARK_WORKER_MEMORY and SPARK_WORKER_CORES parameters to maximize the use of available hardware resources. Secure communication between nodes was established through authentication using 4096-bit SSH keys. The functionality of the cluster was tested using

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Khaleel, Radhwan, and Nasseer Basheer. "Stand-Alone Melanoma Diagnostic System Using the Raspberry PI 4." European Journal of Computer Science and Information Technology 11, no. 6 (2023): 1–12. http://dx.doi.org/10.37745/ejcsit.2013/vol11n6112.

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Skin cancer, in general, is a growing threat to human health. Melanoma skin cancer is one of the most severe types of skin cancer in humans. In the initial stages, melanoma affects the skin in general, and it may develop and spread to other parts of the human body in its advanced stages. Early detection can reduce its severity and improve treatment outcomes. In this study, the proposed system includes image processing techniques and feature extraction by combining Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), in addition to enhancing the extracted features with the dia

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Lee, Seungjik Zaynidinov Hakimjon Yusupov Ibrohimbek Mamirov Xudoyberdi. "PROPOSAL OF SMART HOME SECURITY SYSTEM USING RASPBERRY PI 4." INTERNATIONAL BULLETIN OF APPLIED SCIENCE AND TECHNOLOGY 3, no. 6 (2023): 578–85. https://doi.org/10.5281/zenodo.8041126.

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IoT (Internet of Things) technology, the core technology of the 4<sup>th</sup> industrial revolution, is currently being applied to various fields. Among them, the most basic application is the security system. The security system automatically detects a moving object or person approaching a restricted area, and if the object or person moves, it can immediately take a picture and store it. By taking a picture and storing it, you can recognize what the moving object is and who it is if it is a person. You can open the door by checking who it is, and you can also find out what time someone came.

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Özcan, Recai, and Kemal Tütüncü. "Raspberry Pi ile Gerçek Zamanlı Bitki Algılama Uygulaması." International Conference on Recent Academic Studies 1 (May 12, 2023): 5–10. http://dx.doi.org/10.59287/icras.663.

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Elektronik ve bilgisayar teknolojilerinin gelişimine paralel olarak yapay zekâ uygulamaları birçok alanda gelişme imkânı bulmuştur. Bu alanlardan birisi de hassas tarımda yapay zekanın kullanımıdır. Yapay zekanın alt dalı olan derin öğrenme teknikleriyle güçlü donanıma sahip bilgisayarlar kullanılarak hassas tarım için birçok başarılı bilgisayarlı görü uygulamaları geliştirilmiştir. Ancak bu uygulamaların gerçek zamanlı çalışabilen bir robotik sisteme entegre edilmesi yüksek maliyet gerektirmektedir. Bu sebeple hassas tarıma yönelik gerçek zamanlı uygulamalar tasarlayabilmek ve robotik makinel

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Bansal, Aman, Anshu Sharaf, Anand Keshri, Abrar Alam, and Shruti Sharma. "FACIAL RECOGNITION DOOR LOCK USING RASPBERRY PI." International Journal of Technical Research & Science 6, no. 07 (2021): 23–29. http://dx.doi.org/10.30780/ijtrs.v06.i07.004.

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In today's world, we are confronted with security challenges on every level. As a result, we must use modern technologies to address these difficulties. The Face recognition module is being used in this project to capture human photos and compare them to photographs stored in a database. The detection of persons who enter or leave the house is the most crucial element of any home security system. Instead, then using passwords or pins to monitor this, unique faces can be used because they are a biometric property. We want to build this system on the Raspberry Pi 4 to make the house only accessi

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ANAM, FAJAR CHOIRUL, FAISAL SAMSURI, and JONI WELMAN SIMATUPANG. "Prototipe Chromameter untuk Deteksi Bumper Berbasis Raspberry Pi-4 dan Sensor AS7341." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 11, no. 3 (2023): 677. http://dx.doi.org/10.26760/elkomika.v11i3.677.

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ABSTRAKAda kebutuhan untuk peningkatan kualitas kontrol pengecekan akurasi warna bumper yang diproduksi di PT. Sugity Creatives. Kebutuhan ini disebabkan oleh berbagai pekerjaan repetitif, tidak efisien, dan relatif lama. Sesuai pengamatan, pengecekan membutuhkan waktu 45 detik, validasi data manual selama 15 detik dan pencatatan di kertas selama 60 detik. Sehingga total proses adalah 120 detik. Di artikel ini, kami mengusulkan sebuah prototipe menggunakan Konica Minolta CR400 chromameter. Prototipe ini terdiri dari dua komponen utama yaitu Raspberry Pi-4 sebagai pengontrol dan penyimpan data,

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Fahmi Anggarah. "Perancangan Aplikasi Sistem Pencarian Objek Menggunakan Algoritma Correlation Tracker Pada Pustaka DLIB." Ocean Engineering : Jurnal Ilmu Teknik dan Teknologi Maritim 1, no. 3 (2023): 102–10. https://doi.org/10.58192/ocean.v1i3.565.

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Pelacakan objek merupakan suatu proses untuk menentukan dan mengikuti posisi dari sebuah objek bergerak yang diinginkan menggunakan kamera secara realtime. Ada berbagai macam algoritma dalam pembuatan sistem pelacakan objek, salah satunya adalah algoritma correlation tracker pada pustaka DLIB. Sistem pelacakan objek ini menggunakan Raspberrry Pi 4 yang berisi program python untuk pelacakan objek. Pi Camera digunakan sebagai input gambar video realtime. Hasil penggunaan algoritma correlation tracker yang ada pada pustaka DLIB, yaitu proses pelacakan objek dapat berjalan dengan baik. Terdapat 5

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Bueno, Gloria, Lucia Sanchez-Vargas, Alberto Diaz-Maroto, et al. "Real-Time Edge Computing vs. GPU-Accelerated Pipelines for Low-Cost Microscopy Applications." Electronics 14, no. 5 (2025): 930. https://doi.org/10.3390/electronics14050930.

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Environmental microscopy is crucial for analyzing microorganisms, but traditional optical microscopes are often expensive, bulky, and impractical for field use. AI-driven image recognition, powered by deep learning models like YOLO, enhances microscopy analysis but typically requires high computational resources. To address these challenges, we present two cost-effective pipelines integrating AI with low-cost microscopes and edge computing. Both approaches use the OpenFlexure Microscope and Raspberry Pi devices. The first performs real-time inference with a Raspberry Pi 5 and Hailo-8L accelera

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Feng, Xiangsheng. "Gesture Recognition Control System Based on Raspberry PI." Journal of Physics: Conference Series 1744, no. 4 (2021): 042019. http://dx.doi.org/10.1088/1742-6596/1744/4/042019.

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Tasripan, Tasripan, Hendra Kusuma, and Alfian Nur Rafli Huzaini. "Design and Implementation of Environmental Warning Systems for Drivers with Hearing Impairments." European Journal of Electrical Engineering and Computer Science 8, no. 3 (2024): 7–13. http://dx.doi.org/10.24018/ejece.2024.8.3.621.

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Hearing impairment encompasses conditions where individuals experience difficulties in hearing, categorized into deafness and hard of hearing. The employment opportunities provided by online taxi companies in Indonesia, particularly for motor or car drivers, offer a sense of independence to those with hearing impairments. However, driving necessitates environmental awareness, especially of auditory signals. Hence, there's a need for a surrounding environment warning system to convert crucial sounds into visual alerts, focusing on sounds like sirens and railway crossing warnings. This study int

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Asten, Philipus. "Analisis Kinerja Mikrokomputer Raspberry Pi Pada Smart Greenhouse Berbasis Internet Of Things (IoT) Menggunakan Algoritma Naive Baye." Journal of Information and Technology 3, no. 2 (2023): 55–60. http://dx.doi.org/10.32938/jitu.v3i2.5231.

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Smart Greenhouse adalah sistem yang menggunakan teknologi Internet of Things (IoT) untuk mengontrol dan memantau lingkungan pertumbuhan tanaman secara otomatis. Dalam melakukan analisis kinerja mikrokomputer Raspberry Pi dalam Smart Greenhouse berbasis IoT, dengan menerapkan algoritma Naive Bayes untuk menganalisis data dan membuat keputusan berdasarkan kondisi lingkungan tanaman. Hasil analisis terhadap mikrokomputer Raspberry Pi pada Smart Greenhouse menggunakan algoritma Naive Bayes dapat digunakan untuk memprediksi variabel lingkungan, seperti suhu dan kelembapan, sesuai data yang diperole

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Jailani, Abdul Qadir, Toni Kusuma Wijaya, and Endang Susanti. "PERANCANGAN SISTEM MONITORING KEADAAN KEAMANAN RUMAH BERBASIS KAMERA RASPBERRY PI DAN PUSHBULLET." SIGMA TEKNIKA 3, no. 2 (2020): 180–87. http://dx.doi.org/10.33373/sigma.v3i2.2746.

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AbstarkSalah satu permasalahan di lingkungan perumahan yaitu pecurian terhadap rumah. Berbagai usaha dilakukan agar terhindar dari pencurian. Seperti memasang kamera pemantau di sekitar rumah. Namun usaha tersebut kurang maksimal di sebabkan tidak ada pemberitahuan secara langsung kepada pemilik rumah. Dalam penelitian ini, masalah tersebut dapat diselesaikan dengan merancang Sistem monitoring keadaan keamanan rumah berbasis kamera Raspberry pi dan pushbullet. Dengan memanfaatkan program motion yang ditulis dalam bahasa python sistem ini bekerja menangkap gambar, merekam video dan mengirimkan

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Mahfud, Mahfud, Indra Maulana, and Muhammad Syafri Syamsudin. "RANCANG BANGUN SISTEM PEMBELAJARAN JARAK JAUH UNTUK SEKOLAH MENENGAH KEJURUAN BERBASIS SERVER PORTABLE RASPBERRY Pi 4." PERISKOP : Jurnal Sains dan Ilmu Pendidikan 1, no. 1 (2020): 1–13. http://dx.doi.org/10.58660/periskop.v1i1.7.

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Learning media is a means of communication in the teaching and learning process in the form of hardware and software to achieve learning processes and outcomes effectively and efficiently, and can overcome the limitations of learning space and time. The purpose of this research is to design and produce a distance learning system product using raspberry pi as a portable server. The method used is the method of research and development (Research and Development) with the technique of observation, interviews and literature studies that are tailored to the needs of learning media in SMK Negeri 1 C

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Hartono, Hana Via Eka Amalia, Martono Dwi Atmadja, and M. Abdullah Anshori. "Perbandingan Performansi Mini PC 1.2 GHz dan 1.5 GHz sebagai Server Layanan Video Call menggunakan Codec H264." Jurnal Jartel: Jurnal Jaringan Telekomunikasi 10, no. 2 (2020): 118–23. http://dx.doi.org/10.33795/jartel.v10i2.93.

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Permasalahan yang ada pada server untuk layanan video call yaitu penggunaan server biasanya menggunakan PC yang telah terinstall FreePBX dan diperlukan beberapa perangkat keras yang harus disediakan seperti CPU dengan spesifikasi memory yang digunakan 312 MB, CPU 800 MHz Pentium III. Terdapat alternatif untuk membangun server dengan spesifikasi yang lebih baik dari CPU Konvensional dalam menunjang layanan video call dengan menggunakan Mini PC. Salah satu jenis Mini PC yang populer yaitu Raspberry Pi. Berdasarkan hasil penelitian perbandingan dua server Mini PC mampu melayani kapasitas pelangga

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Hausherr, Daniel Pascal, and Dirk Berben. "(All-in-One) Power Supply System for Mobile and Network-Wired Raspberry Pi-Based Internet of Things Applications." Hardware 1, no. 1 (2023): 54–69. http://dx.doi.org/10.3390/hardware1010005.

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Internet of Things (IoT) applications based on the single-board computer Raspberry Pi depend on reliable and sufficiently powerful energy systems to allow system diversity, flexibility, and available computing power. On this account, we developed an all-in-one module to simplify both the autonomous and network-wired power supply of Raspberry Pi-based systems. The module generates a stable voltage of 5.1 VDC with an available maximum current of 3 A to power a single Raspberry Pi model 3B+ or 4 and furthermore provides an additional power source of 3 VDC–12 VDC at a maximum of 3 A for use by arb

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Musthofa, Achmad Malikur Robbani, and Widi Aribowo. "Rancang Bangun Sistem Kontrol dan Monitoring Lampu Penerangan Jalan Umum (PJU) Menggunkan Yolo dan Node-red." JURNAL TEKNIK ELEKTRO 14, no. 1 (2024): 95–101. https://doi.org/10.26740/jte.v14n1.p95-101.

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Sistem kontrol dan monitoring Penerangan Jalan Umum (PJU) bertujuan untuk mengoptimalkan penggunaan energi dan meningkatkan keamanan lingkungan. Tugas akhir ini meliputi sistem otomatisasi PJU menggunakan YOLOv5 untuk deteksi manusia dan ESP32 sebagai mikrokontroler yang mengendalikan relay 4-channel untuk mengatur lampu LED. Web kamera dan kamera Raspberry Pi digunakan untuk deteksi secara real-time, dengan Raspberry Pi 3 sebagai pusat kontrol yang menjalankan YOLOv5 dan Node-RED untuk mengelola alur kerja dan antarmuka pengguna. Hasil penelitian menunjukkan bahwa sistem mampu menyesuaikan ju

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Ospanova, A., and B. Tuleuov. "Perspectives of use of microcomputer Raspberry Pi in effective Kazakhstan digitalization." BULLETIN of the L.N. Gumilyov Eurasian National University. MATHEMATICS.COMPUTER SCIENCE. MECHANICS Series 125, no. 4 (2018): 95–107. http://dx.doi.org/10.32523/2616-7182-2018-125-4-95-107.

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