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Journal articles on the topic 'Raspberry Pi 4'

Author: Grafiati

Published: 28 June 2021

Last updated: 25 April 2022

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1

Mulyanto, Trio Ade, Mukhtar Habiby, Kusnadi Kusnadi, and Rinaldi Adam. "HOME AUTOMATION SYSTEM DENGAN MENGGUNAKAN RASPBERRY PI 4." Jurnal Digit 11, no. 1 (May 30, 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 rumah dengan memanfaatkan fasilitas GPIO (General Purpose Input Output). Perangkat elektronika yang dapat di control meliputi lampu, kipas angin, suhu ruangan, kamera pengintai dan masih banyak lagi. Perancangan sistem meliputi perancangan alat untuk sistem kontrol arus di mana mengunakan Raspberry Pi untuk mengontrol 6 buah lampu, kipas sensor suhu, sensor motion dan 1 buah adaptor atau kontak saklar dengan memanfaatkan Relay 8 chanel sebagai penghubung dan juga sebagai saklar on / off. Tahap implementasi dilakukan dengan mengambungkan perangkat lunak dan perangkat keras untuk menghasilkan sistem control dengan menghubungkan web server sebagai media interface dan yang terakhir uji coba sistem yang di bangun. Sistem yang di bangun akan menjadi sistem kontrol perangkat elektronik rumah dengan implementasinya menyalakan dan mematikan lampu, mengecek suhu ruangan ,menyalakan kipas dan monitoring keadaan rumah dengaan memanfaatkan web server yang di tanam di dalam raspberry pi sebagai media interface user berupa web yang akan di akses user mengunakan smartphone atau komputer. Dengan ini sistem yang di bangun akan sangat membantu pengguna dalam melakukan pengontrolan perangkat elektronik rumah dimanapun pengguna berada dan menghemat daya listrik. Kata Kunci : Home Automation, Smarthome, Raspberry Pi.

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2

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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3

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 (July 1, 2021): 042101. http://dx.doi.org/10.1088/1742-6596/1964/4/042101.

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4

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 (April 30, 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, Arduino uno , motor driver and pi -cam. The analysis of image will be done using image processing through raspberry pi and for edge detection canny edge algorithm is used. Keywords: OpenCV, Raspberry pi 4, Pi-Cam, Canny Edge Detection, Image Processing.

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5

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 (December 30, 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 Neural Network (LCNN), maka perlu dilakukan percobaan untuk menguji seberapa besar perbedaan kinerja LCNN pada Personal Computer (PC) dan pada mikrokontroler raspberry pi 4 dengan sistem operasi Raspbian. Eksperimen dilakukan dengan menggunakan beberapa parameter kinerja yaitu accuracy, F-1 Score, recall, precision, dan waktu dari pengujian klasifikasi untuk mendapatkan hasil performa dari pembelajaran mendalam. Oleh karena itu, hasil dan arsitektur model akan mengkonfirmasi perbedaan kinerja di masing-masing perangkat dan menunjukkan bagaimana performa model pada perangkat yang dibatasi sumber daya atau berjalan secara waktu nyata. Pengujian menunjukkan bahwa kinerja pada raspberry pi yang merupakan alat dengan sumber daya terbatas tidak mempengaruhi kualitas pengenalan gambar, tetapi mempengaruhi waktu pemrosesan pengenalan, dikarenakan raspberry pi membutuhkan waktu proses yang lebih lama untuk melakukan satu proses pengenalan data atau foto. Hal tersebut akan mengakumulasi waktu yang dibutuhkan untuk pemrosesan data yang banyak, sehingga dapat disimpulkan bahwa raspberry pi dan alat dengan sumber daya terbatas sangat tidak efektif untuk melakukan pelatihan pengenalan dan melakukan proses pengenalan yang berisi banyak data atau foto dalam sekali prosesnya.

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6

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 (February 1, 2021): 012081. http://dx.doi.org/10.1088/1757-899x/1055/1/012081.

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7

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 (June 3, 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 fungsi Voice Assistant sebagai Home Automation. Dimana masing-masing pengujian tersebut memiliki tahapan yang berbeda-beda tergantung dengan apa yang akan diuji. Kemudian didapatkan hasil bahwa tampilan grafis Smart Mirror, fungsi sensor pendukung, fungsi komponen, dan fungsi Voice Assistant sebagai Home Automation dapat berfungsi dengan baik. Namun, pada pengukuran kelembapan udara terdapat kesalahan dimana data yang terukur tidak terbaca. Untuk durasi lamanya waktu dari beban merespon adanya perintah. Masing-masing beban yang digunakan pada penelitian ini dapat berhasil merespon adanya perintah dengan durasi waktu antara 5 – 8,5 detik. Dimana, beban yang paling cepat merepon perintah ialah lampu merk Bardi dan beban yang paling lama untuk merespon perintah ialah saklar 2.

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8

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 (April 1, 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 central processing unit (CPU) from the Raspberry Pi 4. The object detection processing on video data is complete at 1871.408 seconds with 1.474 FPS using the CPU from the Raspberry Pi 4 and finished at 1477.141 seconds with 1.868 FPS using Movidius NCS 2. From these differences, it can be seen that the application of Movidius NCS 2 succeeded in increasing the object detection processing in this study by 26.69% with the YOLOv3-tiny model approach on the Raspberry Pi 4 embedded system.

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9

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 (July 6, 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 cluster system is built using five raspberry Pi type 4B modules as cluster nodes, and the Web server system is built using docker container virtualization technology. Meanwhile, cluster management uses Docker Swarm technology. Performance testing (Quality of Service) of the cluster system is done by simulating a number of loads (requests) and measuring the response of the system based on the parameters of Throughput and Delay (latency). The test results show that the Raspberry Pi Cluster system using Docker Swarm can be used to build a High Availability Server system that is able to handle very high requests that reach Throughput = 161,812,298 requests / sec with an Error rate = 0%.

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10

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 (January 10, 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 and visualization services.

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11

M. Hodgess, Erin. "High Performance Computing on the Raspberry PI." International Journal of Computational Science and Information Technology 10, no. 1 (February 28, 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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12

Feng, Xiangsheng. "Gesture Recognition Control System Based on Raspberry PI." Journal of Physics: Conference Series 1744, no. 4 (February 1, 2021): 042019. http://dx.doi.org/10.1088/1742-6596/1744/4/042019.

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13

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 (August 21, 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 capable of controlling 4 AC voltage electronics as well as 4 relays with each relay capable of bearing a maximum load of 2200 watts using a power supply on the Raspberry Pi which has a minimum of 0.7 amperes and Control of electrical load can be done within a distance of 0 meters - 15 meters from the wireless router

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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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15

Novikov, D. V., A. S. Stankevich, E. G. Silkis, A. M. Torubarov, and G. A. Perepelkin. "THE MORS-4 SPECTRA RECORDING SYSTEM WITH THE RASPBERRY PI 3 MODEL B MICROCOMPUTER." NAUCHNOE PRIBOROSTROENIE 28, no. 3 (August 29, 2018): 24–28. http://dx.doi.org/10.18358/np-28-3-i2428.

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16

Ramadhan, Lies Teddy Galang, Florentinus Budi Setiawan, Slamet Riyadi, and Leonardus Heru Pratomo. "Implementasi Object Tracking untuk Deteksi Titik Laser Menggunakan Raspberry Pi 4." SISTEMASI 10, no. 2 (May 30, 2021): 423. http://dx.doi.org/10.32520/stmsi.v10i2.1288.

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17

Chlastawa, Łukasz. "Mobile image processing system based on the Raspberry Pi 4 platform." Science, Technology and Innovation 12, no. 1 (August 13, 2021): 16–25. http://dx.doi.org/10.5604/01.3001.0015.2458.

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The article presents an image processing system based on the Raspberry Pi (RPi) platform. At the beginning of the article, the basic assumptions and purpose of the system are discussed. The following section presents the structure and operation of the system. The window application managing the system and allowing to perform contextual and spectral transformations on images as well as the measurement of parameters such as image processing time and mean square error (MSE) was discussed. The transformations performed were based both on ready formulas contained in the OpenCV library and the author's implementations, including the function implementing the Fast Fourier Transform algorithm radix-2. Examples of transformations were presented along with their usefulness. In the end, the development potential of the created system is presented and its application in specific solutions is proposed.

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Aravinda, N. L., Mohammad Jabirullah, and DubasiKirtana. "An Intelligent helmet system using IoT and Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042076. http://dx.doi.org/10.1088/1757-899x/981/4/042076.

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19

Libertin, Donalson. "Sistem Pemantauan Ruangan Laboratorium Dengan Raspberry Pi Camera." ELECTRICES 2, no. 1 (May 22, 2020): 11–16. http://dx.doi.org/10.32722/ees.v2i1.1960.

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In the Electrical Engineering Laboratory 2nd Floor room, there is a variety of equipment, but the equipment is often lost. So the room needed a monitoring system to see the activity in the room to minimize theft. The purpose of this study is to design a monitoring system using Raspberry Pi Zero W as a link between the Raspberry Pi Camera and the admin. Room conditions can be monitored online and in realtime on the Web. Based on the results of testing video streaming displayed on the Web, there is a delay of 3-4 seconds so that the image moves slower than the actual situation. This device can send a notification of the condition of the room and pictures at 12: 00-13: 00 when there is movement or someone doing activities in the room. Data is sent via email and google drive @sctolipnj.

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20

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 (May 30, 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 pelanggan pada resolusi video untuk codec H264. Dua server Mini PC mampu melayani komunikasi video call sebanyak 10 client dengan penggunaan CPU dan RAM pada Raspberry Pi 4 dibandingkan Raspberry Pi 3 dengan rata-rata penggunaan CPU 39.8% dan RAM Usage 282 MB dilihat menggunakan perintah HTOP pada putty. Penentuan jumlah kapasitas pengguna ditentukan berdasarkan jumlah client yang berkomunikasi secara bersamaan, dan penggunaan CPU serta RAM pada Mini PC.

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Swapna, V., and R. Dr Arun Prasath. "RF-Automatic Traffic Clearance System for Ambulance using Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042006. http://dx.doi.org/10.1088/1757-899x/981/4/042006.

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Subramanya Chari, K., Maturi Thirupathi, and Ch Hariveena. "IoT-based Flood Monitoring and Alerting System using Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042078. http://dx.doi.org/10.1088/1757-899x/981/4/042078.

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23

Rakesh, Pasuladi, and I. V. Mr Prakash. "Raspberry Pi based E–Health System over Internet of Things." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042008. http://dx.doi.org/10.1088/1757-899x/981/4/042008.

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24

Radhika, Kashaboina, and Ramasamy Dr Velmani. "Bluetooth and GSM based Smart Security System using Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042009. http://dx.doi.org/10.1088/1757-899x/981/4/042009.

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25

Abadi, S. C., M. Eriyadi, D. Usman, Y. M. Hamdani, and A. Suryadi. "Raspberry Pi based SCADA system using Codesys for workshop facilities." IOP Conference Series: Materials Science and Engineering 1098, no. 4 (March 1, 2021): 042076. http://dx.doi.org/10.1088/1757-899x/1098/4/042076.

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26

Latifah, A., W. Ramdhani, M. R. Nasrulloh, and R. Elsen. "Ultrasonic sensor for monitoring corn growth based on Raspberry Pi." IOP Conference Series: Materials Science and Engineering 1098, no. 4 (March 1, 2021): 042087. http://dx.doi.org/10.1088/1757-899x/1098/4/042087.

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27

Atiqur, Rahman, and Yun Li. "Automated smart car parking system using raspberry Pi 4 and iOS application." International Journal of Reconfigurable and Embedded Systems (IJRES) 9, no. 3 (November 1, 2020): 229. http://dx.doi.org/10.11591/ijres.v9.i3.pp229-234.

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In interconnection and automation of different physical gadgets, vehicles, home machines and different things, the internet of things (IoT) innovation plays a critical role.These objects associate and deal information with the assistance of software, different sensors, and actuators. A human's standard of life and living are improved with this automation of gadgets, which is a forthcoming need. In this paper we talked about a similar requirement for instance, a smart car parking system which empowers a driver to discover a parking area and a free slot in that parking area inside a city. This paper focus on drcreasing the time squandered on discovering parking area. This in turn diminishes the fuel utilization and way of life. With the exponential increment in the quantity of vehicles and total population, vehicle accessibility, use out, about starting late, finding a space for parking the vehicle is turning out to be increasingly more troublesome with realizing the amount of conflicts, for example, automobile overloads. This paper is connected to making a trustworthy system that accept authority over the undertaking of recognizing free slots in a parking area and keeping the record of vehicles left in an extremly methodical way. The predicted system decreases human effort at the parking area generally, for example, in case of looking of free slots by the driver and calculating the portion for each vehicle using parking area. The different advances engaged with this system are vehicle unique proof utilizing RFID labels; free slot discovering utilizing Ultrasonic sensors and payment count is done based on time of parking.

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Sagala, Albert, Deni Parlindungan Lumbantoruan, Epelin Manurung, Iroma Situmorang, and Adi Gunawan. "Secured Communication Among HMI and Controller using RC-4 Algorithmand Raspberry Pi." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 3 (September 1, 2015): 526. http://dx.doi.org/10.11591/tijee.v15i3.1571.

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In the beginning the implementation of a plant with SCADA technology, the network is formed isolated from the outside network (LAN or Internet). So it can be ascertained that the communication that occurs on the SCADA network is safe from the threat of crackers. In fact, SCADA network allows it to be connected to the Internet, so that the data of the plant can be monitored via the Internet, so the information about the state of the plant can be monitored in realtime and can be taken quickly if it is known there are anomalies on the control system. In the research we designed a method of encryption and decryption against the lines of communication between the HMI (Human Machine Interface) and the Controller on an industrial minipant contained in Lab CSRC IT Del. Raspberry Pi is used as a gateway between the HMI and the Controller. While Algorithm RC 4 are used as the algorithm for encrypting data between the HMI and the Controller. In the results, we can use the Rapsberry Pi to secure the communication between the HMI and Controller.

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Pramudya Istiqfariandi, Dewa, Gunawan Gunawan, Alifia Azzahra, Krisna Krisna, and Mumtaz Rahmawan. "Pengembangan Visibel yang Mampu Membantu Penyandang Tunanetra Melaksanakan Kegiatan." Jurnal Health Sains 2, no. 10 (October 21, 2021): 1858–69. http://dx.doi.org/10.46799/jsa.v2i10.316.

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Indera penglihatan menjadi salah satu komponen paling penting pada tubuh yang dapat mengatur setiap kegiatan yang akan dijalankan seseorang secara tepat dan cepat. Namun sayangnya, tidak semua orang memiliki kesempatan untuk memiliki anggota tubuh yang lengkap, termasuk juga pada penglihatannya. artikel ini menyajikan desain baru kacamata pintar bantu untuk siswa tunanetra. Tujuannya adalah untuk membantu dalam beberapa tugas sehari-hari menggunakan keunggulan format desain yang dapat dikenakan. Sebagai pembuktian konsep, artikel ini hanya menyajikan satu contoh aplikasi, yaitu teknologi pengenalan teks yang dapat membantu membaca dari bahan hardcopy. Terdapat 5 tahapan metode peneilitan kami: 1) Studi Literatur 2) Identifikasi Masalah 3) Perancangan Sistem 4) Perancangan alat 5) Pengujian alat komputer saat ini merupakan bidang penelitian yang penting. Ini mencakup metode seperti akuisisi citra, pemrosesan, analisis, dan pemahaman, Visibel terdiri dari input, kontroler, dan output. Visibel dikendalikan oleh Mikrokontroler Raspberry Pi dengan menerima citra dari stereo camera yang terintegrasi oleh dua buah sensor kamera yang terpisah sebagai input. Selanjutnya, gambar input tersebut diproses dalam Raspberry Pi. Terdapat beberapa tahapan proses hingga didapatkannya prediksi jarak. Proses pertama adalah konversi luminance, yakni mengubah format gambar dari RGB (Red, Green, Blue) menjadi skala gambar Grayscale. Selanjutnya tahapan pre-processing, gambar diproses dengan meminimalisir noise menggunakan Filter Gaussian. Dari hasil penelitian Selanjutnya ialah skema rangkaian alat. Visibel dikendalikan oleh Mikrokontroler Raspberry Pi dengan menerima citra dari stereo camera yang terintegrasi oleh dua buah sensor kamera yang terpisah sebagai input. Selanjutnya, citra stereo tersebut diproses dalam Raspberry Pi 4. Citra stereo diproses hingga didapatkannya prediksi jarak, lalu kemudian diubah menjadi binaural audio sebagai output yang akan keluar melalui earphone. Kemudian terdapat power button sebagai tombol power, sensitivity power, serta volume button juga diproses dalam mikrokontroller Raspberry PI 4. Di sini dapat kita lihat ilustrasi operasi kerja dari Visibel di mana alat ini memiliki ruang deteksi. Stereo camera akan berperan untuk mengambil data jarak dan arah yang ada di sekitar tunanetra seperti pohon, dinding, orang lain ataupun gundukan. suara binaural yang keluar melalui earphone memiliki frekuensi tertentu. Dimana frekuensi dari suara tersebut bergantung pada jarak halangan, semakin dekat halangan maka semakin cepat pula bitnya. Disinilah perspektif tentang jarak pada halangan itu bekerja. Bila dilihat lagi terdapat 3 halangan, pada jarak 3 meter, 4 meter dan 5 meter. Halangan terdekat akan diprioritaskan dalam pembuatan suara binaural dengan meningkatkan volumenya sehingga pengguna dapat terlebih dahulu menghindari halangan terdekat

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Pratama, Septian Angga. "REALISASI ALAT UKUR SUHU DAN KELEMBABAN BERBASIS RASPBERRY PI." JTT (Jurnal Teknologi Terpadu) 7, no. 1 (April 26, 2019): 62–65. http://dx.doi.org/10.32487/jtt.v7i1.636.

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Suhu dan kelembapan merupakan salah satu parameter yang paling sering diukur. Pengukuran terhadap parameter suhu dan kelembapan sangat berguna untuk mempelajari sebuah proses fisika, kimia, atau, biologi. Pada beberapa dekade terakhir, pemanasan global atau global warming menjadi isu global terkait lingkungan hidup dimana pencemaran dan kerusakan terhadap lingkungan menjadi faktor penyebab tingginya suhu bumi. Oleh karena itu fokus utama penelitian ini adalah mengenai pengukuran suhu dan kelembapan udara pada suatu ruangan dengan dimensi 15 x 10 m apakah perlu dengan adanya tambahan pendingin pada ruangan tersebut atau tidak untuk mengetahui tingkat serta tingkat linearitas sensor DHT 11 yang akan digunakan untuk mengukur dua parameter sekaligus, dengan suatu mikrokontroller berupa Raspberry Pi. Pengukuran dilakukan dengan cara mengambil 24 sampel selama 4 jam hasil yang ditampilkan dari monitor yang terhubung dari Raspberry Pi menunjukan bahwa sensor DHT 11 tidak linier yaitu suhu R2 = 0.8221, sedangkan kelembaban R2 = 0.8893 dikarenakan adanya pengaruh suhu dari luar ruangan dan dalam ruangan serta adanya pengaruh dari kuantitas manusia yang berada di dalam ruangan tersebut.

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Belcore, E., M. Piras, A. Pezzoli, G. Massazza, and M. Rosso. "RASPBERRY PI 3 MULTISPECTRAL LOW-COST SENSOR FOR UAV BASED REMOTE SENSING. CASE STUDY IN SOUTH-WEST NIGER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 207–14. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-207-2019.

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<p><strong>Abstract.</strong> The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors have kept up with it, providing more efficient and effective solutions. One of the most sought-after characteristics of on-board sensors is the low costing associated to good quality of the collected data. This paper proposes a very low-cost multiband sensor developed on a Raspberry device and two Raspberry Pi 3 cameras that can be used in photogrammetry from drone applications. The UAV-integrated radiometric sensor and its performance were tested in in two villages of South-west Niger for the detection of temporary surface water bodies (or Ephemeral water bodies): zones of seasonal stagnant water within villages threatening the viability and people’s health. The Raspberry Pi 3 cameras employed were a regular RGB Pi camera 2 (Red, Green, Blue) and a NoIR Pi 3 camera v2 (regular RGB without IR filter) with 8MPX resolution. The cameras were geometrically calibrated and radiometrically tested before the survey in the field. The results of the photogrammetry elaborations were 4 orthophotos (a RGB and NoIRGB orthophoto for each village). The Normalized Difference Water Index (NDWI) was calculated. The index allowed the localization and the contouring of the temporary surface water bodies present in the villages. The data were checked against the data collected with a Sony (ILCE-5100). Very high correspondence between the different data was detected. Raspberry-based sensors demonstrated to be a valid tool for the data collection in critical areas.</p>

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Tarek, Hesham, Hesham Aly, Saleh Eisa, and Mohamed Abul-Soud. "Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment." Electronics 11, no. 1 (January 3, 2022): 140. http://dx.doi.org/10.3390/electronics11010140.

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Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4.

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Et.al, Teddy Surya Gunawan. "Development of Digital Signage for Primary School using Raspberry Pi." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 1394–99. http://dx.doi.org/10.17762/turcomat.v12i3.911.

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Nowadays, digital signage is a modern advertisement alternative that took over billboards and other traditional advertisements. In the context of education, digital signage provides students, teachers, and parents with an effective way of communication. This research proposed designing and developing low-cost digital signage using Raspberry Pi to be stationed at primary school. An LCD monitor and Raspberry Pi 4 single-board computer were utilized to display various informative content, such as examination date, daily schedule, and other announcements. Two methods were proposed, including Screenly and WordPress on a web server. The WordPress version needs to install PHP, MySQL, and Nginx web server, which can be accessed and updated remotely. Results showed that the free version of Screenly would be adequate for a simple announcement, but the developed WordPress version will be more appropriate and flexible for the primary school purpose.

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Nirosha, G., and Ramasamy Dr Velmani. "Raspberry Pi based Sign to Speech Conversion System for Mute Community." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042005. http://dx.doi.org/10.1088/1757-899x/981/4/042005.

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Pamulaparthy, Manogna, and K. Jeevana Jyothi. "Autonomous Smart Energy Meter over Internet of Things using Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042012. http://dx.doi.org/10.1088/1757-899x/981/4/042012.

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Jabirullah, Mohammad, M. Amru, and D. Raviteja. "IoT based Child Safety Management using Raspberry Pi and RFID Technology." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042079. http://dx.doi.org/10.1088/1757-899x/981/4/042079.

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Amru, M., Allam Venkata Naga Mahesh, and Potharaju Ramesh. "IoT-based Health Monitoring System with Medicine Remainder using Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042081. http://dx.doi.org/10.1088/1757-899x/981/4/042081.

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Shalini, Ch, and I. V. Mr Prakash. "Iot based Industrial Sensor Monitoring and Alerting System using Raspberry Pi." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042010. http://dx.doi.org/10.1088/1757-899x/981/4/042010.

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Wisnudhanti, Kartika, and Feri Candra. "Image Classification of Pandawa Figures Using Convolutional Neural Network on Raspberry Pi 4." Journal of Physics: Conference Series 1655 (October 2020): 012103. http://dx.doi.org/10.1088/1742-6596/1655/1/012103.

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S, Priyadharshini, Natarajan S, and Kavitha S. "USB Device Driver Development of Image Processing Using Raspberry Pi 4 via Thingspeak." International Journal of Electronics and Communication Engineering 8, no. 6 (June 25, 2021): 1–3. http://dx.doi.org/10.14445/23488549/ijece-v8i6p101.

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YOSHIGA, Kousuke, Nobuyasu TOMOKUNI, and Noriho KOYACHI. "Measuring the environment with a depth camera using Raspberry Pi 4 and ROS2." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 1P2—G02. http://dx.doi.org/10.1299/jsmermd.2021.1p2-g02.

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Gunawan, Muhammad Agung, Suko Wiyanto, and Fajar Kholid. "PENERAPAN METODE BACK PROPAGATION PADA RASPBERRY PI 4 UNTUK MENGENAL SUARA TEMBAKAN SENJATA RINGAN SS2- V1." Jurnal Elkasista 2, Mei (May 31, 2021): 34–39. http://dx.doi.org/10.54317/elka.v2imei.158.

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Perkembangan ilmu pengetahuan dan teknologi saat ini telah mengalami kemajuan pesat yang berpengaruhi terhadap kemajuan sistem pertahanan termasuk dalam sistem operasional di satuan TNI-AD. Penggunaan sistem pertahanan senjata biasanya digunakan untuk mendeteksi sumber suara letusan atau tembakan. Penerapan metode back propagation pada raspberry dengan menggunakan sensor microphone boya diharapkan mampu membantu personel yang bertugas untuk mendeteksi sumber suara letusan tembakan. Sensor merupakan alat yang berfungsi untuk mendeteksi adanya perubahan lingkungan secara fisik yang digunakan untuk berbagai kebutuhan sesuai dengan spesifikasi alat yang diinginkan. Penelitian ini menggunakan metode fft yang dapat mengubah time domain menjadi frekuensi domain dengan cepat. Metode back propagation berfungsi untuk mengenali bobot atau presentase jenis suara tembakan SS2. Sensor mikrofon menerima suara tembakan yang nantinya dengan penggunaan raspberry pi 4 akan menghasilkan output yang mempresentasikan suara senjata ringan SS2- V1 pada layar lcd dan speaker. Output yang didapat berupa arah suara tembakan dan jenis suara tembakan.

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Meivel, S., Nidhi Sindhwani, Rohit Anand, Digvijay Pandey, Abeer Ali Alnuaim, Alaa S. Altheneyan, Mohamed Yaseen Jabarulla, and Mesfin Esayas Lelisho. "Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm." Computational Intelligence and Neuroscience 2022 (February 1, 2022): 1–13. http://dx.doi.org/10.1155/2022/2103975.

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The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

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Chen, Pei-Jarn, Tian-Hao Hu, and Ming-Shyan Wang. "Raspberry Pi-Based Sleep Posture Recognition System Using AIoT Technique." Healthcare 10, no. 3 (March 11, 2022): 513. http://dx.doi.org/10.3390/healthcare10030513.

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The relationship between sleep posture and sleep quality has been studied comprehensively. Over 70% of chronic diseases are highly correlated with sleep problems. However, sleep posture monitoring requires professional devices and trained nursing staff in a medical center. This paper proposes a contactless sleep-monitoring Internet of Things (IoT) system that is equipped with a Raspberry Pi 4 Model B; radio-frequency identification (RFID) tags are embedded in bed sheets as part of a low-cost and low-power microsystem. Random forest classification (RFC) is used to recognize sleep postures, which are then uploaded to the server database via Wi-Fi and displayed on a terminal. The experimental results obtained using RFC were compared to those obtained via the support vector machine (SVM) method and the multilayer perceptron (MLP) algorithm to validate the performance of the proposed system. The developed system can be also applied for sleep self-management at home and wireless sleep monitoring in medical wards.

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Rodríguez-Orozco, Eduardo, Enrique García-Guerrero, Everardo Inzunza-Gonzalez, Oscar López-Bonilla, Abraham Flores-Vergara, Jose Cárdenas-Valdez, and Esteban Tlelo-Cuautle. "FPGA-based Chaotic Cryptosystem by Using Voice Recognition as Access Key." Electronics 7, no. 12 (December 9, 2018): 414. http://dx.doi.org/10.3390/electronics7120414.

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A new embedded chaotic cryptosystem is introduced herein with the aim to encrypt digital images and performing speech recognition as an external access key. The proposed cryptosystem consists of three technologies: (i) a Spartan 3E-1600 FPGA from Xilinx; (ii) a 64-bit Raspberry Pi 3 single board computer; and (iii) a voice recognition chip manufactured by Sunplus. The cryptosystem operates with four embedded algorithms: (1) a graphical user interface developed in Python language for the Raspberry Pi platform, which allows friendly management of the system; (2) an internal control entity that entails the start-up of the embedded system based on the identification of the key access, the pixels-entry of the image to the FPGA to be encrypted or unraveled from the Raspberry Pi, and the self-execution of the encryption/decryption of the information; (3) a chaotic pseudo-random binary generator whose decimal numerical values are converted to an 8-bit binary scale under the VHDL description of m o d ( 255 ) ; and (4) two UART communication algorithms by using the RS-232 protocol, all of them described in VHDL for the FPGA implementation. We provide a security analysis to demonstrate that the proposed cryptosystem is highly secure and robust against known attacks.

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Florentinus Budi Setiawan, Franciska Amalia Kurnianingsih, Slamet Riyadi, and Leonardus Heru Pratomo. "Pattern Recognition untuk Deteksi Posisi pada AGV Berbasis Raspberry Pi." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 10, no. 1 (February 25, 2021): 49–56. http://dx.doi.org/10.22146/jnteti.v10i1.738.

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Perkembangan teknologi di bidang otomatisasi dan robotika berkembang sangat pesat, karena memiliki tingkat efesiensi tinggi dari segi tenaga dan waktu. Pada sistem pergudangan, salah satu robot yang digunakan adalah Automated Guided Vehicle (AGV). AGV adalah alat transportasi berupa robot yang dikendalikan secara otomatis, yang berfungsi sebagai pengangkut barang, dengan menggunakan sistem navigasi agar bergerak ke arah yang telah ditentukan. Salah satu sistem navigasi AGV yang telah ada ialah dengan mengikuti pola garis pada lantai. Sistem tersebut kurang efisien karena lambat laun pola garis tersebut akan pudar dan tidak dapat terdeteksi kembali akibat gaya gesek dari roda AGV itu sendiri. Oleh karena itu, sangat diperlukan pengembangan sistem navigasi AGV untuk meminimalkan hambatan tersebut. Sistem pattern recognition ini menggunakan pola yang diletakkan pada langit-langit bangunan dan kamera sebagai sensor yang menghadap ke atas sehingga AGV mampu dengan leluasa mendeteksi pola. Kemudian, pola yang sudah terdeteksi diolah melalui perangkat komputer berupa Raspberry Pi 4 Model B yang telah diprogram. Hasil pengujian menunjukkan bahwa sistem ini mampu mendeteksi posisi dan berhasil menampilkan titik koordinat (x,y) dari AGV serta akan tetap berjalan sampai kapan pun hingga program diubah sesuai yang diperintahkan.

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Barida Baah, Onate Egerton Taylor, and Chioma Lizzy Nwagbo. "A novel approach for federated machine learning using Raspberry Pi." Global Journal of Engineering and Technology Advances 6, no. 3 (March 30, 2021): 063–68. http://dx.doi.org/10.30574/gjeta.2021.6.3.0042.

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The problems of privacy and security is becoming a major challenge when it comes to the distributed systems, federated machine learning system especially when data are been transmitted or learned on a network , this necessitated the reasons for this research work which is all about wireless federated machine learning process using a Raspberry Pi. The Raspberry Pi 4 is a single hardware board with built in Linux operating system. We used data set of names from nine (9) different languages and then develop a training model using recurrent neural network to train this names compare to the names in the existing language like French, Scottish to predict if the names are from any of this language, this is done wirelessly with the Wi-Fi network in a federated machine learning environment for experimental setup with PySft’s that is installed in the python environment. The system was able to predict that name from which the language it originate from, the methodology that is implore in the research work is the Rapid Application Development (RAD). The benefits of this system are to ensure privacy, reduces the computing power, ensure real time learning and most importantly it is cost effective.

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Ya Suranov, A. "Raspberry Pi-based mobile system of the pupil size evaluation." Journal of Physics: Conference Series 2142, no. 1 (December 1, 2021): 012021. http://dx.doi.org/10.1088/1742-6596/2142/1/012021.

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Abstract The article considers development of a human pupil measuring system on the Raspberry platform. The system is aimed at evaluating the variations of the human pupil diameter or area in the process of watching test images or video recordings. To reduce the interference from the eye surface the camera uses a band-stop color glass filter PS 13. In order to increase the pupil image contrast, IR LED backlighting of the eye is implemented. To provide the mobility of the system, battery power of the single-board computer was used while the registered image and the measurement results were transmitted via a Wi-Fi channel. The video camera and the single-board computer Raspberry Pi 4 with the battery bay are attached to the head-mounted flexible belt. The article gives the operation duration evaluation of the battery-supplied system. During the first stage of image processing, binarization by the threshold was performed. The threshold is determined by the image brightness histogram. Since the study is focused on relative variations of the pupil size, the pupil diameter or area values in pixels were chosen as informative parameters. The image recording and processing frequency in the system equalled 25 Hz that provides accurate recording of the pupil variations.

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Abdul Hadi, Muhammad, Rian Ferdian, and Lathifah Arief. "Klasifikasi Tingkat Ancaman Kriminalitas Bersenjata Menggunakan Metode You Only Look Once (YOLO)." CHIPSET 2, no. 01 (April 30, 2021): 33–40. http://dx.doi.org/10.25077/chipset.2.01.33-40.2021.

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The research aim to recognize potential weapon threats through object detection on camera. This research utilize YOLO (You Only Look Once) method in object detection which implemented on Raspberry Pi 4. The process was by detecting object from the camera and classify the object class in 2 available classes : Gun and Knife. Meanwhile, in the classifying process, it also count the object in every classes. When the system detect object in the process, it will send notification in terms of threat level through android application so that the user or operator can mitigate the threat immediately. From the research, we achieve the mAP of 85.12% in which YOLOv4 tiny is used and the testing is done inside a room environment. In its application in detecting weapon in Raspberry Pi 4, the result is around 1.53 fps (frame per second), in which is accommodate to be implemented on, but with a very limited fps.

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A., Shevchenko, and Puzyrov S. "DEVELOPMENT OF THE HARDWARE AND SOFTWARE PLATFORM FOR MODERN IOT SOLUTIONS BASED ON FOG COMPUTING USING CLOUD-NATIVE TECHNOLOGIES." Computer systems and network 2, no. 1 (March 23, 2017): 102–12. http://dx.doi.org/10.23939/csn2020.01.102.

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The concept of digital transformation is very relevant at the moment due to the epidemiological situation and the transition of the world to the digital environment. IoT is one of the main drivers of digital transformation. The Internet of Things (IoT) is an extension of the Internet, which consists of sensors, controllers, and other various devices, the so-called "things," that communicate with each other over the network. In this paper, the development of hardware and software for the organization of fog and edge computing was divided into three levels: hardware, orchestration, application. Application level also was divided into two parts: software and architectural. The hardware was implemented using two versions of the Raspberry Pi: Raspberry Pi 4 and Raspberry Pi Zero, which are connected in master-slave mode. The orchestration used K3S, Knative and Nuclio technologies. Technologies such as Linkerd service network, NATS messaging system, implementation of RPC - GRPC protocol, TDengine database, Apache Ignite, Badger were used to implement the software part of the application level. The architecture part is designed as an API development standard, so it can be applied to a variety of IoT software solutions in any programming language. The system can be used as a platform for construction of modern IoT-solutions on the principle of fog\edge computing. Keywords: Internet of Things, IoT-platform, Container technologies, Digital Twin, API.

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