Relevant bibliographies by topics / Raspberry Pi 3 Model B

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Raspberry Pi 3 Model B'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Raspberry Pi 3 Model B"

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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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Sedayu, Agung, Elvan Yuniarti, and Edi Sanjaya. "Rancang Bangun Home Automation Berbasis Raspberry Pi 3 Model B dengan Interface Aprlikasi Media Sosial Telegram sebagai Kendali." Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics 1, no. 2 (April 2, 2019): 42–47. http://dx.doi.org/10.15408/fiziya.v1i2.9254.

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Telah berkambangnya tekonologi home automation pada zaman ini dan salah satunya dengan pemanfaatan smartphone sebagai salah satu pengendalinya. Pada penelitian ini memanfaatkan sebuah aplikasi media sosial telegram dan sebuah single board computer sebagai kontrol pada sebuah home automation. Penelitian ini bertujuan untuk membangun sebuah home automation dengan menggunakan Raspberry Pi dan aplikasi media sosial telegram sebagai kendalinya. Dengan studi pustaka dan metode eksperimen penelitian ini berhasil merancang bangun sebuah home automation menggunakan Raspberry Pi 3 Model B dengan interface aplikasi telegram sebagai kendalinya. Dibutuhkan sebuah Bot API telegram agar Raspberry Pi dan aplikasi telegram dapat terhubung. Perangkat yang digunakan dalam penelitian ini meliputi: single board computer Raspberry Pi 3 Model B, 2 buah relay untuk 2 perangkat elektronik (lampu dan kipas), 1 buah MCB dan smartphone yang telah terinstall aplikasi telegram yang digunakan sebagai pengontrol perangkat elektronik. Hasil dari penelitian ini sudah sesuai dengan tujuannya yaitu merancang bangun sebuah home automation dengan menggunakan Raspberry Pi dan aplikasi media social telegram sebagai kendalinya mensimulasikannya pada peralatan elektronika.
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Endang Supriyadi, Maya Sofiana, and Surya Dwipangga. "Sistem Kendali Lampu Defect Dan Reject Berbasis Web Server Menggunakan Raspberrry Pi 3 Model B." Jurnal Teknik Informatika 7, no. 1 (February 2, 2021): 09–15. http://dx.doi.org/10.51998/jti.v7i1.346.

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Abstract— This research covers the design and construction of a Web-based Defect & Reject Light Control Intelligent System with Raspberry Pi with the aim of detecting the quality conditions of manufactured goods at the final inspection section of PT. Suryaraya Rubberindo Industries. This system requires several components such as a Raspberry Pi 3 Microcontroller, Relays, mini filament LED lamps and several other supporting components. The design and construction of a Web-Based Defect & Reject Light Control Intelligent System with the Raspberry Pi comes from experimental results, besides that it is also supported by several literary documents found in journals and reference books. This Intelligent Web-Based Defect & Reject Light Control System with the Raspberry Pi uses a website design as an input to turn off or turn on a lamp. Two mini filament lamps are used as output components that are made like Signal lamps. Based on the results of the experiments conducted, the Web-Based Defect & Reject Light Control Intelligent System with the Raspberry Pi can work quite well. When this system detects the condition of the production goods in a state that does not meet the specified company standards but has a low level of seriousness (defect), the system will give a signal for a yellow light with a value of 1, whereas if the level of seriousness is high (reject) then the control system will give a signal to turn on the red light the value of the number 1 stored in the database Intisari— Penelitian ini meliputi racang dan bangun Sistem Cerdas Kendali Lampu Defect & Reject Berbasis Web denganRaspberry Pi dengan tujuan untuk mendeteksi kondisi kualitas barang produksi pada bagian final Inspection di PT. SuryarayaRubberindo Industries. Sistem ini memerlukan beberapa komponen seperti Mikrokontroller Raspberry Pi 3, Relay,Lampu berjenis LED filamen mini dan beberapa komponen pendukung lainnya. Rancang Rangun Sistem Cerdas KendaliLampu Defect & Reject Berbasis Web dengan Raspberry Pi tersebut berasal dari hasil percobaan, selain itu didukung pulaoleh beberapa literatur dokumen yang terdapat pada jurnal dan buku referensi. Sistem Cerdas Kendali Lampu Defect & RejectBerbasis Web dengan Raspberry Pi ini menggunakan rancangan website sebagai input untuk mematikan atau menyalakan sebuah lampu. Dua buah lampu filamen mini digunakan sebagai komponen output yang dibuat seperti lampu Signal.Berdasarkan hasil percobaan yang dilakukan, Sistem Cerdas ini dapat bekerja dengan cukup baik. Ketika sistem ini mendeteksikondisi barang produksi dalam keadaan tidak memenuhi standard perusahaan yang telah ditentukan akan tetapi memilikitingkat keseriusan rendah (defect) maka sistem akan memberikan sinyal untuk lampu kuning bernilai angka 1 sedangkan jika tingkat keseriusan tinggi (reject) maka sistem kendali akan memberikan sinyal untuk menyalakan lampu merah bernilai angka 1 yang tersimpan pada database.
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Dhakate, Prajwal. "NFC based Smart Attendance System using Raspberry Pi 3 Model B+." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 1830–35. http://dx.doi.org/10.22214/ijraset.2020.5293.

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Sudarsono, Joshua Fernaldy, Gede Sukadarmika, and Linawati Linawati. "Rancang Bangun Alat Ukur Kualitas Jaringan Berbasis Raspberry Pi 3 Model B." Majalah Ilmiah Teknologi Elektro 20, no. 1 (March 1, 2021): 53. http://dx.doi.org/10.24843/mite.2021.v20i01.p06.

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Internet telah menjadi kebutuhan bagi masyarakat modern dalam melakukan berbagai aktifitasnya saat ini. Pengguna Internet yang meningkat sangat pesat di setiap tahun menunjukkan bahwa kebutuhan masyarakat terhadap Internet yang semakin tinggi. Di Indonesia pertumbuhan pengguna Internet diprediksi meningkat 10,2 persen setiap tahunnya dari tahun 2018 hingga 2023. Namun demikian, pertumbuhan jumlah pelanggan sering kali tidak diikuti dengan kemampuan provider Internet untuk meningkatkan fasilitas maintanance dan untuk menjaga kualitas layanan kepada pelanggannya. Salah satu penyebabnya adalah keterbatasan perangkat yang sering kali menjadi kendala bagi teknisi untuk mealakukan maintenance sesegera mungkin. Penelitian ini bertujuan untuk dapat merancang dan membangun perangkat yang dapat digunakan sebagai alat ukur kualitas jaringan yang lebih sederhana dan ekonomis sehingga mendukung mobilitas teknisi untuk membagun maupun maintenance jaringannya. Perangkat yang dibangun pada penelitian ini berbasis Raspberry Pi 3 Model B. Perbandingan hasil pengukuran performa perangkat antara lain daya tahan baterai, ping, upload dan download rate antara perangkat yang dibangun dengan penggunaan laptop memberikan hasil yang hampir sama. Peangkat yang dibangun ini memiliki keunggulan pada sisi dimensi yang lebih kecil dan ringan serta biaya yang jauh lebih ekonomis.
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Muttaqin, Imam Wildan, and Arif Rahman. "Sistem Presensi Berbasis RFID Menggunakan Raspberry Pi 3." Buletin Ilmiah Sarjana Teknik Elektro 1, no. 1 (August 19, 2019): 27. http://dx.doi.org/10.12928/biste.v1i1.850.

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Sistem presensi mahasiswa banyak dilakukan secara konvensional. Sistem presensi otomatis yang ada pun memiliki kinerja yang lambat dan tidak mampu diperbarui secara otomatis. Sistem presensi mahasiswa berbasis RFID menggunakan Raspberry Pi 3 diharapkan mampu menangani masalah-masalah tersebut. Sistem pada penelitian ini dibangun menggunakan modul RFID jenis MIFARE RC522, Raspberry Pi 3 model B, dan RTC DS1307. Prinsip kerja sistem ini yaitu menerima masukan berupa hasil identifikasi ID pada kartu RFID, kemudian hasilnya diolah Raspberry Pi 3 sekaligus menentukan respon, dan merekap data presensi pada server. Hasil presensi berupa nama mahasiswa dan mata kuliah ditampilkan pada LCD 16x2. Sebagai hasil akhir, server mengirim data presensi yang dapat ditampilkan pada komputer klien melalui situs web. Pengujian sistem menunjukkan jarak maksimal identifikasi ID sejauh 4,5 cm dengan rata-rata waktu pembacaan selama 150,53 ms. Penambahan RTC DS1307 menjadikan sistem tidak harus terhubung dengan internet untuk update secara otomatis. Selain itu, penelitian ini juga membuktikan bahwa penghalang kayu, karet, keramik, kaca, plastik, dan akrilik, tidak mempengaruhi jarak dan waktu dalam proses identifikasi kartu RFID. Namun penghalang jenis logam dapat menghalangi proses identifikasi kartu RFID.
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Sałuch, Mateusz, Daniel Tokarski, Tomasz Grudniewski, Marta Chodyka, JerzyAntoni Nitychoruk, Paweł Woliński, Beata Jaworska, and Grzegorz Adamczewski. "Raspberry PI 3B + microcomputer as a central control unit in intelligent building automation management systems." MATEC Web of Conferences 196 (2018): 04032. http://dx.doi.org/10.1051/matecconf/201819604032.

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This article aims to show the possible savings in electricity costs in smart building installations with the use of new version of Raspberry Pi 3 model B + as the control unit in intelligent building automation systems. It presents a comparison of the consumption of electricity in two units used in the central control systems, i.e. a small Windows-based computer and a Raspberry microcomputer. The power consumption of these units was measured during the rest period and during standard operations in the intelligent installation system. The conducted measurements proved that the use of the new updated version of Raspberry Pi 3 model B + as the central control unit in intelligent building management systems is more economical and energy-saving.
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Yenni, Helda, and M. Ari Ardianto. "ALAT DIGITAL PENCETAK KUE BAWANG MENGGUNAKAN RASPBERRY PI 3 MODEL B BERBASIS ANDROID." JTT (Jurnal Teknologi Terapan) 6, no. 1 (April 30, 2020): 93. http://dx.doi.org/10.31884/jtt.v6i1.246.

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Kue bawang merupakan salah satu makanan ringan tradisional di Indonesia. Makanan berbahan dasar tepung terigu dan tapioka ini bercita rasa gurih dan garing. Saat ini, kue bawang sudah merambah bisnis kuliner. Peralatan utama dalam pembuatan kue ini disebut ampia. Ampia yang digunakan masih berupa perangkat manual dalam operasionalnya termasuk mengatur ketebalan adonan setelah digiling. Kendala yang dihadapi berupa keterbatasan tenaga manusia untuk menggerakkan alat dan keterbatasan ketelitian untuk mendapatkan ukuran ketebalan yang presisi sesuai dengan yang diinginkan. Pada industri skala besar, hal tersebut dapat berpengaruh pada produktifitas. Solusi dari permasalahan tersebut berupa penerapan teknologi pada industri kuliner dengan membuat alat digital pencetak kue bawang secara otomatis. Kontrol utama adalah Raspberry pi 3, aplikasi smartphone android untuk menjalankan dan mematikan mesin pencetak kue bawang secara otomatis. Sensor ultrasonik digunakan untuk mendeteksi adonan, alat ini dilengkapi dengan motor penggerak yaitu yang digunakan untuk menggiling, mencetak serta memotong adonan kue dan motor servo untuk mengatur ketebalan adonan. Sensor Rotary Encoder ky-040 yang berfungsi sebagai pengatur panjang pemotongan adonan kue bawang. Pada penelitian ini sistem yang dirancang mampu menghasilkan sistem kontrol menggunakan aplikasi smartphone Android, sehingga dapat menghemat tenaga dan waktu dalam proses pembuatan kue bawang serta ukuran dapat diatur sesuai dengan yang diinginkan. Kata Kunci: kue bawang, Raspberry Pi 3, otomatis, Android
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Muck, P. Y., and M. J. Homam. "Iot Based Weather Station Using Raspberry Pi 3." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i4.30.22085.

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Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.
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Agustian, Indra, Faisal Hadi, and M. Khairul Amri Rosa. "Pre-Diagnosis Gangguan Ginjal Melalui Citra Iris Mata Menggunakan Raspberry PI Dengan Metode Convolutional Neural Network (CNN)." JURNAL AMPLIFIER : JURNAL ILMIAH BIDANG TEKNIK ELEKTRO DAN KOMPUTER 9, no. 1 (May 30, 2019): 16–25. http://dx.doi.org/10.33369/jamplifier.v9i1.15396.

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ABSTRAKPenelitian ini melakukan perancangan aplikasi pengenalan gangguan ginjal dini melalui citra digital iris mata menggunakan metode convolutional neural network (CNN) dengan antarmuka Raspberry Pi 3 model B+. Hasil akurasi terbaik yang diperoleh dengan memvariasikan banyak epoch, nilai learning rate, ukuran kernel, komposisi database, dan fungsi pooling layer adalah 94% pada saat epoch 12, 92% pada nilai 0,0001, 95% pada ukuran 3x3, 95% pada komposisi 100 train dan 50 validation, 90% menggunakan fungsi max pooling. Kata kunci: gangguan ginjal, iridology, convolutional neural network, raspberry pi.
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Dissertations / Theses on the topic "Raspberry Pi 3 Model B"

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Hadzima, Jaroslav. "Algoritmy hlubokého učení na embedded platformě." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400703.

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Táto práca popisuje v súčastnosti široko používané architektúry a modely pre Hlboké Učenie, riešiace úlohu detekcie a klasifikácie objektov vo videu. Dôraz tu bude kladený na ich použiteľnosť na vstavaných zariadeniach. Postupne preberieme kroky a odvôvodňovanie pri výbere najlepšieho vstavaného systému pre našu aplikáciu. Ukážková aplikáci pozostáva hlavne z detekcie vozidiel a detekcie voľných parkovacích miest s využitím algoritmov Hlbokého Učenia. Táto aplikácia umožňuje monitorovať počet vozidiel, nachádzajúcich sa na parkovisku a zároveň rozhodnúť, či sa nachádzajú na prakovacom mieste alebo nie. Následne tu budú prebrané kroky nutné ku konfigurácii zariadenia s dôrazom na optimalizáciu hardvéru pre dosiahnutie čo najväčšej rýchlosti. V ďaľšej časti bude poskytnuté porovnanie vybraných modelov, ktoré budú porovnávané hlavne v kategóriách ako rýchlosť alebo F1 skóre. Najlepší kandidát bude použitý na riešenie našej aplikácie a následné testovanie jej vlastností s názvom Inteligentné parkovisko.
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Aspernäs, Andreas, and Thommy Simonsson. "IDS on Raspberry Pi : A Performance Evaluation." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-43997.

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This is a report on the possibility of using a Raspberry Pi as an intrusion detection system in a home environment to increase network security. The focus of this study was on how well two different generations of Raspberry Pi would be able to  handle network traffic while acting as an intrusion detection system. To examine this a testing environment was set up containing two workstation computers connected to a Raspberry Pi, each computer hosting a virtual machine. Tests measuring the network throughput as well as the CPU and memory usage were performed on each of the Raspberry Pi devices. Two models of Raspberry Pis were used; Raspberry Pi model B+ and Raspberry Pi 2 model B; each of them running the operating system Arch Linux ARM. The results of these tests were that both of the Raspberry Pis could be used as an intrusion detection system but has some limitations that could impede usage depending on the requirements of the user. Raspberry Pi 2 model B show benefits of its updated hardware by suffering lower throughput degradation than Raspberry Pi model B+, while using less of it's total CPU and memory capacity.
Den här rapporten behandlar möjligheten att använda en Raspberry Pi som ett intrångdetekteringssystem i en hemma miljö för att öka nätverkssäkerheten. Fokusen i den här studien ligger på hur väl de två senaste generationerna av Raspberry Pi skulle kunna hantera nätverkstrafik samtidigt som den undersöker nätverkstrafiken och söker efter hot. För att kontrollera hur väl en Raspberry Pi kan fungera som ett intrångdetekteringssystem har en laborationsmiljö upprättats bestående av två fysiska maskiner som vardera används för att virtualisera en virtuell maskin. Tester för att mäta datagenomströmning, processor och minnesbelastning utfördes på var och en av Raspberry Pi. Två modeller av Raspberry Pi användes; Raspberry Pi model b+ och Raspberry Pi 2 model b, både körde operativsystemet Arch Linux ARM. Resultatet av testerna visade att det går att använda båda enheterna för att upprätta ett intrångdetekteringssystem, men det finns vissa begränsningar i enheterna vilket kan begränsa implementationsmöjligheterna. Raspberry Pi 2 model B uppvisade bättre resultat i form av att den är lägre belastad och har en högre datagenomströmning till skillnad från Raspberry Pi model B+. Raspberry Pi 2 model B har nyare och snabbare hårdvara vilket är den troliga orsaken till att den presterar bättre.
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Árva, Gábor. "Embedded zpracování videa pro dohledový systém." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316403.

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Diplomová práca sa zaoberá návrhom embedded dohľadového systému, ktoré je implementované na Raspberry Pi 3 B zariadenie. Uvedený systém obsahuje algoritmy pre detekcie pohybu a detekcie objektov, ktoré sú realizované pomocou OpenCV funkcie. Vyhodnocené informácie sú prístupné na webový server.
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Náhlík, Ondřej. "Speciální bezpečnostní systém." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221221.

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This master’s thesis is focused on design and realisation of special security system for space and property protection. System is devided into two parts, transmitting and receiving. Transmitting part is formed by Raspberry Pi Model B+, motion sensor, webcam and USB LTE modem Huawei E398. Receiving part consists of computer with suitable application for communication with FTP server. Data transfer in mobile systems is described in the first part of this thesis, transmitting part is designed in the second part. There is one transmitting option chosen in the third part and there are also described applications of the transmitting part. The last part of this thesis is focused on the receiving part and its application.
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Pandiscia, Nicola. "Analisi di sequenze video per rilevazioni demografiche ed emotive da software su microcontroller." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il seguente progetto è volto ad implementare sul microcontroller "Raspberry Pi v.4 Model B" un software utilizzante a mo' di scatola nera, anche, in parte, una rete neurale che sulla base di una classificazione precedentemente realizzata da soggetti terzi e sulla base di opportuni modelli preaddestrati sfrutti un meccanismo di apprendimento supervisionato per stimare ragionevolmente, secondo opportuni criteri, il sesso, la fascia d'età, lo stato emotivo (caricaturale, ossia forzato) e la distanza approssimativa di uno o più soggetti ripresi frontalmente in volto da una telecamera in tempo reale.
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CHEN, YU-JEN, and 陳宥任. "Autopilot Model-car Implemented by Raspberry Pi 3 Model B Plus with Camera using CNN Deep Learning Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nt6s73.

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碩士
國立臺北科技大學
車輛工程系
107
This paper study a monocular vision-based autopilot model-car prototype. It is based on python. Using machiane learning and computer vision on the embedded system, Raspberry Pi 3 B+ to apply deep learning to autopilot model-car. It uses a 1:12 RC model-car for the stage, Raspberry Pi calculation and front camera to construct platform. First, move on the driveway by operating the joystick. Collecting information on the image and the joystick. Take a large amount of data to train the CNN model. Transfer the model back to Raspberry Pi, and put the car into the test environment. Calculate the direction and speed of the current travel and then convert it into a PWM signal, send the direction signal to the servo motor, and send the speed signal to the electronic transmission to control the motor speed. Continuing to collect data, test and improve the model to improve the success rate.
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Chuang, Chun-Wei, and 莊均維. "Paperless system Based on Raspberry Pi Model B." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/x2x59p.

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碩士
國立臺中科技大學
資訊工程系碩士班
102
Meeting is an activity in everyday work force to exchange face to face views and communications. A large number of supporting documents will be needed to support the meetings. Paperless purpose is decreased the amount of papers used in the process. However, systems which are developed in server are expensive to maintain for small units. In this paper, we proposed a paperless meeting system based on Raspberry Pi Model B. The main focus of the proposed system is to provide a paperless meeting service system which is low implementation costs. The service is design from Red5 and FreeSWITCH. The system is implemented in Raspberry Pi. Experimental results show that implementation costs of the proposed method can effectively reduce implementation costs in the existing meeting system, and the function of meeting can also be providing similar services of the server.
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Book chapters on the topic "Raspberry Pi 3 Model B"

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Flurry, Greg. "Raspberry Pi 3 Model B+ Setup." In Java on the Raspberry Pi, 21–48. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7264-0_2.

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Sahni, Nishant, Kailash Srinivasan, Karan Vala, and Saurabh Malgaonkar. "Study and Research on Raspberry PI 2 Model B Game Design and Development." In Information and Communication Technology for Sustainable Development, 475–83. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3932-4_50.

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Zubov, Dmytro. "A Case Study on the Spatial Cognition of Surrounding Objects by the B&VI People Using Sound Patterns and Ultrasonic Sensing." In Emerging Trends and Applications of the Internet of Things, 105–16. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2437-3.ch004.

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In this paper, two assistive projects on the spatial cognition by blind and visually impaired (B&VI) people are presented using the sound patterns and ultrasonic sensing. The first device supports the sport activities of B&VI, the golf game specifically. Every golf flagstick has the sound marking device with the active buzzer and WiFi remote control by the person with good vision. The NodeMcu Lua ESP8266 ESP-12 WiFi boards in devices are controlled by the cross-platform HTML web-sites, and hence any WiFi smartphone and / or computer can be in use to start the HTML web-page. Mini portable WiFi router links all devices in the network. End-users are securely connected using the password to wireless router. Ten assistive devices were handed in Instituto para Ciegos y Débiles Visuales “Ezequiel Hernández Romo” together with WiFi router. The second device supports the orientation of B&VI by measuring the distance to the obstacle based on the ultrasonic sensor HC-SR04 and Arduino Uno. The distance is pronounced to the B&VI using headphone and MP3 player with SD card. Nowadays, Universidad Politécnica de San Luis Potosí is negotiating with several organizations to create a production line. All devices are of the budget price up to USD 10. All devices were tested successfully. This is joint work of Instituto para Ciegos y Débiles Visuales “Ezequiel Hernández Romo”, Universidad Politécnica de San Luis Potosí, and Tecnológico de Monterrey with ongoing project “Artificial Eyes” based on Raspberry Pi 3 Model B board with an ultrasonic sensor and camera for the image and/or video processing of the surrounding environment, as well as the friendly integration into the local networks using onboard WiFi and Bluetooth.
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Conference papers on the topic "Raspberry Pi 3 Model B"

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Varghese, Levin, Gerard Deepak, and A. Santhanavijayan. "An IoT Analytics Approach for Weather Forecasting using Raspberry Pi 3 Model B+." In 2019 Fifteenth International Conference on Information Processing (ICINPRO). IEEE, 2019. http://dx.doi.org/10.1109/icinpro47689.2019.9092107.

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Viola, Jairo, Sina Dehghan, and YangQuan Chen. "Embedded RIOTS: Model Predictive Control Towards Edge." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97046.

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Abstract RIOTS is a general purpose optimal problem solver written as a MATLAB toolbox with mixed-language programming (C, Fortran, Matlab, Simulink). This first paper introduces how to make RIOTS run under an embedded platform RP3B (Raspberry Pi 3 B) with Windows 10. We presented the system architecture and a complete demo on running RIOTS as the inner kernel for MPC, using a house made thermal control system based on Peltier modules.
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Laluma, Riffa Haviani, Riofalzy Giantara, Bambang Sugiarto, Gunawan, Chandra Afriade Siregar, and Slamet Risnanto. "Automation System of Water Treatment Plant using Raspberry Pi.3 Model B+ Based on Internet of Things (IoT)." In 2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2019. http://dx.doi.org/10.1109/tssa48701.2019.8985516.

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Sarnin, Suzi Seroja, Aida Akbar, Wan Norsyafizan W. Mohamad, Azlina Idris, Nani fadzlina Naim, and Norsuzila Yaracob. "Maleficent Mirror with ALEXA Voice Services as an Internet of Things Implement Using Raspberry Pi 3 Model B." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650106.

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Vira Yudha, Garand, and Rini Wisnu Wardhani. "Design of a Snort-based IDS on the Raspberry Pi 3 Model B+ Applying TaZmen Sniffer Protocol and Log Alert Integrity Assurance with SHA-3." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527511.

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Vavrenyuk, Aleksandr B., Darya V. Matveeva, Nikita M. Lukyantsev, and Victor V. Makarov. "Analysis of an Efficiency of Parallelization of Algorithms Running on Computing Cluster Based on Single-Board Diskless Computers Raspberry PI 3 Model B." In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. http://dx.doi.org/10.1109/elconrus51938.2021.9396277.

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Zanella, Maicon, Maurício Santos, Rafael Piccoli, and Samuel Ferrigo. "Uso experimental de 6LowPAN em redes BLE." In Escola Regional de Alto Desempenho da Região Sul. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eradrs.2020.10746.

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O Bluetooth Low Energy é uma nova tecnologia que permite a comunicação de dispositivos com baixíssimo consumo de energia. Através do 6LowPAN, essa tecnologia pode ser integrada a arquitetura TCP/IP. Dessa forma o projeto pretende criar uma conexão entre dispositivos BLE, endereçando as interfaces com IPv6 por meio do protocolo 6LowPAN. A avaliação do modelo realizada por simulações de conexão entre um aparelhos Raspberry Pi 3 (Model B) com um Notebook Linux, conectando-os e realizando testes com protocolos ICMP, HTTP, FTP, Telnet, SSH, VNC e SMB.
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Vavrenyuk, Aleksandr B., Dmitriy B. Shishov-Turchin, Alexey N. Alexeev, and Victor V. Makarov. "Multi-User System for Remote Access to the Resources of the Educational Computer Cluster Based on Single Board Diskless Computer Raspberry PI 3 Model B as a Service." In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. http://dx.doi.org/10.1109/elconrus51938.2021.9396374.

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Silvestre, Iago, and Leandro Becker. "Performance Analysis of Embedded Control Algorithms used in UAVs." In Simpósio Brasileiro de Engenharia de Sistemas Computacionais. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sbesc_estendido.2020.13110.

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Performance analysis of embedded systems is critical when dealing with Cyber-Physical Systems that require stability guarantees. They typically operate having to respect deadlines imposed during the design of the related control system. In a recent past performance analysis was typically done only by executing the code, and making measures, on the target embedded platform. Nowadays, code execution/measuring can also be done on simulation software, which offers greater degree of liberty for designers to configure the system for the desired tests. This paper presents results obtained from analyzing the performance of two control algorithms developed for controlling an Unmanned Aerial Vehicle (UAV) running on simulated and real embedded platforms. Such analysis is important for twofold reasons: better understand the timing behavior or the algorithms and evaluate architectural issues related with the target embedded platform. Raspberry Pi 3 Model B+ (with Cortex-A53 processor) is used as reference platform and serves as basis for creating different simulated versions for the analysis. Initial results highlighted the important role played by cache memory in the performance of the control algorithms and were able to detect a major bottleneck in one of the control algorithms that could compromise system stability.
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Magalhães, W. F., H. M. Gomes, L. B. Marinho, G. S. Aguiar, and P. Silveira. "Investigating Mobile Edge-Cloud Trade-Offs of Object Detection with YOLO." In VII Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/kdmile.2019.8788.

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With the advent of smart IoT applications empowered with AI, together with the democratization of mobile devices, moving the computation from cloud to edge is a natural trend in both academia and industry. A major challenge in this direction is enabling the deployment of Deep Neural Networks (DNNs), which usually demand lots of computational resources (i.e. memory, disk, CPU/GPU, and power), in resource limited edge devices. Among the possible strategies to tackle this challenge are: (i) running the entire DNN on the edge device (sometimes not feasible), (ii) distributing the computation between edge and cloud or (iii) running the entire DNN on the cloud. All these strategies involve trade-offs in terms of latency, communication, and financial costs. In this article we investigate such trade-offs in a real-world scenario involving object detection from video surveillance feeds. We conduct several experiments on two different versions of YOLO (You Only Look Once), a state-of-the-art DNN designed for fast and accurate object detection and location. Our experimental setup for DNN model partitioning includes a Raspberry PI 3 B+ and a cloud server equipped with a GPU. Experiments using different network bandwidths are performed. Our results provide useful insights about the aforementioned trade-offs.
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