Relevant bibliographies by topics / Raspberry Pi 4

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Academic literature on the topic 'Raspberry Pi 4'

Author: Grafiati

Published: 28 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Raspberry Pi 4"

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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Dissertations / Theses on the topic "Raspberry Pi 4"

Živčák, Adam. "Správa Raspberry Pi 4 clusteru pomocí Nix." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445519.

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The scope of this thesis is to design and implement a system for deploying, managing and monitoring a Raspberry Pi cluster using Nix technologies. The thesis describes the benefits of the functional approach of Nix and the subsystems that are based on it. The thesis also results in a supporting web application, providing an intuitive environment for working with cluster configuration deployments and clearly displaying information about the utilization of individual nodes using dashboards. The final part of the thesis is devoted to testing cluster performance using sample distributed computing

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Hirš, David. "Systém prevence průniků využívající Raspberry Pi." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442389.

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The number of discovered vulnerabilities rapidly increases. For example in 2019 there were discovered 20 362 vulnerabilities. The probability of cyber-attacks realization is high. Therefore it is necessary to propose and implement automated and low-cost Intrusion Prevention or Intrusion Detection Systems (IPS/IDS). This implemetation can focus on home use or small corporate networks. The main goal of the system is to detect or mitigate cyber-attack impact as fast as possible. The master's thesis proposes IPS/IDS based on Raspberry Pi that can detect and prevent various cyber-attacks. Contents

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Marmayohan, Nivethan, and Abdirahman Farah. "Scene analysis using Tensorflow & YOLO algorithms on Raspberry pi 4." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45540.

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Objektdetektion är en av de viktigaste mjukvarukomponenterna i nästa generation trafikövervakning. Deep learnings-algoritmer för objektdetektion, exempelvis YOLO (You Only Look Once), är snabba och noggranna algoritmer i realtid. Realtidsdetektion och igenkänning av objekt är viktiga uppgifter för bildbehandling. I denna studie presenteras ett inbäddat system för detektion och igenkänning av objekt i normal videohastighet (realtid). Indata är följaktligen en videoström som härstammar från en trafikmiljö i Halmstad. Hårdvaran är Raspberry pi 4 i vilken programvarupaketen Tensorflow, YOLO sam

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Krejčí, Jan. "Implementace komunikačních protokolů pro IoT s využitím rozšiřujícího modulu UniPi pro Raspberry Pi." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-317015.

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Presented diploma thesis is focused on the implementation of Wireless M-Bus protocol to embedded device RaspberryPi with expansion board UniPi. The protocol is implemented in Python with Wireless M-Bus devices communicating via IQRF transceiver connected to the UART bus. The theoretical part is focused on an overview of embedded devices for the IoT, the possibility of their expansion. Further, the UniPi expansion board and Wireless M-Bus transceiver are detailed. First part of the thesis focuses on the Wireless M-bus layers, which provides a basic knowledge for understanding the practical part

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Cho, Minn, and Philipe Granhäll. "An Analysis on Bluetooth Mesh Networks and its Limits to Practical Use." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301847.

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A mesh network is a technology that is being repopularized and becoming commonly used by the general public. As this increase in use is observed, technologies such as Bluetooth are being adapted to create mesh variants. In this thesis, a Bluetooth mesh network is created and tested using raspberry pi 4’s and the Bluetooth interface, btferret. This thesis attempts to approach the limits of this technology using accessible tools, outlining the performance the network possesses to serve as a guideline to determine if it suitable for use for tasks at hand. Experimentation is split into two overarc

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Lefler, Přemysl. "Optimalizace operačního systému s jádrem Linux pro zařízení napájené z akumulátoru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442374.

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his thesis focuses on optimization of Linux-based operating system for a accumulator-powered device Raspberry Pi 4. Compared to other devices commonly used in Internet of Things projects, the Raspberry Pi 4 offers many functions within one device. However, the disadvantage is the high consumption of electricity. The aim of this thesis is to achieve greatest possible savings in electricity consumption of the Raspberry Pi 4 device, with regard to functionality of the device as a server for data collection from sensors.

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Ferm, Oliwer. "Real-time Object Detection on Raspberry Pi 4 : Fine-tuning a SSD model using Tensorflow and Web Scraping." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39455.

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Edge AI is a growing area. The use of deep learning on low cost machines, such as the Raspberry Pi, may be used more than ever due to the easy use, availability, and high performance. A quantized pretrained SSD object detection model was deployed to a Raspberry Pi 4 B to evaluate if the throughput is sufficient for doing real-time object recognition. With input size of 300x300, an inference time of 185 ms was obtained. This is an improvement as of the previous model; Raspberry Pi 3 B+, 238 ms with a input size of 96x96 which was obtained in a related study. Using a lightweight model is for the

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Papponi, Tommaso. "Sviluppo, implementazione e verifica sperimentale di un sensore di sole basato sulla piattaforma Raspberry." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24891/.

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Tra i possibili riferimenti per la determinazione d’assetto, nei satelliti viene spesso sfruttata la linea di direzione del sole, rilevata e stimata attraverso un apposito dispositivo. Nel presente lavoro di tesi viene descritto lo sviluppo e la validazione di un sensore di sole digitale a basso costo, basato su un microcontrollore ed una fotocamera CMOS, entrambi di Raspberry Pi. Il prototipo, progettato durante il tirocinio curriculare, è stato ottimizzato con scopo finale l’integrazione nel banco prova Alma-Test Bed, sviluppato all’interno dell’Università di Bologna per la validazione di si

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Mozart, Andraws David, and Larsson Marcus Thornemo. "Crowd Avoidance in Public Transportation using Automatic Passenger Counter." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106090.

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Automatic Passenger Counting (APC) systems are some of the many Internet-Of-Things (IoT) applications and have been increasingly adopted by public transportation companies in recent years. APCs provide valuable data that can be used to give an real time passenger count, which can be a convenient service and allow customers to plan their travels accordingly. The provided data is also valuable for resource streamlining and planning, which potentially increases revenues for the public transportation companies. This thesis briefly studies and evaluates different APC technologies, highlights the ad

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Zatloukal, Jiří. "Senzorika a řízení pohonů 4 kolového mobilního robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-230890.

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The diploma thesis is dealing with the proposal and realization of the sensor and drive system of the four wheel mobile robot. The control unit is a miniature computer Raspberry Pi. The robot will be employed in the future for the environment mapping and location. For this purpose robot exploits the different types of sensors. The information of these sensors is being processed by the Xmega microcontroller. Another microcontroller together with H-bridge DRV-8432 is used to control the direct current drives.

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Books on the topic "Raspberry Pi 4"

Shovic, John C. Raspberry Pi IoT Projects. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6911-4.

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Gay, Warren W. Raspberry Pi Hardware Reference. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0799-4.

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Soper, Mark Edward. Expanding Your Raspberry Pi. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2922-4.

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Venu, Sibeesh. Asp.Net Core and Azure with Raspberry Pi 4. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0.

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Abdul Kadhar, K. Mohaideen, and G. Anand. Data Science with Raspberry Pi. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6825-4.

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Guillen, Guillermo. Sensor Projects with Raspberry Pi. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5299-4.

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Membrey, Peter, and David Hows. Learn Raspberry Pi with Linux. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-4822-4.

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Norris, Donald J. Machine Learning with the Raspberry Pi. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5174-4.

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Donat, Wolfram. Learn Raspberry Pi Programming with Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3769-4.

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Newmarch, Jan. Raspberry Pi GPU Audio Video Programming. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2472-4.

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Book chapters on the topic "Raspberry Pi 4"

Venu, Sibeesh. "About Raspberry Pi." In Asp.Net Core and Azure with Raspberry Pi 4. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_1.

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Venu, Sibeesh. "Configuring Your Raspberry Pi." In Asp.Net Core and Azure with Raspberry Pi 4. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_2.

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Modrzyk, Nicolas. "Vision on Raspberry Pi 4." In Real-Time IoT Imaging with Deep Neural Networks. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5722-7_3.

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Koretsky, Robert M. "The GNU Emacs Editor." In Raspberry Pi OS System Administration. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003473268-4.

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Reddy, Desu Sai Pranav, Y. Sreevatsal Pranav, Padmavathi Kora, and V. Arvind. "Smart Mirror Using Raspberry Pi 4." In Intelligent Manufacturing and Energy Sustainability. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8497-6_3.

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Koretsky, Robert M. "Questions, Problems, and Projects." In Raspberry Pi OS System Administration with systemd. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23405-4.

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Karthika, P., and P. Vidhya Saraswathi. "Machine Learning Security Allocation in IoT using Raspberry Pi." In Data Security in Internet of Things Based RFID and WSN Systems Applications. CRC Press, 2020. http://dx.doi.org/10.1201/9780429294990-4.

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Koretsky, Robert M. "Virtualization Methodologies." In Raspberry Pi OS Text Editors, git, and LXC. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003455813-4.

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Gehlot, Anita, Rajesh Singh, Lovi Raj Gupta, Bhupendra Singh, and Mahendra Swain. "Basics of Arduino." In Internet of Things with Raspberry Pi and Arduino. CRC Press, 2019. http://dx.doi.org/10.1201/9780429284564-4.

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Venu, Sibeesh. "Creating and Deploying a .NET Core Application to Raspberry Pi." In Asp.Net Core and Azure with Raspberry Pi 4. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_4.

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Conference papers on the topic "Raspberry Pi 4"

Mane, Vijay, Pranav Chougule, Siddhant Pusate, and Shyam Pawar. "Interactive Smart Robot using raspberry pi 4." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.11021011.

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Matsuda, Toshihiro, Yue Su, Yang Xiang, Kien Nguyen, and Hiroo Sekiya. "Raspbian Versus LineageOS: An Experimental Study on Raspberry Pi 4." In 2024 23rd International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2024. https://doi.org/10.1109/iscit63075.2024.10793669.

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Aggarwal, Sajal, and Virender Ranga. "A Raspberry Pi 4 and Arduino-based Automated Intercom Door Lock System." In 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE, 2024. http://dx.doi.org/10.1109/acet61898.2024.10730356.

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Singh, Jay bahadur, Anurag Dwivedi, Divyansh Awasthi, Abhishek Yadav, Ashish Kumar Bhardwaj, and Akhilesh Tiwari. "Design and Implementation of Facial Recognition Attendance System Using Raspberry Pi 4." In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2024. https://doi.org/10.1109/upcon62832.2024.10983257.

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Sivakumar, P., R. Vijay Radha Surya, S. Reehan Fazil, R. Subhiksha, R. Hariharan, and Ramesh A. "Empowering Visual Interaction: Raspberry Pi 4 and OLED Displays for Dynamic Feedback Applications." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10801129.

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K, Sabarishraj, Jayanthy S, Judeson Antony Kovilpillai J, Sabari Santhosh S, and Swathi R. "Efficient Implementation of Firmware Over-the-Air Update using Raspberry Pi 4 and STM32F103C8T6." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677143.

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El-Hajj, Mohammed, and Shiva Biersteker. "Evaluating the Security and Performance of CoAP Protocol on Raspberry Pi 4 for IoT Applications." In 2024 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). IEEE, 2024. https://doi.org/10.1109/apwimob64015.2024.10792962.

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Sanjaya, W. S. Mada, Akhmad Roziqin, Thirda Febrilian Putra, et al. "Electronic Nose with Artificial Neural Network Method on Raspberry Pi 4 for Detecting Pork and Beef." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10874021.

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Teradal, Rahul J., Sakshi J. Chougule, Sanika N. Chopade, Abhishek B. Chougule, Vivek V. Kajagar, and Virupaxi Dalal. "Implementation of Smart Parking Access Control and Visitor Authentication using Raspberry Pi 4 And Machine Learning." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988296.

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Shabbir, Noman, Rameel Ahmed, Muhammad Wasif Raza, Ayesha Zeb, Hassan Elahi, and Hasan Waheed. "Comparative Performance and Resource Utilization Analysis of OCR Models for Number Plate Recognition on Raspberry Pi 4." In 2025 International Conference on Communication Technologies (ComTech). IEEE, 2025. https://doi.org/10.1109/comtech65062.2025.11034455.

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Reports on the topic "Raspberry Pi 4"

Corley, Charles, Heather Quinn, and Earl Swartzlander. Accelerated Nuclear Radiation Effects on the Raspberry Pi 3B+ and Pi 4. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1992220.

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Videa, Aldo, and Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, 2021. http://dx.doi.org/10.15788/1715288737.

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Passenger data such as real-time origin-destination (OD) flows and waiting times are central to planning public transportation services and improving visitor experience. This project explored the use of Internet of Things (IoT) Technology to infer transit ridership and waiting time at bus stops. Specifically, this study explored the use of Raspberry Pi computers, which are small and inexpensive sets of hardware, to scan the Wi-Fi networks of passengers’ smartphones. The process was used to infer passenger counts and obtain information on passenger trajectories based on Global Positioning Syste

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