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Relevant bibliographies by topics / Raspberry Pi 4B

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Academic literature on the topic 'Raspberry Pi 4B'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Raspberry Pi 4B"

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James, Nicholas, Lee-Yeng Ong, and Meng-Chew Leow. "Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster." Future Internet 14, no. 8 (2022): 220. http://dx.doi.org/10.3390/fi14080220.

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Raspberry Pi (Pi) is a versatile general-purpose embedded computing device that can be used for both machine learning (ML) and deep learning (DL) inference applications such as face detection. This study trials the use of a Pi Spark cluster for distributed inference in TensorFlow. Specifically, it investigates the performance difference between a 2-node Pi 4B Spark cluster and other systems, including a single Pi 4B and a mid-end desktop computer. Enhancements for the Pi 4B were studied and compared against the Spark cluster to identify the more effective method in increasing the Pi 4B’s DL pe

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Luo, Qirong, Xichang Cai, and Tongyuan Liu. "Multi-Plate Microbial Monitoring Terminal Based on Raspberry Pi 4B." Journal of Electronic Research and Application 8, no. 3 (2024): 28–33. http://dx.doi.org/10.26689/jera.v8i3.7196.

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We utilized Raspberry Pi 4B to develop a microbial monitoring system to simplify the microbial image-capturing process and facilitate the informatization of microbial observation results. The Raspberry Pi 4B firmware, developed under Python on the Linux platform, achieves sum verification of serial data, file upload based on TCP protocol, control of sequence light source and light valve, real-time self-test based on multithreading, and an experiment-oriented file management method. The system demonstrated improved code logic, scheduling, exception handling, and code readability.

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Wahyu Dewanto, Agung Fathurrahman, and Agus Bejo. "Evaluasi Platform Perangkat Keras Sistem Tertanam untuk Unit Kontrol Parkir Otomatis." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 12, no. 4 (2023): 287–92. http://dx.doi.org/10.22146/jnteti.v12i4.6277.

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Sistem parkir otomatis merupakan salah satu teknologi manajemen parkir yang saat ini banyak digunakan di berbagai instansi. Sistem parkir otomatis merupakan sistem parkir yang bekerja dengan menempatkan sebuah mesin portal parkir. Mesin portal parkir tersebut kemudian secara otomatis dapat membuka dan menutup portal serta merekam nomor kendaraan saat masuk dan keluar dengan menggunakan sebuah kunci akses berupa smart card. Salah satu kendala dalam penerapan sistem parkir otomatis adalah terjadinya kemacetan apabila kondisi lalu lintas sedang tinggi. Hal tersebut terjadi karena unit kontrol pad

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Костючко, С., Н. Багнюк, O. Кузьмич, М. Поліщук та Л. Кирилюк. "Біометрична ідентифікація засобами Python та Raspberry Pi." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, № 42 (30 березня 2021): 142–46. http://dx.doi.org/10.36910/6775-2524-0560-2021-42-20.

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У даній статті пропонується один з варіантів біометричної ідентифікації, а саме розпізнавання обличь користувачів засобами (одного з найпопулярніших і сучасного) одноплатного комп’ютера Raspberry Pi 4b та з використанням мови програмування Python. Була проведена низка симуляцій та створена база даних користувачів.

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Zhao, WenHan, Feng Wen, Chen Han, Zhoujian Chu, Qingyue Yao, and Kesong Ji. "Development of GIS Switch State Judgment System Based on Image Recognition." Journal of Physics: Conference Series 2065, no. 1 (2021): 012009. http://dx.doi.org/10.1088/1742-6596/2065/1/012009.

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Abstract Aiming at the fast opening and closing speed of the GIS isolation/grounding switch, manual observation is more difficult, so it is difficult to judge the current switch status. This paper proposes an OpenCV-based image identification algorithm to identify the position of the switch movable contact during the opening and closing process of the isolating switch, thereby judging the state of the isolating switch. This system uses Raspberry Pi as the main hardware core, the server drives the CMOS camera through Raspberry Pi 4B, collects image information in the GIS optical observation win

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Putri Madona, Jepri Simatupang, and Ahmad Yani H. "Multisensory Health Monitoring Device Based on Raspberry Pi 4B." Journal of Advanced Research in Applied Sciences and Engineering Technology 42, no. 1 (2024): 42–56. http://dx.doi.org/10.37934/araset.42.1.4256.

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The availability of health monitoring devices that can be used independently, conveniently, and portably is increasing in line with busy lifestyles and the difficulty of scheduling medical tests. Measuring vital body signals with various devices makes measurements longer, less effective, and relatively more expensive. The proposed research can monitor vital body signals, such as heart rate, body temperature, respiratory rate, oxygen saturation, GSR, blood pressure, and snoring, which are integrated into a Raspberry Pi 4B-based device, with results displayed on an LCD screen. Data acquisition r

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Santos, Paulo Ricardo Sousa, Anderson Felipe Garcia Lopes, and Wanderson Roger Azevedo Dias. "Usando RaspBerry Pi para redução energética no IFRO." Revista de Gestão e Secretariado 15, no. 4 (2024): e3596. http://dx.doi.org/10.7769/gesec.v15i4.3596.

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Este estudo se aprofunda na avaliação de dispositivos computacionais de alto desempenho, enfatizando a importância de componentes como processadores octa-core, clusters robustos e GPUs de alta performance que são essenciais para minimizar o tempo de processamento em operações computacionais. A pesquisa seleciona a Raspberry Pi (RPi), uma plataforma de hardware reconhecida por sua versatilidade e capacidade de processamento, como o centro de seus testes de benchmarking. Os testes comparativos foram conduzidos em três diferentes modelos da RPi: o 2B, o 3B e o 4B. Cada modelo foi submetido a uma

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Amish, Kukudkar, Kale Prathamesh, Kanje Sushil, and M.B.Mali Dr. "Driver Drowsiness Recognition System Using Raspberry Pi." Research and Applications: Emerging Technologies 7, no. 2 (2025): 1–6. https://doi.org/10.5281/zenodo.15316057.

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<em>This paper introduces a Driver Drowsiness Recognition System to enhance road safety by continuously monitoring the alertness of drivers. The system uses the Raspberry Pi 4B as the main controller and combines image processing with fatigue detection algorithms. A switch-based system monitors seatbelt use, while a camera conducts continuous monitoring prior to and during driving. The system has several levels of safety built into it, such as detection of alcohol consumption, seatbelt checks, and real-time observation of drowsiness and yawning through computer vision methods. Facial features

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Kim, Hyun-Don. "AI Headphone Design for the Hearing-Impaired." Korea Industrial Technology Convergence Society 27, no. 4 (2022): 21–26. http://dx.doi.org/10.29279/jitr.2022.27.4.21.

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We designed an artificial intelligence (AI) headphone for hearing impaired people and proposed a convolution neural network (CNN)-based sound classifier with a low computational network that can run on an embedded PC (Raspberry Pi 4B) in real-time. Because our AI headphone can classify 20 types of dangerous or environmental sounds (e.g., siren, car horn, scream, gunshot, etc.) and recognize specific voice keywords (e.g., person name, be careful!, stop!, etc.), it can assist hearing impaired people in reducing the risk of accident exposure and improving convenience. In addition, we use vibratio

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Papakyriakou, Dimitrios, and Ioannis S. Barbounakis. "Performance Analysis of Raspberry Pi 4B (8GB) Beowulf Cluster: STREAM Benchmarking." International Journal of Computer Applications 186, no. 78 (2025): 41–55. https://doi.org/10.5120/ijca2025924687.

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Books on the topic "Raspberry Pi 4B"

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Dakin, Eugene. I Wish I Knew How to...Program Raspberry Pi 4B Electronics with Xojo: Buster Edition Mar 2021 Edition 32-Bit Raspberry Pi OS. Scientific Specialties Ltd, 2021.

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Book chapters on the topic "Raspberry Pi 4B"

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Zubov, Dmytro, and Andrey Kupin. "Performance Evaluation of Raspberry Pi 4B Microcomputer: Case Studies on MPICH Cluster, VMware ESXi ARM Fling, and Windows 11 ARM OS." In Information and Communication Technologies in Education, Research, and Industrial Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20834-8_10.

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Conference papers on the topic "Raspberry Pi 4B"

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Wang, Chunliu, Keling Wu, and Yan Zhang. "Design and Implementation of Intelligent Dressing Mirror Based on Raspberry Pi 4B." In 2025 International Conference on Digital Analysis and Processing, Intelligent Computation (DAPIC). IEEE, 2025. https://doi.org/10.1109/dapic66097.2025.00010.

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Khajuria, Raghav, Abhishek Kumar, Tanishq Anand, Shrajan Jain, Prateek Dayal, and Somnath Banerjee. "Efficient Video Streaming on Raspberry Pi 4B: Reducing CPU Utilization Through Optimization Techniques." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00076.

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Jurabek, Khudayberganov, Rakhimov Bakhtiyorjon, and Berdiyev Alisher. "Modeling an Acoustic Noise Cancellation Filter Based on the Raspberry Pi 4B Model." In 2024 International Conference on Information Science and Communications Technologies (ICISCT). IEEE, 2024. https://doi.org/10.1109/icisct64202.2024.10956670.

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Li, Kangle, Jianing Liang, Yiran Yin, Xiaojie Fan, Changxiao Ma, and Yinqing Xie. "Design of a Raspberry Pi 4B-Based Guide Robot with YOLO-v10 Integration." In 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2025. https://doi.org/10.1109/cisce65916.2025.11065537.

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Silva, Gabriel de Oliveira, Danilo Pereira Escudero, and Wanderson Roger Azevedo Dias. "Analisando Velocidade de Leitura e Escrita de Arquivos do SysBench Executando em Raspberry Pi." In Escola Regional de Alto Desempenho Norte 2 e Escola Regional de Aprendizado de Máquina e Inteligência Artificial Norte 2. Sociedade Brasileira de Computação, 2022. http://dx.doi.org/10.5753/erad-no2.2022.228120.

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O microcomputador Raspberry Pi (RPi), desenvolvido pela Fundação Raspberry Pi, foi escolhido para os testes realizados neste projeto por ser de baixo custo financeiro. Assim, esse artigo tem como principal objetivo apresentar os resultados das execuções realizadas no benchmark SysBench que mensuraram a performance da velocidade de leitura e escrita de arquivos no RPi modelos 2B, 3B e 4B, executando na distribuição do Raspberry Pi OS. Os experimentos constataram que o RPi modelo 4B, teve aproximadamente o dobro de velocidade de leitura e escrita de arquivos que os modelos 2B e 3B, executando co

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Kumar, Kanhaiya, Lokesh Varshney, A. Ambikapathy, et al. "Soft computing based solar follower using Raspberry pi 4B." In THE 12TH ANNUAL INTERNATIONAL CONFERENCE (AIC) 2022: The 12th Annual International Conference on Sciences and Engineering (AIC-SE) 2022. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0177498.

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Silva, Gabriel de Oliveira, Wanderson Roger Azevedo Dias, and Danilo Pereira Escudero. "Análise do Desempenho Computacional da Raspberry Pi Executando o Benchmark SysBench." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/wscad_estendido.2022.226666.

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A plataforma Raspberry Pi (RPi), que contém um microcomputador, foi desenvolvida pela Fundação Raspberry Pi e escolhida para ser usada nesse projeto por se tratar de uma plataforma de baixo custo financeiro. Assim, este artigo tem como principal objetivo apresentar os resultados das execuções que mensuraram o poder computacional (performance) da plataforma RPi modelos 2B, 3B e 4B, por meio de uma análise comparativa do desempenho computacional realizada com o uso do benchmark SysBench executando na distribuição do Sistema Operacional Linux Raspbian. Analisando a métrica CPU do benchmark SysBen

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Wang, Wei, Wang Li, and Zongliang Zhang. "A Parallel PCA-SIFT Algorithm Based on Raspberry Pi 4B." In EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering. ACM, 2023. http://dx.doi.org/10.1145/3650400.3650555.

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A. Neto, Antônio José, José M. dos Santos, José A. C. Neto, and Edward D. Moreno. "Clusters Big Data utilizando Raspberry Pi e Apache Hadoop - Uma Quasi-Revisão Sistemática da Literatura." In Anais Estendidos do Simpósio Brasileiro de Engenharia de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbesc_estendido.2022.228147.

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Este trabalho tem como objetivo identificar como estão sendo desenvolvidos os clusters big data de baixo custo, utilizando Raspberry Pi e Apache Hadoop, e como os mesmos estão sendo validados e monitorados. Para tal fim, foi elaborada uma Quasi-Revisão Sistemática da Literatura (QRSL), resultando em 9 artigos relevantes aptos a responder 3 questões de pesquisa. A QRSL identificou que os modelos de Raspberry Pis mais utilizados no desenvolvimento dos clusters são a Raspberry Pi 4B e a Raspberry Pi 2B, e que para sua validação os benchmarks Terasort e Wordcount são os mais citados na literatura,

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Wang, Jiahao, and Ling Long. "Raspberry Pi 4B-based Design of Home Intelligent Access Control System." In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777551.

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