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Journal articles on the topic 'Raspberry Pi 4B'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Rosół, Maciej, and Wojciech Kula. "An Energy-Efficient Field-Programmable Gate Array Rapid Implementation of a Structural Health Monitoring System." Energies 17, no. 11 (2024): 2626. http://dx.doi.org/10.3390/en17112626.

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System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during c

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Diono, Diono, Muhammad Syafei Gozali, and Yohannes Ridho Soru. "Pengklasifikasian Warna dan Bentuk Produk Menggunakan Kamera ELP- USB8MP02G-MFV dengan Berbasis YOLOV7." JURNAL INTEGRASI 17, no. 1 (2025): 40–45. https://doi.org/10.30871/ji.v17i1.9266.

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Semakin berkembangnya teknologi kecerdasan buatan memungkinkan sistem dapat mendeteksi berbagai objek.Pada penelitian pengklasifikasian warna dan bentuk produk menggunakan kamera ELP-USB8MP02G-MFV dengan berbasis YOLOV7 bertujuan untuk untuk memodifikasi konveyor yang berada pada mesin molding.Karena konveyor hanya memiliki fungsi untuk menyalurkan barang dari mesin molding menuju bin dan lamanya waktu yang digunakan untuk menunggu bin penuh menjadi alasan mengapa konveyor ini dimodifikasi. Modifikasi dilakukan dengan cara menambahkan kamera yang telah dihubungkan dengan Raspberry Pi 4B pada k

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Papakyriakou, Dimitrios, and Ioannis S. Barbounakis. "High Performance Linpack (HPL) Benchmark on Raspberry Pi 4B (8GB) Beowulf Cluster." International Journal of Computer Applications 185, no. 25 (2023): 11–19. http://dx.doi.org/10.5120/ijca2023923005.

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

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

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Biswajit, Mallick. "Design and Development of an Intelligent Raspberry Pi-Powered Smart Mirror." Research and Applications: Embedded System 8, no. 2 (2025): 24–32. https://doi.org/10.5281/zenodo.15371121.

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<em>This work presents the development of a Smart Mirror system using Raspberry Pi 4 as part of an advanced robotics project. The smart mirror functions as both a traditional mirror and an interactive digital display, offering users a range of useful information through various modules. Key features include weather updates (based on geographic coordinates), newsfeed, YouTube screencast, compliments, holiday lists, and real-time financial data display. By integrating different functionalities into a single device, the smart mirror aims to make daily routines more efficient and enjoyable, provid

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Budi Utami Fahnun and Reza Pangestu. "SISTEM REMOTE KONTROL PADA ROBOT MOBIL VIA WEB BERBASIS RASPBERRY PI." Jurnal Ilmiah Teknik 1, no. 2 (2022): 143–53. http://dx.doi.org/10.56127/juit.v1i2.204.

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Seiring perkembangan teknologi yang sangat pesat, suatu keamanan saat ini menjadi hal yang sangat penting, ditambah dengan tingkat kejahatan yang sangat tinggi. Banyak upaya yang bisa dilakukan orang guna mengamankan ruangan salah satunya dengan memasang kunci pengamanan pada pintu atau dengan memasang kamera CCTV, tetapi hal itu belum cukup untuk mencegah kejahatan. Seiring dengan berkembangnya Internet of Things (IoT), akan sangat mustahil apabila pengawasan dan pemantauan hanya mengandalkan kemampuan manusia, Tujuan penelitian ini adalah merancang sistem pemantau dengan memanfaatkan Raspber

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Budi Utami Fahnun and Reza Pangestu. "SISTEM REMOTE KONTROL PADA ROBOT MOBIL VIA WEB BERBASIS RASPBERRY PI." Jurnal Ilmiah Teknik 1, no. 2 (2022): 143–53. http://dx.doi.org/10.56127/juit.v1i2.204.

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Seiring perkembangan teknologi yang sangat pesat, suatu keamanan saat ini menjadi hal yang sangat penting, ditambah dengan tingkat kejahatan yang sangat tinggi. Banyak upaya yang bisa dilakukan orang guna mengamankan ruangan salah satunya dengan memasang kunci pengamanan pada pintu atau dengan memasang kamera CCTV, tetapi hal itu belum cukup untuk mencegah kejahatan. Seiring dengan berkembangnya Internet of Things (IoT), akan sangat mustahil apabila pengawasan dan pemantauan hanya mengandalkan kemampuan manusia, Tujuan penelitian ini adalah merancang sistem pemantau dengan memanfaatkan Raspber

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Volynets, Yevhen, Svitlana Khmel, and Olena Pyechkurova. "Visualizing Cognitive States on a Raspberry Pi Platform for Real-Time Biofeedback Using a Brain-Computer Interface." NaUKMA Research Papers. Computer Science 7 (May 12, 2025): 112–19. https://doi.org/10.18523/2617-3808.2024.7.112-119.

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This research article presents a prototype system that integrates an EEG-based brain-computer interface (BCI) and the Raspberry Pi hardware platform for the real-time visualization of a user’s cognitive states. The key advantages of this approach include:1. The ability to directly control external devices, such as LEDs, using brain signals, which enables the creation of biofeedback systems.2. The flexibility and scalability of the Raspberry Pi-based solution, making it suitable for various applications, from smart home systems to educational training.The study utilized the Emotiv INSIGHT EEG s

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Babić, Jelena. "SISTEM ZA MERENJE, PRAĆENJE I UPRAVLJANJE STANJIMA U BAŠTI." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 39, no. 04 (2024): 526–29. http://dx.doi.org/10.24867/26ih02babic.

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U ovom radu je implementiran MQTT protokol između Mosquitto brokera, jednog Raspberry Pi 4B računara i četiri ESP32-D0WDQ6 mikrokontrolera. Cilj zadatka je bio da se pomoću podataka sa senzora o temperaturi i vlažnosti vazduha, vlažnosti zemljišta, količini padavina i intenzitetu svetlosti upravlja ventilom za vodu i zaklonom od Sunca, i da se dati podaci skladište u bazi podataka na centralnom serveru i da se u realnom vremenu prikazuju na veb stranici Grafana.

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Ramadhani, Muhammad Rafi', Fetty Anggraeny, and Eka Prakarsa Mandyartha. "Rancang Bangun Sistem Kamera Pendeteksi Api Sederhana Menggunakan Raspberry Pi." Jurnal Informatika dan Sistem Informasi 2, no. 2 (2021): 162–70. http://dx.doi.org/10.33005/jifosi.v2i2.302.

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Api merupakan elemen penting dalam kehidupan manusia. Semenjak zaman purba hingga zaman modern, api telah digunakan di hampir setiap sektor dalam kehidupan manusia. Contohnya adalah memasak, alat untuk mengusir hewan buas, penerangan, pembakaran pada mesin uap dan lain-lain. Namun terkadang, keberadaan api juga tak jarang menimbulkan bencana dan kerugian. Sebut saja kebakaran hutan, kebakaran akibat korsleting listrik dan lain-lain. ditengah teknologi yang berkembang di era sekarang. Terdapat banyak sekali metode untuk sebagai antisipasi awal apabila terjadi bencana kebakaran. Salah satunya ad

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Robbani, Abdul Jabbar, Fifi Alfiaturrohmah, Maulana Rafi Nurdiansyah, Amanda Salsabila Maharani, and Aditya Dwi Putro. "Implementasi Smart Home pada Platform Apple Homekit dan Google Home dengan Raspberry Pi 4B." JTIM : Jurnal Teknologi Informasi dan Multimedia 5, no. 4 (2024): 377–87. http://dx.doi.org/10.35746/jtim.v5i4.480.

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This research examines the significant impact of technological advances, especially in the Internet of Things (IoT) paradigm, on various aspects of human life strongly manifested during the In-dustrial Revolution Era 4.0. The main focus of this research is on the application of advanced and innovative IoT concepts in the context of smart homes, integrating popular platforms such as Apple HomeKit and Google Home. The temperature sensor (DHT11) and light sensor (LDR) play a key role as important input elements, enabling the optimization of smart home automation functions. Raspberry Pi 4B was cho

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Darmawan, Hendri, Mike Yuliana, Moch Zen Samsono Hadi, and Rahardhita Widyatra Sudibyo. "Fine-tuning GAN Models with Unpaired Aerial Images for RGB to NDVI Translation in Vegetation Index Estimation." International Journal on Advanced Science, Engineering and Information Technology 14, no. 5 (2024): 1654–62. http://dx.doi.org/10.18517/ijaseit.14.5.20063.

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Calculating the Normalized Difference Vegetation Index (NDVI) requires expensive multispectral cameras, posing challenges such as high costs and the need for technical expertise. This research aims to develop a method to transform RGB images obtained from Unmanned Aerial Vehicle (UAV) into NDVI images. Our proposed method leverages the CycleGAN model, an unsupervised image-to-image translation framework, to learn the intricate mapping between RGB values and NDVI. The model was trained on unpaired datasets of RGB and NDVI images, sourced from paddy fields located in Gresik and Yogyakarta, Indon

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Utami, Retnaning Tyas, Tedy Rismawan, and Rahmi Hidayati. "Pengenalan Objek Menggunakan YOLO pada Alat Bantu Tunanetra Berbasis Raspberry Pi." Techno.Com 23, no. 2 (2024): 350–61. http://dx.doi.org/10.62411/tc.v23i2.10317.

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Keterbatasan sensorik yang melekat pada penyandang tunanetra menghalangi rutinitas mereka yang menyebabkan kesulitan dalam menjalankan aktivitas sehari-hari. Kurangnya teknologi pada alat bantu seperti tongkat yang menjadi kebutuhan para tunanetra menyebabkan kesulitan tunanetra menjalani aktivitas secara normal. Alat bantu yang mampu mengenali objek menjadi salah satu solusi untuk membantu para tunanetra. Dalam penelitian ini, dibangun sebuah sistem pengenalan objek menggunakan YOLO pada alat bantu tongkat tunanetra berbasis Raspberry Pi 4b. Sistem ini dibuat untuk membantu tunanetra mendapat

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Triwiyanto, Triwiyanto, Endro Yulianto, Sari Luthfiyah, et al. "Hand Exoskeleton Development Based on Voice Recognition Using Embedded Machine Learning on Raspberry Pi." Journal of Biomimetics, Biomaterials and Biomedical Engineering 55 (March 28, 2022): 81–92. http://dx.doi.org/10.4028/p-ghjg94.

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The choice of using speech to control the exoskeleton is based on the number of exoskeletons that are controlled using the EMG signal, where the EMG signal itself has the weakness of the complexity of the signal which is influenced by the position of the electrodes, as well as muscle fatigue. The purpose of this research is to develop an exoskeleton device using voice control based on embedded machine learning on a Raspberry Pi minicomputer. In this study, two feature extraction types namely mel-frequency cepstral coefficient (MFCC) and zero-crossing (ZC), and two machine learning algorithms,

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Zagitov, A., E. Chebotareva, A. Toschev, and E. Magid. "Comparative analysis of neural network models performance on low-power devices for a real-time object detection task." Computer Optics 48, no. 2 (2024): 242–52. http://dx.doi.org/10.18287/2412-6179-co-1343.

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A computer vision based real-time object detection on low-power devices is economically attractive, yet a technically challenging task. The paper presents results of benchmarks on popular deep neural network models, which are often used for this task. The results of experiments provide insights into trade-offs between accuracy, speed, and computational efficiency of MobileNetV2 SSD, CenterNet MobileNetV2 FPN, EfficientDet, YoloV5, YoloV7, YoloV7 Tiny and YoloV8 neural network models on Raspberry Pi 4B, Raspberry Pi 3B and NVIDIA Jetson Nano with TensorFlow Lite. We fine-tuned the models on our

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Prasad, Nikam, Baru Tejas, Bacchewar Samarth, and Kulkarni Aparna. "Linux Meets HMI: Solving Compatibility Challenges with Yocto." Research and Applications: Emerging Technologies 7, no. 1 (2025): 50–54. https://doi.org/10.5281/zenodo.15152026.

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<em>This literature review investigates the development of Human-Machine Interface (HMI) applications using the Yocto Project on Raspberry Pi 4B. By examining recent advancements, various applications of model-driven frameworks, adaptive interfaces, device driver integration, and automation testing in embedded systems were explored. The survey highlights significant contributions in optimizing performance, enhancing user experience, and supporting modular, scalable architectures. Comparative insights are drawn from related studies to establish a comprehensive understanding of current challenge

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Firmansyah, Anton, Andri Suyadi, Alif Akram Khalish, Al Farick Zulhanudin, and Syafrudin Syafrudin. "Prototipe lampu lalu lintas menggunakan PLC dan SCADA berbasis computer vision dengan raspberry pi 4B." JURNAL ELTEK 23, no. 1 (2025): 32–45. https://doi.org/10.33795/eltek.v23i1.6380.

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Pertumbuhan populasi dan peningkatan jumlah kendaraan di wilayah perkotaan telah menimbulkan tantangan serius dalam manajemen lalu lintas, terutama di persimpangan yang sering mengalami kemacetan. Sistem lampu lalu lintas konvensional yang tidak mampu merespons kondisi lalu lintas secara real-time menyebabkan pengaturan durasi lampu yang tidak efisien, memperburuk kemacetan, meningkatkan emisi karbon, serta menyebabkan pemborosan bahan bakar. Seiring dengan perkembangan teknologi, machine learning digunakan untuk mengoptimalkan pengaturan lalu lintas secara adaptif. Dalam penelitian ini, dikem

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Gao, Tianyu, and Jozsef Suto. "Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency Evaluation on Raspberry Pi 4B." Sensors 25, no. 6 (2025): 1794. https://doi.org/10.3390/s25061794.

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This work investigates the efficiency and power consumption of using the Intel® (Santa Clara, CA, USA) Neural Compute Stick 2 (NCS2) on the Raspberry Pi 4B platform to accelerate image classification and object tracking. The motivation behind this study is to enable the real-time operation of complex neural networks in embedded systems, potentially reducing the cost of deep learning neural network deployment and expanding industrial applications. This study also supplements the OpenVINO™ 2022.3.2 documentation by recording the application of the Raspberry Pi 4B combined with the NCS2 in the la

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Pan, Dong-Xu, Hao-Lin Ye, and Chih-Ying Chuang. "Intelligent Cat Recognition and Feeding System Based on Raspberry Pi and OpenCV Vision Technology." International Journal of Advanced Engineering Research and Science 12, no. 6 (2025): 19–27. https://doi.org/10.22161/ijaers.126.3.

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To address the issue of feeding outdoor cats, this study designed an intelligent cat recognition and feeding system. In terms of hardware, it integrates a Raspberry Pi 4B with OpenCV to combine ultrasonic sensor cameras, servos, and pressure sensors, forming a dynamic monitoring, characteristic identification, and precise feeding operation process. In software, OpenCV is used for cat face recognition, and Python scripts are employed to coordinate the work of sensors and actuators. The response time of the system can be controlled to be below 4 seconds. In the accurate recognition and feeding m

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Tauladani, Sarah Putri, Rieke Adriati Wijayanti, and Rachmad Saptono. "Implementation YOLOv5 Method for Detecting Safety Equipment Completeness Images on Site Tower (Case Study: PT. Nexwave Surabaya)." JURNAL JARTEL: Jurnal Jaringan Telekomunikasi 14, no. 2 (2024): 145–55. https://doi.org/10.33795/jartel.v14i2.5264.

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The safety equipment completeness detection system is vital for workplace accident prevention. One effective method for this system is YOLOv5, known for its speed and accuracy due to its optimized deep neural network architecture. In this research, we developed a system titled "Implementation of YOLOv5 for Detecting Safety Equipment Completeness on Site Towers (Case Study: PT. Nexwave Surabaya)." We trained this system with a custom dataset from PT. Nexwave Surabaya, comprising 380 images of 5 detection classes: helmets, gloves, safety shoes, vests, and harnesses. The system is built on a Rasp

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Hou, Lixin, Zeye Liu, Jixuan You, et al. "Tomato Sorting System Based on Machine Vision." Electronics 13, no. 11 (2024): 2114. http://dx.doi.org/10.3390/electronics13112114.

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In the fresh tomato market, it is crucial to sort and sell tomatoes based on their quality. This is important to enhance the competitiveness and profitability of the market. However, the manual sorting process is subjective and inefficient. To address this issue, we have developed an automatic tomato sorting system that uses the Raspberry PI 4B as the control platform for the robot arm. This system has been integrated with a human–computer interaction interface sorting system. Our experimental results indicate that this sorting method has an accuracy rate of 99.1% and an efficiency of 1350 tom

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Aaryan Mehta, Priyam Parikh, and Parth Shah. "Raspberry-Pi Based Physical Media to Audio Conversion device for Visually Impaired Individuals." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 4 (2024): 249–60. http://dx.doi.org/10.32628/ijsrset24114127.

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The proposed product is a device for real-time scanning and conversion of text from physical media to audio for the aid of visually impaired individuals. The focus of the project is to make a device which brings the experience of visually impaired individuals as close to that of the ordinarily abled/educated as possible when it comes to access to resources, books, and physical reading material. This device is targeted towards libraries, reading rooms, and schools for visually impaired individuals. The prototype is developed using a FDM 3D printer with PLA material and using a laser cutting mac

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Suder, Jakub, Kacper Podbucki, Tomasz Marciniak, and Adam Dąbrowski. "Low Complexity Lane Detection Methods for Light Photometry System." Electronics 10, no. 14 (2021): 1665. http://dx.doi.org/10.3390/electronics10141665.

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The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color spac

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Alekhya Kodavatiganti. "The Human-Robot Interactive Helmet." World Journal of Advanced Engineering Technology and Sciences 14, no. 1 (2025): 142–49. https://doi.org/10.30574/wjaets.2025.14.1.0011.

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The human-robot Interactive helmet (HRI Helmet) project was created to enhance the safety features for cyclists. Advanced technological integration enabled real-time health monitoring, GPS navigation, and emergency communication. Equipped with a Raspberry Pi 4B, pulse sensors, GPS module and the Google Maps API, this helmet can deliver several vital safety features: heart rate tracking, turn-by-turn navigation, and SOS functionality that activates automatically and manually. Although there have been challenges, like setting up the microphones and permission issues with the serial port, the pro

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Lê, Vũ Nam, Vũ Liêm Khổng, Văn Thư Nguyễn та Đình Quý Phạm. "Phát triển hệ thống bắt bám mục tiêu thời gian thực bằng Raspberry Pi". Journal of Military Science and Technology 90 (25 жовтня 2023): 127–33. http://dx.doi.org/10.54939/1859-1043.j.mst.90.2023.127-133.

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Kỹ thuật bắt bám mục tiêu sử dụng các thuật toán tìm kiếm và dự đoán của máy tính để định vị và dõi theo các mục tiêu một cách tự động mà không cần con người can thiệp. Việc áp dụng kỹ thuật bắt bám mục tiêu vào nhiệm vụ theo dõi, quan sát sẽ giúp nhiệm vụ này trở nên hiệu quả và dễ dàng hơn. Một yêu cầu quan trọng của nhiệm vụ này đó là tốc độ bắt bám phải đủ nhanh để đảm bảo yêu cầu thời gian thực, đồng thời vẫn đảm bảo độ chính xác và ổn định. Ngoài ra, các thiết bị quan sát thường sử dụng phần cứng nhỏ gọn như máy tính nhúng với hiệu năng thấp, các camera lại có độ phân giải cao. Trong bài

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Zhang, Chenhao, Zhiren Xiao, and Chenhao Fu. "Design and implementation of wild plant grabbing system and car in Tibet based on Raspberry Pi 4B." Advances in Engineering Technology Research 10, no. 1 (2024): 59. http://dx.doi.org/10.56028/aetr.10.1.59.2024.

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This article introduces a plant collection system applied in Tibet to solve the challenges of traditional manual collection in the plateau environment. The system uses a remotely controlled Raspberry Pi trolley[1] to collect plants, which improves the collection efficiency and the safety of collectors. Hardware modules include basic functional systems and artificial intelligence subsystems for control, navigation and plant recognition. The AI[2] subsystem uses the YOLOv5s[3] model for plant recognition and cooperates with sensors to implement environmental monitoring. The software interface pr

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Schäfer, Jörg, Baldev Raj Barrsiwal, Muyassar Kokhkharova, Hannan Adil, and Jens Liebehenschel. "Human Activity Recognition Using CSI Information with Nexmon." Applied Sciences 11, no. 19 (2021): 8860. http://dx.doi.org/10.3390/app11198860.

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Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people. There are numerous possibilities to use the Wi-Fi-based HAR solution for human-centric applications in intelligent surveillance, such as human fall dete

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Khairunissa, Natasha, and Rifki Suwandi. "Sistem Pendeteksi Ketersediaan Tempat Duduk pada Perpustakaan berbasis Computer Vision." CHIPSET 6, no. 01 (2025): 76–85. https://doi.org/10.25077/chipset.6.01.76-85.2025.

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Libraries as information centers often face challenges in seating management, especially when visitor numbers are high. This can cause difficulties for new patrons in finding empty seats, as well as disrupting other patrons' activities. Existing solutions, such as reservation systems and sensors, are often less effective and face accuracy issues. This research proposes a computer vision-based empty seat detection system to improve space efficiency. Using Roboflow 3.0 object detection model trained on Roboflow GPU and implemented on Raspberry Pi 4B with webcam, this system detects and displays

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Papakyriakou, Dimitrios, and Ioannis S. Barbounakis. "Benchmarking and Review of Raspberry Pi (RPi) 2B vs RPi 3B vs RPi 3B+ vs RPi 4B (8GB)." International Journal of Computer Applications 185, no. 3 (2023): 37–52. http://dx.doi.org/10.5120/ijca2023922693.

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Jiang, Renjie, Chengyun Wei, Yong Fan, Shuqin Wu, and Wu Xie. "Design and experimental research on intelligent harvesting device for citrus fruits." Journal of Physics: Conference Series 3032, no. 1 (2025): 012004. https://doi.org/10.1088/1742-6596/3032/1/012004.

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Abstract Aiming at the problems of low efficiency and high labor intensity in traditional manual picking of citrus fruits, a set of intelligent picking devices integrating a five-degree-of-freedom mechanical arm, a shear gripper, and an omnidirectional mobile chassis was designed. Using the Raspberry Pi 4B main control and binocular vision camera detection, combined with the YOLOv5 deep learning model, the threedimensional coordinate recognition and positioning of citrus fruits and the control of the mechanical arm picking were achieved, completing the precise shearing of the target fruit stal

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Suder, Jakub, Kacper Podbucki, and Tomasz Marciniak. "Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection." Energies 16, no. 18 (2023): 6677. http://dx.doi.org/10.3390/en16186677.

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In this paper, the comparison of the power requirements during real-time processing of video sequences in embedded systems was investigated. During the experimental tests, four modules were tested: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX, and NVIDIA Jetson Orin AGX. The processing speed and energy consumption have been checked, depending on input frame size resolution and the particular power mode. Two vision algorithms for detecting lines located in airport areas were tested. The results show that the power modes of the NVIDIA Jetson modules have sufficient computing res

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Machowski, Jakub, and Mariusz Dzieńkowski. "Selection of the type of cooling for an overclocked Raspberry Pi 4B minicomputer processor operating at maximum load conditions." Journal of Computer Sciences Institute 18 (March 30, 2021): 55–60. http://dx.doi.org/10.35784/jcsi.2437.

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The Raspberry Pi is a computer platform that is widely used in education, has a very large community and extensive documentation. Therefore, it can be a good and cheap alternative to a traditional computer, a TV streaming device or a console for less demanding games. In the case of observing a lower efficiency of the microcomputer, one of many possibilities of improvement, which this device offers is overclocking the processor. It is associated with a proper selection of parameters (voltage, clocking) and software in order to achieve the highest possible performance of the dedicated Raspbian s

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S. Hi. Rauf, Faris Zulkarnain, Djati Handoko, Ilham S. Pradana, and Dimas Alifta. "Comparison of YOLOv3-tiny and YOLOv4-tiny in the Implementation Handgun, Shotgun, and Rifle Detection Using Raspberry Pi 4B." Jurnal Elektronika dan Telekomunikasi 24, no. 1 (2024): 52. http://dx.doi.org/10.55981/jet.602.

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Criminal activities frequently involve carryable weapons such as handguns, shotguns, and rifle classes. Frequently, the targets of these weapons that are captured are concealed from plain sight by the people of the crowd. The detection process for these weapons can be assisted by using deep learning. In this case, we intend to identify the model of the firearm that was detected. This research aims to apply one of the deep learning concepts, namely You Only Look Once (YOLO). The authors use versions of YOLOv3-tiny and Yolov4-tiny for the detection and classification of types of weapons, which a

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Memon, Khuhed, Norashikin Yahya, Mohd Zuki Yusoff, et al. "Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation." Sensors 24, no. 21 (2024): 7091. http://dx.doi.org/10.3390/s24217091.

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Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medi

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Xiao, Xue, Chen Chen, Martin Skitmore, Heng Li, and Yue Deng. "Exploring Edge Computing for Sustainable CV-Based Worker Detection in Construction Site Monitoring: Performance and Feasibility Analysis." Buildings 14, no. 8 (2024): 2299. http://dx.doi.org/10.3390/buildings14082299.

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This research explores edge computing for construction site monitoring using computer vision (CV)-based worker detection methods. The feasibility of using edge computing is validated by testing worker detection models (yolov5 and yolov8) on local computers and three edge computing devices (Jetson Nano, Raspberry Pi 4B, and Jetson Xavier NX). The results show comparable mAP values for all devices, with the local computer processing frames six times faster than the Jetson Xavier NX. This study contributes by proposing an edge computing solution to address data security, installation complexity,

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Zhou, Brian, Jason Geder, Kamal Viswanath, Alisha Sharma, and Julian Lee. "Power-Aware Inverse-Search Machine Learning for Low Resource Multi-Objective Unmanned Underwater Vehicle Control (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23714–16. http://dx.doi.org/10.1609/aaai.v38i21.30538.

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Flapping-fin unmanned underwater vehicle (UUV) propulsion systems enable high maneuverability for tasks ranging from station-keeping to surveillance but are often constrained by their limited computational power and battery capacity. Previous research has demonstrated that time-series neural network models can accurately predict the thrust and power of certain fin kinematics based on the specified gait coupled with the fin configuration, but can not fit an inverse neural network that takes a thrust request and tunes the kinematics by weighting thrust generation, smooth movement transitions, an

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Zhou, Guiyu, Bo Zhang, Qinghao Li, Qin Zhao, and Shengyao Zhang. "Optimizing success rate with Nonlinear Mapping Control in a high-performance raspberry Pi-based light source target tracking system." PLOS ONE 20, no. 2 (2025): e0319071. https://doi.org/10.1371/journal.pone.0319071.

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This study addresses the limitations of linear mapping in two-dimensional gimbal control for moving target tracking, which results in significant control errors and slow response times. To overcome these issues, we propose a nonlinear mapping control method that enhances the success rate of light source target tracking systems. Using Raspberry Pi 4B and OpenCV, the control system performs real-time recognition of rectangular frames and laser spot images. The tracking system, which includes an OpenMV H7 Plus camera, captures and processes the laser spot path. Both systems are connected to an ST

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Liu, Xiaotang, Zheng Xing, Huanai Liu, et al. "Combination of UAV and Raspberry Pi 4B: Airspace detection of red imported fire ant nests using an improved YOLOv4 model." Mathematical Biosciences and Engineering 19, no. 12 (2022): 13582–606. http://dx.doi.org/10.3934/mbe.2022634.

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<abstract> <p>Red imported fire ants (RIFA) are an alien invasive pest that can cause serious ecosystem damage. Timely detection, location and elimination of RIFA nests can further control the spread of RIFA. In order to accurately locate the RIFA nests, this paper proposes an improved deep learning method of YOLOv4. The specific methods were as follows: 1) We improved GhostBottleNeck (GBN) and replaced the original CSP block of YOLOv4, so as to compress the network scale and reduce the consumption of computing resources. 2) An Efficient Channel Attention (ECA) mechanism was introd

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Alon, Alvin Sarraga. "Machine Vision Recognition System for Iceberg Lettuce Health Condition on Raspberry Pi 4b: A Mobile Net SSD v2 Inference Approach." International Journal of Emerging Trends in Engineering Research 8, no. 4 (2020): 1073–78. http://dx.doi.org/10.30534/ijeter/2020/20842020.

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Nagy, Naya, Sarah Alnemer, Lama Mohammed Alshuhail, et al. "Module-Lattice-Based Key-Encapsulation Mechanism Performance Measurements." Sci 7, no. 3 (2025): 91. https://doi.org/10.3390/sci7030091.

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Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, an

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