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Academic literature on the topic 'Raspberry Pi 4'

Author: Grafiati

Published: 28 June 2021

Last updated: 25 April 2022

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Journal articles on the topic "Raspberry Pi 4"

Mulyanto, Trio Ade, Mukhtar Habiby, Kusnadi Kusnadi, and Rinaldi Adam. "HOME AUTOMATION SYSTEM DENGAN MENGGUNAKAN RASPBERRY PI 4." Jurnal Digit 11, no. 1 (May 30, 2021): 60. http://dx.doi.org/10.51920/jd.v11i1.180.

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ABSTRAK Home automation merupakan salah satu kemajuan teknologi dan komunikasi dengan otomasi kontrol terhadap rumah. Dengan metode ini, perangkat-perangkat elektronik rumah dapat di kontrol mengunakan sistem kendali jarak jauh tanpa harus menyetuh perangkat elektronik yang ada di rumah. Raspberry Pi digunakan sebagai pemroses untuk menciptakan sebuah sistem. Raspberry Pi merupakan mini computer yang fungsi-fungsi dasarnya sama seperti sebuah personal komputer biasa dengan ukuran sebesar kartu kredit. Raspberry Pi dapat mengontrol perangkat-perangkat elektronik yang berada di rumah dengan memanfaatkan fasilitas GPIO (General Purpose Input Output). Perangkat elektronika yang dapat di control meliputi lampu, kipas angin, suhu ruangan, kamera pengintai dan masih banyak lagi. Perancangan sistem meliputi perancangan alat untuk sistem kontrol arus di mana mengunakan Raspberry Pi untuk mengontrol 6 buah lampu, kipas sensor suhu, sensor motion dan 1 buah adaptor atau kontak saklar dengan memanfaatkan Relay 8 chanel sebagai penghubung dan juga sebagai saklar on / off. Tahap implementasi dilakukan dengan mengambungkan perangkat lunak dan perangkat keras untuk menghasilkan sistem control dengan menghubungkan web server sebagai media interface dan yang terakhir uji coba sistem yang di bangun. Sistem yang di bangun akan menjadi sistem kontrol perangkat elektronik rumah dengan implementasinya menyalakan dan mematikan lampu, mengecek suhu ruangan ,menyalakan kipas dan monitoring keadaan rumah dengaan memanfaatkan web server yang di tanam di dalam raspberry pi sebagai media interface user berupa web yang akan di akses user mengunakan smartphone atau komputer. Dengan ini sistem yang di bangun akan sangat membantu pengguna dalam melakukan pengontrolan perangkat elektronik rumah dimanapun pengguna berada dan menghemat daya listrik. Kata Kunci : Home Automation, Smarthome, Raspberry Pi.

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Ganesan, M., R. Hemanth., S. Gunalan., and J. Hemprasad. "Raspberry PI Based Smart Walking Stick." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 042090. http://dx.doi.org/10.1088/1757-899x/981/4/042090.

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Prasath kumar, S., P. Rayavel, N. Anbarasi, B. Renukadevi, and D. Maalini. "Raspberry pi based secured cloud data." Journal of Physics: Conference Series 1964, no. 4 (July 1, 2021): 042101. http://dx.doi.org/10.1088/1742-6596/1964/4/042101.

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Srivastava, Ankit, Prabhat Singh, Sushil Kumar Verma, Kumar Kartikey, and Prof Shubham Shukla. "Path Planning Robot Using PI-CAM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 66–70. http://dx.doi.org/10.22214/ijraset.2022.41133.

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Abstract: Currently, the path planning robot is one among the foremost researched topics in autonomous robotics technology. Path planning has important applications in many areas, for example industrial robotics, autonomous systems, and 3D digital representation that can perform various types of simulations iteratively about product performances. The utilization of versatile robots for modern purposes like arranging, moving, and putting is at this point not an inclination however a need. Our path planning robot is using OpenCV, image processing and various hardware’s such as raspberry pi 4, Arduino uno , motor driver and pi -cam. The analysis of image will be done using image processing through raspberry pi and for edge detection canny edge algorithm is used. Keywords: OpenCV, Raspberry pi 4, Pi-Cam, Canny Edge Detection, Image Processing.

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Hermawan, Indra, Defiana Arnaldy, Maria Agustin, M. Farishanif Widyono, David Nathanael, and Meutia Tri Mulyani. "Sistem Pengenalan Benih Padi menggunakan Metode Light Convolutional Neural Network pada Raspberry PI 4 B." Jurnal Teknologi Terpadu 7, no. 2 (December 30, 2021): 120–26. http://dx.doi.org/10.54914/jtt.v7i2.443.

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Baru-baru ini, metode pembelajaran mendalam dengan Convolution Neural Network (CNN) telah banyak digunakan untuk tugas klasifikasi gambar. CNN memiliki keunggulan yang tak tertandingi dalam mengekstraksi fitur gambar diskriminatif. Namun, banyak metode berbasis CNN yang ada dirancang untuk lebih dalam dan lebih besar dengan lapisan yang lebih kompleks. Sehingga membuatnya sulit untuk diterapkan pada perangkat seluler atau pada perangkat waktu nyata yang menggunakan mikrokontroler seperti raspberry pi, Arduino, dan lain sebagainya. Hal tersebut diatasi dengan menggunakan Light Convolution Neural Network (LCNN), maka perlu dilakukan percobaan untuk menguji seberapa besar perbedaan kinerja LCNN pada Personal Computer (PC) dan pada mikrokontroler raspberry pi 4 dengan sistem operasi Raspbian. Eksperimen dilakukan dengan menggunakan beberapa parameter kinerja yaitu accuracy, F-1 Score, recall, precision, dan waktu dari pengujian klasifikasi untuk mendapatkan hasil performa dari pembelajaran mendalam. Oleh karena itu, hasil dan arsitektur model akan mengkonfirmasi perbedaan kinerja di masing-masing perangkat dan menunjukkan bagaimana performa model pada perangkat yang dibatasi sumber daya atau berjalan secara waktu nyata. Pengujian menunjukkan bahwa kinerja pada raspberry pi yang merupakan alat dengan sumber daya terbatas tidak mempengaruhi kualitas pengenalan gambar, tetapi mempengaruhi waktu pemrosesan pengenalan, dikarenakan raspberry pi membutuhkan waktu proses yang lebih lama untuk melakukan satu proses pengenalan data atau foto. Hal tersebut akan mengakumulasi waktu yang dibutuhkan untuk pemrosesan data yang banyak, sehingga dapat disimpulkan bahwa raspberry pi dan alat dengan sumber daya terbatas sangat tidak efektif untuk melakukan pelatihan pengenalan dan melakukan proses pengenalan yang berisi banyak data atau foto dalam sekali prosesnya.

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Maragatham, T., P. Balasubramanie, and M. Vivekanandhan. "IoT Based Home Automation System using Raspberry Pi 4." IOP Conference Series: Materials Science and Engineering 1055, no. 1 (February 1, 2021): 012081. http://dx.doi.org/10.1088/1757-899x/1055/1/012081.

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Prasetya, Aldo Dwi, Muhammad Daffa Raihan Ma'arif, Shania Syaharani, Imam Halimi, and Dezetty Monika. "Smart Mirror Berbasis Raspberry Pi 4 untuk Home Automation." ELECTRICES 3, no. 1 (June 3, 2021): 34–39. http://dx.doi.org/10.32722/ees.v3i1.4136.

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Smart Mirror merupakan cermin dua arah dengan tampilan elektronik di belakang kaca. Smart Mirror dapat menampilkan berbagai jenis informasi dalam bentuk widget dan akan berguna bagi individu yang ingin melakukan banyak tugas dan tetap mendapat informasi. Alat ini dapat dikontrol dengan voice command atau dengan berupa sentuhan pada IR Frame untuk mengaktifkan voice detector. Untuk mengetahui apakah alat ini berfungsi dilakukan metode pengujian dengan melihat sistem dan proses secara keseluruhan pada alat dimulai dari tampilan grafis Smart Mirror, fungsi sensor pendukung, fungsi komponen, dan fungsi Voice Assistant sebagai Home Automation. Dimana masing-masing pengujian tersebut memiliki tahapan yang berbeda-beda tergantung dengan apa yang akan diuji. Kemudian didapatkan hasil bahwa tampilan grafis Smart Mirror, fungsi sensor pendukung, fungsi komponen, dan fungsi Voice Assistant sebagai Home Automation dapat berfungsi dengan baik. Namun, pada pengukuran kelembapan udara terdapat kesalahan dimana data yang terukur tidak terbaca. Untuk durasi lamanya waktu dari beban merespon adanya perintah. Masing-masing beban yang digunakan pada penelitian ini dapat berhasil merespon adanya perintah dengan durasi waktu antara 5 – 8,5 detik. Dimana, beban yang paling cepat merepon perintah ialah lampu merk Bardi dan beban yang paling lama untuk merespon perintah ialah saklar 2.

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Budiyanta, Nova Eka, Catherine Olivia Sereati, and Ferry Rippun Gideon Manalu. "Processing time increasement of non-rice object detection based on YOLOv3-tiny using Movidius NCS 2 on Raspberry Pi." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 1056–61. http://dx.doi.org/10.11591/eei.v11i2.3483.

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This study aims to increase the processing time of detecting non-rice objects based on the you only look once v3-tiny (YOLOv3-tiny) model. The system was developed on the Raspberry Pi 4 embedded system with the Movidius neural compute stick 2 (NCS 2) implementation approach. Data object in the form of gravel on a bunch of rice in the video. The video data was obtained using a webcam with a resolution of 1920 x 1080 pixels with a total of 2759 frames. From the test results, frames per second (FPS) have increased by 1.27x in the Movidius NCS 2 implementation compared to processing using the central processing unit (CPU) from the Raspberry Pi 4. The object detection processing on video data is complete at 1871.408 seconds with 1.474 FPS using the CPU from the Raspberry Pi 4 and finished at 1477.141 seconds with 1.868 FPS using Movidius NCS 2. From these differences, it can be seen that the application of Movidius NCS 2 succeeded in increasing the object detection processing in this study by 26.69% with the YOLOv3-tiny model approach on the Raspberry Pi 4 embedded system.

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

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In the Industrial 4.0 era, almost all activities and transactions are carried out via the internet, which basically uses web technology. For this reason, it is absolutely necessary to have a high-performance web server infrastructure capable of serving all the activities and transactions required by users without any constraints. This research aims to design a high-performance (high availability) web server infrastructure with low cost (low cost) and energy efficiency. low power) using Cluster Computing technology on the Raspberry Pi Single Board Computing and Docker Container technology. The cluster system is built using five raspberry Pi type 4B modules as cluster nodes, and the Web server system is built using docker container virtualization technology. Meanwhile, cluster management uses Docker Swarm technology. Performance testing (Quality of Service) of the cluster system is done by simulating a number of loads (requests) and measuring the response of the system based on the parameters of Throughput and Delay (latency). The test results show that the Raspberry Pi Cluster system using Docker Swarm can be used to build a High Availability Server system that is able to handle very high requests that reach Throughput = 161,812,298 requests / sec with an Error rate = 0%.

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ANDRIOAIA, DRAGOS-ALEXANDRU, GEORGE CULEA, and PETRU-GABRIEL PUIU. "ENVIRONMENTAL TEMPERATURE AND HUMIDITY MONITORING SYSTEM USING RASPBERRY PI 4 AND THINGSPEACK." Journal of Engineering Studies and Research 27, no. 3 (January 10, 2022): 20–23. http://dx.doi.org/10.29081/jesr.v27i3.283.

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In recent years, IoT platforms have become increasingly used due to their untapped potential. This paper aims to create an IoT system to monitor temperature and humidity in an enclosure The Raspberry Pi 4 SBC (Single-Board Computer) development board and ThingSpeak cloud platform will be used to make this system. Data from the DHT11 humidity and temperature sensor will be collected by the Raspberry PI 4 SBC development board, which will transmit it via the WiFi connection to the IoT ThingSpeak platform cloud for further analysis. The IoT ThingSpeak platform provides data storage, processing and visualization services.

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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 jobs.

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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 of this thesis are focus on description of cyber-attacks based on ISO/OSI model's Link and Network layers. Then there is description of IPS/IDS systems and theirs open source representatives. The practical part is focus on experimental workspace, hardware consumption of choosen detection systems, cyber-attacks scenarios and own implementation of detection program. Detection program is based on these chosen systems and puts them together to be easily manageable.

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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 samt träningskonceptet ”Transfer learning” har implementerats. Resultaten presenteras i form av kvantifiering av realtidskörning på FPS (frames per second), detektion noggrannhet, CPU-temperatur och CPU-frekvens i olika experiment. En slutsats är att Raspberry pi 4 kan utföra objektklassificering och detektion med hög noggrannhet i en del scenarier för trafikövervakning med YOLO-algoritmer. Ett scenario för att klassificera objekt med långsam hastighet till exempel gående, skulle det vara genomförbar med att klassificera och detektera med en högnoggrannhet. För objekt med höghastighet som bilar och cyklister så har Raspberry pi 4 svårt att detektera och klassificera objekter.
Object detection is one of the essential software components in the next generation of traffic monitoring. Real-time detection and recognition of objects are essential tasks for image processing. Therefore, deep learning algorithms for object detection such as YOLO (You Only Look Once) are increasingly used in image analysis, since they run in normal video frame rate (real-time) and are reasonably accurate. This study presents an embedded system and its results for detecting and recognizing objects in real-time. Results are based on a video stream originating from a traffic environment in the city of Halmstad (Sweden). The embedded system is implemented in Raspberry pi 4 using the software Tensorflow and different deep learning algorithms of the YOLO software package. Real-time analyses on frames per second, accuracy in mean average precision, CPU temperature, and CPU frequency are reported for experiments comprising transfer learning. A main conclusion is that Raspberry pi 4 can perform object classification and detection with high accuracy in certain scenarios for traffic monitoring with YOLO algorithms. For example, classifying objects with the speed of a pedestrian would be feasible with classifying and detecting with high accuracy. On the other hand, with high-speed objects such as cars and cyclists, it is a more challenging task for Raspberry pi 4 to detect and classify objects.

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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. The theoretical part concludes overview of captured devices including a description of their data units. In the practical part is the implementation of the sample application for receriving data from a Wireless M-Bus sensors. The application is able to read data from devices transmitting encrypted communication.

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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 overarching methods where a test for latency and throughput is conducted. The thesis goes on to expose these tests to different stressors, categorized as either internal or external. The data collected aims to show the impacts of internal properties, in this case size of the packets transmitted, the size of the network, and finally the number of hops a packet is able to make within the network. The external factors tested for consists of various environmental properties in the form of obstacles and interference. Walls and a microwaves were used as obstacles while WiFi and other Bluetooth signals were used for interference. The results show that Bluetooth Low Energy (BLE) mesh networks are clearly affected by several internal and external factors. From the experimentation conducted, the thesis illustrates the relative effects of each property the tests are exposed to.
Ett mesh nätverk är en teknik som blivit populär igen och används ofta av allmänheten. Eftersom denna ökade användning observeras, tekniker som Bluetooth anpassas för att skapa mesh nätverksvarianter. I denna avhandling skapas och testas ett Bluetoothnätverk med Raspberry pi 4’s och Bluetoothgränssnittet, btferret. Denna uppsats försöker nå gränserna för denna teknik med hjälp av tillgängliga verktyg, definiera nätverks prestandan som en riktlinje för att avgöra om det är lämpligt för användning för uppgifter till hands. Resultaten visar att BLE mesh nätverk har tydliga begränsningar som avslöjar sig i olika sammanhang. I denna raport så undersöks paket storlek och antal hopp som ett paket kan göra inom nätverket utan signifikant prestandafall. Dessutom har olika andra faktorer, såsom väggar och andra störande radiofrekvenser visat sig påverka nätverket. Från alla experiment som genomförts så illustreras relativa effekt av det olika faktorer.

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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 benefit of higher throughput as a trade-off for lower accuracy. To compensate for the loss of accuracy, using transfer learning and tensorflow, a custom object detection model has been trained by fine-tuning a pretrained SSD model. The fine-tuned model was trained on images scraped from the web with people in winter landscape. The pretrained model was trained to detect different objects, including people in various environments. Predictions shows that the custom model performs significantly better doing detections on people in snow. The conclusion from this is that web scraping can be used for fine-tuning a model. However, the images scraped is of bad quality and therefore it is important to thoroughly clean and select which images that is suitable to keep, given a specific application.
Användning av djupinlärning på lågkostnadsmaskiner, som Raspberry Pi, kan idag mer än någonsin användas på grund av enkel användning, tillgänglighet, och hög prestanda. En kvantiserad förtränad SSD-objektdetekteringsmodell har implementerats på en Raspberry Pi 4 B för att utvärdera om genomströmningen är tillräcklig för att utföra realtidsobjektigenkänning. Med en ingångsupplösning på 300x300 pixlar erhölls en periodtid på 185 ms. Detta är en stor förbättring med avseende på prestanda jämfört med den tidigare modellen; Raspberry Pi 3 B+, 238 ms med en ingångsupplösning på 96x96 som erhölls i en relaterad studie. Att använda en kvantiserad modell till förmån för hög genomströmning bidrar till lägre noggrannhet. För att kompensera för förlusten av noggrannhet har, med hjälp av överföringsinlärning och Tensorflow, en skräddarsydd modell tränats genom att finjustera en färdigtränad SSD-modell. Den finjusterade modellen tränas på bilder som skrapats från webben med människor i vinterlandskap. Den förtränade modellen var tränad att känna igen olika typer av objekt, inklusive människor i olika miljöer. Förutsägelser visar att den skräddarsydda modellen detekterar människor med bättre precision än den ursprungliga. Slutsatsen härifrån är att webbskrapning kan användas för att finjustera en modell. Skrapade bilder är emellertid av dålig kvalitet och därför är det viktigt att rengöra all data noggrant och välja vilka bilder som är lämpliga att behålla gällande en specifik applikation.

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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 sistemi di controllo d’assetto per nanosatelliti. Nella prima parte del documento, partendo dai cenni teorici che stanno alla base del funzionamento di un sensore di sole digitale, vengono descritte le strutture hardware e software di un sensore di sole basato su una camera e un microcontrollore Raspberry Pi e sono, inoltre, descritti il montaggio e il primo tuning del prototipo. La seconda parte è, invece, incentrata sulla validazione sperimentale del sensore attraverso test di ripetibilità iterativi. Attraverso la selezione di un’opportuna distanza focale, viene garantito un FOV di 88° gradi sull’asse di imbardata e di 71° sull’asse di beccheggio. Attraverso i test su un banco prova basato su una fonte di luce collimata, è stato verificato che gli errori nella determinazione delle rotazioni sul piano perpendicolari alla fonte di luce sono inferiori a 0.32°.

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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 advantages and disadvantages of these, and presents an Edge-prototype based on Computer Vision and Object Detection. The presented APC was tested in a lab environment and with recordings of people walking in and out of a designated area in the lab. Test results from the lab environment show that the presented low-cost APC efficiently detects passengers with an accuracy of 98.6% on pre-recorded videos. The APC was also tested in real time and the results show that the low-cost APC only achieved an accuracy of 66.7%. This work has laid the ground for further development and testing in a public transport environment.

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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"

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

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

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

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

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

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

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

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

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Pajankar, Ashwin. Raspberry Pi Supercomputing and Scientific Programming. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2878-4.

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

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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, 1–18. Berkeley, CA: 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, 19–28. Berkeley, CA: 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, 67–108. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5722-7_3.

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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, 49–68. Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9780429294990-4.

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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, 29–43. First edition. | New York, N.Y. : CRC Press/Taylor & Francis Group, 2019.: 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, 43–61. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_4.

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Venu, Sibeesh. "Azure IoT Central." In Asp.Net Core and Azure with Raspberry Pi 4, 191–228. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_10.

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Venu, Sibeesh. "Setting Up the Prerequisites to Develop the Application." In Asp.Net Core and Azure with Raspberry Pi 4, 29–42. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_3.

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Venu, Sibeesh. "Playing with Azure IoT Hub and Our Application." In Asp.Net Core and Azure with Raspberry Pi 4, 63–86. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_5.

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Venu, Sibeesh. "Finally, A Windows Terminal That You Can Customize." In Asp.Net Core and Azure with Raspberry Pi 4, 87–93. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6443-0_6.

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Conference papers on the topic "Raspberry Pi 4"

Mythili, R., Pullyala Nithin Reddy, B. Keerthivasan, and V. Sooriya. "Encrypted NAS using Raspberry Pi 4." In 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). IEEE, 2021. http://dx.doi.org/10.1109/iceeccot52851.2021.9707921.

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Buzura, Loredana, Gabriel Groza, Radu Papara, and Ramona Galatus. "Assisted OCT diagnosis embedded on Raspberry Pi 4." In 2021 IEEE 27th International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2021. http://dx.doi.org/10.1109/siitme53254.2021.9663686.

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Mora, Eduardo Alfonso Huerta, Victor Alejandro Gonzalez Huitron, Abraham Efraim Rodriguez Mata, and Hector Rodriguez Rangel. "Plant disease detection with convolutional neural networks implemented on Raspberry Pi 4." In 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2020. http://dx.doi.org/10.1109/ropec50909.2020.9258684.

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Soares, Felipe, Lucas Fernandes, Atslands Da Rocha, Paulo Rego, José Maia, and José De Souza. "Avaliação de Desempenho de Computadores Raspberry Pi com Algoritmos para o Reconhecimento Automático de Placas Veiculares." In Simpósio Brasileiro de Engenharia de Sistemas Computacionais. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sbesc_estendido.2020.13088.

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Este trabalho apresenta uma avaliação de desempenho (acurácia, tempo de execução, uso de RAM e consumo de energia) de dois algoritmos para reconhecimento automático de placas (Automatic License-Plate Recognition), ALPR, em hardware de baixo custo comumente usado em ambientes de Internet das Coisas, computadores Raspberry Pi, modelos 3B, 3B + e 4B. Os objetivos dessa analise são verificar qual algoritmo entrega o melhor custo-benefício aos dispositivos sobre as métricas avaliadas, se os computadores podem executar este tipo de aplicação e como o desempenho melhorou nos diferentes modelos. Os resultados mostram que todos esses dispositivos podem lidar bem com a aplicação, embora o processamento de vídeo em tempo real não seja viável. Para o conjunto de dados testado, o algoritmo mais leve, que dispensa uma das etapas realizada pelo outro, quando executado no Raspberry Pi 4 superou os demais em todos os aspectos.

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Ancheva, Veselina, and Valentina Voinohovska. "MINICOMPUTER RASPBERRY PI 4 AS AN EFFECTIVE INSTRUMENT IN CONDUCTING STEM EDUCATION IN INFORMATICS CLASSES." In 13th annual International Conference of Education, Research and Innovation. IATED, 2020. http://dx.doi.org/10.21125/iceri.2020.0594.

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Azlan, Mohammad Azerul, Abd Kadir Mahamad, and Sharifah Saon. "Face Recognition of Passenger for Bus Services." In Conference on Faculty Electrical and Electronic Engineering 2021/2. UTHM, 2021. http://dx.doi.org/10.30880/eeee.2021.02.01.007.

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Most university students are using the bus provided by the university's management to move from one place to another place. The analysis are required to improvise the quality of the of bus services such as the amount of passenger that using the bus and information of passengers such as gender. The objectives of this project are to develop face recognition system based on gender using Raspberry Pi 4 and Intel Neural Compute Stick 2 and to test and validate the performance of the developed system for face classification and passenger counting system. Also this system is able to store passenger information into Google Firebase Cloud with Internet of Things. This system is used Raspbian in Raspberry Pi 4 with the libraries that used for face classification and recognition such as OpenCV and OpenVINO. This project able to detect faces of the passengers soon as they ride the bus and determine gender of the passengers and count passengers according gender and the information of the passengers will stored in Google Firebase. There are some recommendation that need to be added in this project to improve efficiency of the system.

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Hariawan, Febrian Rachmad, and Septia Ulfa Sunaringtyas. "Design an Intrusion Detection System, Multiple Honeypot and Packet Analyzer Using Raspberry Pi 4 for Home Network." In 2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering. IEEE, 2021. http://dx.doi.org/10.1109/qir54354.2021.9716189.

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Helbet, Robert, Vasile Monda, Andrei Cristian Bechet, and Paul Bechet. "Low Cost System for Terrestrial Trunked Radio Signals Monitoring Based on Software Defined Radio Technology and Raspberry Pi 4." In 2020 International Conference and Exposition on Electrical And Power Engineering (EPE). IEEE, 2020. http://dx.doi.org/10.1109/epe50722.2020.9305536.

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Đuraševic, Slađana, Uroš Pešovic, Dejan Vujičic, Dušan Markovic, Snežana Tanaskovic, Dalibor Tomic, and Vladeta Stevovic. "PRAĆENJE AKTIVNOSTI PČELA PRIMENOM RAČUNARSKE VIZIJE." In SAVETOVANJE o biotehnologiji sa međunarodnim učešćem. University of Kragujevac, Faculty of Agronomy, 2021. http://dx.doi.org/10.46793/sbt26.107dj.

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Computer vision as a tool enables automated processing of visual information and provides the possibility of significant improvement of the agricultural production process. This paper presents the results of the application of the YOLO algorithm for monitoring bees at the entrance to the hive. The applied model achieved a detection accuracy of 92.86% and was implemented on a Raspberry PI 4 computer system. This small computer system can be used for further field testing, where the activity of bees at the entrance to the hive is monitored via video recording.

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Tjahjono, Budi, Ade Sulaeman, Fransiskus Adikara, and Kundang Juman. "Implementation of Load Balancing Technology Using Raspberry Pi as a Server for Computer Based Examination." In Proceedings of the 2nd International Conference on Quran and Hadith Studies Information Technology and Media in Conjunction with the 1st International Conference on Islam, Science and Technology, ICONQUHAS & ICONIST, Bandung, October 2-4, 2018, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-10-2018.2295570.

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