Relevant bibliographies by topics / Raspberry Pi 2 model B

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

Academic literature on the topic 'Raspberry Pi 2 model B'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Raspberry Pi 2 model B"

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

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Telah berkambangnya tekonologi home automation pada zaman ini dan salah satunya dengan pemanfaatan smartphone sebagai salah satu pengendalinya. Pada penelitian ini memanfaatkan sebuah aplikasi media sosial telegram dan sebuah single board computer sebagai kontrol pada sebuah home automation. Penelitian ini bertujuan untuk membangun sebuah home automation dengan menggunakan Raspberry Pi dan aplikasi media sosial telegram sebagai kendalinya. Dengan studi pustaka dan metode eksperimen penelitian ini berhasil merancang bangun sebuah home automation menggunakan Raspberry Pi 3 Model B dengan interface aplikasi telegram sebagai kendalinya. Dibutuhkan sebuah Bot API telegram agar Raspberry Pi dan aplikasi telegram dapat terhubung. Perangkat yang digunakan dalam penelitian ini meliputi: single board computer Raspberry Pi 3 Model B, 2 buah relay untuk 2 perangkat elektronik (lampu dan kipas), 1 buah MCB dan smartphone yang telah terinstall aplikasi telegram yang digunakan sebagai pengontrol perangkat elektronik. Hasil dari penelitian ini sudah sesuai dengan tujuannya yaitu merancang bangun sebuah home automation dengan menggunakan Raspberry Pi dan aplikasi media social telegram sebagai kendalinya mensimulasikannya pada peralatan elektronika.
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Wardana, I. Nyoman Kusuma, Julian W. Gardner, and Suhaib A. Fahmy. "Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction." Sensors 21, no. 4 (February 4, 2021): 1064. http://dx.doi.org/10.3390/s21041064.

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Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.
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Wiguna, Esa Hayyu, and Arkhan Subari. "RANCANG BANGUN SISTEM MONITORING KETINGGIAN AIR DAN KELEMBABAN TANAH PADA PENYIRAM TANAMAN OTOMATIS DENGAN HMI (HUMAN MACHINE INTERFACE) BERBASIS RASPBERRY PI MENGGUNAKAN SOFTWARE NODE-RED." Gema Teknologi 19, no. 3 (October 31, 2017): 1. http://dx.doi.org/10.14710/gt.v19i3.21878.

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Esa Hayyu Wiguna, Arkhan Subari, in this paper explain that the monitoring system is a system used to monitor and control work processes in a plant design. This system is widely used and applied in the industrial world to find out the performance of a plant. To do the monitoring system, a lot of software can be used, which is then called the HMI (Human Machine Interface). The monitoring system with an interface in the form of HMI can be presented in various forms, such as buttons, or can also be displayed in the visualization of the plant while working. This monitoring system through an HMI interface uses supporting hardware in the form of a Raspberry Pi as a device to process the data that will be displayed on the display screen, while displaying its visualization uses an LCD touch screen. This LCD touch screen is connected to the Raspberry Pi via the LCD driver. The graphic form that will be displayed on the LCD touch screen is designed using Node-RED software. The visualization that will be displayed on the Touch Screen LCD will be adjusted to the working system of automatic plant sprinklers. This monitoring system using an HMI interface can display the plant's working system through indicators of water level and soil moisture. To test tube 2 water level measured through ultrasonic sensors through HMI has an error ratio of 1.01%, while for soil moisture measured through soil moisture sensors has an error ratio of 1.51%. Keywords: Monitoring System, Human Machine Interface (HMI), Raspberry Pi, Node-RED. ReferencesHaryanto, Heri dan Sarif Hidayat. 2012. Perancangan HMI (Human Machine Interface) Untuk Pengendalian Kecepatan Motor DC. Jurnal S1 Jurusan Elektro Fakultas Teknik Terpublikasi. Banten: Universitas Sultan Ageng Tirtayasa.Udayana, Gede Agus, I Gede Mahendra Darmawiguna, dan I Made Gede Sumarya. 2016. Pengembangan Prototipe Portal Otomatis Dengan Pendeteksian Plat Nomor Kendaraan Berbasis Raspberry Pi. Artikel Jurusan Pendidikan Teknik Informatika Terpublikasi. Bali: Universitas Pendidikan Ganesha.Man, Joseph. 2016. Raspberry Pi 3 Model B Technical Specifications. https://www.element14.com/community/docs/DOC-80899/l/raspberry-pi-3-model-b-technical-specifications. Diakses tanggal 14 Agustus 2017.Kurniawan, Halim. 2005. Aplikasi Penjawab Pesan Singkat Automatis dengan Bahasa Python. Makalah Seminar Tugas Akhir S1 Jurusan Teknik Elektro Terpublikasi. Semarang: Universitas Diponegoro.Node-RED. 2013. Node-RED; Flow-based programming for the Internet of Things. https://nodered.org/. Diakses tanggal 02 Mei 2017.Tim J, M. 2016. Developing with Node-RED. https://software.intel.com/en-us/articles/developing-with-node-red. Diakses tanggal 02 Mei 2017.
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Utami, Pipit, Abdul Aziz Sidiq Tri Putra, Djoko Santoso, Nuryake Fajaryati, Bonita Destiana, and Mohd Erfy Ismail. "VIDEO MOVING SURVEILLANCE YANG TERINTEGRASI YOUTUBE MENGGUNAKAN RASPBERRY PI 3." Elinvo (Electronics, Informatics, and Vocational Education) 3, no. 1 (August 14, 2018): 113–23. http://dx.doi.org/10.21831/elinvo.v3i1.20797.

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Static CCTV facilities in class need to be optimized in classroom learning, especially in recording learning activities as implementation of learning in the 21st century and strategic steps to face the Industrial Revolution 4.0. Educators need to play a role in utilizing CCTV in learning. This article presents the development of YouTube Integrated Video Moving Surveillance devices using Raspberry Pi 3. The development stages consist of analysis, design, development and evaluation. The analysis shows that: (1) the limitations of CCTV motion are followed up with the addition of motorcycles; (2) limited access to video recording data is followed up by sending in real time using YouTube; and (3) controlling the system performance of the device using Raspberry Pi 3 model B. The evaluation results show that all electronic measurement parameters are in accordance with the success target and the functional test shows that the device can function to record classroom learning activities that can be monitored in real time via YouTube with good quality (average delay is 22,89s). The results of the development of this device are expected to be an alternative use of technology in learning, especially in the supervision and assessment of student learning activities.
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Suchendra, Devie Ryana, Dewi Putri Suryani, and Muhammad Ikhsan Sani. "Home Lighting Control Based on Time Scheduling using Crontab." IJAIT (International Journal of Applied Information Technology) 2, no. 01 (May 25, 2018): 1. http://dx.doi.org/10.25124/ijait.v2i01.924.

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Access control system in smart home concept is a support system that affects especially on controlling the electronic devices. Along with advances in technology, the conventional access control system was developed into an electronic-based access control system. A conventional access control system such as manual electric switches is now beginning to be developed with an electric switch that can be controlled wirelessly from the web. Raspberry pi 2 model B is one of the minicomputers which can be used in an automatic access control system. By using GPIO (General Purpose Input Output) on Raspberry Pi, can be created by a wireless access control system, safe and effective. The purpose of this study is designing a prototype relay control system wirelessly model that can be accessed through the web, the result indicates the possibility of relay control to be remote. Crontab scheduling functions on the controller's relay are used, then the device can function based on a predetermined schedule, it is very helpful to turn on and off electrical devices home remotely. The form of a username and password authentication will be used to protect the system from being accessed by anyone. In the final phase of research, the system is evaluated at day and night using Crontab function. Meanwhile, the response time of the relay is within 1-2 seconds range.
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Delgado-Reyes, Gustavo, Pedro Guevara-Lopez, Igor Loboda, Leobardo Hernandez-Gonzalez, Jazmin Ramirez-Hernandez, Jorge-Salvador Valdez-Martinez, and Asdrubal Lopez-Chau. "State Vector Identification of Hybrid Model of a Gas Turbine by Real-Time Kalman Filter." Mathematics 8, no. 5 (April 27, 2020): 659. http://dx.doi.org/10.3390/math8050659.

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A model and real-time simulation of a gas turbine engine (GTE) by real-time tasks (RTT) is presented. A Kalman filter is applied to perform the state vector identification of the GTE model. The obtained algorithms are recursive and multivariable; for this reason, ANSI C libraries have been developed for (a) use of matrices and vectors, (b) dynamic memory management, (c) simulation of state-space systems, (d) approximation of systems using equations in matrix finite difference, (e) computing the mean square errors vector, and (f) state vector identification of dynamic systems through digital Kalman filter. Simulations were performed in a Single Board Computer (SBC) Raspberry Pi 2® with a real-time operating system. Execution times have been measured to justify the real-time simulation. To validate the results, multiple time plots are analyzed to verify the quality and convergence time of the mean square error obtained.
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Sowah, Robert A., Dale E. Boahene, Dalton C. Owoh, Rexford Addo, Godfrey A. Mills, Wiafe Owusu-Banahene, Gifty Buah, and Baffour Sarkodie-Mensah. "Design of a Secure Wireless Home Automation System with an Open Home Automation Bus (OpenHAB 2) Framework." Journal of Sensors 2020 (October 30, 2020): 1–22. http://dx.doi.org/10.1155/2020/8868602.

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There is rapid interest growing in the use of smart, connected devices. The developing world market for smart technology is evolving to adopt and adapt to the interconnected world of devices leading to the Internet of Things (IoT) everywhere. This research paper presents the design, development, and deployment of a prototype for the secure wireless home automation system with OpenHAB 2. We employed the use of two (2) high-performance microcontrollers, namely, the Arduino Mega 2560, interfaced with a 16-channel relay, and Raspberry Pi Model B, running the OpenHAB software. The Raspberry Pi functioned as the server to develop a prototype of an automated smart home that is remotely controllable from both a web application and an Android mobile app. In designing a wireless controlled switch for home appliances, two security procedures were implemented, namely, the token-based JSON Web Token (JWT) interface and Advanced Encryption Standard (AES) procedures for authentication and data encryption. Our system delivered a home automation system that leverages on the power of the latest version of OpenHAB to maximize productivity and overall home security while making it adaptable to the management of individual devices. When tested, both the developed hardware and software modules performed extremely well to meet the goal of a secured home automation system. Industry-standard penetration testing tools and frameworks, including Aircrack-ng, were utilized; wireless network audit began with a full sweep of the wireless frequencies with excellent results. It also ensures the efficient use of energy in the home as devices are intelligently controlled from both mobile and web applications. The results of the design and implementation of the additional layer for the security of the OpenHAB framework provide various theoretical and practical implications for home automation.
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Wu, Yu Tzu, Matheus K. Gomes, Willian HA da Silva, Pedro M. Lazari, and Eric Fujiwara. "Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures." Biomedical Engineering and Computational Biology 11 (January 2020): 117959722091282. http://dx.doi.org/10.1177/1179597220912825.

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Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.
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Santise, M., K. Thoeni, R. Roncella, S. W. Sloan, and A. Giacomini. "PRELIMINARY TESTS OF A NEW LOW-COST PHOTOGRAMMETRIC SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W8 (November 14, 2017): 229–36. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w8-229-2017.

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This paper presents preliminary tests of a new low-cost photogrammetric system for 4D modelling of large scale areas for civil engineering applications. The system consists of five stand-alone units. Each of the units is composed of a Raspberry Pi 2 Model B (RPi2B) single board computer connected to a PiCamera Module V2 (8 MP) and is powered by a 10 W solar panel. The acquisition of the images is performed automatically using Python scripts and the OpenCV library. Images are recorded at different times during the day and automatically uploaded onto a FTP server from where they can be accessed for processing. Preliminary tests and outcomes of the system are discussed in detail. The focus is on the performance assessment of the low-cost sensor and the quality evaluation of the digital surface models generated by the low-cost photogrammetric systems in the field under real test conditions. Two different test cases were set up in order to calibrate the low-cost photogrammetric system and to assess its performance. First comparisons with a TLS model show a good agreement.
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Villaseñor-Aguilar, Marcos J., J. Enrique Botello-Álvarez, F. Javier Pérez-Pinal, Miroslava Cano-Lara, M. Fabiola León-Galván, Micael-G. Bravo-Sánchez, and Alejandro I. Barranco-Gutierrez. "Fuzzy Classification of the Maturity of the Tomato Using a Vision System." Journal of Sensors 2019 (July 4, 2019): 1–12. http://dx.doi.org/10.1155/2019/3175848.

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Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components R and B were three and four for the case of component G. The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of 536,995×10-6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.
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Dissertations / Theses on the topic "Raspberry Pi 2 model B"

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Aspernäs, Andreas, and Thommy Simonsson. "IDS on Raspberry Pi : A Performance Evaluation." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-43997.

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This is a report on the possibility of using a Raspberry Pi as an intrusion detection system in a home environment to increase network security. The focus of this study was on how well two different generations of Raspberry Pi would be able to  handle network traffic while acting as an intrusion detection system. To examine this a testing environment was set up containing two workstation computers connected to a Raspberry Pi, each computer hosting a virtual machine. Tests measuring the network throughput as well as the CPU and memory usage were performed on each of the Raspberry Pi devices. Two models of Raspberry Pis were used; Raspberry Pi model B+ and Raspberry Pi 2 model B; each of them running the operating system Arch Linux ARM. The results of these tests were that both of the Raspberry Pis could be used as an intrusion detection system but has some limitations that could impede usage depending on the requirements of the user. Raspberry Pi 2 model B show benefits of its updated hardware by suffering lower throughput degradation than Raspberry Pi model B+, while using less of it's total CPU and memory capacity.
Den här rapporten behandlar möjligheten att använda en Raspberry Pi som ett intrångdetekteringssystem i en hemma miljö för att öka nätverkssäkerheten. Fokusen i den här studien ligger på hur väl de två senaste generationerna av Raspberry Pi skulle kunna hantera nätverkstrafik samtidigt som den undersöker nätverkstrafiken och söker efter hot. För att kontrollera hur väl en Raspberry Pi kan fungera som ett intrångdetekteringssystem har en laborationsmiljö upprättats bestående av två fysiska maskiner som vardera används för att virtualisera en virtuell maskin. Tester för att mäta datagenomströmning, processor och minnesbelastning utfördes på var och en av Raspberry Pi. Två modeller av Raspberry Pi användes; Raspberry Pi model b+ och Raspberry Pi 2 model b, både körde operativsystemet Arch Linux ARM. Resultatet av testerna visade att det går att använda båda enheterna för att upprätta ett intrångdetekteringssystem, men det finns vissa begränsningar i enheterna vilket kan begränsa implementationsmöjligheterna. Raspberry Pi 2 model B uppvisade bättre resultat i form av att den är lägre belastad och har en högre datagenomströmning till skillnad från Raspberry Pi model B+. Raspberry Pi 2 model B har nyare och snabbare hårdvara vilket är den troliga orsaken till att den presterar bättre.
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Náhlík, Ondřej. "Speciální bezpečnostní systém." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221221.

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This master’s thesis is focused on design and realisation of special security system for space and property protection. System is devided into two parts, transmitting and receiving. Transmitting part is formed by Raspberry Pi Model B+, motion sensor, webcam and USB LTE modem Huawei E398. Receiving part consists of computer with suitable application for communication with FTP server. Data transfer in mobile systems is described in the first part of this thesis, transmitting part is designed in the second part. There is one transmitting option chosen in the third part and there are also described applications of the transmitting part. The last part of this thesis is focused on the receiving part and its application.
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Hadzima, Jaroslav. "Algoritmy hlubokého učení na embedded platformě." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400703.

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Táto práca popisuje v súčastnosti široko používané architektúry a modely pre Hlboké Učenie, riešiace úlohu detekcie a klasifikácie objektov vo videu. Dôraz tu bude kladený na ich použiteľnosť na vstavaných zariadeniach. Postupne preberieme kroky a odvôvodňovanie pri výbere najlepšieho vstavaného systému pre našu aplikáciu. Ukážková aplikáci pozostáva hlavne z detekcie vozidiel a detekcie voľných parkovacích miest s využitím algoritmov Hlbokého Učenia. Táto aplikácia umožňuje monitorovať počet vozidiel, nachádzajúcich sa na parkovisku a zároveň rozhodnúť, či sa nachádzajú na prakovacom mieste alebo nie. Následne tu budú prebrané kroky nutné ku konfigurácii zariadenia s dôrazom na optimalizáciu hardvéru pre dosiahnutie čo najväčšej rýchlosti. V ďaľšej časti bude poskytnuté porovnanie vybraných modelov, ktoré budú porovnávané hlavne v kategóriách ako rýchlosť alebo F1 skóre. Najlepší kandidát bude použitý na riešenie našej aplikácie a následné testovanie jej vlastností s názvom Inteligentné parkovisko.
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Pandiscia, Nicola. "Analisi di sequenze video per rilevazioni demografiche ed emotive da software su microcontroller." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il seguente progetto è volto ad implementare sul microcontroller "Raspberry Pi v.4 Model B" un software utilizzante a mo' di scatola nera, anche, in parte, una rete neurale che sulla base di una classificazione precedentemente realizzata da soggetti terzi e sulla base di opportuni modelli preaddestrati sfrutti un meccanismo di apprendimento supervisionato per stimare ragionevolmente, secondo opportuni criteri, il sesso, la fascia d'età, lo stato emotivo (caricaturale, ossia forzato) e la distanza approssimativa di uno o più soggetti ripresi frontalmente in volto da una telecamera in tempo reale.
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Chuang, Chun-Wei, and 莊均維. "Paperless system Based on Raspberry Pi Model B." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/x2x59p.

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碩士
國立臺中科技大學
資訊工程系碩士班
102
Meeting is an activity in everyday work force to exchange face to face views and communications. A large number of supporting documents will be needed to support the meetings. Paperless purpose is decreased the amount of papers used in the process. However, systems which are developed in server are expensive to maintain for small units. In this paper, we proposed a paperless meeting system based on Raspberry Pi Model B. The main focus of the proposed system is to provide a paperless meeting service system which is low implementation costs. The service is design from Red5 and FreeSWITCH. The system is implemented in Raspberry Pi. Experimental results show that implementation costs of the proposed method can effectively reduce implementation costs in the existing meeting system, and the function of meeting can also be providing similar services of the server.
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CHEN, YU-JEN, and 陳宥任. "Autopilot Model-car Implemented by Raspberry Pi 3 Model B Plus with Camera using CNN Deep Learning Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nt6s73.

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碩士
國立臺北科技大學
車輛工程系
107
This paper study a monocular vision-based autopilot model-car prototype. It is based on python. Using machiane learning and computer vision on the embedded system, Raspberry Pi 3 B+ to apply deep learning to autopilot model-car. It uses a 1:12 RC model-car for the stage, Raspberry Pi calculation and front camera to construct platform. First, move on the driveway by operating the joystick. Collecting information on the image and the joystick. Take a large amount of data to train the CNN model. Transfer the model back to Raspberry Pi, and put the car into the test environment. Calculate the direction and speed of the current travel and then convert it into a PWM signal, send the direction signal to the servo motor, and send the speed signal to the electronic transmission to control the motor speed. Continuing to collect data, test and improve the model to improve the success rate.
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Book chapters on the topic "Raspberry Pi 2 model B"

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

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

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R., Hemalatha, Radha S., and Muthumeenakshi K. "Disease Monitoring of Cucumber in Polyhouse Through IoT-Based Mobile Application." In Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture, 273–88. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-1722-2.ch017.

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Most countries have an economy that is dependent on agriculture—either in a magnificent or small way, from employment generation to national income contribution—implying that agriculture is inevitable. Polyhouse farming is a new and widely accepted method of farming in the present days. The polyhouse is made in such a way that it can provide water and fertilizers in required amounts in a controlled manner, which can result in high yields. Polyhouse requires severe monitoring of crops as stagnant air, and lack of air circulation will lead to breeding of insects and materialistic loss. Hence, this chapter proposes an IoT-based disease-monitoring prototype for an agricultural/polyhouse application. The prototype is designed and tested to identify the disease onset in cucumbers. This work initially focuses on recognizing the critical cucumber diseases in polyhouse using NodeMCU and Raspberry-Pi-based hardware model. The decisions to be made and the major changes in the sensed parameters if any will be intimated to the farmers using a specifically designed mobile application.
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Zubov, Dmytro. "A Case Study on the Spatial Cognition of Surrounding Objects by the B&VI People Using Sound Patterns and Ultrasonic Sensing." In Emerging Trends and Applications of the Internet of Things, 105–16. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2437-3.ch004.

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In this paper, two assistive projects on the spatial cognition by blind and visually impaired (B&VI) people are presented using the sound patterns and ultrasonic sensing. The first device supports the sport activities of B&VI, the golf game specifically. Every golf flagstick has the sound marking device with the active buzzer and WiFi remote control by the person with good vision. The NodeMcu Lua ESP8266 ESP-12 WiFi boards in devices are controlled by the cross-platform HTML web-sites, and hence any WiFi smartphone and / or computer can be in use to start the HTML web-page. Mini portable WiFi router links all devices in the network. End-users are securely connected using the password to wireless router. Ten assistive devices were handed in Instituto para Ciegos y Débiles Visuales “Ezequiel Hernández Romo” together with WiFi router. The second device supports the orientation of B&VI by measuring the distance to the obstacle based on the ultrasonic sensor HC-SR04 and Arduino Uno. The distance is pronounced to the B&VI using headphone and MP3 player with SD card. Nowadays, Universidad Politécnica de San Luis Potosí is negotiating with several organizations to create a production line. All devices are of the budget price up to USD 10. All devices were tested successfully. This is joint work of Instituto para Ciegos y Débiles Visuales “Ezequiel Hernández Romo”, Universidad Politécnica de San Luis Potosí, and Tecnológico de Monterrey with ongoing project “Artificial Eyes” based on Raspberry Pi 3 Model B board with an ultrasonic sensor and camera for the image and/or video processing of the surrounding environment, as well as the friendly integration into the local networks using onboard WiFi and Bluetooth.
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Sivagami, Arasu, Michael Angelo Kandavalli, and Bhaskarrao Yakkala. "Design and Evaluation of an Automated Monitoring and Control System for Greenhouse Crop Production." In Next-Generation Greenhouses for Food Security. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97316.

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An embedded system integrated with sensors based on nanomaterial is proposed for closely monitoring and control microclimate parameters 24 hours a day to maximise production over the whole crop growth season by introducing greenhouse for the cultivation of plants or specific plant species. The system will also eliminate errors in human intervention to optimise production of crops. This system consists of sensors and actuators, an Analogue to Digital Converter (ADC) and a Raspberry Pi. The system will determine whether a defined threshold is passed by any climate parameter and systematically changes via the controller. The current work reduces human input through automated irrigation to optimally utilize a scarce resource, namely water. Climatic parameters for plant growth such as, moisture, humidity, temperature, water pressure in drip pipe, soil salinity etc. are monitored and optimized. Furthermore, work was extended to include GSM to control the entire farm remotely. For its success, it is very important to choose a greenhouse location. For instance, the problems are quite different when choosing an adjoining greenhouse, for instance a sunroom or greenhouse. The greenhouse location should be chosen for sunlight, proximity to power and water sources, wind, drain and freeze pockets, and the proximity of the garden and house. The intention behind accomplishment and devise of GSM based Fertigation System is to construct and evaluate the requirement of water in the yield as farming is the major resource of production which habitually depends on the water accessibility. Irrigation of water is usually done by manual method. To ease the work of the farmer GSM based automatic Fertigation (includes chemigation too) system can be implemented so that water wastage can be reduced and also the fertilizer can be added accordingly. Also the Soil Salinity can be checked and reduced if exceeds certain limit. By using GSM, only GSM command via GSM mobile can control the start and stop action of a motor that feeds the field with the water. GSM is used for controlling the entire process and the entire system backbone. It can be used from any distance to control irrigation. The results are assessed by electronic simulator PROTEUS using the desired optimised parameters, the design of this automated greenhouse system with PIC controller. As the inputs to the microcontroller and as an LCD screen record the respective outputs, the model produces a soil moisture sensor, light sensor and temperature sensor. The system performance is accurate and repeatable for measuring and controlling the four parameters that are crucial for plant growth - temperature, humidity, soil moisture and light intensity. With the reduction in electricity consumption, maintenance and complexity, and a flexible and precise environment control form for agriculture, the new system successfully cured quite a couple of defects in existing systems. Nano composite film sensors (Graphene and Graphene mixed in order to optimise the input of fertilisers for chemical composition determination. Using nano technology in agriculture enforces the firm bond between the engineer and farmer. Nano material film-based gas sensors were used to measure the presence of oxygen and CO2.using graphene nano composite sensors integrated into an embedded system, to detect the presence and levels of gases. Improve crop growth with combined red and blue light for lighting under the leavened and solar-powered LED lighting modules. This was achieved by graph/solar cells. The light was measured at the photosynthesis flux (PPFD) of 165 μmol m-2 s-1 by 10 cm of its LED module. LED lights were provided between 4:00 a.m. and 4:00 p.m. in the daytime treatments and night treatments from 10 to 10 hours. The use of the nighttime interlumination of LEDs was also economical than the interlumination of charts. Thus, nightlighting LEDs can effectively improve plant growth and output with less energy than the summer and winter times. Solar panels are best functioning during times of strong sunlight today, but begin to wan when they become too hot and cloudy. By allowing Solar Panels to produce electricity during harsh weather conditions and increase efficiency, a breakthrough in graphene-based solar panels can change everything. Ultimately with a fully autonomous system, agricultural productivity and efficiency, the length of the growing season, energy consumption and water consumption were recorded and monitored by exporting the data over GSM environment. With the steady decrease in the cost of high-performing hardware and software, the increased acceptance of self-employed farming systems, and the emerging agricultural system industry, the results will be reliable control systems covering various aspects of quality and production quantity.
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Conference papers on the topic "Raspberry Pi 2 model B"

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Torres, Andrei B. B., Atslands R. Rocha, and José Neuman De Souza. "Análise de Desempenho de Brokers MQTT em Sistema de Baixo Custo." In XV Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2016.9727.

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Este artigo apresenta uma análise de desempenho (uso de CPU, consumo de memória e envio de mensagens) de brokers MQTT em um hardware de baixo custo, o Raspberry Pi 2 Modelo B. Os objetivos da análise são averiguar qual implementação de broker MQTT é a mais adequada às limitações do hardware e se o Raspberry Pi 2 é realmente capaz de funcionar como gateway de uma rede de sensores e atuadores para a Internet das Coisas (IoT). Os resultados mostraram que o Raspberry Pi 2 consegue lidar com grandes números de conexões, sendo a implementação em Erlang (eMQTT) a que obteve melhor resultado em vazão de dados, enquanto a implementação em C (Mosquitto) apresentou a menor carga de processamento e consumo de memória.
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He, Qihao, Bruce Segee, and Vincent Weaver. "Raspberry Pi 2 B+ GPU Power, Performance, and Energy Implications." In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0038.

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Balderston, Dayton, John Eric Kelley, James Crowder, Thomas DeAgostino, and Christopher Depcik. "Repurposing of a Hybrid Vehicle Nickel Metal Hydride Battery Pack for Electrical Grid Storage." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70897.

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Climate change concerns are driving incentives to increase the fuel economy of passenger vehicles. Consequently, this has resulted in a growing prevalence of electrified vehicles (EVs) consisting of hybrid, plug-in hybrid, and fully electric vehicles. Unfortunately, EVs are often removed from the road when 70 to 80% of the original energy capacity remains in their battery pack. In order to maintain or increase the value of EV battery packs in an end-of-vehicle life scenario, there are three potential solutions: remanufacturing for re-use, recycling, or repurposing. However, the complexity of handling dissimilar battery chemistries makes both remanufacturing and recycling a significant challenge. Hence, repurposing may prove to be a more viable short-term goal of the industry. In order to explore this potential outcome, a team of undergraduate students studied the continuous cycling effects of used and refurbished Toyota® Prius nickel metal hydride battery packs. A Raspberry Pi 2 Model B microcomputer recorded relevant data, including battery pack voltage, energy input, and energy output. In combination, a Laboratory Virtual Instrument Engineering Workbench (LabVIEW™) control system used this logged information to regulate charging and discharging of the battery pack. In addition, to enhance the environmental sustainability of the project, this control system acquired solar information from a nearby weather station, subsequently ensuring that the battery pack only recharged during times of peak solar radiation. Analysis of the pack’s energy balance helped to characterize the cycle life of the pack and its potential in repurposing. Others can emulate the methodology employed as a way to instruct students about the potential left in used vehicular battery packs and their possible integration with the electrical grid.
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Murthy, M. Narayana, and P. AjaySaiKiran. "A Smart Office Automation System Using Raspberry Pi (Model-B)." In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). IEEE, 2018. http://dx.doi.org/10.1109/icctct.2018.8550894.

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

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Laluma, Riffa Haviani, Riofalzy Giantara, Bambang Sugiarto, Gunawan, Chandra Afriade Siregar, and Slamet Risnanto. "Automation System of Water Treatment Plant using Raspberry Pi.3 Model B+ Based on Internet of Things (IoT)." In 2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2019. http://dx.doi.org/10.1109/tssa48701.2019.8985516.

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

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

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Abstract RIOTS is a general purpose optimal problem solver written as a MATLAB toolbox with mixed-language programming (C, Fortran, Matlab, Simulink). This first paper introduces how to make RIOTS run under an embedded platform RP3B (Raspberry Pi 3 B) with Windows 10. We presented the system architecture and a complete demo on running RIOTS as the inner kernel for MPC, using a house made thermal control system based on Peltier modules.
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Vavrenyuk, Aleksandr B., Darya V. Matveeva, Nikita M. Lukyantsev, and Victor V. Makarov. "Analysis of an Efficiency of Parallelization of Algorithms Running on Computing Cluster Based on Single-Board Diskless Computers Raspberry PI 3 Model B." In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. http://dx.doi.org/10.1109/elconrus51938.2021.9396277.

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

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