Extrapolating Z-Axis Data for a 2D Image on a Single Board Computer

2021 ◽  
pp. 503-512
Author(s):  
V. Anupama ◽  
A. Geetha Kiran
Keyword(s):  
Single Board Computer

scholarly journals Is It Possible to Apply Agile Methods to Contribute to the Linux Kernel?

2020 ◽  
pp. 291-297
Author(s):  
Thatiane de Oliveira Rosa ◽  
Alfredo Goldman
Keyword(s):  
Software Development ◽  
Agile Methods ◽  
Years Of Experience ◽  
Linux Kernel ◽  
Teaching Activity ◽  
Single Board Computer ◽  
Computer Based

Abstract In this document, we describe the experience of teaching Agile Methods for developing projects related to the Linux Kernel, during the XP Lab course. In 2018, the first project related to this context emerged. This project had the objective of making adjustments to the driver for Linux IIO subsystem. The second project was developed in 2019 and aimed to refactor the Ethernet driver used in the kernel of a Brazilian Single Board Computer. Based on 19 years of experience offering the XP Lab course, we consider the development of these projects to be a challenging teaching activity, which deserves to be presented and discussed with students, educators, and professionals. Our aim is to show that it is possible to adapt Agile Values to different software development settings.

scholarly journals Real time monitoring of water Quality using IoT and Deep learning

2021 ◽  
Vol 297 ◽  
pp. 01059
Author(s):  
Saloua Senhaji ◽  
Mohamed Hamlich ◽  
Mohammed Ouazzani Jamil
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Environmental Protection Agency ◽  
Short Term Memory ◽  
Chemical Parameters ◽  
Water Parameters ◽  
Term Memory ◽  
Single Board Computer ◽  
Physico Chemical

Access to safe drinking water is one of the most pressing issues facing many developing countries. Water must meet Environmental Protection Agency (E.P.A.) requirements. The normal method of measuring physico-chemical parameters is to take samples manually and send them to the laboratory to check the water quality. In this paper, we proposed a new intelligent design of a real-time water quality monitoring system using Deep Learning technology. This system is composed of several sensors that allow us to measure water parameters (physico-chemical parameters), bacteriological parameters and organoleptic parameters) and to detect the presence of certain substances (undesirable substances, toxic substances) and of a single-board/mobile computer module, Internet and other accessories. Water parameters are automatically detected by the single-board computer. Raspberry Pi3 model B. The single board computer receives the data from the sensors and this data is sent to the web server using the Internet module. It is able to detect the water quality situation worldwide. The data will be analysed in real time. The application of deep learning to these areas has been an important research topic. The Long-Short Term Memory (LSTM) network has been shown to be well suited for processing and predicting large events with long intervals and delays in the time series. LSTM networks have the ability to retain long-term memory.

scholarly journals Determinación del factor de potencia mediante el uso de SBC en clientes

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Luis Tipán ◽  
José Rumipamba
Keyword(s):  
Raspberry Pi ◽  
Single Board Computer

El entregar un servicio eléctrico de calidad es el principal objetivo de las empresas distribuidoras de energía. Existen problemas de afección en la distribución de energía y uno de ellos son los efectos negativos causados por los armónicos presentes en las cargas lineales y no lineales utilizadas por clientes residenciales e industriales, afectándose directamente en forma negativa el factor de potencia. En este trabajo se mide el factor de potencia producido por cargas típicas que se encuentran en áreas residenciales por medio de un medidor inteligente basado el uso de SBC (Single Board Computer), como son la Raspberry Pi y el Arduino. Además, se evalúan los efectos producidos por este factor de potencia para luego determinar su influencia en la distorsión de voltaje en un sistema de distribución.

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