Electrical measurements error analysis laboratory exercise using an open-source hardware platform

2019 ◽  
Author(s):  
Mateo Marcelic ◽  
Bruno Sandric ◽  
Ivana Leto ◽  
Marko Jurcevic
Keyword(s):  
Error Analysis ◽  
Open Source ◽  
Electrical Measurements ◽  
Hardware Platform ◽  
Laboratory Exercise ◽  
Analysis Laboratory ◽  
Open Source Hardware

scholarly journals Wi-PoS: A Low-Cost, Open Source Ultra-Wideband (UWB) Hardware Platform with Long Range Sub-GHz Backbone

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1548 ◽  
Author(s):  
Ben Van Herbruggen ◽  
Bart Jooris ◽  
Jen Rossey ◽  
Matteo Ridolfi ◽  
Nicola Macoir ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Open Source ◽  
Long Range ◽  
Indoor Localization ◽  
Average Energy ◽  
Multipath Fading ◽  
Outdoor Environment ◽  
Ultra Wideband ◽  
Hardware Platform ◽  
Open Source Hardware

Ultra-wideband (UWB) localization is one of the most promising approaches for indoor localization due to its accurate positioning capabilities, immunity against multipath fading, and excellent resilience against narrowband interference. However, UWB researchers are currently limited by the small amount of feasible open source hardware that is publicly available. We developed a new open source hardware platform, Wi-PoS, for precise UWB localization based on Decawave’s DW1000 UWB transceiver with several unique features: support of both long-range sub-GHz and 2.4 GHz back-end communication between nodes, flexible interfacing with external UWB antennas, and an easy implementation of the MAC layer with the Time-Annotated Instruction Set Computer (TAISC) framework. Both hardware and software are open source and all parameters of the UWB ranging can be adjusted, calibrated, and analyzed. This paper explains the main specifications of the hardware platform, illustrates design decisions, and evaluates the performance of the board in terms of range, accuracy, and energy consumption. The accuracy of the ranging system was below 10 cm in an indoor lab environment at distances up to 5 m, and accuracy smaller than 5 cm was obtained at 50 and 75 m in an outdoor environment. A theoretical model was derived for predicting the path loss and the influence of the most important ground reflection. At the same time, the average energy consumption of the hardware was very low with only 81 mA for a tag node and 63 mA for the active anchor nodes, permitting the system to run for several days on a mobile battery pack and allowing easy and fast deployment on sites without an accessible power supply or backbone network. The UWB hardware platform demonstrated flexibility, easy installation, and low power consumption.

Do-It-Yourself Design for Social Robots: An Open-Source Hardware Platform to Encourage Innovation

2017 ◽  
Vol 24 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Cesar Vandevelde ◽  
Francis Wyffels ◽  
Bram Vanderborght ◽  
Jelle Saldien
Keyword(s):  
Open Source ◽  
Social Robots ◽  
Hardware Platform ◽  
Do It Yourself ◽  
Open Source Hardware

scholarly journals Red de sensores de humedad y temperatura de bajo costo

2019 ◽  
pp. 11-18
Author(s):  
Luis Manuel Mendoza-Pinto ◽  
María Jesús Espinosa-Trujillo ◽  
Jesús Humberto Peet-Manzón
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Wireless Sensors ◽  
Humidity Sensor ◽  
Low Cost ◽  
Good Choice ◽  
General Development ◽  
Hardware Platform ◽  
Open Source Hardware ◽  
To Receive

This article presents the development of a low cost network of wireless sensors that use an open source hardware platform, consisting of an ESP8266 and a digital temperature-humidity sensor to measure the parameters in an area determined by the range of the sensor. The general development of the system includes the use of open source software to receive information through the network. Tests of sensor effectiveness were performed at three different points in an air-conditioned area. The first sensor was placed outside the area, the second in the middle and the last one at the exit of the air conditioning. The results obtained allowed to know the behavior of temperature and humidity in the area and the effectiveness of the sensor network to measure the variables, the results of the measurements are presented in detail. Because the system is highly scalable, inexpensive and easy to build compared to other systems, it is a good choice for a wide variety of applications.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

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