scholarly journals Open-source Hardware – Microcontrollers and Physics Education – Integrating DIY Sensors and Data Acquisition with Arduino

2015 ◽  
Author(s):  
Brian Huang
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Physics Education ◽  
Open Source Hardware

scholarly journals Configurable IoT Open-Source Hardware and Software I-V Curve Tracer for Photovoltaic Generators

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7650
Author(s):  
Isaías González ◽  
José María Portalo ◽  
Antonio José Calderón
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Smart Grids ◽  
Operating Conditions ◽  
Raspberry Pi ◽  
Modern Trend ◽  
Curve Tracer ◽  
Open Source Hardware ◽  
Curve Tracing ◽  
Photovoltaic Generators

Photovoltaic (PV) energy is a renewable energy resource which is being widely integrated in intelligent power grids, smart grids, and microgrids. To characterize and monitor the behavior of PV modules, current-voltage (I-V) curves are essential. In this regard, Internet of Things (IoT) technologies provide versatile and powerful tools, constituting a modern trend in the design of sensing and data acquisition systems for I-V curve tracing. This paper presents a novel I-V curve tracer based on IoT open-source hardware and software. Namely, a Raspberry Pi microcomputer composes the hardware level, whilst the applied software comprises mariaDB, Python, and Grafana. All the tasks required for curve tracing are automated: load sweep, data acquisition, data storage, communications, and real-time visualization. Modern and legacy communication protocols are handled for seamless data exchange with a programmable logic controller and a programmable load. The development of the system is expounded, and experimental results are reported to prove the suitability and validity of the proposal. In particular, I-V curve tracing of a monocrystalline PV generator under real operating conditions is successfully conducted.

scholarly journals LOW COST SEISMIC DATA ACQUISITION SYSTEM BASED ON OPEN SOURCE HARDWARE AND SOFTWARE TOOLS

2020 ◽  
Author(s):  
Arvid Ramdeane ◽  
Lloyd Lynch
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Seismic Data ◽  
Low Cost ◽  
Data Acquisition System ◽  
Software Tools ◽  
Acquisition System ◽  
Seismic Data Acquisition ◽  
Private Network ◽  
Open Source Hardware

The University of the West Indies Seismic Research Centre, Trinidad and Tobago, operates a network of over 50 stations for earthquake and volcanic monitoring in the Eastern Caribbean islands. These stations form a seismic network consisting of various types of instrumentation, and communication systems. Over a period of 11 years, the Centre has embarked on an initiative of upgrading and expanding the current network with combinations of broadband and/or strong motion sensors, high dynamic range digitizers and networking equipment to link each station to centralized observatories via high speed digital data transmission medium. To realize such an upgrade and expansion, the Centre has developed a seismic data acquisition system prototype built using open-source hardware and software tools. The prototype is intended to be low-cost using off the shelf hardware components and open-source seismic related software handling data acquisition and data processing in two separate modules. The prototype uses a three-channel accelerometer sensor and can process data into standard MiniSEED format for easy data archiving and seismic data analysis. A global position module provides network time protocol time synchronization within 1 millisecond for accurate timestamping of data. Data can be stored locally on the prototype in twenty-minute data files or securely transferred to a central location via internet with the use of virtual private network capabilities. The prototype is modular in design allowing for components to be replaced easily and the system software can be updated remotely thus reducing maintenance cost.

scholarly journals pyPhotometry: Open source Python based hardware and software for fiber photometry data acquisition

2018 ◽  
Author(s):  
Thomas Akam ◽  
Mark E. Walton
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Low Cost ◽  
Excitation Light ◽  
Signal Validation ◽  
Data Acquisition Board ◽  
Wide Range ◽  
Open Source Hardware ◽  
And Control ◽  
Led Drivers

Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators (e.g. GCaMP) through an optical fiber. We present a system of open source hardware and software for fiber photometry data acquisition consisting of a compact, low cost, data acquisition board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.

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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