scholarly journals Requirements for design reuse in open-source hardware: a state of the art

Procedia CIRP ◽  
2021 ◽  
Vol 100 ◽  
pp. 792-797
Author(s):  
A. Ezoji ◽  
J.F. Boujut ◽  
R. Pinquié
Keyword(s):  
Open Source ◽  
State Of The Art ◽  
Design Reuse ◽  
Open Source Hardware

scholarly journals Towards a Better Understanding of Open Source Hardware Design Reuse in Company-Community Collaboration

2021 ◽  
pp. 149-154
Author(s):  
Amer Ezoji ◽  
Romain Pinquie ◽  
Jean François Boujut
Keyword(s):  
Open Source ◽  
Design Reuse ◽  
Community Collaboration ◽  
Design Problems ◽  
Speed Up ◽  
The One ◽  
Open Source Hardware ◽  
A Company ◽  
Common Understanding ◽  
Tools And Methods

AbstractThe open-source-software movement that emerged in the late 90s has recently extended to hardware. In this paper, we try to better understand how the reuse of design solutions facilitates company-community collaboration. On the one hand, based on existing research studies, we analyze three fundamental questions - 1) who wants to reuse a design? 2) Why do they want to reuse a design?, and 3) How do they reuse a design? - from a company perspective and a community perspective. On the other hand, we identified that companies and communities must create a common understanding of the design problems and solution and they could benefit in reusing design artifact to speed up the development time and improve the quality and transferability of the results. However, this research shows that companies and communities don’t use the same type of tools and methods to reuse design knowledge which may cause some problems for collaboration [2].

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

scholarly journals Shock tube data processing tools using open source hardware and software platforms

2020 ◽  
Author(s):  
K. Thirumalesh ◽  
Salgeri Puttaswamy Raju ◽  
Hiriyur Mallaiah Somashekarappa ◽  
Kumaraswamy Swaroop
Keyword(s):  
Data Processing ◽  
Shock Tube ◽  
Open Source ◽  
Software Platforms ◽  
Open Source Hardware

scholarly journals Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution

2021 ◽  
Vol 13 (15) ◽  
pp. 8182
Author(s):  
José María Portalo ◽  
Isaías González ◽  
Antonio José Calderón
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Smart Grids ◽  
Raspberry Pi ◽  
Operator Interface ◽  
Open Source Hardware ◽  
Real Time Information ◽  
And Storage ◽  
Time Information ◽  
Pv Generator

Smart grids and smart microgrids (SMGs) require proper monitoring for their operation. To this end, measuring, data acquisition, and storage, as well as remote online visualization of real-time information, must be performed using suitable equipment. An experimental SMG is being deployed that combines photovoltaics and the energy carrier hydrogen through the interconnection of photovoltaic panels, electrolyser, fuel cell, and load around a voltage bus powered by a lithium battery. This paper presents a monitoring system based on open-source hardware and software for tracking the temperature of the photovoltaic generator in such an SMG. In fact, the increases in temperature in PV modules lead to a decrease in their efficiency, so this parameter needs to be measured in order to monitor and evaluate the operation. Specifically, the developed monitoring system consists of a network of digital temperature sensors connected to an Arduino microcontroller, which feeds the acquired data to a Raspberry Pi microcomputer. The latter is accessed by a cloud-enabled user/operator interface implemented in Grafana. The monitoring system is expounded and experimental results are reported to validate the proposal.

A mobile sensor package for real-time greenhouse monitoring using open-source hardware

2019 ◽  
Author(s):  
Lars Larson ◽  
Elad Levintal ◽  
Jose Manuel Lopez Alcala ◽  
Dr. Lloyd Nackley ◽  
Dr. John Selker ◽  
...  
Keyword(s):  
Open Source ◽  
Real Time ◽  
Mobile Sensor ◽  
Sensor Package ◽  
Open Source Hardware

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Image

2021 ◽  
Author(s):  
J.M. Murray ◽  
P. Pfeffer ◽  
R. Seifert ◽  
A. Hermann ◽  
J. Handke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Atherosclerotic Plaque ◽  
State Of The Art ◽  
The Mean ◽  
Time Savings ◽  
Plaque Quantification ◽  
Atherosclerosis Research ◽  
Hematoxylin Eosin ◽  
Microscopy Images

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool. It includes 2 models, trained and tested on 1089 hematoxylin-eosin stained mouse model atherosclerotic brachiocephalic artery sections. The models were compared to 3 human raters. Vesseg can be accessed at https://vesseg .online or downloaded. The models show mean Soerensen-Dice scores of 0.91±0.15 for plaque and 0.97±0.08 for lumen pixels. The mean accuracy is 0.98±0.05. Vesseg is already in active use, generating time savings of >10 minutes per slide. Conclusions: Vesseg brings state-of-the-art deep learning methods to atherosclerosis research, providing drastic time savings, while allowing for continuous improvement of models and the underlying pipeline.

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