scholarly journals Utilizing social media and video games to control #DIY microscopes

2017 ◽  
Vol 3 ◽  
pp. e139
Author(s):  
Maxime Leblanc-Latour ◽  
Craig Bryan ◽  
Andrew E. Pelling
Keyword(s):  
Social Media ◽  
Open Source ◽  
Video Game ◽  
Low Cost ◽  
Image Data ◽  
Time Lapse ◽  
Raspberry Pi ◽  
Laboratory Equipment ◽  
User Input ◽  
Web Camera

Open-source lab equipment is becoming more widespread with the popularization of fabrication tools such as 3D printers, laser cutters, CNC machines, open source microcontrollers and open source software. Although many pieces of common laboratory equipment have been developed, software control of these items is sometimes lacking. Specifically, control software that can be easily implemented and enable user-input and control over multiple platforms (PC, smartphone, web, etc.). The aim of this proof-of principle study was to develop and implement software for the control of a low-cost, 3D printed microscope. Here, we present two approaches which enable microscope control by exploiting the functionality of the social media platform Twitter or player actions inside of the videogame Minecraft. The microscope was constructed from a modified web-camera and implemented on a Raspberry Pi computer. Three aspects of microscope control were tested, including single image capture, focus control and time-lapse imaging. The Twitter embodiment enabled users to send ‘tweets’ directly to the microscope. Image data acquired by the microscope was then returned to the user through a Twitter reply and stored permanently on the photo-sharing platform Flickr, along with any relevant metadata. Local control of the microscope was also implemented by utilizing the video game Minecraft, in situations where Internet connectivity is not present or stable. A virtual laboratory was constructed inside the Minecraft world and player actions inside the laboratory were linked to specific microscope functions. Here, we present the methodology and results of these experiments and discuss possible limitations and future extensions of this work.

scholarly journals Utilizing Social Media and Video Games to Control #DIY Microscopes

2016 ◽  
Author(s):  
Maxime Leblanc-Latour ◽  
Craig Bryan ◽  
Andrew Pelling
Keyword(s):  
Social Media ◽  
Open Source ◽  
Video Game ◽  
Low Cost ◽  
Image Data ◽  
Time Lapse ◽  
Raspberry Pi ◽  
Laboratory Equipment ◽  
User Input ◽  
Web Camera

ABSTRACTOpen-source lab equipment is becoming more widespread with the popularization of fabrication tools such as 3d-printers, laser cutters, CNC machines, open source microcontrollers and open source software. Although many pieces of common laboratory equipment have been developed, software control of these items is sometimes lacking. Specifically, control software that can be easily implemented and enable user-input and control over multiple platforms (PC, smartphone, web, etc.). The aim of this proof-of-principle study was to develop and implement software for the control of a low-cost, 3d-printed microscope. Here, we present two approaches, which enable microscope control by exploiting the functionality of the social media platform Twitter or player actions inside of the videogame Minecraft. The microscope was constructed from a modified web-camera and implemented on a Raspberry Pi computer. Four aspects of microscope control were tested, including single image capture, focus control and time-lapse imaging. The Twitter-embodiment enabled users to send “tweets” directly to the microscope. Image data acquired by the microscope was then returned to the user through a Twitter reply and stored permanently on the photo-sharing platform Flickr, along with any relevant metadata. Local control of the microscope was also implemented by utilizing the video game Minecraft, in situations where Internet connectivity is not present or stable. A virtual laboratory was constructed inside the Minecraft world and player actions inside the laboratory were linked to specific microscope functions. Here, we present the methodology and results of these experiments and discuss possible limitations and future extensions of this work.

scholarly journals Raspberry Pi Powered Imaging for Plant Phenotyping

2017 ◽  
Author(s):  
Jose C. Tovar ◽  
J. Steen Hoyer ◽  
Andy Lin ◽  
Allison Tielking ◽  
Monica Tessman ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
High Throughput ◽  
Plant Diversity ◽  
Low Cost ◽  
Image Acquisition ◽  
Image Data ◽  
Raspberry Pi ◽  
Source Image ◽  
Plant Image

ABSTRACTPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.Methods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.Conclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

scholarly journals Open Source Completely 3-D Printable Centrifuge

2019 ◽  
Author(s):  
Salil S. Sule ◽  
Aliaksei L. Petsiuk ◽  
Joshua M. Pearce
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Medical Diagnostics ◽  
Resource Constrained ◽  
Fused Filament Fabrication ◽  
Web Camera ◽  
Relative Centrifugal Force ◽  
Recycled Plastics ◽  
Human Powered ◽  
Open Source Systems

Centrifuges are commonly required devices in medical diagnostics facilities as well as scientific laboratories. Although there are commercial and open source centrifuges, costs of the former and required electricity to operate the latter, limit accessibility in resource-constrained settings. There is a need for low-cost, human-powered, verified and reliable lab-scale centrifuge. This study provides the designs for a low-cost 100% 3-D printed centrifuge, which can be fabricated on any low-cost RepRap-class fused filament fabrication (FFF) or fused particle fabrication (FPF)-based 3-D printer. In addition, validation procedures are provided using a web camera and free and open source software. This paper provides the complete open source plans including instructions for fabrication and operation for a hand-powered centrifuge. This study successfully tested and validated the instrument, which can be operated anywhere in the world with no electricity inputs obtaining a radial velocity of over 1750rpm and over 50N of relative centrifugal force. Using commercial filament the instrument costs about US$25, which is less than half of all commercially available systems; however, the costs can be dropped further using recycled plastics on open source systems for over 99% savings. The results are discussed in the contexts of resource-constrained medical and scientific facilities.

scholarly journals Open-Source Electronics Platforms as Enabling Technologies for Smart Cities: Recent Developments and Perspectives

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 404 ◽  
Author(s):  
Daniel Costa ◽  
Cristian Duran-Faundez
Keyword(s):  
Open Source ◽  
Smart City ◽  
Smart Cities ◽  
Low Cost ◽  
Raspberry Pi ◽  
Computational Power ◽  
Different Types ◽  
Recent Developments ◽  
Programmable Devices ◽  
Hardware Platforms

With the increasing availability of affordable open-source embedded hardware platforms, the development of low-cost programmable devices for uncountable tasks has accelerated in recent years. In this sense, the large development community that is being created around popular platforms is also contributing to the construction of Internet of Things applications, which can ultimately support the maturation of the smart-cities era. Popular platforms such as Raspberry Pi, BeagleBoard and Arduino come as single-board open-source platforms that have enough computational power for different types of smart-city applications, while keeping affordable prices and encompassing many programming libraries and useful hardware extensions. As a result, smart-city solutions based on such platforms are becoming common and the surveying of recent research in this area can support a better understanding of this scenario, as presented in this article. Moreover, discussions about the continuous developments in these platforms can also indicate promising perspectives when using these boards as key elements to build smart cities.

scholarly journals VisBio: a Flexible Open-Source Visualization Package for Multidimensional Image Data

2006 ◽  
Vol 14 (3) ◽  
pp. 6-11
Author(s):  
Curtis T. Rueden ◽  
Kevin W. Eliceiri
Keyword(s):  
Open Source ◽  
Curve Fitting ◽  
Image Data ◽  
Time Lapse ◽  
Dramatic Improvement ◽  
Exponential Curve ◽  
Lifetime Imaging ◽  
The Past ◽  
Resultant Data ◽  
Fitting Algorithm

Over the past few years there has been a dramatic improvement in microscopy acquisition techniques, in effective imaging modalities as well as raw hardware performance. As the microscopist's available tools become more sophisticated and diverse—e.g., time-lapse, Z sectioning, multispectra, lifetime, nth harmonic, polarization, and many combinations thereof—we face a corresponding increase in complexity in the software for understanding and interpreting the resultant data. With lifetime imaging, for example, it is overwhelming to study the raw numbers; instead, an exponential curve-fitting algorithm must be applied to extract meaningful lifetime values from the mass of photon counts recorded by the instrument.

scholarly journals Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

2021 ◽  
pp. 20-25
Author(s):  
Tomás Serrano-Ramírez ◽  
Ninfa del Carmen Lozano-Rincón ◽  
Arturo Mandujano-Nava ◽  
Yosafat Jetsemaní Sámano-Flores
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Vision System ◽  
Low Cost ◽  
Artificial Vision ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Smart Cameras ◽  
Suggested Technique ◽  
Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

scholarly journals AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

2020 ◽  
Author(s):  
Chaitra Hegde ◽  
Zifan Jiang ◽  
Pradyumna Byappanahalli Suresha ◽  
Jacob Zelko ◽  
Salman Seyedi ◽  
...  
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Characteristic Curve ◽  
Rapid Assessment ◽  
Infrared Camera ◽  
Visible Spectrum ◽  
Wait Time ◽  
Far Infrared ◽  
Raspberry Pi ◽  
Body Parts

AbstractWith the recent COVID-19 pandemic, healthcare systems all over the world are struggling to manage the massive increase in emergency department (ED) visits. This has put an enormous demand on medical professionals. Increased wait times in the ED increases the risk of infection transmission. In this work we present an open-source, low cost, off-body system to assist in the automatic triage of patients in the ED based on widely available hardware. The system initially focuses on two symptoms of the infection fever and cyanosis. The use of visible and far-infrared cameras allows for rapid assessment at a 1m distance, thus reducing the load on medical staff and lowering the risk of spreading the infection within hospitals. Its utility can be extended to a general clinical setting in non-emergency times as well to reduce wait time, channel the time and effort of healthcare professionals to more critical tasks and also prioritize severe cases.Our system consists of a Raspberry Pi 4, a Google Coral USB accelerator, a Raspberry Pi Camera v2 and a FLIR Lepton 3.5 Radiometry Long-Wave Infrared Camera with an associated IO module. Algorithms running in real-time detect the presence and body parts of individual(s) in view, and segments out the forehead and lip regions using PoseNet. The temperature of the forehead-eye area is estimated from the infrared camera image and cyanosis is assessed from the image of the lips in the visible spectrum. In our preliminary experiments, an accuracy of 97% was achieved for detecting fever and 77% for the detection of cyanosis, with a sensitivity of 91% and area under the receiver operating characteristic curve of 0.91. Heart rate and respiratory effort are also estimated from the visible camera.Although preliminary results are promising, we note that the entire system needs to be optimized before use and assessed for efficacy. The use of low-cost instrumentation will not produce temperature readings and identification of cyanosis that is acceptable in many situations. For this reason, we are releasing the full code stack and system design to allow others to rapidly iterate and improve the system. This may be of particular benefit in low-resource settings, and low-to-middle income countries in particular, which are just beginning to be affected by COVID-19.

scholarly journals One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5812
Author(s):  
Andres Henao ◽  
Philippe Apparicio ◽  
David Maignan
Keyword(s):  
Knowledge Sharing ◽  
Open Source ◽  
Scientific Community ◽  
Low Cost ◽  
Video Recording ◽  
Raspberry Pi ◽  
Essential Tool ◽  
Geographical Position ◽  
Motorized Vehicles ◽  
Comparability Of Results

During the last decade, bicycles equipped with sensors became an essential tool for research, particularly for studies analyzing the lateral passing distance between motorized vehicles and bicycles. The objective of this article is to describe a low-cost open-source sensor called one metre plus (1m+) capable of measuring lateral passing distance, registering the geographical position of the cyclist, and video-recording the trip. The plans, codes, and schematic design are open and therefore easily accessible for the scientific community. This study describes in detail the conceptualization process, the characteristics of the device, and the materials from which they are made. The study also provides an evaluation of the product and describes the sensor’s functionalities and its field of application. The objective of this project is to democratize research and develop a platform/participative project that offers tools to researchers worldwide, in order to standardize knowledge sharing and facilitate the comparability of results in various contexts.

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