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.

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 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 A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 652 ◽  
Author(s):  
José R. García-Martínez ◽  
Juvenal Rodríguez-Reséndiz ◽  
Edson E. Cruz-Miguel
Keyword(s):  
Velocity Profile ◽  
Open Source ◽  
Low Cost ◽  
High Stress ◽  
Rate Of Change ◽  
Open Architecture ◽  
Raspberry Pi ◽  
Field Programmable ◽  
Acceleration And Deceleration ◽  
S Curve

The velocity profiles are used in the design of trajectories in motion control systems. It is necessary to design smoother movements to avoid high stress in the motor. In this paper, the rate of change in acceleration value is used to develop an S-curve velocity profile which presents an acceleration and deceleration stage smoother than the trapezoidal velocity profile reducing the error at the end of the duty-cycle pre-established in one degree of freedom (DoF) application. Furthermore, a new methodology is developed to generate a seven-segment profile that works with negative velocity and displacement constraints applying an open source architecture in a hybrid electronic platform compounded by a system on a chip (SoC) Raspberry Pi 3 and a field programmable gate array (FPGA). The performance of the motion controller is measured through the comparison of the error obtained in real-time application with a trapezoidal velocity profile. As a result, a low-cost platform and an open architecture system are achieved.

Adapting a Low-Cost and Open-Source Commercial Pipetting Robot for Nanoliter Liquid Handling

2020 ◽  
pp. 247263032097359
Author(s):  
E. Enoch A. W. Councill ◽  
Nathanial B. Axtell ◽  
Thy Truong ◽  
Yiran Liang ◽  
Adam L. Aposhian ◽  
...  
Keyword(s):  
Open Source ◽  
Scientific Community ◽  
Low Cost ◽  
Mass Spectrometry Analysis ◽  
Liquid Chromatography Mass Spectrometry ◽  
Processing Efficiency ◽  
Spectrometry Analysis ◽  
Liquid Handling ◽  
Fluorescence Measurements ◽  
Volume Measurements

Low-volume liquid handling capabilities in bioanalytical workflows can dramatically improve sample processing efficiency and reduce reagent costs, yet many commercial nanoliter liquid handlers cost tens of thousands of dollars or more. We have successfully adapted a low-cost and open-source commercial pipetting robot, the Opentrons OT-1, to accurately aspirate and dispense nanoliter volumes. Based on fluorescence measurements, the modified OT-1 was able to reproducibly transfer 50 nL of water with less than 3% measurement error and 5% coefficient of variation (CV). For 15 nL transfers, the volume measurements indicated less than 4% error and 4% CV. We applied this platform to the preparation of low-nanogram proteomic samples for liquid chromatography–mass spectrometry analysis, demonstrating that the modified OT-1 is an effective platform for nanoliter liquid handling. At a total materials cost of less than $6000, including the commercial liquid handler and all modifications, this system is also far less expensive than other platforms with similar capabilities, placing automated nanoliter handling within reach of a far broader scientific community.

scholarly journals Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 822 ◽  
Author(s):  
Lawrence Oriaghe Aghenta ◽  
Mohammad Tariq Iqbal
Keyword(s):  
Internet Of Things ◽  
Open Source ◽  
Supervisory Control ◽  
Low Cost ◽  
Raspberry Pi ◽  
Voltage Sensors ◽  
Scada System ◽  
Iot Platform ◽  
Field Instrumentation ◽  
Distributed Processes

Supervisory Control and Data Acquisition (SCADA) is a technology for monitoring and controlling distributed processes. SCADA provides real-time data exchange between a control/monitoring centre and field devices connected to the distributed processes. A SCADA system performs these functions using its four basic elements: Field Instrumentation Devices (FIDs) such as sensors and actuators which are connected to the distributed process plants being managed, Remote Terminal Units (RTUs) such as single board computers for receiving, processing and sending the remote data from the field instrumentation devices, Master Terminal Units (MTUs) for handling data processing and human machine interactions, and lastly SCADA Communication Channels for connecting the RTUs to the MTUs, and for parsing the acquired data. Generally, there are two classes of SCADA hardware and software; Proprietary (Commercial) and Open Source. In this paper, we present the design and implementation of a low-cost, Open Source SCADA system by using Thinger.IO local server IoT platform as the MTU and ESP32 Thing micro-controller as the RTU. SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. The SCADA system proposed in this work is based on the Internet of Things SCADA architecture which incorporates web services with the conventional (traditional) SCADA for a more robust supervisory control and monitoring. It comprises of analog Current and Voltage Sensors, the low-power ESP32 Thing micro-controller, a Raspberry Pi micro-controller, and a local Wi-Fi Router. In its implementation, the current and voltage sensors acquire the desired data from the process plant, the ESP32 micro-controller receives, processes and sends the acquired sensor data via a Wi-Fi network to the Thinger.IO local server IoT platform for data storage, real-time monitoring and remote control. The Thinger.IO server is locally hosted by the Raspberry Pi micro-controller, while the Wi-Fi network which forms the SCADA communication channel is created using the Wi-Fi Router. In order to test the proposed SCADA system solution, the designed hardware was set up to remotely monitor the Photovoltaic (PV) voltage, current, and power, as well as the storage battery voltage of a 260 W, 12 V Solar PV System. Some of the created Human Machine Interfaces (HMIs) on Thinger.IO Server where an operator can remotely monitor the data in the cloud, as well as initiate supervisory control activities if the acquired data are not in the expected range, using both a computer connected to the network, and Thinger.IO Mobile Apps are presented in the paper.

scholarly journals An inexpensive remotely-operated video recording system for continuous behavioral observations

2019 ◽  
Author(s):  
W.David Weber ◽  
Heidi S. Fisher
Keyword(s):  
Open Source ◽  
Contextual Information ◽  
Video Recording ◽  
Bright Light ◽  
Recording System ◽  
Raspberry Pi ◽  
Live Streaming ◽  
Recording Technology ◽  
Peromyscus Californicus ◽  
Observer Effects

AbstractVideo recording technology is an important tool for studies of animal behavior because it reduces observer effects and produces a record of experiments, interactions among subjects, and contextual information, however it remains cost prohibitive for many researchers. Here we present an inexpensive method for building a remotely-operated video recording system to continuously monitor behavioral or other biological experiments. Our system employs Raspberry Pi computers and cameras, open-source software, and allows for wireless networking, live-streaming, and the capacity to simultaneously record from several cameras in an array. To validate this system, we continuously monitored home-cage behavior of California mice (Peromyscus californicus) in a laboratory setting. We captured video in both low- and bright-light environments to record behaviors of this nocturnal species, and then quantified mating interactions of California mouse pairs by analyzing the videos with an open-source event logging software. This video recording platform offers users the flexibility to modify the specifications for a range of tasks and the scalability to make research more efficient and reliable to a larger population of scientists.

scholarly journals PiRATeMC: A highly flexible, scalable, and affordable system for obtaining high quality video recordings for behavioral neuroscience.

2021 ◽  
Author(s):  
Samuel W Centanni ◽  
Alexander CW Smith
Keyword(s):  
Open Source ◽  
Online Community ◽  
Video Quality ◽  
Video Recording ◽  
Bright Light ◽  
Infrared Light ◽  
Raspberry Pi ◽  
Behavioral Experiments ◽  
Recording Equipment ◽  
Camera System

With the recent development and rapidly accelerating adoption of machine-learning based rodent behavioral tracking tools such as DeepLabCut, one common variable that can impact the quality and consistency of results is the camera system. Many experimenters use webcams, GoPros, or other commercially available cameras that are not only relatively expensive, but offer very little flexibility over recording parameters. These cameras are not optimized for recording many types of behavioral experiments, which can lead to suboptimal video quality. Furthermore, it is a challenge, if not impossible, to synchronize multiple cameras with each other, or to send/receive a trigger with external signals such as a TTL pulse or a network connection. We have developed an affordable ecosystem of behavioral recording equipment, PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture), that relies on Raspberry Pi Camera Boards that are ideal for recording in both bright light, low light, and dark conditions under infrared light. PiRATeMC offers users control over nearly every recording parameter. This setup can easily be scaled up and synchronously controlled in clusters via a self-contained network to record a large number of simultaneous behavioral sessions without burdening institutional network infrastructure. Furthermore, the Raspberry Pi is an excellent platform for novice and inexperienced programmers interested in using an open-source recording system, with a large online community that is very active in developing novel open-source tools. Moreover, it easily interfaces with Arduinos and other microcontrollers, allowing simple synchronization and interfacing of video recording with nearly any behavioral equipment using GPIO pins to send or receive 3.3V or 5V signals, I2C, or serial communication.

scholarly journals BioSamplr: An open source, low cost automated sampling system for bioreactors

2020 ◽  
Author(s):  
John P. Efromson ◽  
Shuai Li ◽  
Michael D. Lynch
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Raspberry Pi ◽  
Sampling System ◽  
Sample Interval ◽  
3D Printed ◽  
Bioreactor Experiment ◽  
Log File ◽  
Time Required ◽  
The Cost

AbstractAutosampling from bioreactors reduces error, increases reproducibility and offers improved aseptic handling when compared to manual sampling. Additionally, autosampling greatly decreases the hands-on time required for a bioreactor experiment and enables sampling 24 hrs a day. We have designed, built and tested a low cost, open source, automated bioreactor sampling system, the BioSamplr. The BioSamplr can take up to ten samples from a bioreactor at a desired sample interval and cools them to a desired temperature. The device, assembled from low cost and 3D printed components, is controlled wirelessly by a Raspberry Pi, and records all sampling data to a log file. The cost and accessibility of the BioSamplr make it useful for laboratories without access to more expensive and complex autosampling systems.

scholarly journals Broad-scale Applications of the Raspberry Pi: A Review and Guide for Biologists

2021 ◽  
Author(s):  
Jolle Wolter Jolles
Keyword(s):  
Scientific Community ◽  
Large Scale ◽  
Low Cost ◽  
Ease Of Use ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
Ecosystem Monitoring ◽  
Diverse Range ◽  
Wide Range ◽  
Macro Scale

The field of biology has seen tremendous technological progress in recent years, fuelled by the exponential growth in processing power and high-level computing, and the rise of global information sharing. Low-cost single-board computers are predicted to be one of the key technological advancements to further revolutionise this field. So far, an overview of current uptake of these devices and a general guide to help researchers integrate them in their work has been missing. In this paper I focus on the most widely used single board computer, the Raspberry Pi. Reviewing its broad applications and uses across the biological domain shows that since its release in 2012 the Raspberry Pi has been increasingly taken up by biologists, both in the lab, the field, and in the classroom, and across a wide range of disciplines. A hugely diverse range of applications already exist that range from simple solutions to dedicated custom-build devices, including nest-box monitoring, wildlife camera trapping, high-throughput behavioural recordings, large-scale plant phenotyping, underwater video surveillance, closed-loop operant learning experiments, and autonomous ecosystem monitoring. Despite the breadth of its implementations, the depth of uptake of the Raspberry Pi by the scientific community is still limited. The broad capabilities of the Raspberry Pi, combined with its low cost, ease of use, and large user community make it a great research tool for almost any project. To help accelerate the uptake of Raspberry Pi’s by the scientific community, I provide detailed guidelines, recommendations, and considerations, and 30+ step-by-step guides on a dedicated accompanying website (raspberrypi-guide.github.io). I hope with this paper to generate more awareness about the Raspberry Pi and thereby fuel the democratisation of science and ultimately help advance our understanding of biology, from the micro- to the macro-scale.

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