scholarly journals Atmospheric Data Collecting Cubesat using Raspberry PI

2021 ◽  
Vol 9 (VI) ◽  
pp. 3880-3888
Author(s):  
Banala Krishna Gopal
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Space Missions ◽  
Raspberry Pi ◽  
Power Efficient ◽  
Ground Station ◽  
Technological Advances ◽  
Atmospheric Data ◽  
Data Collecting

As advances in technology make payloads and instruments for space missions smaller, lighter, and more power efficient, a distinct segment market is emerging for low-cost missions on very small spacecrafts such as - micro, nano, and picosatellites. Due to the fact that even after many technological advances the usage of miniature satellites the remote sensing of atmospheric is still not a widely explored aspect, to overcome this we idealized a system to build a CUBESAT which can be built with minimal efforts. We proposed this system with an objective to build a CUBESAT to detect different weather aspects of our earth at the troposphere layer which is the lowest layer of earth. We implemented our project using the Raspberry Pi due to its versatility in multi-processing and connectivity. Here the Raspberry-Pi is going to be configured with transceiver modules in the CUBESAT’s sender-end to gather atmospheric data associated with temperature, gasses present, humidity and pressure using CUBESAT sensors and after the reception of data at ground station by Arduino configured as receiver, data is going to be stored in an accessible website for viewing and further computations.

scholarly journals Designing of an Advanced Compression Bioreactor with an Implementation of a Low-Cost Controlling System Connected to a Mobile Application

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 915
Author(s):  
Gözde Dursun ◽  
Muhammad Umer ◽  
Bernd Markert ◽  
Marcus Stoffel
Keyword(s):  
Mobile Application ◽  
Low Cost ◽  
Experimental Information ◽  
Raspberry Pi ◽  
Tissue Constructs ◽  
Cost Controlling ◽  
Controlling System ◽  
And Control ◽  
Tissue Models

(1) Background: Bioreactors mimic the natural environment of cells and tissues by providing a controlled micro-environment. However, their design is often expensive and complex. Herein, we have introduced the development of a low-cost compression bioreactor which enables the application of different mechanical stimulation regimes to in vitro tissue models and provides the information of applied stress and strain in real-time. (2) Methods: The compression bioreactor is designed using a mini-computer called Raspberry Pi, which is programmed to apply compressive deformation at various strains and frequencies, as well as to measure the force applied to the tissue constructs. Besides this, we have developed a mobile application connected to the bioreactor software to monitor, command, and control experiments via mobile devices. (3) Results: Cell viability results indicate that the newly designed compression bioreactor supports cell cultivation in a sterile environment without any contamination. The developed bioreactor software plots the experimental data of dynamic mechanical loading in a long-term manner, as well as stores them for further data processing. Following in vitro uniaxial compression conditioning of 3D in vitro cartilage models, chondrocyte cell migration was altered positively compared to static cultures. (4) Conclusion: The developed compression bioreactor can support the in vitro tissue model cultivation and monitor the experimental information with a low-cost controlling system and via mobile application. The highly customizable mold inside the cultivation chamber is a significant approach to solve the limited customization capability of the traditional bioreactors. Most importantly, the compression bioreactor prevents operator- and system-dependent variability between experiments by enabling a dynamic culture in a large volume for multiple numbers of in vitro tissue constructs.

scholarly journals Barriers to training in laparoscopic surgery in low- and middle-income countries: A systematic review

2021 ◽  
pp. 004947552199818
Author(s):  
Ellen Wilkinson ◽  
Noel Aruparayil ◽  
J Gnanaraj ◽  
Julia Brown ◽  
David Jayne
Keyword(s):  
Laparoscopic Surgery ◽  
Low Cost ◽  
Surgical Care ◽  
Successful Implementation ◽  
Middle Income ◽  
Middle Income Countries ◽  
Technological Advances ◽  
Local Access ◽  
And Training ◽  
Low And Middle Income

Laparoscopic surgery has the potential to improve care in resource-deprived low- and-middle-income countries (LMICs). This study aims to analyse the barriers to training in laparoscopic surgery in LMICs. Medline, Embase, Global Health and Web of Science were searched using ‘LMIC’, ‘Laparoscopy’ and ‘Training’. Two researchers screened results with mutual agreement. Included papers were in English, focused on abdominal laparoscopy and training in LMICs. PRISMA guidelines were followed; 2992 records were screened, and 86 full-text articles reviewed to give 26 key papers. Thematic grouping identified seven key barriers: funding; availability and maintenance of equipment; local access to experienced laparoscopic trainers; stakeholder dynamics; lack of knowledge on effective training curricula; surgical departmental structure and practical opportunities for trainees. In low-resource settings, technological advances may offer low-cost solutions in the successful implementation of laparoscopic training and improve access to surgical care.

scholarly journals Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 432
Author(s):  
Guenther Retscher ◽  
Alexander Leb
Keyword(s):  
Performance Improvement ◽  
Low Cost ◽  
Information Service ◽  
Raspberry Pi ◽  
University Library ◽  
Stop And Go ◽  
Promising Solution ◽  
Localization Performance ◽  
The University ◽  
Suitable Location

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals A low cost remote sensing system using PC and stereo equipment

2011 ◽  
Vol 79 (12) ◽  
pp. 1240-1245 ◽  
Author(s):  
Joel F. Campbell ◽  
Michael A. Flood ◽  
Narasimha S. Prasad ◽  
Wade D. Hodson
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Sensing System
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