scholarly journals Low Cost System for Laboratory-based Course in IT Education using Raspberry Pi

2020 ◽  
Author(s):  
Tae-Hoon Kim ◽  
Ricardo Calix ◽  
Dhruvkumar Patel
2018 ◽  
Vol 119 (1) ◽  
pp. 337-346 ◽  
Author(s):  
Gergely Silasi ◽  
Jamie D. Boyd ◽  
Federico Bolanos ◽  
Jeff M. LeDue ◽  
Stephen H. Scott ◽  
...  

Skilled forelimb function in mice is traditionally studied through behavioral paradigms that require extensive training by investigators and are limited by the number of trials individual animals are able to perform within a supervised session. We developed a skilled lever positioning task that mice can perform within their home cage. The task requires mice to use their forelimb to precisely hold a lever mounted on a rotary encoder within a rewarded position to dispense a water reward. A Raspberry Pi microcomputer is used to record lever position during trials and to control task parameters, thus making this low-footprint apparatus ideal for use within animal housing facilities. Custom Python software automatically increments task difficulty by requiring a longer hold duration, or a more accurate hold position, to dispense a reward. The performance of individual animals within group-housed mice is tracked through radio-frequency identification implants, and data stored on the microcomputer may be accessed remotely through an active internet connection. Mice continuously engage in the task for over 2.5 mo and perform ~500 trials/24 h. Mice required ~15,000 trials to learn to hold the lever within a 10° range for 1.5 s and were able to further refine movement accuracy by limiting their error to a 5° range within each trial. These results demonstrate the feasibility of autonomously training group-housed mice on a forelimb motor task. This paradigm may be used in the future to assess functional recovery after injury or cortical reorganization induced by self-directed motor learning. NEW & NOTEWORTHY We developed a low-cost system for fully autonomous training of group-housed mice on a forelimb motor task. We demonstrate the feasibility of tracking both end-point, as well as kinematic performance of individual mice, with each performing thousands of trials over 2.5 mo. The task is run and controlled by a Raspberry Pi microcomputer, which allows for cages to be monitored remotely through an active internet connection.


This paper deals with development of a Vehicle Security and Entertainment System, which is being used to monitor, track the vehicle, and to offer local entertainment system. The development system makes used of two embedded devices to split the entertainment system from the security system to ensure isolation and security. The security system is equipped with camera, distress signal switch and GPS/GPRS module to track, report a problem, and monitor the vehicle by sending data to a centralized database server where vehicle owner can access and retrieve these data to guarantee the safety of the passengers and the vehicle too. The second system is the entertainment system, where this system uses a powerful Intel atom embedded device and local network to allow users to connect and offer entertaining services. These services include, E-Book library and multimedia streaming. The main concept of research to develop a low cost system to secure and entertain passengers on vehicles like buses, train and even cars. The development is cost effective and as well as can be modified to add extra modules or to develop extra entertainment services. If the vehicle is stolen the system is able to send a distress signal to the owner or company. They can help the passengers by monitoring through the vehicle camera. In this research we have successfully developed and tested the system.


Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 153 ◽  
Author(s):  
Mery Diana ◽  
Juntaro Chikama ◽  
Motoki Amagasaki ◽  
Masahiro Iida

Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we use the general average pooling layer to replace the fully connected layers on the convolutional neural network (CNN) model, used in the previous study, to reduce the number of network properties without decreasing the model performance in developing image classification for image search tasks. We apply the cosine similarity to measure the characteristic similarity between the feature vector of image input and extracting feature vectors from testing images in the database. The result of the cosine similarity calculation will show the image as the result of the searching image task. In the implementation, we use Raspberry Pi 3 as a low-cost hardware and CIFAR-10 dataset for training and testing images. Base on the development and implementation, the accuracy of the model is 68%, and the system generates the result of the image search base on the characteristic similarity of the images.


Author(s):  
Samuel Aidala ◽  
Zachary Eichenberger ◽  
Nickolas Chan ◽  
Kyle Wilkinson ◽  
Chinedum Okwudire

Desktop fused filament fabrication (FFF) 3D printers have been growing in popularity among hobbyist and professional users as a prototyping and low-volume manufacturing tool. One issue these printers face is the inability to determine when a defect has occurred rendering the print unusable. Several techniques have been proposed to detect such defects but many of these approaches are tailored to one specific fault (e.g., filament runout/jam), use expensive hardware such as laser distance sensors, and/or use machine vision algorithms which are sensitive to ambient conditions, and hence can be unreliable. This paper proposes a versatile, reliable, and low-cost system, named MTouch, to detect millimeter-scale defects that tend to make prints unusable. At the core of MTouch is an actuated contact probe designed using a low-power solenoid, magnet, and hall effect sensor. This sensor is used to check for the presence, or absence, of the printed object at specific locations. The MTouch probe demonstrated 100% reliability, which was significantly higher than the 74% reliability achieved using a commercially available contact probe (the BLTouch). Additionally, an algorithm was developed to automatically detect common print failures such as layer shifting, bed separation, and filament runout using the MTouch probe. The algorithm was implemented on a Raspberry Pi mini-computer via an Octoprint plug-in. In head-to-head testing against a commercially available print defect detection system (The Spaghetti Detective), the MTouch was able to detect faults 44% faster on average while only increasing the print time by 8.49%. In addition, MTouch was able to detect faults The Spaghetti Detective was unable to identify such as layer shifting and filament runout/jam.


2019 ◽  
Vol 15 (28) ◽  
Author(s):  
Alex Esteban Barbosa Torres

Introduction: This paper describes the research – developed in 2007 by ORCA and SciBas – that modifies an actuator device (AD) – DC electric pump – making it Smart.  Problem: There are currently no intelligent ADs of open architecture with the possibility of being easily manipulated in didactic plants. Objective: To identify and implement circuits to control an AD with signals. Methodology: The AD is developed following the family-standard IEEE 1452: implementing communication interfaces that characterize intelligent transducers connected to systems based on microprocessors, instruments and networks. The integration is made in a configuration platform that displays, records and manipulates AD information through a PC. Results: Obtaining a low-cost system based on Raspberry Pi - as an intelligent core - where the following are integrated: a PSOC 4 microcontroller to interact with the signals of the actuator, a circuit to manage the energy between the pump and the microcontroller, and an integrated signal that goes to a processor Originality: Modify a DC electric pump -as part of a didactic plant system- to have an intelligent AD architecture. The checking of filling and flow rates indicate advantages over traditional instrumentation: noise immunity, effective isolation of circuits from sources, work sequences, effective storage of actuator information, simple calibration, ease of signal transmission and remote control, and the possibility of intelligent presentations. Limitations: In non-didactic plants it is necessary to extend the research to the industrial sector and increase the quality of the elements.


2007 ◽  
Vol 40 (11) ◽  
pp. 53
Author(s):  
BRUCE K. DIXON
Keyword(s):  
Low Cost ◽  

Author(s):  
Ramin Sattari ◽  
Stephan Barcikowski ◽  
Thomas Püster ◽  
Andreas Ostendorf ◽  
Heinz Haferkamp

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