scholarly journals Development of Smart Vehicle Security and Entertainment System (SSES) using Raspberry Pi

2020 ◽  
Vol 9 (3) ◽  
pp. 4077-4083 ◽  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Security System ◽  
Local Network ◽  
Raspberry Pi ◽  
Main Concept ◽  
Embedded Devices ◽  
Vehicle Owner ◽  
Cost System ◽  
Vehicle Security

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.

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
Keyword(s):  
Low Cost ◽  
Raspberry Pi ◽  
Cost System ◽  
It Education

scholarly journals Individualized tracking of self-directed motor learning in group-housed mice performing a skilled lever positioning task in the home cage

2018 ◽  
Vol 119 (1) ◽  
pp. 337-346 ◽  
Author(s):  
Gergely Silasi ◽  
Jamie D. Boyd ◽  
Federico Bolanos ◽  
Jeff M. LeDue ◽  
Stephen H. Scott ◽  
...  
Keyword(s):  
Motor Learning ◽  
Radio Frequency Identification ◽  
Home Cage ◽  
Low Cost ◽  
Motor Task ◽  
Training Group ◽  
Cortical Reorganization ◽  
Raspberry Pi ◽  
Internet Connection ◽  
Cost System

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.

DESIGN OF SMARTSCHOOL WEBCAM SECURITY SYSTEM USING RASPBERRY PI WITH MOTION DETECTION IN SMK PGRI SUKODADI

JOURNAL ASRO ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 202
Author(s):  
Firman Yudianto ◽  
Fajar Annas Susanto
Keyword(s):  
Motion Detection ◽  
Industry 4.0 ◽  
Low Cost ◽  
Security System ◽  
Educational Institutions ◽  
Raspberry Pi ◽  
File Size ◽  
Moving Image ◽  
Moving Images ◽  
Web Camera

Currently a low cost security system is needed and easy to apply especially at educational institutions that willimplement smart school and industry 4.0. Needed devices are raspberry pi and web camera. Raspberry pi willonly save moving images taken from the web camera. Because by storing an image whose file size is not toolarge will ease the performance of the server. In this study, a design for the raspberry pi based motion detectionsystem will be applied at SMK PGRI Sukodadi Lamongan Regency which has not have security system. Thissystem will save the file in the form of an image that will be put together into a moving image that looks like avideo that will displayed in a LED monitor.Keywords: smart school, motion detection, moving image.

scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

scholarly journals Smart Authentication and Secured Engine Unlocking System for Automobiles

2021 ◽  
pp. 653-659
Author(s):  
Bhargava R ◽  
Amulya H C ◽  
Jyothi K P ◽  
Keerthana R
Keyword(s):  
Real Time ◽  
Human Life ◽  
Security System ◽  
Raspberry Pi ◽  
Biometric Authentication ◽  
Servo Motor ◽  
Smart System ◽  
The Real ◽  
Alert Message ◽  
Vehicle Security

As human life started to evolve on this earth, the craving for smart automobiles has increased, and adding a vehicle security system to secure the automobile from theft in parking and in unsecured places is important. This paper proposes the design and development of smart system to prevent theft that uses biometric authentication to access the door and to start the engine of the automobile. This system initially uses the fingerprint module that takes the real time fingerprint of a person trying to open the vehicle door and compares it with the authorized person’s fingerprint and then allows or denies the access to door, and secondly the camera takes the image of a person trying to start the engine and compares with the authorized person’s image to allow or deny the access to the engine. In case of detection of unauthorized fingerprint, the GSM module sends the message to the owner and in case unauthorized person detected by camera it sends the captured image with alert message to owner. The system is developed using raspberry pi, GSM module, fingerprint module, pi camera, dc and servo motor.

scholarly journals Affordable Remote Monitoring of Plant Growth and Facilities using Raspberry Pi Computers

2019 ◽  
Author(s):  
Brandin Grindstaff ◽  
Makenzie E. Mabry ◽  
Paul D. Blischak ◽  
Micheal Quinn ◽  
J. Chris Pires
Keyword(s):  
Remote Monitoring ◽  
Data Transfer ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Raspberry Pi ◽  
List Type ◽  
Environmental Sensors ◽  
Data Logging ◽  
The Stability

ABSTRACTPremise of the study: Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated remote monitoring of such facilities with low-cost hardware can greatly improve both the reproducibility and the accurate maintenance of their conditions.Methods and Results: Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed a cost-effective system for monitoring growth chamber conditions, which we have called ‘GMpi.’ Coupled with our software, GMpi_Pack, our setup automates sensor readings, photography, alerts when conditions fall out of range, and data transfer to cloud storage services.Conclusions: The GMpi offers low-cost access to environmental data logging, improving reproducibility of experiments, as well as reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing customization and expansion to include other features such as machine vision.

scholarly journals Low-cost Voice Based Braille Script Communication for Teaching Aid

2021 ◽  
pp. 457-467
Author(s):  
Shaik Asif. Hussain ◽  
◽  
Shaik Javeed. Hussain ◽  
Raza Hasan ◽  
Salman Mahmood
Keyword(s):  
Embedded System ◽  
Traditional Method ◽  
Low Cost ◽  
Cost Effective ◽  
Assistive Device ◽  
Raspberry Pi ◽  
Proximity Sensor ◽  
Ir Sensors ◽  
Visually Challenged ◽  
First Time

Though the Traditional method of teaching Braille script for the blind is simple, yet it has some potential drawbacks. Handling the marbles and the slate for a first-time does make learning very difficult. In most cases, the teacher will also be blind, so for each representation, the teacher must reach each student’s slate and change the arrangement of the marbles. This is a harder and time taking job. This project focuses on the design and development of an embedded system based electronic assistive device which eases the problem of teaching visually challenged beginner. This Project is implemented using an ordinary Braille slate with IR sensors and Raspberry Pi 2 Model B board which is cost-effective and simple. The Software is implemented in Simulink of MATLAB R2020. The placing of the marbles in the slate is sensed by the IR Proximity sensor. If the combination of the marbles placed is correct, then the Raspberry Pi’s Text to speak converter produces the audio sound output of the corresponding letter. This method provides an easy way of teaching Braille Script with less effort for the teacher.

Design of a GSM Cell – Phone based Vehicle Monitoring & Theft Security System

2013 ◽  
pp. 272-276
Author(s):  
Amol S. Dhotre ◽  
Abhishek S. Chandurkar ◽  
Sumedh. S. Jadhav
Keyword(s):  
Mobile Phone ◽  
Digital Signal ◽  
Security System ◽  
Vehicle Owner ◽  
Central Processing ◽  
Message Service ◽  
Processing Signal ◽  
Vehicle Security ◽  
External Program ◽  
Integrated Or

This project will focus on developing an enhancement of the vehicle alarm security system via SMS. The system will manipulate a mobile phone to send SMS (Short Message Service). Even though the SMS can be sent using the features available in the mobile, the objective of this project is to activate the SMS sending by the mobile phone using external program, connected physically to the mobile phone. The study of telecommunication is an interesting field because it involves digital signal processing, signal and systems, programming and more. This inspires people to improvise the technology into daily use. In this project, the technology of telecommunication, to be specific; SMS, is integrated or improvised to the present vehicle security system. Instead of human to human telecommunication, this system creates new entity which is machine to human telecommunication. This system is an upgrading and improving vehicle security system by integrating SMS features to alert vehicle owners whenever intrusion occurs. This project involves hardware and software parts construction and the integration of both parts to create the system. We succeed in achieving the objective and in fact, add another feature to the system which will initiates a call to the owner after sending the SMS. In the end of this project, we will document all the hardware and software development and provide a simulation model of the system. An interfacing mobile is also connected to the microcontroller, which is in turn, connected to the engine. Once, the vehicle is being stolen, the information is being used by the vehicle owner for further processing. The information is passed onto the central processing insurance system which is in the form of the sms, the microcontroller unit reads the sms and sends it to the Global Positioning System (GPS) module and using the triangulation method, GPS module feeds the exact location in the form of latitude and longitude to the user’s mobile. By reading the signals received by the mobile, one can control the ignition of the engine.

scholarly journals Enabling Image-Based Streamflow Monitoring at the Edge

2020 ◽  
Vol 12 (12) ◽  
pp. 2047 ◽  
Author(s):  
Fabio Tosi ◽  
Matteo Rocca ◽  
Filippo Aleotti ◽  
Matteo Poggi ◽  
Stefano Mattoccia ◽  
...  
Keyword(s):  
Embedded System ◽  
High Speed ◽  
Low Cost ◽  
Cost Effective ◽  
Engineering Practice ◽  
Embedded Devices ◽  
Cloud Processing ◽  
Internet Connections ◽  
Processing Device

Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have proved to be reliable systems to non-intrusively monitor water bodies in remote places at variable flow regimes. Nonetheless, to tackle their computational and energy requirements, offload processing and high-speed internet connections in the monitored environments, which are often difficult to access, is mandatory hence limiting the effective deployment of such techniques in several relevant circumstances. In this paper, we advance and simplify streamflow velocity monitoring by directly processing the image stream in situ with a low-power embedded system. By leveraging its standard parallel processing capability and exploiting functional simplifications, we achieve an accuracy comparable to state-of-the-art algorithms that typically require expensive computing devices and infrastructures. The advantage of monitoring streamflow velocity in situ with a lightweight and cost-effective embedded processing device is threefold. First, it circumvents the need for wideband internet connections, which are expensive and impractical in remote environments. Second, it massively reduces the overall energy consumption, bandwidth and deployment cost. Third, when monitoring more than one river section, processing “at the very edge” of the system efficiency improves scalability by a large margin, compared to offload solutions based on remote or cloud processing. Therefore, enabling streamflow velocity monitoring in situ with low-cost embedded devices would foster the widespread diffusion of gauge cameras even in developing countries where appropriate infrastructure might be not available or too expensive.

scholarly journals Low-Cost Image Search System on Off-Line Situation

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 153 ◽  
Author(s):  
Mery Diana ◽  
Juntaro Chikama ◽  
Motoki Amagasaki ◽  
Masahiro Iida
Keyword(s):  
Low Cost ◽  
Model Performance ◽  
Cosine Similarity ◽  
Raspberry Pi ◽  
Image Search ◽  
Search Tasks ◽  
Cost System ◽  
Network Properties ◽  
General Average ◽  
Fully Connected

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.

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