scholarly journals COVID-19 Authorized Entry with Face Mask Detection using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 1862-1867
Author(s):  
Anju Ajay
Keyword(s):  
Large Scale ◽  
Facial Recognition ◽  
Face Mask ◽  
Raspberry Pi ◽  
Wireless Internet ◽  
Great Demand ◽  
Embedded Device ◽  
Internet Connection ◽  
Battery Power ◽  
Python Programming

There are no effective face mask detection applications in the current COVID-19 scenario, which is in great demand for transportation, densely populated places, residential districts, large-scale manufacturers, and other organizations to ensure safety. In addition, the lack of big datasets of photographs with mask has made this task more difficult. With the use of Python programming, the Open CV library, Keras, and tensor flow, this project presents a way for recognizing persons without wearing a face mask using the facial recognition methodology. This is a self-contained embedded device that was created with the Raspberry Pi Electronic Development Board and runs on battery power. We make use of a wireless internet connection using USB modem. In comparison to other existing systems, our proposed method is more effective, reliable, and consumes significantly less data and electricity

scholarly journals A Survey on Covid Safety System

2021 ◽  
Vol 9 (11) ◽  
pp. 589-593
Author(s):  
Hinal Sodagra
Keyword(s):  
Body Temperature ◽  
Face Mask ◽  
Raspberry Pi ◽  
Infrared Sensor ◽  
Shopping Malls ◽  
Solution System ◽  
Public Places ◽  
Hand Sanitizer ◽  
Security Clearance ◽  
Python Programming

Abstract: In this paper a Raspberry Pi based automated solution system focused on the real-time face monitoring of people to detect both face masks and body temperature with the help of MLX90614 sensor has been proposed. This is implemented using Python Programming with OpenCV Library, TensorFlow, Dlib Module. A security clearance system is deployed that will allow that person to enter if they are wearing a face mask and their body temperature is in check with WHO guidelines. A programmed hand sanitizer apportioning machine is mechanized, non-contact, liquor-based hand sanitizer gadget. Liquor is essentially a dissolvable, and furthermore a generally excellent sanitizer when contrasted with fluid cleanser or strong cleanser, likewise it needn't bother with water to wash off since it is unpredictable furthermore, disintegrates in a split second after application to hands. It is too demonstrated that a convergence of >70% liquor can execute Covid in hands. Here, we have used IR sensor detects the hand put close to it, the Arduino Uno is utilized as a microcontroller, which detects the distance and the outcome isthe pump starts running out the hand sanitizer. Thus, the above said system will help the society by saving time and also helps in contaminating the spread of coronavirus. This can be implemented in public places such as colleges, schools, offices, shopping malls, etc. to inspect people. Keywords: Deep Learning, Open CV, Keras, Python, Tensor Flow, Computer Vision, Raspberry Pi, COVID-19, DLib, Arduino, Sensor, Sanitizer, Infrared sensor

scholarly journals RANCANG BANGUN SISTEM PRESENSI BERBASIS IOT

2020 ◽  
pp. 60-67
Author(s):  
Stephanie Imelda Pella ◽  
Frans Likadja ◽  
Molina Odja ◽  
Wenefrida T Ina
Keyword(s):  
Programming Language ◽  
Integrated System ◽  
The Other ◽  
Local Network ◽  
Raspberry Pi ◽  
The Internet ◽  
Internet Connection ◽  
User Query ◽  
Python Programming ◽  
Database Engine

The purpose of this research is to design and implement an attendance system based on internet of things (IoT) . The proposed system integrated two types of attendance systems, face recognition based attendance system (FRA) and fingerprint-based attendance system (FPA), with a central server. The FRA was developed in a Raspberry Pi mini-computer using Python programming language and Openc CV library. The FPA, on the other hand, was developed using Node MCU ESP8266 and Fingerprint scanner AS608 with Adafruit Fingerprint library. Both FRA and FPA are connected to a web server with a database engine through the internet connection and sensing attendance data using the HTTP_POST method. The server was developed using Apache Webserver, PHP programming language, and MySQL database engine. The server serves two main purposes, which are to record the attendance data sent by the FPA and FRA, and generate an attendance report based on the user query. The system testing was done in a local network. The result showed that both the subsystems and the integrated system worked well

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

scholarly journals Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

2021 ◽  
Vol 13 (12) ◽  
pp. 6900
Author(s):  
Jonathan S. Talahua ◽  
Jorge Buele ◽  
P. Calvopiña ◽  
José Varela-Aldás
Keyword(s):  
Facial Recognition ◽  
Face Mask ◽  
Recognition System ◽  
Classification Model ◽  
World Health ◽  
The Face ◽  
Recognition Systems ◽  
Facial Recognition System ◽  
Health Organization ◽  
Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

scholarly journals PERANCANGAN SISTEM DETEKSI OBJEK PADA ROBOT KRSBI BERBASIS MINI PC RASPBERRY PI 3

2019 ◽  
Vol 12 (1) ◽  
pp. 56-64
Author(s):  
Ilfan Sugianda ◽  
Thamrin Thamrin
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Raspberry Pi ◽  
Process Data ◽  
Single Board Computer ◽  
Object Based ◽  
Football Match ◽  
Omni Wheel ◽  
Python Programming ◽  
Camera Sensor

KRSBI Wheeled is One of the competitions on the Indonesian Robot Contest,. It is a football match that plays 3 robot full autonomous versus other teams. The robot uses a drive in the form of wheels that are controlled in such a way, to be able to do the work the robot uses a camera sensor mounted on the front of the robot, while for movement in the paper author uses 3 omni wheel so the robot can move in all directions to make it easier towards the ball object. For the purposes of image processing and input and output processing the author uses a Single Board Computer Raspberry PI 3 are programmed using the Python programming language with OpenCV image processing library, to optimize the work of Single Board Computer(SBC) Raspberry PI 3 Mini PC assisted by the Microcontroller Arduino Mega 2560. Both devices are connected serially via the USB port. Raspberry PI will process the image data obtained webcam camera input. Next, If the ball object can be detected the object's position coordinates will be encoded in character and sent to the Microcontroller Arduino Mega 2560. Furthermore, Arduino mega 2560 will process data to drive the motors so that can move towards the position of the ball object. Based on the data from the maximum distance test results that can be read by the camera sensor to be able to detect a ball object is �5 meters with a maximum viewing angle of 120 �.

Python Programming for the Raspberry Pi

2021 ◽  
pp. 165-213
Author(s):  
Charles Bell
Keyword(s):  
Raspberry Pi ◽  
Python Programming

A New Business Dimension

2001 ◽  
pp. 75-91
Author(s):  
Andre L. Brandao
Keyword(s):  
Antenna Arrays ◽  
Large Scale ◽  
Digital Signal ◽  
Spatial Dimension ◽  
Link Quality ◽  
Radio Link ◽  
Wireless Internet ◽  
Space Division Multiple Access ◽  
New Business ◽  
Market Opportunities

Space division multiple access (SDMA) is a promising technique useful for increasing capacity, reducing interference and improving overall wireless communication link quality. With a large-scale penetration expected for wireless Internet, the radio link will require significant reduction in cost and increase in capacity, benefits that the proper exploitation of the spatial dimension can offer. Market opportunities with SDMA are significant, as a number of companies have been recently formed to bring products based on this new concept to the wireless marketplace. The approach to SDMA is broad, ranging from "switched-beam techniques" to "adaptive antennas." Basically the technique employs antenna arrays and digital signal processing to achieve the necessary increases incapacity and quality needed in the wireless world.

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.

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