Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV

2014 ◽  
Author(s):  
Wilson Feipeng Abaya ◽  
Jimmy Basa ◽  
Michael Sy ◽  
Alexander C. Abad ◽  
Elmer P. Dadios
Keyword(s):  
Low Cost ◽  
Night Vision ◽  
Raspberry Pi

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 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.

Implementasi Metode Unsupervised Learning Pada Sistem Keamanan Dengan Optimalisasi Penyimpanan Kamera IP

2021 ◽  
Vol 5 (6) ◽  
pp. 1099-1105
Author(s):  
Desta Yolanda ◽  
Mohammad Hafiz Hersyah ◽  
Eno Marozi
Keyword(s):  
Unsupervised Learning ◽  
Night Vision ◽  
Raspberry Pi ◽  
Monitoring Systems ◽  
Test Results ◽  
Storage Process ◽  
File Size ◽  
Web Based ◽  
Security Monitoring ◽  
Human Faces

Security monitoring systems using face recognition can be applied to CCTV or IP cameras. This is intended to improve the security system and make it easier for users to track criminals is theft. The experiment was carried out by detecting human faces for 24 hours using different cameras, namely an HD camera that was active during the day and a Night Vision camera that was active at night. The application of Unsupervised Learning method with the concept of an image cluster, aims to distinguish the faces of known or unknown people according to the dataset built in the Raspberry Pi 4. The user interface media of this system is a web-based application built with Python Flask and Python MySQL. This application can be accessed using the domain provided by the IP Forwarding device which can be accessed anywhere. According to the test results on optimization of storage, the system is able to save files only when a face is detected with an average file size of ± 2.28 MB for 1x24 hours of streaming. So that this storage process becomes more efficient and economical compared to the storage process for CCTV or IP cameras in general.

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

2019 ◽  
Vol 13 (1) ◽  
pp. 47-61
Author(s):  
Guenther Retscher ◽  
Jonathan Kleine ◽  
Lisa Whitemore
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Ground Truth ◽  
Mobile User ◽  
Location Based Services ◽  
Raspberry Pi ◽  
Indoor Environments ◽  
Point Location ◽  
Point Determination ◽  
The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

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