Arduino based door unlocking system with real time control

Nath, Subhrapratim; Banerjee, Paramita; Biswas, Rathindra Nath; Mitra, S.; Naskar, Mrinal Kanti

doi:10.1109/ic3i.2016.7917989

Cited by 34 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The preceding work is where we deal with algorithms and used different electronic devices for door unlock system. The author in [11] proposed a "real-time control using Arduino-based door unlocking system"; in this study, they used radio frequency identification (RFID) codes to scan card for door unlock. If a person wants to enter the room first, it is needed to scan the card then someone can able to enter the room.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Hussain

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Hussain

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Wang and Katabi (2013) present a RFID positioning system that is robust to multipath and non-line-of-sight scenarios and can locate misplaced books with a median accuracy of 11 cm in a library. Nath et al (2016) propose a door unlocking system based on RFID for libraries containing multiple doors.…”

Section: Rfid and Librariesmentioning

confidence: 99%

Libraries in Internet of Things (IoT) era

Liang

Yong

2018

LHT

View full text Add to dashboard Cite

Purpose Internet of Things (IoT) has been widely adopted in the commercial sector. Although IoT enables traditional libraries to convert into digital ones, the implementation of the IoT in libraries is slow. The purpose of this paper is to report on the current state of research on applications of IoT in libraries, describe challenges that IoT applications face in libraries and discus directions of adopting IoT in libraries in the future. Design/methodology/approach To conduct this research, the literature of IoT and its application in libraries were reviewed by examining existing literature in Institute of Electrical and Electronics Engineers (IEEE) Xplore. Findings The literature review finds that radio-frequency identification has been adopted by digital libraries. The slow implementation of IoT is caused by security and privacy issues, lack of standards and the lack of financial, technological and organizational resources. This study provides a prospective for the application of IoT in libraries; the technologies of IoT have the potential in betterment of library services. Research limitations/implications The limitation of this study is that only IEEE Xplore is included. Other database should be explored in future research. Originality/value The application of IoT in libraries is an emerging issue; a systematic and extensive review of recent research on applications of IoT in libraries is unavailable. This paper presents an overview of IoT in libraries, findings and potential research opportunities.

show abstract

“…Untuk membuka kunci atau menutup kuncinya, menggunakan kontrol dari Android [8]. Ada pula yang menggunakan RFID sebagai kunci untuk membuka pintunya [9].…”

Section: Pendahuluan 11 Latar Belakangunclassified

Drimm: Drink Mixing Machine Untuk Membantu Pedagang Minuman Menentukan Takaran Air Yang Konsisten

Halimi

Fauzan

Habibi

et al. 2019

jitter

View full text Add to dashboard Cite

Abstrak Elektrolit merupakan salah satu dari cairan yang ada pada tubuh manusia. Elektrolit ini digunakan untuk menjaga keseimbangan cairan yang ada pada tubuh. Kekurangan elektrolit dapat mengakibatkan dehidrasi. Dehidrasi dapat diatasi dengan minum minuman yang mengandung elektrolit. Namun, minuman yang dijual oleh pedagang itu belum tentu mengandung elektrolit yang aman dikonsumsi oleh tubuh manusia, sehingga diperlukan alat untuk membantu pedagang untuk membuat minuman dengan kandungan elektrolit yang aman bagi tubuh manusia. Dari masalah tersebut, penulis membuat sebuah alat untuk mencampur minuman. Alat ini diberi nama DRIMM (Drink Mixing Machine). DRIMM akan memberi takaran campuran air minuman dengan takaran yang sudah diolah dengan menggunakan metode Regresi Linier Berganda. Dari hasil percobaan yang dilakukan, alat dapat membuat campuran air dengan konsentrasi elektrolit rata-rata sebesar 654 PPM. Sehingga kandungan elektrolit yang ada pada minuman tersebut sesuai dengan dosis yang aman dikonsumsi oleh tubuh. Alat ini, diharapkan dapat membantu pedagang dalam proses pembuatan minumannya. Serta membuat pedagang tidak perlu khawatir dengan kandungan elektrolit didalam minumannya. Kata Kunci: Elektrolit, Arduino, Mikrokontroller, Mixing, Regresi Linier Berganda

show abstract

Arduino based door unlocking system with real time control

Cited by 34 publications

References 3 publications

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Libraries in Internet of Things (IoT) era

Drimm: Drink Mixing Machine Untuk Membantu Pedagang Minuman Menentukan Takaran Air Yang Konsisten

Contact Info

Product

Resources

About