Economical, energy efficient and portable home security system based on Raspberry Pi 3 using the concepts of OpenCV and MIME

Abhilash, D; Chandrashekar,; S, Shalini

doi:10.1109/ccube.2017.8394155

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Using convolution factorization, this effective method creates a network with dispersed connections based on the building of face detection networks with low computing cost and adequate capacity. With the input of VGA resolution, EagleEye operates at 21 frames per second on an embedded device based on the ARM Cortex-A53 (Raspberry Pi1 3b+ [30]) with the best accuracy compared to methods with the same order of computational complexity. On a different note, some people would rather design a processor specifically tailored to image processing applications using FPGAs.…”

Section: Embedded Techniques For Secured Applicationsmentioning

confidence: 99%

An Optimized Effective Authentication Process for E-Health Application

Choura,

Chaabane,

Baklouti

et al. 2024

Int. J. Onl. Eng.

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Because of the availability of more than an actor and a wireless component in an e-health application, providing more security and safety to users of this type of applications is expected. Moreover, ensuring protection of data user available or shared within different services from any security attack becomes an important requirement. In this paper, we are interested essentially in the authentication process, and we propose an improved Landmarkbased algorithm as a tool to extract, firstly, key features from analysed faces, and hence to accelerate the authentication operation. The suggested approach beats other state-of-the-art works in terms of accuracy and speed-up attaining time execution constraint, according to experimental evaluations.

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Section: Embedded Techniques For Secured Applicationsmentioning

confidence: 99%

An Optimized Effective Authentication Process for E-Health Application

Choura,

Chaabane,

Baklouti

et al. 2024

Int. J. Onl. Eng.

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“…Raspberry Pi is widely used for portable IoT applications because of its versatility. The Raspberry Pi has been used in a number of projects for a variety of purposes, including monitoring the health status of patients [ 9 ], a security system [ 10 ], and a testing system device [ 11 ] to name a few. The Raspberry Pi has also been used for projects based on EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System

Benomar

Cao

Vishwanath

et al. 2022

Sensors

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Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.

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