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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM 6

Design of a Smart Home Door Security System with Face Detect...

引用

6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 IEEE.

关键词： Ultrasonic sensors

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Monte Carlo Simulation in a Digital Calibration Certificate 15th

Monte Carlo Simulation in a Digital Calibration Certificate

引用

15th EAI International Conference on Automation and Control in Theory and Practice, ARTEP 2023

作者： Sammarah, George Halaj, Martin Bartalský, Lukáš Palenčár, Jakub Slovak University of Technology in Bratislava Faculty of Mechanical Engineering Institute of Automation Measurement and Applied Informatics Bratislava Slovakia

ISBN: (纸本)9783031319662

To establish trust in the measurement results, the metrological traceability of the measuring devices utilized must be ensured. This can be accomplished by confirming or calibrating the instruments. In reality, the information received during calibration is given in the traditional calibration certificate, and the user of the certified instrument decides how to utilize it in the measurement. Here comes the role of the digital calibration certificate that unlocks several features of this kind of calibration certificate that the user can use during its own measurements. In our case, the digital calibration certificate provides the option of supplying a large amount of additional data that may be usefully employed in various different tests. There are several methods for solving this kind of a problem. The paper discusses one method to this problem which is called the "Monte Carlo Method" and shows it using a specific case to give you a closer sight of this problem. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Calibration

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Implementation of Convolutional Neural Network with EfficientNet-B0 Architecture for Brain Tumor Classification 8

Implementation of Convolutional Neural Network with Efficien...

引用

8th International Conference on informatics and Computing, ICIC 2023

作者： Hadi, Vinsensius Haryo Bhaskoro Mutiara, Achmad Benny Refianti, Rina Gunadarma University Informatics Engineering Depok Indonesia Gunadarma University Computer Science and Information Technology Depok Indonesia

ISBN: (纸本)9798350342604

Brain tumor is a type of cancer that is difficult to detect in the early stages and can be a fatal disease. Early detection of brain tumors is crucial to increase the chances of successful treatment. The use of imaging technologies such as CT-Scan and MRI has helped in detecting brain tumors, but accurate and fast diagnosis remains a challenge. In this study, the Convolutional Neural Network (CNN) method was used as an alternative method to detect brain tumors faster and more accurately. Some CNN architectures used include VGGNet, InceptionResNet, and the latest architecture called EfficientNet-B0. This research aims to compare the performance of EfficientNet-B0 with other CNN architectures in classifying brain tumors on MRI images. The results of this study are expected to provide an understanding of the effectiveness of using EfficientNet-B0 compared to other architectures in classifying brain tumors on MRI images. The dataset used consists of 3264 images with glioma, normal, pituitary, and meningioma classes. Testing was conducted with several scenarios for epochs and testing against optimizers such as Adam and RMSProp. The results from testing the model using confusion matrix showed that in experiments with an epoch scenario of 25 and Optimizer obtained an average precision value reaching 98%, average recall reaching 98%, and average F1-Score reaching 98%. The implementation of the best-designed model in the form of a system successfully predicted types of glioma, normal, pituitary, and meningioma tumors. Additionally, the classification model will be implemented into a website using Python programming language along with Flask framework. © 2023 IEEE.

关键词： Python

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Human Activity Recognition Based on Wearable Devices and Feedforward Neural Networks

Human Activity Recognition Based on Wearable Devices and Fee...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Hutabarat, James Parluhutan Ahmadi, Nur Adiono, Trio School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Bandung Institute of Technology Bandung40132 Indonesia

ISBN: (纸本)9798350381290

Human activity recognition (HAR) using wearable sensors has garnered significant attention in recent years. This technology can continuously track human activities which is essential for remote health monitoring, elderly care, and rehabilitation. In this study, we propose a simple feedforward neural network (FFNN) model equipped with four fully connected layers to enhance the accuracy of HAR. Experimental results conducted on the UCI-HAR (UCI Human Activity Recognition) dataset show that the proposed FFNN model achieve an accuracy and F1 score of 0.96. It outperforms other existing algorithms, which are K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), highlighting the effectiveness of a simplified neural network architecture in enhancing HAR performance. © 2023 IEEE.

关键词： Support vector machines

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Vehicle type recognition: a case study of MobileNetV2 for an image classification task 28th

Vehicle type recognition: a case study of MobileNetV2 for an...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Kobiela, Dariusz Groth, Jan Hajdasz, Michal Erezman, Mateusz Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Algorithms and Systems Modelling Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gabriela Narutowicza 11/12 Gdansk80-233 Poland

The goal of the research was to demonstrate the full data science lifecycle through a use case of the MobileNetv2 model for vehicle image classification task using various validation and test sets, each with different difficulty level. Diverse model variations were employed, each designed to recognize images of ground vehicles and classify them into one of five possible classes: car, truck, motorcycle, bicycle, or bus. In terms of validation accuracy, the highest results were obtained by the model trained with uniformly designed train and val sets (with data normalization and augmentation), where train set also contained validation set. This model also obtained the highest accuracy results on both test sets. The superiority of MODEL 3 BASELINE is confirmed by other metrics as well: test loss, f1-score, AUC and confusion matrices (for both test sets). Results between MODEL 1 BASELINE and MODEL 2 BASELINE differed according to the test set 1 and 2 and other metrics and it was not possible to declare the superiority of one method of datasets preparation over another (original class distribution [no data normalization and no data augmentation] versus uniformly designed [with data normalization and augmentation]). The article also presents challenges and findings - the problems, key issues, and their solutions that arose during the process of data collection and tagging, as well as the preparation and evaluation of the model. © 2024 The Authors.

关键词： Classification (of information)

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Characteristic Analysis of Upper Bound Call Blocking Probability of Emergency CAC for Three Types of Calls 14

Characteristic Analysis of Upper Bound Call Blocking Probabi...

引用

2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Narikiyo, Sota Miyata, Sumiko Baba, Ken-Ichi Yamaoka, Katsunori Shibaura Institute of Technology Graduate School of Engineering and Science Tokyo Japan Shibaura Institute of Technology College of Engineering Tokyo Japan Kogakuin University School of Informatics Tokyo Japan Tokyo Institute of Technology School of Engineering Tokyo Japan

ISBN: (纸本)9798350360134

During emergencies such as disasters, congestion occurs due to the rapid increase in calls for different purposes. Calls have different priority levels depending on their purpose. Therefore, we need to prioritize high-priority emergency calls such as 911 and guarantee their Quality of Service (QoS). To solve this problem, Call Admission Control (CAC), which guarantees QoS for calls, has been introduced in circuit switching systems. In general, CAC classifies calls into two types: emergency calls and general calls. Another CAC method classifies general calls into two types general-in calls and general-out calls, because some general calls have different priorities. This conventional paper is the upper bound of the general-in call is dynamically derived for a given traffic intensity using a newly proposed setting method. However, the upper bound of the general-in calls should not be derived using a proprietary setting method. Therefore, in this study, the upper bound of general-in calls is considered as a control parameter and is analyzed to a CAC method for three types of calls according to traffic intensity. © 2024 IEEE.

关键词： Quality of service

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Optimization of Random Forest Algorithm for Paddy Growth Stage Classification Using Bayesian Optimization and Oversampling

Optimization of Random Forest Algorithm for Paddy Growth Sta...

引用

2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Gunawan, Elisabeth Afkharinah, Nurul Izza Masnur, Nadira Fawziyya Mutijarsa, Kusprasapta Agustan, Agustan Yulianto, Swasetyo School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Geospatial Research Center Jakarta Indonesia

ISBN: (纸本)9798350381290

Remote sensing and machine learning (ML) technologies that have developed very rapidly allow data retrieval and processing for monitoring paddy field conditions with minimal human intervention. One of the machine learning algorithms that is widely used for paddy growth stage classification is Random Forest. This study rebuilt the Random Forest model with datasets derived from a combination of Area Sample Framework (KSA) data and Sentinel-1A imagery attributes. The highest accuracy result by this model is still © 2023 IEEE.

关键词： Machine learning

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The effect of augmentation and filtration on noisy environment's acoustic signals to detect abnormalities in industrial machines based on artificial neural networks 14

The effect of augmentation and filtration on noisy environme...

引用

14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Qurthobi, Ahmad Maskeliunas, Rytis Department of Multimedia Engineering Faculty of Informatics Kaunas University of Technology Studentust. 50 Kaunas51368 Lithuania

The use of acoustic methods for failure detection is increasing in recent times. By using the acoustic method, some features related to the condition of the equipment under test can also be extracted. The main objective of this study was to compare the effect of using filters and augmentation on audio signals in the data training process on the accuracy and loss generated during training and testing. The model is developed using an artificial neural network. The MIMII dataset, which can be accessed openly, is being used for training and testing purposes. Based on the four scenarios designed, it can be seen that the movement of changes in accuracy values in tests involving the use of original data and augmented data is faster (close to 100% after the twentieth epoch) than the other methods. This is because the augmentation process in the form of a time shift does not change the signal in the frequency domain. Moreover, the test signal used is a periodic signal. © 2023 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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A Multimodal Brain Tumor Segmentation Method: Parallel Fusion of Transformer and CNN

A Multimodal Brain Tumor Segmentation Method: Parallel Fusio...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zheng, Fang Zhou, Hang Li, Guoliang Ni, Fuchuan Huazhong Agricultural University Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Key Laboratory of Smart Farming Technology for Agricultural Animals Ministry of Agriculture and Rural Affairs College of Informatics Wuhan China Huazhong Agricultural University College of Informatics Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China

ISBN: (纸本)9798350386226

While convolutional neural network(CNN) have historically achieved significant success in 3D medical image segmentation, recent advancements in Vision Transformer (ViT) have led to increased popularity of Transformer architectures in this field. Leveraging the global information extraction capabilities of Transformers, we propose a novel parallel structure combining CNN and Transformer for multimodal brain tumor segmentation, named PTCNet. We introduce two key improvements to enhance multimodal segmentation:1) Introducing a novel channel attention module that replaces spatial self-attention in the Transformer to improve cross-modal information extraction.2) Enhancing the reversible feature extraction module based on residual structures to better detect small lesions. We evaluated our method on the BraTS 2023 brain metastasis dataset, achieving an average DSC of 61.42%, which is 1.12% higher than the second place. The innovative structure and improved modules of PTCNet significantly enhance performance in multimodal brain tumor segmentation tasks. Code and models are available at https://***/naiqizh/PTCNet. © 2024 IEEE.

关键词： Convolutional neural networks

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NUMERICAL ASPECTS OF THE CO-ROTATING VORTEX PAIR PROBLEM IN AEROACOUSTICS 10

NUMERICAL ASPECTS OF THE CO-ROTATING VORTEX PAIR PROBLEM IN ...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Ulveczki, Mihály Ádám Rucz, Péter Faculty of Electrical Engineering and Informatics Dept. of Networked Systems and Services Budapest University of Technology and Economics Hungary

ISBN: (纸本)9788888942674

The co-rotating vortex pair is a well-known test problem for validating aeroacoustic computational frameworks. Both the flow field and the radiated acoustic far-field can be determined analytically, which allows for the direct comparison of all associated quantities. However, in order to simulate the vortex pair using numerical methods, several approximations must be made. One problem is caused by the singular velocity field at the center of the potential vortex, which must be treated by desingularization that also affects the resulting source terms. Another challenge in finite element formulations is the handling of the unbounded domain, which needs to be artificially truncated. In our paper, the numerical aspects of the vortex pair problem are examined. The effects of the necessary approximations including the desingularization of the vortex-core and discretization to different computational methods are investigated. A method relying on the direct integration of the source terms is compared to different finite element solution strategies. Convergence tests are carried out and discussed for formulations of Lighthill's equation and the perturbed convective wave equation in the frequency domain. © 2023 Ulveczki and Rucz. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Finite element method

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