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Real Time Face Recognition System using Raspberry Pi4 3

Real Time Face Recognition System using Raspberry Pi4

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3rd international conference on Advanced Electrical Engineering, ICAEE 2024

作者： Boukerche, Alaa Eddine Chelali, Fatma Zohra University of Science and Technology Houari Boumediene Usthb Faculty of Electrical Engineering Telecommunications Department Algeria University of Science and Technology Houari Boumediene Usthb Speech Communication and Signal Processing Laboratory Faculty of Electrical Engineering Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349351

Modern technologies like computer vision (CV), Artificial Intelligence (AI), the internet of things (IoT), and (AIoT) Artificial Intelligence of Things have advanced in many fields, particularly surveillance systems, which need to process faces in real time to guarantee security. This paper proposed a comparative analysis for a real time face recognition system from video sequences with two architectures including traditional application using a personal computer and a Raspberry Pi, respectively. Our study offers an implementation for a real-time face recognition system employing Viola-Jones algorithm and Deep neural networks techniques such as Convolutional Neural Networks (CNN) for face detection and face recognition. Our face recognition system seeks to replace the conventional approach of keeping track of attendance by implementing a Raspberry Pi-based attendance monitoring system that can be utilized by all institutions and organizations. Significant progress has been made thanks to the use of CNN models which has allowed an accuracy rate of 94%. © 2024 IEEE.

关键词： Convolutional neural networks

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Sim-to-Real image Transfer for RL-based Bin Picking 15

Sim-to-Real Image Transfer for RL-Based Bin Picking

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15th international conference on Information and Communication technology Convergence, ICTC 2024

作者： Park, In Jun Han, Hyonyoung Digital Convergence Research Laboratory Electornics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Robotic bin picking has become increasingly important in the field of automated manufacturing and logistics, where precise handling of objects in complex settings is essential. Reinforcement learning (RL) has emerged as a powerful tool for developing policies to manage such tasks. However, a significant challenge persists in transferring these policies from simulation to real-world environments due to visual discrepancies, often resulting in reduced performance when applied to actual robotic systems. To overcome this issue, we introduce an innovative approach that incorporates spatial transformer networks (STN) into the RL framework. This technique specifically addresses the scale variations between simulated and real-world images, enabling the policy to adapt more effectively and enhancing both the robustness and generalizability of the learned behaviors. Our approach has shown a marked improvement in real-world bin picking tasks, with the integration of STN leading to an 11.5% boost in grasping accuracy, achieving a high overall success rate of 95.5%. By effectively narrowing the visual domain gap, our method reduces the reliance on extensive real-world data collection and training, facilitating quicker and more efficient deployment of robotic bin picking solutions in a variety of industrial contexts. © 2024 IEEE.

关键词： Reinforcement learning

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Embedded Network Equipment Fault Remote Monitoring System based on internet of Things 13th

Embedded Network Equipment Fault Remote Monitoring System Ba...

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13th international conference on Frontier Computing, FC 2023

作者： Liu, Weixue Collage of Mathematics and Compute Science Chifeng University Inner Mongolia Chifeng024000 China

ISBN: (纸本)9789819992980

The embedded network equipment fault remote monitoring is an embedded system that uses the internet to connect and communicate with other equipment. It can be used for industrial automation, medical treatment, home security, etc. The monitoring platform mainly includes three parts: data receiving and transmitting module, database and client. The data receiving and sending module is not only responsible for receiving data and storing it in the database, but also responsible for forwarding the data to the client. The database is responsible for storing and maintaining data, and providing links to clients for querying historical data. The client obtains the real-time data of remote sensors and controllers through the data receiving and transmitting module connected to the server, performs status display and remote monitoring, and accesses the database for historical data query and analysis. Aiming at the two key technologies of signal acquisition and information transmission in the monitoring system, this paper studies and designs a monitoring system applied to the remote status of equipment failures. This system mainly focuses on the function realization of the lower computer, which can complete the accurate acquisition of equipment status signals and reliable transmission through LoRa wireless technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Database systems

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FPGA Interfaced ECG signal Processing based on Machine Learning Classifier

FPGA Interfaced ECG Signal Processing Based on Machine Learn...

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2023 international conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Jeya Malar, R. Mary Mettilda, J. Mageshbabu, M. Siva Nagini, R. Grace, Lily Saron Gireesha, B. Kings Engineering College Department of Information Technology Chennai India Ppg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India SIMATS Saveetha School of Engineering Department of Electronics and Communication Engineering Thandalam Chennai India Universal College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Vallioor India Kings Engineering College Department of Electronics and Communication Engineering Chennai India University College of Science Tumkur University Department of Electronics Karnataka572103 India

ISBN: (纸本)9798350393378

This study aims to propose an FPGA-oriented ECG signal processing system architecture. The method identifies FPGA complexes and classifies beats as either preterm or typical contractions of the ventricular wall. For pulse classification, an element of the Open Source ECG Analysis Software is used, and for FPGA complexity verification, a method based on a phase-space image of an ECG signal is used. Artificial intelligence (AI) is increasingly serving as the basis for autonomous and independent systems. One of the most popular machine learning techniques among many categorization strategies is the Support Vector Machine (SVM) classification. We may further improve the SVM classifier's accuracy and classification process by using convex optimization techniques to identify the best solution. Numerous machine learning techniques, such as SVM classification, need a lot of processing power since they are compute-and data-intensive. Additionally, a number of machine learning algorithms have made it into incorporated and handheld gadgets, which have very particular demands. © 2023 IEEE.

关键词： Electrocardiograms

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Leveraging Deep Sequential Convolutional Neural Networks in Digital Transformation for Sustainable E-Waste Management in the New Digital Era

Leveraging Deep Sequential Convolutional Neural Networks in ...

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2024 international conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Muruganandhan, D. Suresh, M. Muthunagai, R. Santhosh, K. Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Pondicherry India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

Rapid technical breakthroughs in the current digital age have contributed to the creation of electronic waste (e-waste) poses serious threats to health and the environment. This research examines the use of Deep Sequential Convolutional Neural Networks (DSCNNs) to leverage the internet of Things (IoT) for sustainable management of electronic waste. The global e-waste dilemma is introduced with a focus on the need for innovative solutions to mitigate its impact. Existing challenges include the increasing number of discarded electronic devices, ineffective e-waste sorting, and limited recycling options. DSCNN models for their strong visual recognition capabilities, offer viable solutions by automating e-waste classification and recycling. This research explores various DSCNN architectures and their applications in e-waste processing devices to enable faster and more accurate recognition of recyclable materials. The primary goal is to develop an efficient DSCNN-based framework to enhance recycling efficiency, reduce landfill inputs, and promote sustainable practices. Preliminary findings suggest that DSCNN models significantly improve the accuracy and efficiency of e-waste sorting, paving the way for scalable and cost-effective e-waste management platforms. By addressing key environmental issues, this research advocates for the integration of AI technology into waste management processes. © 2024 IEEE.

关键词： Artificial Intelligent Automation Deep Sequential Convolutional Neural Networks Digital Transformation E-Waste Management Environmental Sustainability image Recognition Smart Recycling systems Sustainable Recycling

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1st international conference on Security and Information Technologies with AI, internet Computing, and Big data Applications, SITAIBA 2021

1st International Conference on Security and Information Tec...

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1st international conference on Security and Information Technologies with AI, internet Computing, and Big data Applications, SITAIBA 2021

ISBN: (纸本)9783031054907

The proceedings contain 39 papers. The special focus in this conference is on Security and Information Technologies with AI, internet Computing, and Big data Applications. The topics include: An RFID Ownership Transfer based on Multiple Owners with Different Weights;comments on a Scalable Healthcare Authentication Protocol with Attack-Resilience and Anonymous Key-Agreement;A LWE-based Receiver-Deniable Encryption Scheme;Privacy-Preserved Hierarchical Authentication and Key Agreement for AI-Enabled Telemedicine systems;fuzzy C-Means based Feature Selection Mechanism for Wireless Intrusion Detection;an Active User-Side Detector for Evil Twins;evaluation of Recurrent Neural Network Model Training for Health Care Suggestions;e-learning Behavior Analytics in the Curriculum of Big Data Visualization Application;malware Detection based on image Conversion;Comments on the Visual Binary QR Code;automobile Theft Detection by Driving Behavior Identification Using Deep Autoencoder;Combining a Bi-LSTM-based Siamese Network with Word2Vec Algorithm for Classifying High-Dimensional Dataset;real Time Drowsiness Detection based on Facial Dynamic Features;gradient Deep Learning Boosting and Its Application on the Imbalanced Datasets Containing Noises in Manufacturing;fabric Defect Detection by Applying Structural Similarity Index to the Combination of Variational Autoencode and Generative Adversarial Network;a Novel Defense Mechanism Against Label-Flipping Attacks for Support Vector Machines;study of Contribution Verifiability for Privacy-preserving Federated Learning;design and Application of Handwritten Numeral Recognizer based on Convolutional Neural Network;collision Avoidance in Crowded Zone Using Adversarial Reinforcement Learning;Cryptanaylsis to Sowjanya et al.’s ABEs from ECC;NLP-based Hardware Solution for Censoring Audio on Over-the-Top (OTT) Media Services;IIoT Key Distribution and Authentication technology based on Physical Unclonable Function;Lattice-based Anonymous Mult

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A Smartphone Data Steganography Framework based on K-mean image Selection and Random Embedding in Three image Channels

A Smartphone Data Steganography Framework Based on K-mean Im...

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international conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Alheyasat, Omar Masoud, Mohammad Jaradat, Yousef Manasrah, Ahmad A. Electrical Engineering Department Al-Balqa Applied University Al-Salt Jordan Electrical Engineering Department Al-Zaytoonah University of Jordan Amman Jordan Mechanical Engineering Department Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9789819720033

Concealing information within smart devices during the internet of Things era presents a formidable challenge. In this study, we introduce a novel steganographic framework designed for smartphones, comprising two distinct layers. The initial layer employs an unsupervised K-means algorithm to categorize smartphone images into three clusters. images from the first two clusters are selected for the data embedding process. The clustering mechanism aids in the identification of images with the lowest likelihood of being deleted by the user. To improve data availability, two different images are employed for the embedding process. In the second layer, a linear random number is used to embed the data through an XOR operation in the two least significant bits of each channel in the RGB color model. The framework’s performance is assessed and compared to two other frameworks using metrics such as peak signal-to-noise ratio (PSNR), embedding capacity, and mean square error (MSE). Our findings demonstrate that the proposed framework achieved higher PSNR and greater embedding capacity in comparison with the other frameworks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smartphones

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Integrated Smart Parking System Using internet of Things (IoT) and Digital image Processing with Faster Region based Convolutional Neural Network (Faster R-CNN) and Optical Character Recognition (OCR) 8

Integrated Smart Parking System Using Internet of Things (Io...

引用

8th international conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2024

作者： Lubis, Fahrurrozi Nababan, Erna Budhiarti Nainggolan, Pauzi Ibrahim Syah Putra, Mohammad Fadly Rahmat, Romi Fadillah Sulaiman, Riza Talupan Pangaribuan, Albert Lukas Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Institute of Visual Informatics Universiti Kebangsaan Malaysia Bangi43600 Malaysia

ISBN: (纸本)9798331531249

Parking is an integral part of driving, especially in urban areas. However, inefficient parking systems often cause drivers to spend a significant amount of time just to find a parking spot, even up to 10 hours per month in major cities like Jakarta. Besides wasting time, prolonged parking searches also increase fuel consumption and CO2 emissions. This study aims to develop smart parking application integrated with internet of Things and Computer Vision. The system uses ultrasonic sensors connected to an ESP32 microcontroller to detect real-time parking slot availability, sending status updates to Firebase. Additionally, Faster Region based Convolutional Neural Network (Faster R-CNN) and Easy OCR technologies are used to detect and identify vehicle license plates and parking slot markers, ensuring consistency between reservations and actual parking slot usage. The research findings indicate that the sensors system has the fastest measurement time of 106.3 milliseconds and data transmission time of 73.6 milliseconds. The Faster R-CNN model shows and accuracy of 86% for detecting license plates and parking slot markers, while EasyOCR achieves 100% accuracy in recognizing the characters of license plates and parking slot markers. The average total time for inference and character extraction is 2.7 seconds. This integrated approach provides significant solution for both users and parking vendor. © 2024 IEEE.

关键词： Convolutional neural networks

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IIoT based Electrical Asset Management System: A LoRaWan Deployment Study at Shipyard

IIoT based Electrical Asset Management System: A LoRaWan Dep...

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2022 IEEE international conference in Power Engineering Application, ICPEA 2022

作者： Thinesh, D.R. Sivaneasan, B. Tan, K.T. Mohan, Purnima Murali Singapore

ISBN: (纸本)9781665416290

With the internet of Things (IoT) being a key enabler for Industry 4.0, newer wireless communication technologies for IoT networks promise to achieve a higher communication range with lower power consumption compared to legacy communication systems like Wi-Fi. In this paper, an IIoT-based solution-CoastaL (Condition monitoring for electrical asset and Localization)-is proposed using Long Range WAN (LoRaWAN) technology and deployed in a shipyard spanning across 206 hectares for condition monitoring and localization of electrical assets. The paper presents a comprehensive comparison study of various Low Power WAN (LPWAN) technologies to determine a suitable technology for the CoastaL system. LoRaWAN is the only LPW AN technology that allows a private network implementation and does not require subscriptions while offering high range and cost efficiency as opposed to Sigfox and NB-IoT. The performance of the CoastaL system is measured as a function of receiver signal strength indicator (RSSI) which is based on the distance from the gateway. Using the collected measurement data, an empirical formula on the RSSI was formulated to estimate the signal strength of LoRaWAN devices at shipyards and similar environments. © 2022 IEEE.

关键词： internet of things

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Enhancing Dehazing Performance of Single Optical Satellite images using Gamma Correction and Improved DCP

Enhancing Dehazing Performance of Single Optical Satellite I...

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Regan, D. Priyan, S. Vishnu Siddartha Institute of Science and Technology Dept. of ECE Puttur India Kings Engineering College Dept. of Bio-Medical Engineering Chennai India

ISBN: (纸本)9798350319200

Dehazing is an essential preprocessing step for improving the quality of satellite imagery. Hazy conditions can significantly impact the visibility and clarity of satellite images, making it difficult to extract useful information from them. In recent years, several dehazing techniques have been proposed for satellite imagery, including contrast stretching, dark channel prior, and adaptive gamma correction. In this research work, we propose a novel haze removal approach that combines gamma correction with modified dark channel prior-based haze removal approach in the YCbCr color space. Our approach enhances the luminance component of the image by applying gamma correction to the Y channel, and reduces computational complexity by subsampling and combining the chrominance channels. The performance of the proposed system is assessed using peak signal to noise ratio (PSNR) and structural similarity index (SSIM) and compared with the state-of-the-art methods. Experimental results demonstrate that the proposed our approach outperforms other methods in terms of dehazing and contrast improvement, suggesting its potential for various applications that require high-quality satellite image processing. © 2023 IEEE.

关键词： DCP Gamma correction Satellite image dehazing YCbCr Color space

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