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Athletes movement tracking device for soccer players 18

Athletes movement tracking device for soccer players

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18th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Archevapanich, Tuanjai Sithiyopasakul, Jirayus Sithiyopasakul, Jiran Sithiyopasakul, Paisan Anuwongpinit, Thanavit Purahong, Boonchana Rajamagala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Communication Engineering Nonthaburi Thailand King Mongkut's Institute of Technology Ladkrabang School of Engineering Bankok Thailand King Mongkut's Institute of Technology Ladkrabang School of Engineering Department of Computer Engineering Bankok Thailand

ISBN: (纸本)9780738111278

This article presents the design and development of real-time tracking system using GPS for soccer players. The tracking system is separated into two parts including hardware and software sections. The hardware uses NodeMCUV3 which is connected to the GPS module that can receive a current position data of soccer players. After that, it processes and records the position data into the MicroSD card module. In the software section can display the heat map results from recorded data of soccer players. The main user is a soccer club manager which is responsible for monitoring the data that tested on the movement of the soccer player. For the experimental results, a device is tested, and the movement data is collected. Also, it is found that the tracking device could track the athlete's movements successfully. © 2021 IEEE.

关键词： Global positioning system

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Research on Blockchain: Privacy Protection of Cryptography Blockchain-Based Applications

Research on Blockchain: Privacy Protection of Cryptography B...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Zhang Dayong Nur Haliza Abdul Wahab Khairunnisa A. Kadir Arafat Aldhaqm Haslinah Mohd Nasir Keng Yinn Wong Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Electronics & Computer Engineering Technology Department Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Faculty of Mechanical Universiti Teknologi Malaysa Johor Malaysia

Blockchain is a distributed database technology developed because of cryptocurrency, which has the characteristics of open and transparent ledger data. However, attackers will analyze ledger data to obtain sensitive information, threatening transaction privacy of users. Cryptography is the underlying technology of the blockchain to ensure the integrity and legitimacy of transactions in the blockchain. Providing privacy protection for the blockchain by studying cryptography has become a research hotspot in the blockchain. This paper introduces the cryptography technology in the blockchain, summarizes the privacy protection problems existing in the current blockchain, and expounds the existing cryptography solutions and the advantages and disadvantages of each scheme. Finally, the future research directions of cryptography in blockchain are summarized and prospected.

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Knapsack Encryption with Elliptic Curve Cryptography Based Secured Wireless Network

Knapsack Encryption with Elliptic Curve Cryptography Based S...

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International Conference on Edge Computing and Applications (ICECAA)

作者： V. Arun P.G. Kuppusamy G. Naveen P. Santhuja Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering Siddharth Institue of Engineering & Technology (SIETK) Puttur Andhra Pradesh India Department of Electronics and Communication Engineering Jagannath Institute of Technology Chengalpattu Tamil Nadu India Department of Computer Science and Engineering B V Raju Institute of Technology Hyderabad Telangana India

This research study discusses about the prospective future research areas while presenting the state-of-the-art in wireless network cryptography. Elliptic Curve Cryptography (ECC) offers a safe way for interacting nodes to exchange keys. In order to implement ECC, the message is first converted into an affine point on the Elliptic Curve (EC), and then the knapsack method is applied to the ECC-encrypted message across the finite field. This approach presents Knapsack Encryption with ECC Algorithm (KECC) for improving data security in wireless networks. The data is encrypted by using the knapsack algorithm and the ECC algorithm. The necessary encrypted data is forwarded to the receiver via in-between nodes. As a result, it avoids the man-in-the-middle attack. Experimental results prove that the KECC strategy increases the forward ratio. In addition, it reduces the key generation, encryption, as well as decryption computational time.

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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application

Modern Manufacturing for Alloy Wheel Defect Detection using ...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels Systems architecture User interfaces Mobile applications

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The Optimal Shape of the Surface Hardening Head Obtained by Numerical Modeling

The Optimal Shape of the Surface Hardening Head Obtained by ...

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International Conference on electronics, computers and Artificial Intelligence (ECAI)

作者： Mihaela Cornelia Novac Marius Codrean Mircea Gordan Mihaela Codrean Mihai Oproescu Ovidiu Constantin Novac Francisc Slovac Department of Electrical Engineering Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Department of Elecytronics Faculty of Electronics Communication and Computer Science Computers and Electrical Engineering University of Pitesti Pitesti Romania Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania

This paper presents some aspects of the numerical modeling performed in order to study the process of induction heating applied to a workpiece, using Flux 2D and FEMM (Finite Element Method Magnetics), two software tools specifically designed for electromagnetic simulations. The paper presents a significant technical finding regarding the optimal shape of the surface hardening head. Various shapes of the inductor were analyzed, focusing on solving the eddy current problem using the commercial program FEMM. The induction heating process was numerically simulated using the FLUX 2D program for a semi-finished punch made of OLC 45 steel, specifically for hot surface heat treatment. The simulations were conducted utilizing the program's simulation capabilities.

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Mohammed I. Habelalmateen R Archana Reddy Chetana Reddy N. Nagarjuna Pradeep Kumar S Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor Malaysia Department of Computer Technical Engineering College of Technical Engineering the Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana India Department of Electronics and Communication Oxford College of Engineering Bangalore Department of CSIT CVR College of Engineering Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10 −2.5 , SNR 10 −3.4 and PAPR of . 10 −4.2 , underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems.

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Temperature characteristics of Gate all around nanowire channel Si-TFET 5

Temperature characteristics of Gate all around nanowire chan...

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5th International Conference on Electronic Design, ICED 2020

作者： Agha, Firas Natheer Abdul-Kadir Hashim, Yasir Abdullah, Waheb Abduljabbar Shaif Department of Electrical Engineering College of Engineering University of Mosul Mosul Iraq Department of Computer Engineering Faculty of Engineering Tishk International University Erbil-Kurdistan Iraq Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahan Pekan Pahang Darul Makmur26600 Malaysia

This paper study the impact of working temperature on the electrical characteristics of gate all around nanowire channel Si-TFET and examines the effect of working temperature on threshold voltage, transcondactance (gm), ION/IOFF ratio, drain induced barrier lowering (DIBL), and sub-threshold swing (SS). The (Silvaco) simulation tool has been used to investigate the working temperature on the Si-TFET characteristics. The temperature range in this study is from-25 to 150 C. The results indicate that the TFET must work in electronic circuits with the lower temperature as possible to get better performances. Furthermore, the TFET has good performances as a temperature nanosensor with diode connection mode under ON conditions. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Nanowires

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis

Early Detection of Black Fungus Using Deep Learning Models f...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Esraa Hassan Abeer Saber El-Sayed M. El-kenawy Roheet Bhatnagar Mahmoud Y. Shams Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Information technology Dept. Faculty of Computers and Artificial intelligence Damietta University Damietta Egypt Dept. of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%.

关键词： COVID-19 Deep learning Training Computational modeling Data preprocessing Mouth Skin Organ transplantation Immune system Tumors

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Malaika Sumble Sumair Aziz Muhammad Umar Khan Khushbakht Iqtidar Raul Fernandez-Rojas Department of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Faculty of Science and Technology University of Canberra Canberra ACT Australia Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles.

关键词： Machine learning algorithms Accuracy Cepstral analysis Surveillance Signal processing algorithms Feature extraction Time-domain analysis

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Coronary Artery Disease Prediction Using Enhanced Multi Layer DCNN

Coronary Artery Disease Prediction Using Enhanced Multi Laye...

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IEEE UP Section Conference on Electrical computer and electronics (UPCON)

作者： Varun Malik Smita Sharma Ruchi Mittal A. Kakoli Rao R. John Martin Akhilesh Kumar Khan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida Faculty of Computer Science and Information Technology Jazan University KSA Lloyd Law College Greater Noida

Coronary Artery Disease(CAD) is a prevalent and life-threatening cardiovascular condition affecting millions of individuals globally. Early detection and accurate prediction of CAD are vital for timely intervention and prevention of severe cardiac events. Convolutional Neural Networks (CNNs) and other deep learning approaches have recently demonstrated amazing effectiveness in a number of medical applications, including illness prediction. This research proposes an innovative approach, Enhanced Multi-Layer Deep Convolutional Neural Network (EML-DCNN), for the prediction of Coronary Artery Disease. The proposed EML-DCNN architecture integrates advanced deep learning concepts, feature engineering, and data augmentation techniques to enhance the model's predictive performance. The multi-layer structure of EML-DCNN enables the automatic extraction of intricate patterns and features from medical imaging data, specifically focusing on coronary artery images. Additionally, the model incorporates attention mechanisms and residual connections, enhancing its ability to capture subtle details and relationships within the data. To validate the efficacy of the proposed EML-DCNN model, extensive experiments were conducted on a large and diverse dataset comprising coronary artery images obtained from various medical sources. The results demonstrate superior predictive accuracy, sensitivity, and specificity compared to existing CAD prediction models. Moreover, the model's interpretability is enhanced through visualization techniques, providing valuable insights into the features influencing the prediction process.

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