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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Sota Furusawa Chinthaka Premachandra Dept. of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into “learned subject (class)” and “unlearned subjects (classes)”. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments.

关键词： Accuracy Passwords Biometric authentication Generative adversarial networks Search problems Security Protection Information technology Faces Hyperspectral imaging

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Human Activity Recognition using Smartphone Dataset: A Review

Human Activity Recognition using Smartphone Dataset: A Revie...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Verma, Apurv Verma, Vijayant Patel, Brijesh Dept. of Computer Science and Engineering School of Engineering and I.T. MATS University Raipur India Dept. of Mechanical Engineering School of Engineering and I.T. MATS University Raipur India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Cell Phones are becoming more refined with each new model release. These days phones ordinarily consolidate numerous sensors for various functions such as GPS tracking, temperature sensors, accelerometer, gyroscope, high-determination cameras. In recent years, Human Activity Recognition (HAR) has enabled various applications. The information gathered by HAR may be utilized for a number of reasons, including fitness tracking, health and safety, and entertainment, among others. An abundance of research has been conducted on real-time processing, which has resulted in increased power consumption of mobile devices. It is preferable that the activity identification process be as precise as possible, because of the resource limited feature of a mobile device. So, the research is to use the dataset from the smartphone and use various classification models to find out which classifieris more accurate and provides fasterresults. © 2022 IEEE.

关键词： Smartphones

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Analysis of Common Mode Currents and Harmonic Pollution at Supplying Induction Motors from Static Converters with Variable Modulation Frequency

Analysis of Common Mode Currents and Harmonic Pollution at S...

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2023 Power Quality and Electromagnetic Compatibility at Low-Frequency, PQEMC-LF 2023

作者： Nuca, Iurie Wan, Lu Nicolae, Petre - Marian Nicolae, Ileana - Diana Grassi, Flavia Wu, Xinglong University of Craiova Electrical Engineering Energetic and Aeronautics Dept. Craiova Romania Politecnico di Milano Department of Electronics Information and Bioengineering Milan Italy University of Craiova Computer Science and Information Technology Dept. Craiova Romania

ISBN: (纸本)9798350341331

The problems of Electromagnetic Compatibility (EMC) in three-phase systems are less studied than single-phase or direct current, due to the smaller ratio of emissions relative to the rated power of such systems. This paper is focused on the problem of emissions in three-phase variable frequency drive systems in the low-frequency EMC range, specifically on the common mode current through the connected ground wire between the static converter and power grid. The common mode current is determined to be variable depending on switching frequency. For a deeper understanding Fast Fourier Transform was used to decompose the signal. Main parameter to understand the magnitude of common mode current was the RMS of the current. The decomposition showed a significant DC component which cannot be picked up by the used RF probe, thus presenting a noise offset necessary to be considered when calculating the total effective value of common mode current. The analysis was made on the time-domain representation and the frequency behavior of the converter. The paper suggests that the standard measurement in the low-frequency range and the picked-up signal are investigated enough, as there is a disparity in the impact of emissions in the low-frequency range compared to what off-the-shelf devices have to offer, which are standard compliant, but still present considerable emissions. © 2023 IEEE.

关键词： Static converters

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An Efficient Salt and Pepper Noise Reduction Approach for Video(s) Using Optimized Filter Approach 2

An Efficient Salt and Pepper Noise Reduction Approach for Vi...

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2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2022

作者： Kumain, Sandeep Chand Singh, Maheep Govil, Mahesh Chandra Pilli, Emmanuel Shubhakar Srinagar India Malaviya National Institute of Technology Jaipur Dept. of Computer Science and Engineering Jaipur India

ISBN: (数字)9781665488754

ISBN: (纸本)9781665488754

In today's digital world, most information is shared through images or videos. Images and videos are more infor-mative than textual data. During the image or video capturing process, addition of noise is one of the major problems. The presence of noise in captured data causes results to be misleading, particularly in computer vision and image processing tasks. As a result, preprocessing is required before tackling tasks like edge detection, object detection, object recognition, salient object detection, video summarization, and so on. Gaussian, Salt & Pepper, Poison, and Speckle noise are the most common types of noise which can affect the video. In this article, the author(s) presented an efficient noise reduction approach for reducing the salt and pepper noise present in the image or video. The performance of the proposed approach is compared with traditional and widely used filters for noise reduction, such as mean, mode, and median. The experimental analysis of the proposed approach is done on the dataset where the videos are captured using the camera as well as from the other resources such as Internet. Noisy videos are prepared by mixing the Salt & Pepper noise with different noise densities (levels) in the video dataset. The experimental results show that the proposed approach outperforms the traditional approaches not only in terms of noise reduction but also in preserving the details in the video. © 2022 IEEE.

关键词： Salt and pepper noise

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

arXiv

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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025, CC BY.

关键词： Markov chains

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Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition

Machine Learning-based sEMG Signal Classification for Hand G...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Parshuram N. Aarotale Ajita Rattani Dept. of Biomedical Engineering Wichita State University USA Dept. of Computer Science and Engineering Uni. of North Texas USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

EMG-based hand gesture recognition uses electromyographic (EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand *** paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of 97% using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of 94.95% using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)).

关键词： Hands Deep learning Wavelet transforms Accuracy Biological system modeling Gesture recognition Muscles Feature extraction Electromyography Time-domain analysis

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Intervention Analysis for Predicting Stock Market using Deep Learning Algorithms and Time Series Analysis

Intervention Analysis for Predicting Stock Market using Deep...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Krish Valecha Yash Pandloskar Sowmya Dadheech Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Stock market being intricate and volatile pose challenges for classical Time Series Analysis techniques in terms of generalization and computation on real-world data. In order to improve prediction capacities, new methods like intervention analysis and Deep Learning (DL) are required. The research seeks to unravel the complexities of stock price prediction modelling by comparing Time Series Analysis with Deep Learning augmented by intervention analysis. Upon evaluation on different datasets, the Deep Learning models, specifically the Recurrent Neural Network (RNN) performed better than the other models considered. RNN outperformed other models on Mean Squared Error (MSE) for Tesla and IBM data, with values 3.874 before intervention and 3.593 after intervention and 46.52 before intervention and 42.97 after intervention respectively, thus highlighting the advantages of Deep Learning models for predictions. The goal of investigating intervention analysis methods incorporated into predictive models is to create processes that can predict overall market trends and efficiently identify and address sudden deviations.

关键词： Deep learning Analytical models Recurrent neural networks Accuracy Computational modeling Time series analysis Predictive models Market research Data models Stock markets

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A Simulation Model for EMC Compliance Assessment of Induction Cooktops for Cardiac Implanted Electronic Devices 35

A Simulation Model for EMC Compliance Assessment of Inductio...

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35th General Assembly and Scientific Symposium of the International Union of Radio science, URSI GASS 2023

作者： Campi, T. Cecilia, V. Mattei, E. Boumerdassi, W. Rotellini, M. Tatangelo, G. Feliziani, M. University of l'Aquila L'Aquila Italy Italian National Institute of Health Endocrine-metabolic Diseases and Aging Dept. of Cardiovascular Italy University of Rome Tor Vergata Dept. of Civil and Computer Science Engineering Italy

ISBN: (纸本)9789463968096

The increasing medical use of cardiac implanted electronic devices (CIEDs) for heart patients increases the risk of device malfunction or damage due to electromagnetic interference (EMI). In this work we focus on the EMI sources produced by induction cooktops operating at intermediate frequency (IF). A computational model is proposed to evaluate the induced voltage and the magnetic field in a pacemaker according to the ISO 14117 standard. The method is based on a finite element analysis (FEA) of the magnetic field configuration. A post-processing analysis was then performed with the approximated method to evaluate the maximum voltage induced in the standard-size loop of a pacing lead. © 2023 International Union of Radio science.

关键词： Electromagnetic compatibility

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms

Machine Learning Techniques for Identifying Child Abusive Te...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Mohammad Kamal Uddin Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention.

关键词： Deep learning Support vector machines Radio frequency Measurement Machine learning algorithms Social networking (online) Prevention and mitigation Computational modeling Natural language processing Long short term memory

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