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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Zhao, Liangxiao Li, Gang Wang, Pengjun Ma, Xuejiao Zhou, Ziyu The College of Electrical and Electronic Engineering Wenzhou University Wenzhou325000 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325035 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350361834

Strong physical unsolvable function (PUF) has a wide range of applications in internet of things (IoT) security. However, it is vulnerable to machine learning (ML) modeling attacks. This paper proposes a strong PUF anti-ML attack method based on dynamic-matrix encryption. It obfuscates the challenge by multiplying the dynamic encryption matrix (DEM) with the challenge matrix synchronizing initial values during the registration phase of the device security authentication protocol prevents our obfuscation from being invalidated when the algorithms and structures motivating the obfuscation are made public. Each round of DEM is updated with respect to each previous round of challenge which is similar to the properties of sequential logic circuits in that the current response does not depend only on the current challenge but is related to previous challenge. Experimental results show that even if an attacker collects 1 million challenge response pairs, the prediction accuracies of the several ML attacks we use are below 53%, with negligible effects on randomness, reliability, and uniqueness. © 2024 IEEE.

关键词： Authentication Protocol

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Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments 5

Development of Convolutional Neural Network-Based AI-Dermato...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kahatapitiya, Nipun Shantha Wijethunge, Akila Edirisinghe, Sajith Silva, Bhagya Nathali Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Dept. of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Medical Sciences Dept. of Anatomy Gangodawila Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Dept. of Computer Engineering Nugegoda Sri Lanka School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Buk-gu Daegu Korea Republic of Sri Lanka Institute of Information Technology Faculty of Engineering Dept. of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients. © 2023 IEEE.

关键词： computer vision

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Accelerated Algorithm for Counting the Amount of Numbers in Specified Ranges

Accelerated Algorithm for Counting the Amount of Numbers in ...

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2022 Conference of Russian Young Researchers in Electrical and electronic engineering, ElConRus 2022

作者： Krutikov, Alexander K. Strabykin, Dmitry A. Lapitsky, Alexey A. Vyatka State University Faculty of Automation and Computer Engineering Department of Electronic Computers Kirov Russia

ISBN: (纸本)9781665409933

An accelerated algorithm for calculating the number of array numbers that fit in each of the specified ranges is proposed. The algorithm assumes that the number of ranges is a multiple of the power of two, the bit depth of the maximum number of the array does not exceed the bit depth of the memory cell used to store it. The acceleration of counting numbers is based on the conversion of the analyzed number of the source array to the address of the memory cell corresponding to the range of the number. This conversion reduces the time for counting the number of numbers in the source array that fit into the specified ranges by eliminating memory accesses to read the boundary values of the addresses of the ranges. The time estimates of the algorithm execution are given, the prospects of using the algorithm in solving problems are noted. © 2022 IEEE.

关键词： Array processing

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Unit Commitment and Load Dispatch Problems Considering Inertial Response of Dispatchable Generators

Unit Commitment and Load Dispatch Problems Considering Inert...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Shigenobu, Ryuto Ito, Yuhi Ito, Masakazu Takano, Hirotaka Orihara, Dai Goda, Tadahiro Taoka, Hisao University of Fukui Faculty of Engineering Dept. of Electrical Electronic and Computer Engineering Fukui Japan Gifu University Faculty of Engineering Dept. of Electrical Electronic and Computer Engineering Gifu Japan Fukushima Renewable Energy Institute Aist National Institute of Advanced Industrial Science and Technology Fukushima Japan

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

The growth in penetration of renewable energy sources leads to reduction in the number of grid-tied dis-patchable synchronous generators. Since the contributions of non-dispatchable, renewable energy-based generators to both grid inertia and grid frequency controllability are significantly small, it becomes important to secure sufficient operational margins in the operation scheduling phase. This paper presents a framework that integrates constraints for the inertial response and the primary frequency control to the unit commitment and economic load dispatch (UC-ELD) problem. The authors' proposal contributes cooperative operations of dispatchable and non-dispatchable generators in the levels of inertial and primary responses although the UC-ELD is a framework for operations in longer period. Through numerical simulations and discussion on their results, the validity of the authors' proposal is verified. © 2022 IEEE.

关键词： Renewable energy resources

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Two-Operation Buffering based on Dynamic Optical RAM 17

Two-Operation Buffering based on Dynamic Optical RAM

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Semenov, Andriy Tsyrulnyk, Serhii Baraban, Serhii Semenova, Olena Rudyk, Andrii Khloba, Anton Vinnytsia National Technical University Faculty of Information Electronic Systems Vinnytsia Ukraine Vinnytsia National Agrarian University Department of Computer Sciences and Digital Economy Vinnytsia Ukraine Poznan University of Technology Faculty of Computing and Telecommunications Poznan Poland National University of Water and Environmental Engineering Department of Automation Electrical Engineering and Computer-Integrated Technologies Rivne Ukraine

ISBN: (纸本)9798331520564

The authors of this paper propose a block diagram for a dual-operating buffer on a dynamic optical RAM. The buffer consists of two dynamic optical RAMs, each controlled separately to allow for simultaneous writing and reading of data, thereby improving performance. This setup enables data to be read from the buffer while new data is being written to it. Implementing a dual-operating buffer on a dynamic optical RAM has the potential to enhance the performance of high-speed data transmission in telecommunications networks. The authors conducted numerical calculations to compare failure probabilities between single-operation and dual-operation buffers on dynamic optical RAM. The results indicated that using a dual-operating buffer significantly reduces the probability of failure with the same buffer memory capacity compared to a single-operating buffer. Additionally, the authors analyzed the effectiveness of the proposed dual-operating buffer. The analysis confirmed that exploiting dynamic optical memory for buffering enhances data transfer speed and efficiency due to the inherent properties of optical data transfer, such as high bandwidth and low latency. © 2024 IEEE.

关键词： Dynamic random access storage

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An improved performance model for artificial intelligence-based diabetes prediction

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Journal of Electrical Systems and Information Technology 2025年第1期12卷 1-30页

作者： Okwudili, Ugwu Hillary Ukachukwu, Oparaku Ogbonna Chijindu, V. C. Ezea, Michael Okechukwu Ishaq, Buhari Department of Military Science Faculty of Military Science and Interdisciplinary Studies Nigerian Defence Academy Kaduna Nigeria Department of Electronic and Computer Engineering Faculty of Engineering University of Nigeria Nsukka Nigeria Department of Electrical Engineering Faculty of Engineering Nigeria Maritime University Okerenkoko Warri Nigeria

This research study addresses the pressing issue of diabetes prediction using advanced machine learning techniques, presenting the development of an ensemble model that significantly outperforms existing methods by 1.6% using area under the curve as the primary performance metric. The proposed model achieved an area under the curve (AUC) of 0.946, surpassing the previously best-performing model, extreme gradient boosting (XB) by 0.7%. The improved ensemble model has outrightly outperformed other existing machine learning (ML) models from the literature by 1.6%. This improvement is particularly notable given the challenges posed by outliers and missing values in the dataset, which complicate diabetes prediction. The choice of choosing algorithms such as k-nearest neighbor (KNN), random forest (RF), AdaBoost (AB), and extreme gradient boost (XB) was meticulously informed by their distinct strengths and limitations, which are critical for the efficacy of this research study. KNN is particularly user-friendly, making it accessible for preliminary analyses; however, it exhibits sensitivity to feature scaling; RF, while robust and capable of handling a variety of data distribution, has a propensity for overfitting, which is mitigated through tuning. AB is effective in enhancing the performance of weak learners, yet it may encounter challenges with imbalanced datasets, an aspect that was addressed through soft weighted sampling vote. XB demonstrates remarkable predictive performance, bolstered by its built-in cross-validation and parallel processing; nevertheless, it remains sensitive to outliers, highlighting the importance of thorough data processing. By integrating these algorithms into an ensemble framework, this study effectively mitigated their individual limitations, leading to a more accurate and improved reliable prediction model. The innovative approach of hyperparameter tuning through randomized search further enhanced the model’s performance, marking a signific

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Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs 32

Tackling heavy-tailed noise in distributed estimation: Asymp...

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32nd Telecommunications Forum, TELFOR 2024

作者： Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Vukovic, Manojlo University of Novi Sad Faculty of Technical Sciences Department of Power Electronic and Communication Engineering Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Serbia Carnegie Mellon University Department of Electrical and Computer Engineering United States University of Novi Sad Faculty of Technical Sciences Department of Fundamental Sciences Serbia

ISBN: (纸本)9798350391053

We present an algorithm for distributed estimation of an unknown vector parameter θ∗ ∈ M in the presence of heavy-tailed observation and communication noises. Heavy-tailed noises frequently appear, e.g., in densely deployed Internet of Things (IoT) or wireless sensor network systems. The presented algorithm falls within the class of consensus+innovation estimators and combats the effect of the heavy-tailed noises by adding general nonlinearities in the consensus and innovations update parts. We present results on almost sure convergence and asymptotic normality of the estimator. In addition, we provide novel analytical studies that reveal interesting tradeoffs between the system noises and the underlying network topology. © 2024 IEEE.

关键词： Internet of things

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Periodicity and bifurcation of single pulse and soliton molecules

Periodicity and bifurcation of single pulse and soliton mole...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Ban, Xiaoqiang Li, Xiaohui Li, Qian Zhong, Ming School of Electronic and Computer Engineering Peking University Shenzhen China School of Physics and Information Technology Shaanxi Normal University Xi'an China Faculty of Education Shenzhen University Shenzhen China

Period bifurcation of single pulse and soliton molecule are studied in fiber lasers, where period-timing bifurcation reveals the abundant nonlinear pulse dynamics and may be applied to optical switch. © 2024 IEEE.

ISBN: (纸本)9798350379266

关键词： Fiber lasers

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An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment 3

An Enhanced Deep Super-Resolution Generative Adversarial Net...

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3rd IEEE International Conference on electronic engineering, ICEEM 2023

作者： El-Fattah, Ibrahim Abd Taha, Taha E. El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Department Electronics and Electrical Communications Engineering Alexandria Egypt Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models. © 2023 IEEE.

关键词： Generative adversarial networks

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USK-FEMO: A Face Emotion Dataset using Deep Learning for Effective Learning 2

USK-FEMO: A Face Emotion Dataset using Deep Learning for Eff...

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2nd International Conference on computer System, Information Technology, and Electrical engineering, COSITE 2023

作者： Muhajir, Muhajir Oktiana, Maulisa Muchtar, Kahlil Fitria, Maya Akhyar, Akhyar Pratama, Muhammad Dandy Lin, Chih-Yang Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Malaysia Department of Mechanical Engineering National Central University Taoyuan City Taiwan

ISBN: (纸本)9798350343069

Emotions play an essential role in the learning process and have an impact on how the learning process is eventually carried out. Facial expressions can be used to visually identify a person's emotions. Along with the advancement of computer vision and deep learning techniques, the study of human-computer interaction is increasingly focusing on the recognition of facial expressions. One of the main issues is the availability of sufficient datasets, especially for students. This study examined the deep learning architecture for face emotion classification. In addition, this research also introduces a new emotional dataset acquired from the junior high school student at SMP Negeri 1 Darul Imarah, Aceh Besar Regency, Indonesia. This dataset contains five classes that include the emotions of happiness, sadness, anger, surprise, and boredom. The dataset was then tested using the Mobile-Net architecture, the highest accuracy was achieved with a learning rate of 0.0001% of 88.492%. %. The dataset can be explored via the link https://***/USK-FEMO-DATASET/. © 2023 IEEE.

关键词： Human computer interaction

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