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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Kwanho Kim Kanghyun Jo dept. of Electrical Electronic and Computer Engineering University of Ulsan Ulsan South Korea

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

With the increase of interest in digital twin, the way to link dynamic objects into digital twin is also important task as well as static environment. However, there are many situations that the developer can not know all about real environment. This paper focus on finding camera extrinsic parameters(6-DOF camera pose) to fix misalignment between real and digital twin images. Due to the absent of knowledge about function of image $I(\theta)$ given camera parameter $\theta$, this work can’t use gradient descent algorithm known as a powerful optimization method in deep learning. So this paper propose how to apply GA(Genetic Algorithm) for optimizing camera parameters. Fitness function, one of the most important property in GA, is composed of similarity between two images. This paper propose novel approach to calculate similarity using classical feature extraction and matching methods. Even though this work use classic feature extraction method, errors are estimated within 1.42 m for camera position and 1.22° for rotation in all experiments(Averagely 0.744 m and 0.903°). The code of the algorithm can be found in the following link: https://***/Kwan-Ho-Kim/***.

关键词： Industrial electronics Deep learning Codes Optimization methods Cameras Feature extraction Digital twins

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QuCode: A Novel Conceptual Description of Quantum Algorithms

QuCode: A Novel Conceptual Description of Quantum Algorithms

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International Symposium on Multiple-Valued Logic

作者： Hrithik Ketineni Marek Perkowski Dept. of Electrical and Computer Engineering Portland State University Portland Oregon USA

ISBN: (数字)9798350343083

ISBN: (纸本)9798350343090

Conventional presentations of quantum algorithms overwhelmingly rely on mathematical formalism, posing an unnecessary barrier to conceptual understanding. The growing influence of quantum computing across diverse domains necessitates more accessible education on the subject. To engage a broader audience in quantum computing, we introduce a new approach for teaching quantum algorithms: drawing parallels to classical pseudocode. This approach, which we call “QuCode,” is demonstrated through its application to several black box quantum algorithms: Deutsch-Jozsa, Bernstein-Vazirani, and Simon's algorithm.

关键词： computers Flowcharts Quantum algorithm Education Supply chains Syntactics Observers

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Identification of Black Fungus Diseases Using CNN and Transfer-Learning Approach 22

Identification of Black Fungus Diseases Using CNN and Transf...

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2nd International Conference on Computing Advancements: Age of Computing and Augmented Life, ICCA 2022

作者： Hasan, Md Imran Mahbub, Nosin Ibna Sarkar, Bappa Dept. of Computer Science and Engineering Islamic University Kushtia Bangladesh Dept. of Information and Communication Technology Islamic University Kushtia Bangladesh

ISBN: (纸本)9781450397346

Black Fungus is a dangerous fungal illness, commonly referred to as 'mucormycosis', usually infecting uncompromising people. Mucormycosis is generally rare, affecting less than two individuals per million each year, but it is currently 80 times more frequent in India. People of all ages, including prematurity infants, may be impacted. To minimize the time and effort required by medical experts to investigate black fungus disease and enhance the consistency of image identification of black fungus infections, we suggested a transfer learning based CNN model as the dataset for the black fungal disease is limited. The experimental outcomes of several different architectures, including VGG16, InceptionV3, and Xception, are compared and analyzed in this study, and it is observed that Xception has the best performance, with 97.92% training accuracy and 95.60% testing accuracy. © 2022 ACM.

关键词： Image enhancement

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A Mixed-Order Divergence-Conforming Locally Corrected Nystrom Method for Triangular Cells

A Mixed-Order Divergence-Conforming Locally Corrected Nystro...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： John C. Young Stephen D. Gedney Robert J. Adams Dept. of Electrical & Computer Engineering University of Kentucky Lexington KY USA Dept. of Electrical Engineering University of Coloardo-Denver Denver CO USA

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

A locally corrected Nystrom method is presented that better models a mixed-order, divergence-conforming space on triangular cells. The theory is developed for a space that is complete to the same order for both the unknown quantity and its divergence. The method is implemented for the electric field integral equation, and convergence results are presented for scattering from a perfectly conducting sphere.

关键词： Integral equations Scattering Computational electromagnetics Mathematical models Electric fields Convergence

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Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity

arXiv

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arXiv 2023年

作者： Zhou, Zhanpeng Yang, Yongyi Yang, Xiaojiang Yan, Junchi Hu, Wei Dept. of Computer Science and Engineering MoE Key Lab of AI Shanghai Jiao Tong University China Dept. of Electrical Engineering & Computer Science University of Michigan United States

Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path in the parameter space while maintaining near-constant training and test losses. In this work, we introduce a stronger notion of linear connectivity, Layerwise Linear Feature Connectivity (LLFC), which says that the feature maps of every layer in different trained networks are also linearly connected. We provide comprehensive empirical evidence for LLFC across a wide range of settings, demonstrating that whenever two trained networks satisfy LMC (via either spawning or permutation methods), they also satisfy LLFC in nearly all the layers. Furthermore, we delve deeper into the underlying factors contributing to LLFC, which reveal new insights into the permutation approaches. The study of LLFC transcends and advances our understanding of LMC by adopting a feature-learning perspective. We released our source code at https://***/zzp1012/LLFC. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Dynamic Mode Decomposition for Control Systems with Input Delays

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IFAC-PapersOnLine 2023年第3期56卷 97-102页

作者： Christopher C. Zawacki Eyad H. Abed Dept. of Electrical and Computer Engineering University of Maryland College Park MD 20742 USA

Dynamic mode decomposition with control (DMDc) has emerged as a powerful tool for data-driven system identification in recent years. Our work provides an extension to DMDc, namely the ability to identify systems with a delayed input. Specifically, we propose dynamic mode decomposition with input-delayed Control (DMDidc), which allows the identification of input-delayed linear time-invariant (LTI) systems given sufficient input-output data. We prove the method is able to identify delays that are a fraction of a discrete controllers sampling period and provide an extended predictive feedback control scheme that accommodates such delays. A numerical simulation illustrates the benefits of our method.

关键词： Dynamic Mode Decomposition System Identification Control Systems Input Delay Predictive Feedback Control

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AGORA: A Multi-Provider Edge Computing Resource Management and Pricing Framework

AGORA: A Multi-Provider Edge Computing Resource Management a...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Panagiotis Charatsaris Matthew Salcido Maria Diamanti Abid Mohammad Ali Eirini Eleni Tsiropoulou Symeon Papavassiliou School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Multi-provider multi-user multi-access edge computing provides a recent market-driven networking paradigm facilitating the user data offloading process. In this paper we introduce the AGORA framework, which employs a sophisticated multi-leader multi-follower Stackelberg game that jointly optimizes the data offloading, computing resource allocation, and computing resource pricing, all facilitated through a non-cooperative game-theoretic approach. In order to support the aforementioned modeling and approach, a novel utility function that quantifies the users satisfaction, factoring in the computing service cost, and an innovative profit function for the MEC providers is introduced, emphasizing the market penetration and the computing service provision costs. Numerical results, obtained via modeling and simulation, demonstrate AGORA’s remarkable adaptability, accommodating homogeneous and heterogeneous user computing demands, while simultaneously outperforming proportional fairness resource allocation approaches, and significantly enhancing the MEC providers’ profitability and the users’ satisfaction from the edge computing services.

关键词： Wireless communication Adaptation models Multi-access edge computing Costs Computational modeling Pricing Games

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Overview of PUFs and Emerging Chaotic Structures

Overview of PUFs and Emerging Chaotic Structures

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Physical Assurance and Inspection of Electronics (PAINE), 2020 IEEE

作者： David G. Tindall Aubrey N. Beal Tommy Morris Dept. of Electrical & Computer Engineering The University of Alabama in Huntsville Huntsville AL USA

ISBN: (数字)9798331542252

ISBN: (纸本)9798331542269

We present an abbreviated survey of common Physically Unclonable Function (PUF) structures, and highlight two emerging chaotic structures, targeted towards application-driven practitioners who need reliable generation of device specific values. Interestingly, chaotic PUF structures output entropic values over successive iterations. We also outline three distinct PUF use cases and discuss well-suited electronic structures for each case. We discuss security in limited cases and summarize suitable implementation targets, aspects affecting reliability, and notional generation rates. Altogether, this work summarizes key concepts about PUF structures in an application-driven context to aid both designers and users of these devices in the broader security community.

关键词： Surveys Systems operation Authentication Random access memory Physical unclonable function Entropy Security Transistors Integrated circuit reliability Random number generation

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Post Quantum Code-Based Cryptosystems with Dual Inverse Matrix

Post Quantum Code-Based Cryptosystems with Dual Inverse Matr...

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International Conference on Information Technology in Asia (CITA)

作者： Farshid Haidary Makoui T. Aaron Gulliver Mohammad Dakhilalian Dept. of Electrical & Computer Engineering University of Victoria Victoria BC Canada Dept. of Electrical & Computer Engineering Isfahan University of Technology Isfahan Iran

The Shor algorithm demonstrates the significant risk that quantum attacks pose to the security of widely used cryptographic primitives. However, code-based cryptography has been shown to be resistant to these attacks. To date, no polynomial-time attack exists that can break code-based cryptosystems such as the McEliece cryptosystem. Despite this, these cryptosystems are not employed in practical applications in domains such as online banking, blockchains, and e-commerce platforms. The primary reason is the large sizes of the public and private keys associated with code-based cryptosystems. In this paper, a new code-based cryptosystem is introduced which employs a dual matrix based on the transpose and inverse of the parity check matrix to reduce the key size compared to the McEliece cryptosystem.

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)

作者： Md. Sariful Islam Md Tusher Ahmad Bappy Jubayer Ahmed Shawon Mehedi Hasan Wahidur Rahman Lija Akter Mir Mohammad Azad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pretrained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models' performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients.

关键词： Cataracts Measurement Deep learning Accuracy Image color analysis Static VAr compensators Feature extraction Real-time systems Residual neural networks Principal component analysis

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