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arXiv 2023年

作者： Bokaei, Mohammad Razavikia, Saeed Rini, Stefano Amini, Arash Behrouzi, Hamid Electrical Engineering Department Sharif University of Technology Iran Department of Electronic Systems Aalborg University Denmark School of Electrical Engineering and Computer Science KTH University Sweden Taiwan

In this paper, we investigate the problem of recovering the frequency components of a mixture of K complex sinusoids from a random subset of N equally-spaced time-domain samples. Because of the random subset, the samples are effectively non-uniform. Besides, the frequency values of each of the K complex sinusoids are assumed to vary continuously within a given range. For this problem, we propose a two-step strategy: (i) we first lift the incomplete set of uniform samples (unavailable samples are treated as missing data) into a structured matrix with missing entries, which is potentially low-rank;then (ii) we complete the matrix using a weighted nuclear minimization problem. We call the method a weighted lifted-structured (WLi) low-rank matrix recovery. Our approach can be applied to a range of matrix structures such as Hankel and double-Hankel, among others, and provides improvement over the unweighted existing schemes such as EMaC and DEMaC. We provide theoretical guarantees for the proposed method, as well as numerical simulations in both noiseless and noisy settings. Both the theoretical and the numerical results confirm the superiority of the proposed approach. © 2023, CC BY-NC-ND.

关键词： Numerical methods

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Decentralised Iterative Learning Control for High Performance Collaborative Tracking Problem with Output Constraints

Decentralised Iterative Learning Control for High Performanc...

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UKACC International Conference on Control (CONTROL)

作者： Bin Chen Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. School of Electronics and Computer Science University of Southampton Southampton U.K.

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

High performance collaborative tracking problem, requiring a group of independent subsystems to generate a global output that can precisely track the desired reference in a repetitive manner, has found lots of applications in practice. However, for such an important control task, existing iterative learning control (ILC) methods have not considered the constraint on each subsystem's output, which leads to potential risk within the control process. This paper proposes a novel optimisation based ILC method to address the high performance collaborative tracking problem with output constraints. The proposed ILC framework can guarantee not only each subsystem's output constraint is always satisfied during the control process, but also the monotonic convergence of a well-defined performance index to a possibly minimum value. To avoid huge computational complexity for large scale systems, we further apply the idea of the alternative direction method of multipliers (ADMM) to implement the proposed ILC frame-work in a decentralised manner, which allows the resulting decentralised methods to be applied to large scale and changing systems. Moreover, the decentralised ILC method proposed in this paper is suitable for non-minimum phase, heterogeneous and/or homogeneous systems, which is appealing in practice. Convergence properties of the proposed ILC algorithms are analysed rigorously, and numerical examples are given to demonstrate the algorithms' effectiveness.

关键词： Collaboration Process control Performance analysis Large-scale systems Task analysis Computational complexity Optimization

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Inpainting Diffusion Synthetic and Data Augment with Feature Keypoints for Tiny Partial Fingerprints

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2024年第3期7卷 396-409页

作者： Hsu, Mao-Hsiu Hsu, Yung-Ching Chiu, Ching-Te The Department of Electro-Optical Engineering National Formosa University Yunlin Taiwan The Department of Computer Science National Tsing Hua University Hsinchu Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan

The advancement of fingerprint research within public academic circles has been trailing behind facial recognition, primarily due to the scarcity of extensive publicly available datasets, despite fingerprints being widely used across various domains. Recent progress has seen the application of deep learning techniques to synthesize fingerprints, predominantly focusing on large-area fingerprints within existing datasets. However, with the emergence of AIoT and edge devices, the importance of tiny partial fingerprints has been underscored for their faster and more cost-effective properties. Yet, there remains a lack of publicly accessible datasets for such fingerprints. To address this issue, we introduce publicly available datasets tailored for tiny partial fingerprints. Using advanced generative deep learning, we pioneer diffusion methods for fingerprint synthesis. By combining random sampling with inpainting diffusion guided by feature keypoints masks, we enhance data augmentation while preserving key features, achieving up to 99.1% recognition matching rate. To demonstrate the usefulness of our fingerprint images generated using our approach, we conducted experiments involving model training for various tasks, including denoising, deblurring, and deep forgery detection. The results showed that models trained with our generated datasets outperformed those trained without our datasets or with other synthetic datasets. This indicates that our approach not only produces diverse fingerprints but also improves the model’s generalization capabilities. Furthermore, our approach ensures confidentiality without compromise by partially transforming randomly sampled synthetic fingerprints, which reduces the likelihood of real fingerprints being leaked. The total number of generated fingerprints published in this article amounts to 818,077. Moving forward, we are ongoing updates and releases to contribute to the advancement of the tiny partial fingerprint field. © 2019 IEEE.

关键词： Deep learning

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Access Control Based on Self-Sovereign Identity 16

Access Control Based on Self-Sovereign Identity

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Nita, Stefania Loredana Mihailescu, Marius Iulian Marascu, Valentina Rogobete, Marius Faculty of Information Systems and Cyber Security Military Technical Academy 'Ferdinand i' Department of Computers and Cybersecurity Bucharest Romania 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users' credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust. © 2024 IEEE.

关键词： Electronic document identification systems

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Lightweight Spiking Neural Network Based Detector for Interweave Cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

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IEEE International Conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

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A Machine Learning Approach for High Resolution Fractional Vegetation Cover Estimation Using Planet Cubesat and RGB Drone Data Fusion

A Machine Learning Approach for High Resolution Fractional V...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jacob Nesslage Brittany Lopez Barreto Adam Weingram Erin Hestir Environmental Systems Graduate Group University of California Merced Department of Civil and Environmental Engineering University of California Merced Department of Electrical Engineering and Computer Science University of California Merced

High resolution fractional vegetation cover (HR-FVC) is important for many applications, including precision agriculture, forestry, and conservation. For land managers, HR-FVC is most useful when the data can be produced quickly with minimal effort. In this study, we perform data fusion of RGB drone data and multispectral cubesat data for synthetic daily HR-FVC estimation. First, binary classification of 10cm resolution drone data was used to identify vegetation. An AdaBoost model (Accuracy = 0.868, F1-score = 0.840) was selected for further analysis. HR-FVC training data was then produced from drone vegetation maps by calculating the FVC in a 3m pixel – Planet SuperDove resolution, resulting in 238,270 training points. A random forest regression model was used to predict HR-FVC from Planet SuperDove data. The final model’s performance is comparable to similar studies (R 2 = 0.720, RMSE = 0.213), suggesting the methodology could be viable for applications requiring daily HR-FVC datasets.

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Metagenomic Data Analysis with Machine Learning to Discover Colorectal Cancer-Associated Enzymes

Metagenomic Data Analysis with Machine Learning to Discover ...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Nur Sebnem Ersoz Cihan Kuzudisli Malik Yousef Burcu Bakir-Gungor Department of Bioengineering Faculty of Life and Natural Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat Israel Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

The human gut microbiome comprises over 10 trillion microbes and plays important roles in maintaining metabolism, body homeostasis, impacting immune function. Metagenomics which studies genomic data from clinical and environmental samples is crucial in understanding the interplay between the host and the gut microbiome. Recently, functional profiling of metagenomes helps to identify alterations in microbial functions, particularly enzyme-encoding genes. Colorectal cancer (CRC) is known as one of the leading causes of cancer-related deaths. In this study, we aimed to find the CRC-associated enzymes by analyzing metagenomic data with different machine learning methods. A total of 1262 samples including CRC and control groups from different countries were used in this study. This dataset was obtained by functionally profiling metagenomics data and estimating community level enzyme commission (EC) abundance values. For the analysis of this dataset, RCE-IFE and SVM-RCE machine learning methods, which are group-based feature selection methods, were compared with 6 different individual feature selection methods. 10 times Monte-Carlo Cross Validation was used in our experiments. It was observed that RCE-IFE, Extreme Gradient Boosting and Select K Best methods similarly provided the best performances. Especially in this study, besides the its high performance, the group-based feature selection method RCE-IFE grouped enzymes into clusters unlike TFS, and then identified biologically relevant CRC-associated enzymes.

关键词： Enzymes Metagenomics Microorganisms Monte Carlo methods Colorectal cancer Signal processing Homeostasis

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Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments

arXiv

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arXiv 2023年

作者： Osti, Manish Thakuri, Aashray Qolomany, Basheer Mulahuwaish, Aos Department of Computer Science and Information Systems Saginaw Valley State University University Center MI United States College of Engineering and Computer Science University of Michigan- Dearborn Dearborn United States Cyber Systems Department University of Nebraska at Kearney Kearney United States

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL’s efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning. © 2023, CC BY.

关键词： Machine learning

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