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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali University of Glasgow James Watt School of Engineering Glasgow United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Clemson University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350351255

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. © 2024 IEEE.

关键词： Frequency allocation

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High piezoelectricity after field cooling AC poling in temperature stable ternary single crystals manufactured by continuous-feeding Bridgman method

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Journal of Advanced Ceramics 2022年第1期11卷 57-65页

作者： Cong LUO Tomoaki KARAKI Zhuangkai WANG Yiqin SUN Yohachi(John)YAMASHITA Jiayue XU School of Materials Science and Engineering Shanghai Institute of TechnologyShanghai 201418China Department of Electrical and Computer Engineering Faculty of EngineeringToyama Prefectural UniversityToyama 939-0398Japan Department of Mechanical&Aerospace Engineering North Carolina State UniversityNC 27695-7910USA

After field cooling(FC)alternating current poling(ACP),we investigated the dielectric and piezoelectric properties of[001]_(pc)-oriented 0.24Pb(In_(1/2)Nb_(1/2))O_(3)(PIN)-0.46Pb(Mg_(1/3)Nb_(2/3))O_(3)(PMN)-0.30PbTiO3(PT)(PIMN-0.30PT)single crystals(SCs),which were manufactured by continuous-feeding Bridgman(CF BM)within morphotropic phase boundary(MPB)*** ACP with 4 kVrms/cm from 100 to 70℃,the PIMN-0.30PT SC attained high dielectric permittivity of 8330,piezoelectric coefficient(d_(33))of 2750 pC/N,bar mode electromechanical coupling factorκ_(33)of 0.96 with higher phase change temperature(T_(pc))of 103℃,and high Curie temperature(7c)of 180℃.These values are the highest ever reported as PIMN-xPT SC system with Tpc>100℃.The enhancement of these properties is attributed to the induced low symmetry multi-phase supported by phase *** work indicates that FC ACP is a smart and promising method to enhance piezoelectric properties of relaxor-PT ferroelectric SCs including PIMN-xPT,and provides a route to a wide range of piezoelectric device applications.

关键词： field cooling(FC)alternating current poling(ACP) PIMN-0.30PT high piezoelectricity

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Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks

arXiv

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arXiv 2025年

作者： Telila, Yohannis Kifle Senevirathne, Damitha Tissera, Dumindu Narayan, Apurva Capretz, Miriam A.M. Grolinger, Katarina Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science Western University LondonON Canada

Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anomaly detection have achieved great success, but are typically centralized, involving sharing local data with a central server which raises privacy and security concerns. Federated Learning (FL) has been gaining popularity as it enables distributed learning without sharing local data. However, FL depends on neural networks, which are vulnerable to adversarial attacks that manipulate data, leading models to make erroneous predictions. While adversarial attacks have been explored in the image domain, they remain largely unexplored in time series problems, especially in the energy domain. Moreover, the effect of adversarial attacks in the FL setting is also mostly unknown. This paper assesses the vulnerability of FL-based anomaly detection in energy data to adversarial attacks. Specifically, two state-of-the-art models, Long Short Term Memory (LSTM) and Transformers, are used to detect anomalies in an FL setting, and two white-box attack methods, Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), are employed to perturb the data. The results show that FL is more sensitive to PGD attacks than to FGSM attacks, attributed to PGD’s iterative nature, resulting in an accuracy drop of over 10% even with naive, weaker attacks. Moreover, FL is more affected by these attacks than centralized learning, highlighting the need for defense mechanisms in *** Codes 68 Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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A unified computational and experimental approach to determine laser safety of liver tissue 36

A unified computational and experimental approach to determi...

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Optical Interactions with Tissue and Cells XXXVI 2025

作者： Arroyo, Junior Zhang, Jiaxin Bell, Muyinatu A. Lediju Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9781510683822

Photoacoustic imaging has promising potential to be integrated into surgical guidance systems. However, this integration can potentially be limited by biosafety concerns. While laser safety guidelines exist for skin, corneal, and retinal tissue, there are no guidelines for liver tissue. We introduced the first known numerical simulation approach to determine laser safety for liver tissue and present a novel microscopy analysis framework to validate performance. Our innovative approach integrates Monte Carlo simulations of laser-tissue interaction followed by COMSOL modeling for necrosis prediction. These predictions are demonstrated with pulsed laser exposure using 30 mJ and 73 mJ laser energies with an optical wavelength of 750 nm administered for exposure durations of 1, 10, and 20 minutes. Empirical validations were performed with necrosis values quantified from immunohistochemical sections of swine liver exposed to the same laser conditions in vivo. These necrosis predictions deviated from quantitative histological results by 0.01% to 8.1%. The presented approach provides a promising alternative to traditional, time-intensive experimental methods to otherwise determine laser safety guidelines, which is expected to translate to multiple tissues and laser properties. © 2025 SPIE.

关键词： Laser safety

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Correction to: Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions (International Journal of Intelligent Robotics and Applications, (2024), 10.1007/s41315-024-00333-2)

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International Journal of Intelligent Robotics and Applications 2024年第1期9卷 365-373页

作者： Stefanopoulou, Aliki Raptis, Emmanuel K. Apostolidis, Savvas D. Gkelios, Socratis Kapoutsis, Athanasios Ch. Chatzichristofis, Savvas A. Vrochidis, Stefanos Kosmatopoulos, Elias B. Information Technologies Institute The Centre for Research & ampTechnology Hellas 6th km Harilaou - Thermis Thessaloniki57001 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Campus Xanthi67100 Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos 2 Danais Avenue PafosCY 8042 Cyprus

In the original publication, the author has found few errors in the figures 2, 4, 11 and 12 which significantly impact both the paper's quality and the reputation of the journal. These issues undermine the clarity of the methodology and the novelty of the research. 1. Each column represents a different case in the experiment, corresponding to the number of UAVs in both the obstacle-free and obstacle areas. However, in Fig. 2, the first row contains identical subfigures across all columns, while the subfigures in the remaining rows are identical to each other The incorrect Fig. 2 appears as below: (Figure presented.) However the corrected Fig. 2 should appear as below. (Figure presented.) 2. In Fig. 4, every row showcase the average QoF for the testing scenarios (obstacle-free and obstacle area). However the first row has the exact same figures with the second row. The incorrect Fig. 4 appears as below: (Figure presented.) However the corrected Fig. 4 should appear as below. (Figure presented.) 3. Figures 11 and 12 showcase the distribution of each Quality of Flight (QoF) metric value for every UAV in the conducted trials during the testing scenarios. However, Fig. 11 is the same with Fig. 12. The incorrect Figs. 11 and 12 appears as below: (Figure presented.) However the corrected Figs. 11 and 12 should appear as below. (Figure presented.) Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle-free area Distribution of each Quality of Flight metric value for every UAV in the conducted trials during the coverageof the obstacle area The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

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Utilizing the Random Forest Algorithm to Enhance Alzheimer's disease Diagnosis 3

Utilizing the Random Forest Algorithm to Enhance Alzheimer's...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kaur, Chamandeep Panda, Tuhina Panda, Subhasis Rahman Mohammed Al Ansari, Abdul Nivetha, M. Kiran Bala, B. Jazan University Dept of It Saudi Arabia Gandhi Institute for Education and Technology Computer Science Engineering India Gandhi Institute for Education and Technology Electrical and Computer Science Engineering India Salmanyia Hospital Department of Surgery Bahrain K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Trichy India

ISBN: (纸本)9781665462167

Machine learning is widely used in many aspects of healthcare. The development of medical technology has made it possible to gather better data for early disease symptom diagnosis. This study makes an effort to categorize Alzheimer's disorder. Alzheimer's disease is a fatal disorder that may result in memory loss and mental impairment. To prepare for medical attention, this needs early disease diagnosis. Magnetic resonance imaging (MRI) can be used to accurately and non-invasively diagnose Alzheimer's disease. Effective feature extraction and segmentation techniques are necessary for the accurate diagnosis of MRI images. Utilizing MRI data of the brain's white matter, grey matter, and cerebrospinal fluid, feature selection is carried out. Random forest trees are used in standard machine learning methods like regression and classification. The results of the utilized method were next contrasted with those of other machine learning techniques. As a result, RF model-based interpolation analysis surpasses the RF non-imputation method with greater accuracy, specificity, sensitivity, f-measure, and ROC. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Rice Disease Recognition using Improved Visual Geometry Group Deep Learning Algorithms 23

Rice Disease Recognition using Improved Visual Geometry Grou...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Lee, Shu-Hung Cheng, Chia-Hsin Tsai, Yu-Shun Huang, Yung-Fa Liu, Shing-Hong Chen, Ching-Mu School of Intelligent Manufacturing and Automotive Engineering Guangdong Business and Technology University Zhaoqing526238 China National Formosa University Department of Electrical Engineering Yunlin Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung Taiwan Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung Taiwan National Penghu University of Science and Technology Department of Electrical Engineering Penghu Taiwan

ISBN: (纸本)9798331528041

Rice diseases are one of the major factors affecting rice production. Traditionally, the identification and assessment of rice diseases have been done manually by experts and farmers, which is time-consuming and cannot provide real-time disease prediction, leading to significant losses. Therefore, there is a need for faster and more efficient methods for rice disease recognition. In this paper, we propose a solution for rice disease detection using artificial intelligence in image recognition. We investigate the performance of the visual geometry group (VGG) deep learning algorithms in recognizing rice diseases. Simulation results show that the VGG16 and VGG19 pretrained models achieve validation accuracies of 67% and 72.5%, respectively. © 2024 IEEE.

关键词： Artificial Intelligence Rice Diseases Visual Geometry Group

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Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion

arXiv

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arXiv 2025年

作者： Chagahi, Mehdi Hosseini Delfan, Niloufar Moshiri, Behzad Piran, Md. Jalil Parikhan, Jaber Hatam School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of

Intracranial hemorrhage (ICH) is a critical medical emergency caused by the rupture of cerebral blood vessels, leading to internal bleeding within the skull. Accurate and timely classification of hemorrhage subtypes is essential for effective clinical decision-making. To address this challenge, we propose an advanced pyramid vision transformer (PVT)-based model, leveraging its hierarchical attention mechanisms to capture both local and global spatial dependencies in brain CT scans. Instead of processing all extracted features indiscriminately, A SHAP-based feature selection method is employed to identify the most discriminative components, which are then used as a latent feature space to train a boosting neural network, reducing computational complexity. We introduce an entropy-aware aggregation strategy along with a fuzzy integral operator to fuse information across multiple CT slices, ensuring a more comprehensive and reliable scan-level diagnosis by accounting for inter-slice dependencies. Experimental results show that our PVT-based framework significantly outperforms state-of-the-art deep learning architectures in terms of classification accuracy, precision, and robustness. By combining SHAP-driven feature selection, transformer-based modeling, and an entropy-aware fuzzy integral operator for decision fusion, our method offers a scalable and computationally efficient AI-driven solution for automated ICH subtype classification. © 2025, CC BY-NC-ND.

关键词： computerized tomography

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Minimum-Norm Interpolation Under Covariate Shift 41

Minimum-Norm Interpolation Under Covariate Shift

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41st International Conference on Machine Learning, ICML 2024

作者： Mallinar, Neil Zane, Austin Frei, Spencer Yu, Bin Department of Computer Science University of California San DiegoCA United States Department of Statistics University of California BerkeleyCA United States Department of Statistics University of California Davis CA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

Transfer learning is a critical part of real-world machine learning deployments and has been extensively studied in experimental works with overparameterized neural networks. However, even in the simplest setting of linear regression a notable gap still exists in the theoretical understanding of transfer learning. In-distribution research on high-dimensional linear regression has led to the identification of a phenomenon known as benign overfitting, in which linear interpolators overfit to noisy training labels and yet still generalize well. This behavior occurs under specific conditions on the source covariance matrix and input data dimension. Therefore, it is natural to wonder how such high-dimensional linear models behave under transfer learning. We prove the first non-asymptotic excess risk bounds for benignly-overfit linear interpolators in the transfer learning setting. From our analysis, we propose a taxonomy of beneficial and malignant covariate shifts based on the degree of overparameterization. We follow our analysis with empirical studies that show these beneficial and malignant covariate shifts for linear interpolators on real image data, and for fully-connected neural networks in settings where the input data dimension is larger than the training sample size. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique

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Procedia computer science 2025年 258卷 4105-4116页

作者： Nilesh Bhaskarrao Bahadure Sudhanshu Gonge Jagdish Chandra Patni Bhoomi Shah Nagrajan Raju Prasenjeet D. Patil Department of Computer Science and Engineering School of Technology GSFC University Vadodara Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Thanjavur Tamil Nadu India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality.

关键词： Berkeley wavelet transformation enhancement magnetic resonance imaging (MRI) segmentation morphology

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