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17th Asian Conference on computer Vision, ACCV 2024

作者： Phuong, Thao Nguyen Duy, Vinh Nguyen Sakaino, Hidetomo FPT Consulting Japan FPT Software Minato Japan Department of Computer Science and Communications Engineering Waseda University Shinjuku Japan

ISBN: (纸本)9789819626434

Image generation in 2D and 3D has become an active research topic in Deep Learning. Single or multiple input images with non-orthogonal views are used for another shape and texture with different viewing angles. On the other hand, computer-Aided Design (CAD) relies on handling-based 3D generation, i.e., isometric view images, from three orthographic view line drawings in 2D. However, since unique viewing pairs of such 2D and 3D images are required to train, SOTA models are insufficient to generate desirable images. More spatial and geometrical constraints remain undone due to less corresponding image features between images. This paper proposes IsoTGAN with a GAN-based generator with the Transformer in its generator, where three images of an object’s front, side, and top view are input. The encoder is trained by the spatial and geometrical relation among three view images. A novel Gaussian Enhanced Euclidean attention mechanism and a geometry-constrained loss function are also proposed for further local image feature enhancement. Extensive experiments on the SPARE3D dataset demonstrate that the proposed IsoTGAN outperforms State-of-the-art (SOTA) models, i.e., DINO, regarding local and global image feature accuracy. This helps generate 3D isometric view images in auto-CAD system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D generation attention CAD GAN Isometric view Transformer

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Generative Diffusion Model-Based Deep Learning Framework for Remaining Useful Life Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 18431-18434页

作者： Ha, Sangjun Sung, Mingyu Saeed, Faisal Yun, Sangseok Kim, Il-Min Kang, Jae-Mo Kyungpook National University Department of Artificial Intelligence Daegu41566 Korea Republic of Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Pukyong National University Department of Information and Communications Engineering Busan48513 Korea Republic of Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada

In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: (i) a feature extractor corresponding to the encoder part of our customized U-Net and (ii) a RUL predictor constructed by a multi-layer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pre-trained for high-quality feature learning, and then, is re-trained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme. © 2014 IEEE.

关键词： Prediction models

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Deep Learning-Based Data-Assisted Channel Estimation and Detection

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in communications and Networking 2025年 3卷 534-551页

作者： Hashempoor, Hamidreza Choi, Wan Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

We introduce a novel structure empowered by deep learning models, accompanied by a thorough training methodology, for enhancing channel estimation and data detection in multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Central to our approach is the incorporation of a Denoising Block, which comprises three meticulously designed deep neural networks (DNNs) tasked with accurately extracting noiseless embeddings from the received signal. Alongside, we develop the Correctness Classifier, a classification algorithm adept at distinguishing correctly detected data by leveraging the denoised received signal. By selectively utilizing these identified data symbols as additional pilot signals, we augment the available pilot signals for channel estimation. Our Denoising Block also enables direct data detection, rendering the system well-suited for low-latency applications. To enable model training, we propose a hybrid likelihood objective of the detected symbols. We analytically derive the gradients with respect to the hybrid likelihood, enabling us to successfully complete the training phase. When compared to other conventional methods, experiments and simulations show that the proposed data-aided channel estimator significantly lowers the mean-squared-error (MSE) of the estimation and thus improves data detection performance. Github repository link is https://***/Hamidreza-Hashempoor/5g-dataaided-channel-estimate. © 2023 CCBY.

关键词： Mean square error

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Locally Optimal Periods in Periodic Optically Transparent Two-Metal-Layered Refractive Metasurfaces for Outdoor-to-Indoor Communication

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1253-1257页

作者： Kim, Byeongjin Bang, Seungwoo Kim, Sunghyun Kwon, Doyle Kim, Seongkwan Oh, Jungsuek Seoul National University Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Seoul National University KT Corporation Seoul08826 Korea Republic of

This study presents a novel electromagnetic analysis and mathematical derivation for determining the locally optimal unit cell period that minimizes phase error losses in the macroscopic design of a periodic two-metal-layered refractive metasurface (TRM) for outdoor-to-indoor (O2I) communication. The TRM's periodicity is investigated for three main advantages: reducing design complexity, lowering lifecycle costs, and increasing area efficiency. The existence of locally optimal unit cell periods for each pair of incidence and refraction angles, along with a method to find them, was derived. Finally, a 6 G upper-mid optically transparent TRM with a locally optimal unit cell period was fabricated, and its path losses in both far-field and near-field scenarios were measured to validate the proposed technique. © 2002-2011 IEEE.

关键词： Refraction

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

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Role of ERP as an Integrative Intelligent System for Business Enterprises 1st

Role of ERP as an Integrative Intelligent System for Bus...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Yasser, Ibrahim Khalifa, Fahmi Electronics and Communications Engineering Department Mansoura University Mansoura35516 Egypt Department of Electrical and Computer Engineering Morgan State University BaltimoreMD21251 United States

ISBN: (纸本)9783031823763

Enterprise Resource Planning (ERP) systems are now vital for organizations to integrate and manage their core business processes. This paper provides an overview of ERP systems, their functionalities, benefits, and challenges. ERP systems are comprehensive software solutions that enable organizations to streamline and automate various functions, such as finance, human resources, supply chain management, manufacturing, and customer relationship management. By integrating these functions into a centralized system, ERP promotes data consistency, enhances operational efficiency, and facilitates informed decision-making. Successful ERP implementation requires a thorough understanding of organizational requirements, effective change management strategies, strong leadership, and ongoing support and training. Furthermore, we touch base on emerging trends in ERP systems, such as cloud-based ERP solutions, mobile access, and integration with emerging technologies like artificial intelligence and analytics. These developments offer organizations greater flexibility, scalability, and real-time insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Public relations

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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2025 Systems of Signals Generating and Processing in the Field of on Board communications, SOSG 2025

作者： Arinin, O.V. Bakhmach, D.M. Katsnelson, A.I. Kurbeteva, A.S. Yelizarov, A.A. Moscow Technical University of Communications and Informatics Moscow Russia Hse University Electronic Engineering Department Moscow Russia Hse University Computer Engineering Department Moscow Russia

ISBN: (纸本)9798331532635

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described. © 2025 IEEE.

关键词： Software packages

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd Science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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