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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Block Sparse Vector Codes for Ultra-Reliable and Low-Latency Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Zhang, Yanfeng Zhu, Xu Liu, Yujie Fan, Xi'an Guan, Yong Liang Wong, M. L. Dennis Lau, Vincent K. N. Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan China Harbin Institute of Technology School of Information Science and Technology Shenzhen518055 China Guangxi Normal University Guangxi Key Laboratory of Brain Inspired Computing and Intelligent Chips Guilin541001 China Shenzhen Municipal Key Laboratory of AIoT Communications Shenzhen518055 China Nanyang Technological University Continental-NTU Corporate Lab Singapore Dongguan University of Technology School of Computer Science and Technology Dongguan China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels. © 1972-2012 IEEE.

关键词： Fading channels

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Relationship between number of annotations and accuracy in segmentation of the erector spinae muscle using Bayesian U-Net in torso CT images

Relationship between number of annotations and accuracy in s...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Wakamatsu, Yuichi Kamiya, Naoki Zhou, Xiangrong Hara, Takeshi Fujita, Hiroshi Department of Media Informatics Graduate School of Information Science and Technology Aichi Prefectural University 1522-3 Ibaragabasama Nagakute-shi Aichi480-1198 Japan Department of Information Systems Graduate School of Information Science and Technology Aichi Prefectural University 1522-3 Ibaragabasama Nagakute-shi Aichi480-1198 Japan Department of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University 1-1 Yanagido Gifu-shi Gifu501-1193 Japan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Supervised learning for image segmentation requires annotated images. However, image annotation has the problem that it is time-consuming. This problem is particularly significant in the erector spinae muscle segmentation due to the large size of the muscle. Therefore, this study considers the relationship between the number of annotated images used for training and segmentation accuracy of the erector spinae muscle in torso CT images. We use Bayesian U-Net, which has shown high accuracy in thigh muscle segmentation, for the segmentation of the erector spinae muscle. In the network training, we limit the number of slices for each case and the number of cases to 100%, 50%, 25%, and 10%. In the experiment, we use 30 torso CT images, including 6 cases for the test dataset. Experimental results are evaluated by the mean Dice value of the test dataset. Using 100% of the slices per case, the segmentation accuracy with 100%, 50%, 25%, and 10% of the cases was 0.934, 0.927, 0.926, and 0.890, respectively. On the other hand, using 100% of the cases, the segmentation accuracy with 100%, 50%, 25%, and 10% of the slices per case was 0.934, 0.934, 0.933, and 0.931, respectively. Furthermore, the segmentation accuracy with 100% of the cases and 10% of the slices per case was higher than that of the previous method. We showed that it is feasible to achieve high segmentation accuracy with a limited number of annotated images by selecting several slices from a limited number of cases for training. © 2021 SPIE.

关键词： Image segmentation

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Joint Computation Offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks

arXiv

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arXiv 2024年

作者： Van Chien, Trinh Cong, Mai Dinh Luong, Nguyen Cong Do, Tri Nhu Kim, Dong In Chatzinotas, Symeon The School of Information and Communication Technology Hanoi University of Science and Technology Viet Nam The Faculty of Computer Science Phenikaa University Hanoi Viet Nam The Department of Electrical Engineering Polytechnique Montréal MontréalQCH3T 1J4 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg

In this paper, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a ground target based on an autocorrelation function. The performance of the velocity estimation that is represented by Cramer-Rao lower bound (CRB) depends on the length of the offloading bit sequence and the UAV’s location. Thus, we jointly optimize the task size for offloading and the UAV’s location to minimize the overall computation latency and the CRB of the mean square error for velocity estimation subject to the UAV’s budget. The problem is non-convex, and we propose a genetic algorithm to solve it. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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NFT-Based Blockchain-Oriented Security Framework for Metaverse Applications

arXiv

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arXiv 2023年

作者： Manzoor, Khadija Noor, Umara Rashid, Zahid Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University 1 Gwanak-Ro Gwanak-Gu Seoul08826 Korea Republic of

The Metaverse is rapidly evolving, bringing us closer to its imminent reality. However, the widespread adoption of this new automated technology poses significant research challenges in terms of authenticity, integrity, interoperability, and efficiency. These challenges originate from the core technologies underlying the Metaverse and are exacerbated by its complex nature. As a solution to these challenges, this paper presents a novel framework based on Non-Fungible Tokens (NFTs). The framework employs the Proof-of-Stake consensus algorithm, a blockchain-based technology, for data transaction, validation, and resource management. PoS efficiently consume energy and provide a streamlined validation approach instead of resource-intensive mining. This ability makes PoS an ideal candidate for Metaverse applications. By combining NFTs for user authentication and PoS for data integrity, enhanced transaction throughput, and improved scalability, the proposed blockchain mechanism demonstrates noteworthy advantages. Through security analysis, experimental and simulation results, it is established that the NFT-based approach coupled with the PoS algorithm is secure and efficient for Metaverse applications. © 2023, CC BY.

关键词： Blockchain

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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International Conference on Data engineering

作者： Zuowei Zhang Zhe Liu Zongfang Ma Yiru Zhang Hao Wang Institut de Recherche en Informatique et Systèmes Aléatoires University of Rennes 1 France School of Information and Control Engineering Xi’an University of Architecture and Technology China Department of Computer Science Norwegian University of Science and Technology Norway

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets.

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Block-Sparse Tensor Recovery

arXiv

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arXiv 2024年

作者： Lu, Liyang Wang, Zhaocheng Gao, Zhen Chen, Sheng Poor, H. Vincent The Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China The Tsinghua Shenzhen International Graduate School Shenzhen518055 China Beijing Institute of Technology Beijing100081 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-sparse tensor recovery as a theoretical foundation, where concepts measuring holistic mutual incoherence property (MIP) of the measurement matrix set are defined. A representative algorithm based on the orthogonal matching pursuit (OMP) framework, called tensor generalized block OMP (T-GBOMP), is applied to the theoretical framework elaborated for analyzing both noiseless and noisy recovery conditions. Specifically, we present the exact recovery condition (ERC) and sufficient conditions for establishing it with consideration of different degrees of restriction. Reliable reconstruction conditions, in terms of the residual convergence, the estimated error and the signal-to-noise ratio bound, are established to reveal the computable theoretical interpretability based on the newly defined MIP, which we introduce. The flexibility of tensor recovery is highlighted, i.e., the reliable recovery can be guaranteed by optimizing MIP of the measurement matrix set. Analytical comparisons demonstrate that the theoretical results developed are tighter and less restrictive than the existing ones (if any). Further discussions provide tensor extensions for several classic greedy algorithms, indicating that the sophisticated results derived are universal and applicable to all these tensorized variants. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Combined Envelope Scaling with Modified SLM Method for PAPR Reduction in OFDM-Based VLC Systems

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Journal of Optical Communications 2023年第s1期44卷 S1175-S1180页

作者： Touhami, Ridha Slimani, Djamal Abdulkafi, Ayad Atiyah Hussein, Yaseein Soubhi Alias, Mohamad Yusoff University of Ferhat Abbas Setif 1 Setif19000 Algeria Tikrit University Salahadin Tikrit43001 Iraq Department of Information Technology & Computer Science Ahmed Bin Mohmmed College Doha Qatar Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia

Orthogonal frequency-division multiplexing technique (OFDM) has been adopted widely as a modulation technique for radio frequency (RF) and optical communication systems such as visible-light communication (VLC) due to its high spectral efficiency and low-complexity implementation. VLC-OFDM is recommended in 5 G mobile communication. However, VLC-OFDM suffer from the high peak to-average power ratio (PAPR). In this paper, a modified selective mapping (MSLM) method is applied to the proposed system followed by a new envelope scaling process for further reductions in PAPR of VLC-OFDM system. Simulation results show that the proposed method reduces the PAPR by about 6.8 and 1.7 dB comparing with the original signal and the traditional SLM with a number of rotation vector U = 8, respectively. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Peak to average power ratio

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Monocular 3d Human Pose Estimation Networks for Steady and Coherent Humanoid-Model Driving

SSRN

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SSRN 2024年

作者： Chan, Din-Yuen Yang, Wei-Jong Liou, Jhih-Siang Mao, Songan Department of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Computer Science and Information Engineering National Chiayi University No.300 Syuefu Rd. Chiayi City60004 Taiwan Department of Electrical Engineering National Cheng Kung University 70101 No.1-3 Daxue Rd. East Dist. Tainan City701 Taiwan Qualcomm Incorporated San Diego United States

In this paper, we propose a 3D pose driving (3PD) system containing 2D skeleton pose estimation (2D SPE), 3D association pose estimation (3D APE) networks and 3D angle matched driving (3D AMD) engine. The 2D SPE network consecutively predicts the joint coordinates of human 2D pose in frame bases. Then, the 3D APE network associates the successive 2D poses to predict the 3D skeletons of dynamic poses by temporal convolutional networks. The experimental results demonstrate that the integration of 2D SPE and 3D APE networks can attain the accurate 3D human pose estimation without any auxiliary training for occluded joints. And, the 3D AMD engine can apply the estimated dynamic 3D skeletons to drive a 3D avatar in steady synchronization with the user’s motions. Compared to state-of-the-art network, the proposed 3D human pose estimation network with only 4-frame latency can attain the higher accuracy with regular architecture. © 2024, The Authors. All rights reserved.

关键词： Engines

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Indonesian question classification using feature extraction and selection approach on coarse and fine taxonomy

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AIP Conference Proceedings 2023年第1期2654卷

作者： Irfandy Thalib Widyawan Indah Soesanti 1Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia

The question classification phase is considered as one of the most significant phases in a Question Answering System to help the system find or construct an accurate answer which results in an improvement of the quality of question answering systems. In this work, we proposed a question classification into a 2-layer taxonomy called Coarse-Fine taxonomy. This is the first work for Indonesian question classification into Coarse-Fine taxonomy. We employed a feature selection and machine learning classification using Support Vector Machine algorithm. In the feature selection, we found that Unigram+TFIDF+Word Shape is the best combination that reached the highest accuracy with 92.9% in Coarse category. On the other hand, the combination of Unigram+TFIDF+WH word features is the best combination for Fine category with 79.3% accuracy.

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