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Content-aware robust semantic transmission of images over wireless channels with GANs

Digital Communications and Networks 2024年

作者： Xuyang Chen Daquan Feng Qi He Yao Sun Gaojie Chen Xiang-Gen Xia College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu 611731 China James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UK 5GIC & 6GIC Institute for Communication Systems University of Surrey Guildford GU2 7XH UK Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA

Semantic Communication (SemCom) can significantly reduce the transmitted data volume and keep robustness. Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly, rather than achieving precise bit-by-bit reconstruction. Existing image SemCom systems directly perform semantic encoding and decoding on the entire image, which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise. To this end, we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks (GANs). Specifically, the accurate semantics of the image are extracted by the semantic encoder, and divided into two parts for different downstream tasks: Regions of Interest (ROI) and Regions of Non-Interest (RONI). By reducing the quantization accuracy of RONI, the amount of transmitted data volume is reduced significantly. During the transmission process of semantics, a Signal-to-Noise Ratio (SNR) is randomly initialized, enabling the model to learn the average noise distribution. The experimental results demonstrate that by reducing the quantization level of RONI, transmitted data volume is reduced up to 60.53% compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks. Moreover, our model exhibits increased robustness with variable SNRs.

关键词： Semantic communication GANs Image transmission ROI

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A novel ensemble XGBoost and deep Q-network for pregnancy risk prediction on multi-class imbalanced datasets

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ICT Express 2025年

作者： Kurnianingsih Nobukawa, Sou Widyawati, Melyana Nurul Pramana, Cipta Aji, Nurseno Bayu Thohari, Afandi Nur Aziz Hendrawati, Dwiana Sato-Shimokawara, Eri Kubota, Naoyuki Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Central Java Semarang 50275 Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Chiba Narashino 275-0016 Japan Poltekkes Kemenkes Semarang Tirto Agung Pedalangan Banyumanik Central Java Semarang 50268 Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Fatmawati Tembalang Central Java Semarang 50272 Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Central Java Semarang 50275 Indonesia Faculty of Systems Design Tokyo Metropolitan University Tokyo 1910065 Japan

Addressing imbalanced data is essential for accurate prediction. We propose a novel ensemble method of XGBoost and deep Q-learning networks (DQN) for pregnancy risk prediction. First, we train the majority class utilizing XGBoost. We subsequently utilize DQN to train the minority class into binary classifications. Finally, we use the trained models from DQN and XGBoost in ensemble learning to generate the final classification results. The XGBoost-DQN model achieves high performance with 0.9819 in precision, recall, F1-score, and accuracy, outperforming several baseline classifiers on private data from 5313 pregnant women in Indonesia and showing superior results on public datasets. © 2025 The Author(s)

关键词： Deep Q-network Ensemble learning Imbalanced dataset Pregnancy risk XGBoost tree

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Preface

Studies in Big Data

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Studies in Big Data 2021年 73卷 v-vii页

作者： Chakraborty, Chinmay Banerjee, Amit Kolekar, Maheshkumar H. Garg, Lalit Chakraborty, Basabi Department of Electronics and Communication Engineering Birla Institute of Technology Ranchi Mesra India Department of Electrical Engineering Indian Institute of Technology Patna Patna India Faculty of Software and Information Science Iwate Prefectural University Takizawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta Msida Malta

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NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

arXiv

引用

arXiv 2022年

作者： Guo, Yulan Wang, Longguang Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Chen, Liangyu Chu, Xiaojie Yu, Wenqing Jin, Kai Wei, Zeqiang Guo, Sha Yang, Angulia Zhou, Xiuzhuang Guo, Guodong Xiao, Huaxin Yan, Shen Liu, Yuxiang Cai, Hanxiao Dai, Bin Peng, Feiyue Cao, Pu Nie, Yang Yang, Lu Song, Qing Hu, Xiaotao Xu, Jun Xu, Mai Jing, Junpeng Deng, Xin Xing, Qunliang Qiao, Minglang Guan, Zhenyu Guo, Wenlong Peng, Chenxu Chen, Zan Chen, Junyang Li, Hao Chen, Junbin Li, Weijie Yang, Zhijing Li, Gen Li, Aijin Sun, Lei Zhang, Dafeng Liu, Shizhuo Zhang, Jiangtao Qu, Yanyun Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Liang, Qiaohui Lin, Jianxin Wang, Yijun Yin, Lianying Zhang, Rongju Zhao, Wei Xiao, Peng Xu, Rongjian Zhang, Zhilu Zuo, Wangmeng Guo, Hansheng Gao, Guangwei Zeng, Tieyong Kim, Joohyeok Kim, HyeonA Park, Eunpil Sim, Jae-Young Pi, Huicheng Zhang, Shunli Zhai, Jucai Zeng, Pengcheng Liu, Yang Ma, Chihao Huang, Yulin Chen, Junying National University of Defense Technology China The Chinese University of Hong Kong Hong Kong The University of Sydney Australia University of Würzburg ETH Zürich Switzerland MEGVII Technology China Peking University China Bigo Technology Pte. Ltd Singapore Smart Healthcare Innovation Lab Beijing University of Posts and Telecommunications China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Head of Institute of Deep Learning Baidu Research College of Systems Engineering National University of Defense Technology China College of Liberal Arts and Sciences National University of Defense Technology China Pattern Recognition and Intelligent Vision Lab Beijing University of Posts and Telecommunications China College of Computer Science Nankai University Tianjin China School of Statistics and Data Science Nankai University Tianjin Singapore Beihang University China Zhejiang University of Technology China Guangdong University of Technology China Tencent OVBU SRC-B Xiamen University China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan College of Computer Science and Electronic Engineering Hunan University China Harbin Institude of Technology China The Chinese University of Hong Kong Hong Kong Nanjing University of Posts and Telecommunications China Department of Electrical Engineering Ulsan National Institute of Science and Technology Korea Republic of Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology Korea Republic of Beijing Jiaotong University China City University of Hong Kong Hong Kong South China University of Technology China

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR. Copyright © 2022, The Authors. All rights reserved.

关键词： Stereo image processing

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Characterizing and detecting freezing of gait using multi-modal physiological signals

arXiv

引用

arXiv 2020年

作者： Wang, Ying Beuving, Floris Nonnekes, Jorik Cohen, Mike X. Long, Xi Aarts, Ronald M. van Wezel, Richard Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Academic Center for Epileptology Kempenhaeghe Heeze Netherlands Donders Institute for Brain Cognition and Behaviour Department of Rehabilitation Radboud University Medical Centre Nijmegen Netherlands Department of Rehabilitation Sint Maartenskliniek Nijmegen Netherlands Philips Research Eindhoven Netherlands Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Freezing-of-gait a mysterious symptom of Parkinson’s disease and defined as a sudden loss of ability to move forward. Common treatments of freezing episodes are currently of moderate efficacy and can likely be improved through a reliable freezing evaluation. Basic-science studies about the characterization of freezing episodes and a 24/7 evidence-support freezing detection system can contribute to the reliability of the evaluation in daily life. In this study, we analyzed multi-modal features from brain, eye, heart, motion, and gait activity from 15 participants with idiopathic Parkinson’s disease and 551 freezing episodes induced by turning in place. Statistical analysis was first applied on 248 of the 551 to determine which multi-modal features were associated with freezing episodes. Features significantly associated with freezing episodes were ranked and used for the freezing detection. We found that eye-stabilization speed during turning and lower-body trembling measure significantly associated with freezing episodes and used for freezing detection. Using a leave-one-subject-out cross-validation, we obtained a sensitivity of 97% ± 3%, a specificity of 96% ± 7%, a precision of 73% ± 21%, a Matthews correlation coefficient of 0.82 ± 0.15, and an area under the Precision-Recall curve of 0.94 ± 0.05. According to the Precision-Recall curves, the proposed freezing detection method using the multi-modal features performed better than using single-modal features. Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors

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Noise bias compensation for reconstructed HDR images in two layer coding

IEEJ Transactions on Electronics, Information and Systems

引用

IEEJ Transactions on Electronics, Information and systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The institute of electrical Engineers of Japan.

关键词： Image denoising

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TCAD modeling for SiGe HBT THz detectors

arXiv

引用

arXiv 2019年

作者： Liu, Xueqing Suarez, John Shur, Michael Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Engineering Widener University ChesterPA19013 United States

Terahertz (THz) response of transistor and integrated circuit yields important information about device parameters and has been used for distinguishing between working and defective transistors and circuits. Using a TCAD model for SiGe HBTs we simulate their current-voltage characteristics and their response to sub-THz (300 GHz) radiation. Applying different mixed mode schemes in TCAD, we simulated the dynamic range of the THz response for SiGe HBTs and showed that it is comparable with that of the TeraFET detectors. The HBT response to the variations of the detector design parameters are investigated at different frequencies with the harmonic balance simulation in TCAD. These results are useful for the physical design and optimization for the HBT THz detectors and for the identification of faulty SiGe HBT and Si BiCMOS circuits using sub-THz or THz scanning. Copyright © 2019, The Authors. All rights reserved.

关键词： Electronic design automation

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Adaptive Resonant Energy Realization in FB-ZCS DC-DC Converter Using Dual-Capacitor Circuit

Adaptive Resonant Energy Realization in FB-ZCS DC-DC Convert...

引用

IEEE Energy Conversion Congress and Exposition

作者： Rohit Suryadevara Leila Parsa Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Department of Electrical Engineering University of California Santa Cruz Santa Cruz CA USA

This paper presents a current-fed full-bridge zero-current-switching (FB-ZCS) dc-dc converter with adaptive resonant energy, realized using dual-capacitor resonant circuit. In existing FB-ZCS converters, transformer leakage inductance and a resonant capacitor form the L-C resonant circuit to achieve smooth current commutation and soft-switching. Two types of resonant capacitor configurations are proposed in literature: shunt-connected across the transformer-secondary and series-connected with the transformer-primary. Shunt-connected capacitor has the drawbacks of constant resonant energy and large duty-cycle loss. While series-connected capacitor realizes adaptive resonant energy and reduces duty-cycle loss, it suffers from increased peak-voltage stress for primary-side components and insufficient resonant capacitor voltage at light-loads. This paper presents a dual-capacitor resonant circuit to realize adaptive resonant energy and address the aforementioned limitations. Operation of proposed converter is explained and validated through simulation and experimental results.

关键词： Capacitors Zero current switching Mathematical model RLC circuits Inductance Inductors Loading

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Long-lived Zone-boundary Magnons in an Antiferromagnet

arXiv

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arXiv 2025年

作者： Choe, Jeongheon Lujan, David Ye, Gaihua Nnokwe, Cynthia Ma, Bowen He, Jiaming Gao, Frank Y. Nunley, T. Nathan Leonardo, Aritz Arruabarrena, Mikel Ayuela, Andres Zhou, Jianshi Rodriguez-Vega, Martin Fiete, Gregory A. He, Rui Li, Xiaoqin Department of Physics Center for Complex Quantum Systems The University of Texas at Austin AustinTX78712 United States Center for Dynamics and Control of Materials The University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States EHU Quantum Center University of the Basque Country UPV/EHU Bilbao Spain Donostia20018 Spain Department of Physics Northeastern University BostonMA02115 United States Quantum Materials and Sensing Institute Northeastern University BurlingtonMA01803 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Antiferromagnetic (AFM) insulators exhibit many desirable features for spintronic applications such as fast dynamics in the THz range and robustness to fluctuating external fields. However, large damping typically associated with THz magnons presents a serious challenge for THz magnonic applications. Here, we report long-lived short-wavelength zone boundary magnons in the honeycomb AFM insulator CoTiO3, recently found to host topological magnons. We find that its zone-boundary THz magnons exhibit longer lifetimes than its zone-center magnons. This unusual momentum-dependent long magnon lifetime originates from several factors including the antiferromagnetic order, exchange anisotropy, a finite magnon gap, and magnon band dispersion. Our work suggests that magnon-magnon interaction may not be detrimental to magnon lifetimes and should be included in future searches for topological magnons. © 2025, CC BY.

关键词： Antiferromagnetic materials

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Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing systems

arXiv

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arXiv 2020年

作者： Chen, Xianfu Wu, Celimuge Chen, Tao Liu, Zhi Zhang, Honggang Bennis, Mehdi Liu, Hang Ji, Yusheng VTT Technical Research Centre of Finland Finland Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Department of Mathematical and Systems Engineering Shizuoka University Japan Zhejiang University Hangzhou China Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science Catholic University of America United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan

This paper studies the problem of information freshness-aware task offloading in an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). A third-party real-time application service provider provides computing services to the subscribed mobile users (MUs) with the limited communication and computation resources from the InP based on a long-term business agreement. Due to the dynamic characteristics, the interactions among the MUs are modelled by a non-cooperative stochastic game, in which the control policies are coupled and each MU aims to selfishly maximize its own expected long-term payoff. To address the Nash equilibrium solutions, we propose that each MU behaves in accordance with the local system states and conjectures, based on which the stochastic game is transformed into a single-agent Markov decision process. Moreover, we derive a novel online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks for each MU to approximate the Q-factor and the post-decision Q-factor. Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics. Numerical experiments examine the potentials of the proposed scheme in balancing the age of information and the energy consumption. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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