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A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

computers, Materials & Continua 2020年第1期62卷 157-177页

作者： Shaowei Weng Yiyun Liu Yunqing Shi Bo Ou Chunyu Zhang Cuiping Wang School of Information Engineering Guangdong University of TechnologyGuangzhou510006China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen518060China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07103 USA College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China School of Information Engineering Xizang Minzu UniversityXianyang712082China School of Electronic Information Qingdao UniversityQingdao266071China

This paper proposes a two-step general framework for reversible data hiding(RDH)schemes with controllable contrast *** first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity(EC),while the second step is used for increasing image *** the second step,some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization(HE),which would lead to the image contrast ***,for HE,the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are ***,in our proposed framework,contrast over-enhancement is avoided by controlling the degree of contrast *** the second step can only provide a small amount of data due to controlled contrast enhancement,the first one helps to achieve a large amount of data without degrading visual *** RDH method which can achieve high EC while preserve good visual quality,can be selected for the first *** fact,Gao et al.’s method is a special case of our proposed *** addition,two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.

关键词： Reversible data hiding controlled contrast enhancement general framework PEE-based RDH method IT-based RDH method

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Transclaw U-net: Claw U-net with transformers for medical image segmentation

arXiv

引用

arXiv 2021年

作者： Yao, Chang Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Institute of Image Communication and Information Processing Shanghai Jiao Tong University Department of Electrical Computer and Biomedical Engineering Ryerson University

In recent years, computer-aided diagnosis has become an increasingly popular topic. Methods based on convolutional neural networks have achieved good performance in medical image segmentation and classification. Due to the limitations of the convolution operation, the long-term spatial features are often not accurately obtained. Hence, we propose a TransClaw U-Net network structure, which combines the convolution operation with the transformer operation in the encoding part. The convolution part is applied for extracting the shallow spatial features to facilitate the recovery of the image resolution after upsampling. The transformer part is used to encode the patches, and the self-attention mechanism is used to obtain global information between sequences. The decoding part retains the bottom upsampling structure for better detail segmentation performance. The experimental results on Synapse Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net is better than other network structures. The ablation experiments also prove the generalization performance of TransClaw U-Net. © 2021, CC BY.

关键词： Convolution

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Event-Triggered Differentially Private Average Consensus for Multi-agent Network

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 75-83页

作者： Aijuan Wang Xiaofeng Liao Haibo He the College of Computer Science Chongqing University the School of Electronics and Information Engineering Southwest University the Department of Electrical Computer and Biomedical EngineeringUniversity of Rhode Island Kingston IEEE Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems(MANSs). Based on the MANSs,a new distributed differentially private consensus algorithm(DPCA) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.

关键词： Average consensus differentially private event-triggered communication multi-agent network systems (MANSs)

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Research on Sentiment Dictionary Based on Sentiment Analysis in News Domain 7

Research on Sentiment Dictionary Based on Sentiment Analysis...

引用

7th IEEE International Conference on Big Data Security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2021

作者： Gao, Yihang Su, Peng Zhao, Hui Qiu, Meikang Liu, Meiqin Henan International Joint Laboratory of Theories Key Technologies on Intelligence Networks Henan University Kaifeng China Intelligent Data Processing Engineering Research Center of Henan Province Henan University Kaifeng China Educational Information Technology Laboratory Henan University Kaifeng China Texas Am University-Commerce Department of Computer Science CommerceTX75428 United States College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665439275

The known Chinese sentiment dictionary has universal applicability, lacks the expansion of proprietary domains, and lacks support for network words. This paper proposes a new approach and applies it to the Internet news sentiment analysis text provided by the CCF Big Data Computing Intelligence Contest. First, the corpus is segmented in Chinese to obtain candidate sentiment words that have not appeared in the sentiment dictionary. The candidate sentiment words are calculated in different sentimental texts. The proportion of appearance is obtained, and the emotional tendency of candidate emotional words is obtained, and then it is merged with the emotional vocabulary ontology database to obtain the expanded emotional dictionary. Experiments show that the expanded sentiment dictionary is better than the known Chinese sentiment vocabulary ontology database in the above corpus. © 2021 IEEE.

关键词： Sentiment analysis

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Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots

arXiv

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arXiv 2023年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Department of Automation Tsinghua University Beijing100084 China The School of Intelligent Systems Science and Engineering The GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation

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Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks

arXiv

引用

arXiv 2021年

作者： Théate, Thibaut Wehenkel, Antoine Bolland, Adrien Louppe, Gilles Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Liège Belgium Information Processing and Communications Laboratory Institut Polytechnique de Paris Paris France

The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectation. A distributional RL algorithm may be characterised by two main components, namely the representation of the distribution together with its parameterisation and the probability metric defining the loss. The present research work considers the unconstrained monotonic neural network (UMNN) architecture, a universal approximator of continuous monotonic functions which is particularly well suited for modelling different representations of a distribution. This property enables the efficient decoupling of the effect of the function approximator class from that of the probability metric. The research paper firstly introduces a methodology for learning different representations of the random return distribution (PDF, CDF and QF). Secondly, a novel distributional RL algorithm named unconstrained monotonic deep Q-network (UMDQN) is presented. To the authors’ knowledge, it is the first distributional RL method supporting the learning of three, valid and continuous representations of the random return distribution. Lastly, in light of this new algorithm, an empirical comparison is performed between three probability quasi-metrics, namely the Kullback-Leibler divergence, Cramer distance, and Wasserstein distance. The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Cooperative Backscatter NOMA with Imperfect SIC: Towards Energy Efficient Sum Rate Maximization in Sustainable 6G Networks

arXiv

引用

arXiv 2022年

作者： Ahmed, Manzoor Ali, Zain Khan, Wali Ullah Waqar, Omer Asif, Muhammad Khan, Abd Ullah Javed, Muhammad Awais Al-Wesabi, Fahd N. College of Computer Science and Technology Qingdao University Qingdao266071 China Department of Electrical and Computer Engineering University of California Santa CruzCA United States University of Luxembourg Luxembourg City1855 Luxembourg BC Canada Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China Department of Computer Science National University of Science and Technology Balochistan Campus Quetta Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia Faculty of Computer and IT Sana'a University Yemen

The combination of backscatter communication with non-orthogonal multiple access (NOMA) has the potential to support low-powered massive connections in upcoming sixth-generation (6G) wireless networks. More specifically, backscatter communication can harvest and use the existing RF signals in the atmosphere for communication, while NOMA provides communication to multiple wireless devices over the same frequency and time resources. This paper has proposed a new resource management framework for backscatter-aided cooperative NOMA communication in upcoming 6G networks. In particular, the proposed work has simultaneously optimized the base station's transmit power, relaying node, the reflection coefficient of the backscatter tag, and time allocation under imperfect successive interference cancellation to maximize the sum rate of the system. To obtain an efficient solution for the resource management framework, we have proposed a combination of the bisection method and dual theory, where the sub-gradient method is adopted to optimize the Lagrangian multipliers. Numerical results have shown that the proposed solution provides excellent performance. When the performance of the proposed technique is compared to a brute-forcing search technique that guarantees optimal solution however, is very time-consuming, it was seen that the gap in performance is actually 0%. Hence, the proposed framework has provided performance equal to a cumbersome brute-force search technique while offering much less complexity. The works in the literature on cooperative NOMA considered equal time distribution for cooperation and direct communication. Our results showed that optimizing the time-division can increase the performance by more than 110% for high transmission powers. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

arXiv

引用

arXiv 2022年

作者： Khan, Wali Ullah Ali, Zain Lagunas, Eva Mahmood, Asad Asif, Muhammad Ihsan, Asim Chatzinotas, Symeon Ottersten, Björn Dobre, Octavia A. University of Luxembourg Luxembourg City1855 Luxembourg Department of Electrical and Computer Engineering University of California Santa Cruz United States Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen Univerisyt Guangdong Shenzhen China The School of Computer Science and Electronic Engineering Bangor University BangorLL57 1UT United Kingdom The Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt Karush–Kuhn–Tucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this wo

关键词： Spectrum efficiency

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

引用

SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

引用

arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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