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Semi-LDPC Convolutional Codes:Construction and Low-Latency Windowed List Decoding

Journal of communications and information Networks 2021年第4期6卷 411-419页

作者： Qianfan Wang Suihua Cai Li Chen Xiao Ma School of Electronics and Communication Engineering Sun Yat-sen UniversityGuangzhou 510006China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China

This paper presents a new coding scheme called semi-low-density parity-check convolutional code(semi-LDPC-CC),whose parity-check matrix consists of both sparse and dense sub-matrices,a feature distinguished from the conventional *** propose sliding-window list(SWL)decoding algorithms with a fixed window size of two,resulting in a low decoding latency but a competitive error-correcting *** performance can be predicted by upper bounds derived from the first event error probability and by genie-aided(GA)lower bounds estimated from the underlying LDPC block codes(LDPC-BCs),while the complexity can be reduced by truncating the list with a threshold on the difference between the soft metrics in the serial decoding *** results are presented to validate our analysis and demonstrate the performance advantage of the semi-LDPC-CCs over the conventional LDPC-CCs.

关键词： low-density parity-check convolutional codes(LDPC-CCs) spatially coupled LDPC(SC-LDPC)codes sliding-window list(SWL)decoding

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IoT ETEI: End-to-End IoT Device Identification Method

IoT ETEI: End-to-End IoT Device Identification Method

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IEEE Conference on Dependable and Secure Computing

作者： Feihong Yin Li Yang Yuchen Wang Jiahao Dai Xidian University Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

The past decades have seen the rapid development of Internet of Things (IoT) in various domains. Identifying the IoT devices connected to the network is a crucial aspect of network security. However, existing work on identifying IoT devices based on manually extracted features and prior knowledge, leading to low efficiency and identification accuracy. In this paper, we propose an automatic end-to-end IoT device identification method (IoT ETEI) based on CNN+BiLSTM deep learning model, which outperforms traditional methods from the perspective of overhead and identify accuracy. We demonstrate the effectiveness and flexibility of the proposed method by deploying IoT ETEI in the face of identifying IoT devices on public datasets with the accuracy rate over 99 %, even for IoT devices that use encryption protocols.

关键词： Deep learning Telecommunication traffic Feature extraction Encryption Object recognition Internet of Things security

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Joint Optimization Task Offloading Strategy for Mobile Edge Computing 2

Joint Optimization Task Offloading Strategy for Mobile Edge ...

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2nd IEEE International Conference on information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Tang, Xue Wen, Zhan Chen, Jiali Li, Yiquan Li, Wenzao College of Communication Engineer Chengdu University of Information Technology Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu611730 China

ISBN: (纸本)9781665428767

With increase of Internet of Things (IoT) devices, the burden of cloud computing servers is gradually increasing. Mobile Edge Computing (MEC), as an emerging solution, can relieve the pressure of computing and improve the quality of user experience. One of the critical challenges in MEC is to make effective use of communication and computing resources and select the task processing mode reasonably. In this paper, based on the scenario of a single mobile device (MD) to one mobile edge server (MES), we propose a Joint optimization method base on Discrete Binary Particle Swarm Optimization Algorithm (JOBPSO) for system load. The JOBPSO can optimize MD's task offloading, reduce delay and energy consumption. Finally, the simulation results indicate that the performance of JOBPSO in reducing system load is better than the other two traditional offloading methods. © 2021 IEEE.

关键词： Mobile edge computing

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Outlier Detection Method based on Improved K-means Clustering Algorithm 21

Outlier Detection Method based on Improved K-means Clusterin...

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5th International Conference on Electronic information technology and Computer Engineering, EITCE 2021

作者： Liu, Wenfen Wang, Nan Huang, Yuehua Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin Guangxi China School of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin Guangxi China College of Computer Science and Engineering Guilin University of Aerospace Technology Guilin Guangxi China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (纸本)9781450384322

Outlier detection is one of the important methods of data mining and analysis. The previous K-means outlier detection methods are easy to be misled by outliers in the clustering process and cannot detect outliers well. To solve this problem, we propose a method of outlier detection based on an improved K-means clustering algorithm. This method adaptively detects edge points (including outliers and edge points of other clusters) through the local threshold obtained from the distance distribution of the data points in the cluster to the centroid. The edge points are ignored when the centroid is updated to avoid the situation that the edge points mislead the centroid update. After completing the clustering, the farthest point in each cluster is put into the outlier cluster iteratively. When the average distance of the data in the cluster reaches stability or reaches the maximum number of iterations, the iteration ends. This method retains the ease of use of traditional K-means which accelerates the speed of clustering iteration and finds outliers accurately in data sets. Experimental results show that the algorithm can effectively detect outliers and have good results on multiple data sets. © 2021 ACM.

关键词： Anomaly detection

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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Computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

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Unicast Beamforming in the Presence of a Smart Reactive Jammer: A Stackelberg Game Approach

Unicast Beamforming in the Presence of a Smart Reactive Jamm...

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IEEE Workshop on Signal Processing Advances in Wireless communications (SPAWC)

作者： Bai Shi Huaizhong Shao Jingran Lin Zhikai Zhai School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China Peng Cheng Laboratory Shenzhen China

The malicious jamming attacks will disrupt legitimate public wireless communication systems, which has become a critical issue that must be considered in modern communication system design. This paper considers an anti-jamming unicast transmit beamforming problem with a smart reactive jammer. We model this problem as a Stackelberg game and formulate a bilevel optimization problem, in which the base station maximizes the minimum signal-to-interference-jamming-noise ratio (SIJNR) of legitimate users and the jammer minimizes the maximum SIJNR. This problem is quite challenging due to its hierarchical structure and nonconvexity. A penalty method is developed to solve this problem, which transforms it into a mathematical program with equilibrium constraint (MPEC) problem after implementing a tight semi-definite relaxation (SDR) method. The numerical results demonstrate its superior performance.

关键词：

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MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE TRANSFER

MODULATION SIGNAL RECOGNITION BASED ON SELECTIVE KNOWLEDGE T...

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GLOBECOM 2022 - 2022 IEEE Global communications Conference

作者： Huaji Zhou Xu Wang Jing Bai Zhu Xiao Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Artificial Intelligence Xidian University Xi'an China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781665435413

Deep learning-based recognition of radio signal modulation has emerged as a current research hotspot with significant practical potential. However, in practical applications, radio modulation signal data acquisition is complicated to obtain, and label samples are costly and time-consuming to meet the data dependence of deep learning. Transfer learning allows pretrained networks to be reused on large-scale datasets, making it a kind of solution for modulation signal recognition in limited data. The method of suppressing small singular values in the feature vector is employed in this paper to realize selective knowledge transfer for modulation signal recognition, while stochastic normalization is employed to replace the batch normalization layer to avoid over-fitting. We tested the stochastic normalized selective knowledge transfer method on the RML2016.10A and RML2016.04C datasets, with an SNR of 6dB signal samples, and found that it can lead to average growth of 15.77% and 10.32% when compared to direct training, and 6.1% and 2.73% when compared to vanilla fine-tuning. In addition, we check up under a variety of SNR conditions to ensure that our method is effective.

关键词： Training Deep learning Transfer learning Data acquisition Modulation Global communication Knowledge transfer

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Multimodal Knowledge Graph Completion Method Based on Integrated Modality Adversarial Training and Relation-Enhanced Attention Mechanism

Multimodal Knowledge Graph Completion Method Based on Integr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ruijia Zhang Zheng Dong Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Negative sampling (NS) is widely used in knowledge graph completion (KGC) to generate negative triples for contrastive learning during training. However, existing NS methods are not suitable when multimodal information is incorporated into KGC models. Due to their complex design, these methods are also inefficient. In this paper, we propose the integration of Modality-Aware Adversarial Training (IMAT) to generate higher-quality negative samples for Multimodal Knowledge Graph Completion (MMKGC), and introduce a Relation-Enhanced Cross-modal Attention (RECA) mechanism to evaluate bidirectional attention weights between multimodal features using relational information, thereby improving the model's ability to identify hard negative samples. Our approach represents a joint design of MMKGC models and training strategies, surpassing 16 recent MMKGC methods and achieving new state-of-the-art results on three public MMKGC benchmarks.

关键词： Training Federated learning Design methodology Computational modeling Semantics Focusing Knowledge graphs Contrastive learning Sampling methods Real-time systems

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Context-Aware Enhancement and Transformer Network for Image-Text Retrieval

Context-Aware Enhancement and Transformer Network for Image-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zheng Dong Ruijia Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Image-text matching is an important and challenging task in the field of multimedia analysis, aimed at bridging the semantic gap between visual content and language descriptions. Although this field has significant implications for enhancing cross-modal interaction, most previous work still faces challenges in accurately aligning images with text descriptions, especially when dealing with images containing rich semantic information. To address this issue, we propose a novel context-aware image-text matching model that extracts and summarizes visual region information aligned with multiple text descriptions from a single image. Specifically, we designed an adaptive context-aware self-attention module to extract representations of visual regions and text words. By controlling the complementary semantic relationships within each modality, our model can adaptively capture contextual information for each modality. We then introduce a Transformer-based encoder layer to extract features at multiple levels and aggregate region-level features into image-level features. Finally, fine-grained word-region alignment is conducted to match image features with corresponding text features. To evaluate the effectiveness of our method, we conducted extensive experiments on two benchmark datasets, Flickr30K and MS-COCO. The experimental results show that our model outper-forms several state-of-the-art baselines in image-text matching tasks, demonstrating the effectiveness and practicality of our model in handline cross-modal retrieval tasks.

关键词： Measurement Visualization Adaptation models Federated learning Semantics Transformers Feature extraction Faces Context modeling Cross modal retrieval

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