检索结果-内蒙古大学图书馆

6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021

作者： Bin, Jang Wei, Wu Xilin, Wang Lei, Shen 36th Research Institute of China Electronics Technology Group Corporation Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9780738126043

Aiming at the problem of mixed signal recognition, this paper proposes a modulation recognition algorithm based on multidimensional features of all digital receiver. Use the features of demodulated signal, such as constellation quadrant distribution, frequency tracking distribution distance and baseband data distribution distance, realized the recognition of A/D mixed signal in dense environment. Theoretical analysis and simulation results show that this algorithm has better performance than the widely used modulation recognition algorithm based on cyclic spectrum and higher-order spectrum. It has fewer requirements for prior information and synchronization, and it is easy to be implemented. © 2021 IEEE.

关键词： Modulation

来源：评论

学校读者我要写书评

暂无评论

A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State

引用

Computers, Materials & Continua 2021年第8期68卷 2651-2663页

作者： Zhen-Zhen Li Zi-Chen Li Xiu-Bo Chen Zhiguo Qu Xiaojun Wang Haizhu Pan School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China School of Electronic Engineering Dublin City UniversityDublin9Ireland College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China

In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental ***,we propose a more practical quantum network coding scheme with the assist of non-maximally entangled *** this paper,a practical quantum network coding protocol over grail network is proposed,in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly *** achievable rate region,security and practicability of the proposed protocol are discussed and *** practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.

关键词： Quantum network coding non-maximally entangled state quantum grail network practical protocol

来源：评论

学校读者我要写书评

暂无评论

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Signal Demodulation Algorithm Based on Generative Adversarial Networks 3

A Signal Demodulation Algorithm Based on Generative Adversar...

引用

3rd IEEE International Conference on Civil Aviation Safety and information technology, ICCASIT 2021

作者： Lijun, Wang Minghong, Zhang Yu, Han Lei, Shen Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665425186

In recent years, extensive research has been done to obtain better demodulation performance by combining signal demodulation with deep learning algorithms, such as convolutional neural networks (CNN). However, the demodulation performance of CNN is not much improved compared with traditional coherent demodulation algorithms. This paper proposes a signal demodulation algorithm based on generative adversarial networks (GAN). The proposed GAN-based demodulation network uses the confrontation characteristics between the generator and the discriminator in GAN to greatly improve the demodulation ability of the signal. Numerical results show that the demodulation performance is better than the coherent demodulation algorithm and the CNN-based demodulation algorithm. © 2021 IEEE

关键词： Demodulation

来源：评论

学校读者我要写书评

暂无评论

Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

引用

China Communications 2020年第10期17卷 157-169页

作者： Huaji Zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic *** this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian ***,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into ***,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled *** present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal *** to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic *** validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal *** experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

关键词： generative adversarial network semi-supervised learning electromagnetic signal classification end-to-end classification weighted loss function

来源：评论

学校读者我要写书评

暂无评论

IR Net: information restriction and information recovery for accurate binary neural networks

引用

Neural Computing and Applications 2023年第19期35卷 14449-14464页

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei China Intelligent Manufacturing Institute of Hefei University of Technology Hefei China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but they cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors and suffer from a tradeoff between accuracy and efficiency. In contrast, considering information loss and the mismatch between model capacity and input information quantity caused by network binarization, we propose information Restriction and information Recovery Network (IR $$^2$$ Net) to stimulate the potential of BNNs and achieve improved network accuracy by restricting the input information and recovering feature information. The proposed approach includes (1) information restriction, which evaluates the feature information extracted from the input by a BNN, discards some of the information it cannot focus on, and reduces the amount of the input information to match the model capacity; and (2) information recovery: due to the information loss incurred during forward propagation, the extracted feature information of the network is not sufficient for supporting accurate classification. Shallow feature maps with richer information are selected, and these feature maps are fused with the final feature maps to recover the extracted feature information and further enhance the model capacity to match the amount of input information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better tradeoff between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with a $$\sim$$ 10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net .

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Electromagnetic Signal Classification Method Inspired by the Visual Characteristics of Biological Dual-Channel

SSRN

引用

SSRN 2022年

作者： Dai, Shaogang Zhou, Huaji Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China School of communication engineering Hangzhou Dianzi University Hangzhou310018 China

Automatic modulation classification of electromagnetic signals has an important role in the field of civil and military signal processing, for which research is mainly based on deep learning methods. However, existing deep learning models generally suffer from problems such as network redundancy and non-interpretability. Since the biological visual system has extraordinary visual signal recognition ability, and the system has been widely studied at the micro (neuron), mesoscopic (visual pathway) and macro (visual cortex) levels, this paper constructs a biological dual-channel visual neural network (BDVNN) inspired by the visual characteristics of biological dual-channel for efficient classification of the electromagnetic signals. The input part of BDVNN simulates the dual-channel structure of lateral geniculate nucleus (LGN), building a dual-channel parallel module composed of convolution kernels of different scales to extract the overall coarse features and local fine features from the input signal, respectively. The dual-channel module internally uses atrous convolution to simulate the sparse coding characteristics of LGN, aiming to reduce the model parameters as well as increase the recognition efficiency. The middle part of BDVNN simulates the fusion of primary visual cortex receptive fields to fuse coarse features with fine features, which facilitates the further extraction of advanced features in subsequent layers of the network. In the setting of the number of network layers, the BDVNN model refers to the biological visual pathway hierarchy to meet the lightweight characteristics so as to significantly reduce the training cost. For the RadioML2016.10a dataset, the recognition accuracy of BDVNN is 9.73%, 92.45% and 93.64% under the signal-to-noise ratio of -20dB, 2dB and 18dB, respectively, with 2%~17% improvement over the rest of mainstream algorithms. For the ACARS dataset, the average recognition accuracy of BDVNN for 20 signals is 95.2%, which is also bett

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Ultra-Wideband Spectrum Monitoring Using Software Defined Radio

Ultra-Wideband Spectrum Monitoring Using Software Defined Ra...

引用

International Conference on Communication technology (ICCT)

作者： Huijie Zhu Binhui Chen Yiteng Wang Lijun Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China

This paper presents an ultra-wideband spectrum monitoring system using a software defined radio platform. The system has been developed by using an Ettus X310 universal software radio peripheral (USRP) for data acquisition and a laptop for displaying the graphical user interface. The system can operate in two modes with two different daughter boards installed. One of the operation modes allows simultaneously monitoring up to two different channels, each with a maximum sampling bandwidth of 200 MHz, or simultaneously monitoring up to four different channels, each with a maximum sampling bandwidth of 100 MHz. The other operation mode allows monitoring a single channel with a maximum sampling bandwidth exceeding 300 MHz. Furthermore, unlike the widely adopted GNU Radio software, the proposed system software is friendly to the Microsoft Windows operating system.

关键词： Portable computers Operating systems Data acquisition Bandwidth Monitoring Software radio Ultra wideband technology

来源：评论

学校读者我要写书评

暂无评论

A Survey of Machine Learning-based IoT Intrusion Detection Techniques 6

A Survey of Machine Learning-based IoT Intrusion Detection T...

引用

6th IEEE International Conference on Smart Cloud, SmartCloud 2021

作者： Long, Jing Fang, Fei Luo, Haibo College of Information Science and Engineering Hunan Normal University Changsha410081 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9781665443746

Research on data anomaly intrusion detection in the Internet of Things (IoT) environment is still insufficient, and there are still many practical problems that need to be solved urgently. At present, the deep integration of Artificial Intelligence (AI) technology and IoT, and intelligent technologies such as machine learning have gradually been applied to the field of intrusion detection of the Internet of Things. Therefore, this paper conducts the latest and in-depth research on the efficient and accurate intrusion detection technology used in computers, analyzes the security threats facing the Internet of Things, and summarizes the latest technology and evaluation indicators of intrusion detection. This paper can promote the improvement of large-scale industrial network security detection performance under the new situation to a certain extent and has important theoretical significance and practical application value for ensuring the rapid and healthy development of today's industrial IoT security environment. © 2021 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Weak Signal Detection based on Beta Divergence

Weak Signal Detection based on Beta Divergence

引用

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

作者： Jiahao Liu Haoxuan Liu Wenqiang Pu Rui Zhou Ming-Yi You Qingjiang Shi Science and Technology on Communication Information Security Control Laboratory Jiaxing China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Software Engineering Tongji University Shanghai China

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：