2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)最新文献

Wavelet Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links 基于小波神经网络的波动低功耗无线链路质量预测

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449254

Wei Liu, Jinwei Xu, Yu Xia, Ming Xu, Mao Jing, Shunren Hu, Daqing Huang

{"title":"Wavelet Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links","authors":"Wei Liu, Jinwei Xu, Yu Xia, Ming Xu, Mao Jing, Shunren Hu, Daqing Huang","doi":"10.1109/ICCCS52626.2021.9449254","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449254","url":null,"abstract":"Low power wireless links are prone to fluctuate when the channel environment changes. In order to reduce the impact of link fluctuations on data transmission, it is necessary to predict the link quality quickly and accurately and make dynamic adjustments according to prediction results. However, existing link quality prediction mechanisms lack sufficient consideration of the impact of link fluctuations, which leads to high prediction errors under the links with large fluctuations such as moderate and sudden changed links. In response to this problem, this paper proposed WNN-LQP, a more effective link quality prediction mechanism under the links with large fluctuations. By taking advantage of the higher resolution of link quality indicator in the transition region as well as the stronger learning ability and higher prediction accuracy of wavelet neural network, WNN-LQP could reduce the prediction errors under moderate and sudden changed links effectively. Compared with the similar mechanism, its prediction errors are reduced by 26.9% under both moderate and sudden changed links.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115307050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Joint Coding Scheme Based on Reed-Solomon Codes 基于Reed-Solomon码的联合编码方案

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449143

Hao Wang, Wei Zhang, Yanyan Liu

引用次数: 2

MRI Reconstruction Using Graph Reasoning Generative Adversarial Network 利用图推理生成对抗网络进行MRI重构

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449191

Wenzhong Zhou, Huiqian Du, Wenbo Mei, Liping Fang

{"title":"MRI Reconstruction Using Graph Reasoning Generative Adversarial Network","authors":"Wenzhong Zhou, Huiqian Du, Wenbo Mei, Liping Fang","doi":"10.1109/ICCCS52626.2021.9449191","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449191","url":null,"abstract":"The deep learning-based CS-MRI methods have been demonstrated to be able to reconstruct high-precision MR images. However, it can be observed that most current deep learning-based CS-MRI methods capture long-range dependencies by stacking multiple convolutional layers, which is computationally inefficient. The latent graph neural network has been proposed to efficiently capture long-range dependencies, which can address the above issue. Besides, there are very few works introducing graph neural networks (GNNs) into MRI reconstruction. In this paper, we propose a novel graph reasoning generative adversarial network, termed as GRGAN, by introducing the graph reasoning networks into MRI reconstruction, where the graph reasoning networks are embedded in the generator to capture long-range dependencies more efficiently. In addition, we propose the multi-scale aggregated residual blocks, termed as MARBs, and introduce them into the proposed GRGAN to extract multi-scale feature information effectively. The experimental results demonstrate that the proposed GRGAN surpasses the state-of-the-art deep learning-based CS-MRI methods with fewer model parameters.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAC Contention Protocol Based on Reinforcement Learning for IoV Communication Environments 基于强化学习的车联网通信环境下MAC争用协议

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449308

Zhonghui Pei, Wei Chen, Luyao Du, Hongjiang Zheng

引用次数: 4

Big Data Analytics System Adoption in SMEs of Manufacturing 制造业中小企业大数据分析系统的应用

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449100

W. Tang, Singha Chaveesuk

引用次数: 0

Performance Analysis of Mobile Receivers with Optimal Tilt Angle in Visible Light Communication System 可见光通信系统中最佳倾角移动接收机性能分析

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449314

Lingchuan Kang, Chao Wang, Yu Mu, Xiao-ping Du, Jingshu Xue, Yi-jun Zhu

{"title":"Performance Analysis of Mobile Receivers with Optimal Tilt Angle in Visible Light Communication System","authors":"Lingchuan Kang, Chao Wang, Yu Mu, Xiao-ping Du, Jingshu Xue, Yi-jun Zhu","doi":"10.1109/ICCCS52626.2021.9449314","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449314","url":null,"abstract":"Aiming at the difficulties that the receiver's field of view and random movement seriously affect the robustness of the visible light communication channel, this paper proposes to use the optimal-tilt-angle receiver as one of the solutions. Firstly, we take the channel capacity as the optimization goal and the Kuhn-Tucker conditions as the optimization tool to deduce the mathematical expression of the optimal tilt angle of the receiver in the single LED scene. Secondly, we propose to use the channel capacity difference between the optimal-tilt-angle receiver and the vertical-up receiver to evaluate the improvement of the channel robustness of the optimal-tilt-angle receiver. Finally, the cumulative distribution function and probability density function of the channel capacity difference are deduced when the receiver moves randomly. The simulation results show that the optimal-tilt-angle receiver improves the channel robustness more obviously in the scenes where the LED has a wide half-power semi-angle, the receiver has a narrow field of view, and the receiver has a wide range of movement.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116210233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Neural Network Based Adaptive Spatial Modulation 基于人工神经网络的自适应空间调制

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449310

Jean Paul Twarayisenze, Zhiquan Bai, Abeer Mohamed, K. Pang, Jingjing Wang, Xinghai Yang, K. Kwak

{"title":"Artificial Neural Network Based Adaptive Spatial Modulation","authors":"Jean Paul Twarayisenze, Zhiquan Bai, Abeer Mohamed, K. Pang, Jingjing Wang, Xinghai Yang, K. Kwak","doi":"10.1109/ICCCS52626.2021.9449310","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449310","url":null,"abstract":"Adaptive spatial modulation (ASM) is a closed loop feedback transmission technique for multiple-input multiple-output (MIMO) systems, where different modulation orders can be assigned to the transmit antennas based on the available channel conditions. However, the conventional optimal modulation order selection (MOS) schemes in ASM have high computational complexity. In this paper, a supervised learning aided feed-forward artificial neural network (ANN) is proposed to design the MOS in ASM and achieve an effective tradeoff between the system computational complexity and the bit error rate (BER) performance. Specifically, the proposed ANN is utilized to transform the MOS problem in ASM to a multiclass classification problem based on a low search classification method and predict the optimal MOS candidate which maximizes the minimum Euclidean distance. Simulation results reveal that, for a given spectral efficiency (SE), the proposed ANN based ASM scheme outperforms the classical SM scheme and retains the advantages of the conventional ASM scheme but with lower system computational complexity.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122008360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Novel Intrusion Detection Method Based on WOA Optimized Hybrid Kernel RVM 一种基于WOA优化混合核RVM的入侵检测方法

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449199

Pan Gao, Meng Yue, Zhi-jun Wu

{"title":"A Novel Intrusion Detection Method Based on WOA Optimized Hybrid Kernel RVM","authors":"Pan Gao, Meng Yue, Zhi-jun Wu","doi":"10.1109/ICCCS52626.2021.9449199","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449199","url":null,"abstract":"In recent years, various machine learning algorithms and intelligent optimization algorithms have emerged one after another, and are widely used in intrusion detection. As a highly sparse model, the relevance vector machine (RVM) is very suitable for intrusion detection scenarios with large scale data. The selection of the parameters of the intrusion detection model directly affects the performance of intrusion detection. Therefore, the selection and determination of parameters is a very critical point to obtain better detection performance. At the same time, the classification performance of RVM obviously depends on the kernel function. To ensure the diversity of kernel function, we adopt a hybrid kernel function formed by linear combination. In addition, RVM is easy to fall into the local optimum, and it has large initial value randomness and poor convergence. Aiming at the limitations of the RVM algorithm, we propose a novel WOA-HRVM model, which optimizes the parameters of the hybrid kernel RVM by WOA algorithm to obtain better performance. The proposed WOA-HRVM is evaluated on NSL-KDD and CICIDS2017 dataset. Compared with other algorithms tested, the proposed WOA-HRVM algorithm significantly improves the accuracy and speed of intrusion detection.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116870864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Data-free Knowledge Distillation via Adversarial* 通过Adversarial*的无数据知识蒸馏

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449145

Yu Jin, Zhaofan Qiu, GengQuan Xie, Jinye Cai, Chao Li, Liwen Shen

引用次数: 3

Cryptanalysis of a Special Case of RSA Large Decryption Exponent Using Lattice Basis Reduction Method 用格基约简法对RSA大解密指数的一个特例进行密码分析

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2021-04-23 DOI: 10.1109/ICCCS52626.2021.9449268

Majid Mumtaz, Ping Luo

引用次数: 1