Pub Date : 2013-12-17DOI: 10.3724/SP.J.1087.2013.03482
L. xilinx Wang, Fu Yuan, L. Xiang, Linhua Zheng
Multi-Symbol Detection(MSD) and Turbo Product Code(TPC) can greatly improve the performance of PCM /FM(Pulse Code Modulation / Frequency Modulation) telemetry system. To solve the high computational complexity issues in MSD algorithm,an improved algorithm which reduced the computational complexity of MSD was proposed. Chase decoding algorithm for TPC also reduced the system memories by simplifying the calculation of the soft input information. The simulation results show that despite of 1. 7 dB loss,the improved algorithm still obtains about 8 dB performance gain. Because of lowcomplexity and low system memories,it is more suitable for hardware implementation.
{"title":"Performance of PCM/FM telemetry system based on multi-symbol detection and Turbo product code: Performance of PCM/FM telemetry system based on multi-symbol detection and Turbo product code","authors":"L. xilinx Wang, Fu Yuan, L. Xiang, Linhua Zheng","doi":"10.3724/SP.J.1087.2013.03482","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03482","url":null,"abstract":"Multi-Symbol Detection(MSD) and Turbo Product Code(TPC) can greatly improve the performance of PCM /FM(Pulse Code Modulation / Frequency Modulation) telemetry system. To solve the high computational complexity issues in MSD algorithm,an improved algorithm which reduced the computational complexity of MSD was proposed. Chase decoding algorithm for TPC also reduced the system memories by simplifying the calculation of the soft input information. The simulation results show that despite of 1. 7 dB loss,the improved algorithm still obtains about 8 dB performance gain. Because of lowcomplexity and low system memories,it is more suitable for hardware implementation.","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69949014","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}
Pub Date : 2013-12-17DOI: 10.3724/SP.J.1087.2013.03559
Feng Yong, Han Nan, Ji Dongfeng
For the purpose of events extraction from large-scale short posts of microblogging service,a complete event detection and tracking algorithm was proposed using cloud framework. First,based on the number of forward and comment of the microblog,the posts were expressed as Vector Space Model(VSM). Then the keywords were extracted using RIHDBSCAN(Incremental Hierarchical DBSCAN based on Representative posts) to realize the event detection and tracking. Considering that a single node cannot quickly and efficiently handle the large amount of data,the algorithm would be deployed on Hadoop,a cloud computing platform. The experiment on real microblog data extracted from Sina microblogging platform shows that the proposed method achieves higher performance than that of TF-IDF(Term Frequency-Inverse Document Frequency) and UFITUF(User Frequency-Inverse Thread User Frequency),and the use of cloud framework improves the processing speed.Therefore,it is suitable for data analysis and mining on huge datasets.
为了从微博服务的大规模短帖子中提取事件，提出了一种基于云框架的完整的事件检测与跟踪算法。首先，根据微博的转发数和评论数，将微博表示为向量空间模型(VSM)。然后利用RIHDBSCAN(Incremental Hierarchical DBSCAN based on Representative posts)提取关键词，实现事件检测与跟踪;考虑到单个节点无法快速有效地处理大量数据，该算法将部署在云计算平台Hadoop上。在新浪微博平台提取的真实微博数据上进行的实验表明，该方法比TF-IDF(词频-逆文档频率)和UFITUF(用户频-逆线程用户频率)的性能更高，并且使用云框架提高了处理速度。因此，它适用于海量数据集的数据分析和挖掘。
{"title":"Microblog events detection and tracking with incremental hierarchical DBSCAN based on representative posts using cloud framework","authors":"Feng Yong, Han Nan, Ji Dongfeng","doi":"10.3724/SP.J.1087.2013.03559","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03559","url":null,"abstract":"For the purpose of events extraction from large-scale short posts of microblogging service,a complete event detection and tracking algorithm was proposed using cloud framework. First,based on the number of forward and comment of the microblog,the posts were expressed as Vector Space Model(VSM). Then the keywords were extracted using RIHDBSCAN(Incremental Hierarchical DBSCAN based on Representative posts) to realize the event detection and tracking. Considering that a single node cannot quickly and efficiently handle the large amount of data,the algorithm would be deployed on Hadoop,a cloud computing platform. The experiment on real microblog data extracted from Sina microblogging platform shows that the proposed method achieves higher performance than that of TF-IDF(Term Frequency-Inverse Document Frequency) and UFITUF(User Frequency-Inverse Thread User Frequency),and the use of cloud framework improves the processing speed.Therefore,it is suitable for data analysis and mining on huge datasets.","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69949485","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}
Pub Date : 2013-12-17DOI: 10.3724/SP.J.1087.2013.03567
Wang Chong, L. Xiujuan
A Protein-Protein Interaction(PPI) network clustering model and an algorithm based on the mechanism of the Artificial Immune System(AIS) were proposed to improve the identification accuracy. In this algorithm,the set of cluster centers was regarded as antigens and the neighbor nodes were regarded as antibodies. The antibodies were regarded as the memory cells of clusters by calculating the affinity between the antibodies and antigens. Then excellent antibodies were selected as vaccines,and they were injected into clustering modules to get update. Finally the memory cells were updated after comparing the fitness of the modules before injection. The simulation results on PPI datasets show that,compared with FLOW algorithm,the f-measure of precision and recall value of the new algorithm have got improved.
{"title":"Protein-protein interaction network clustering based on artificial immune system","authors":"Wang Chong, L. Xiujuan","doi":"10.3724/SP.J.1087.2013.03567","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03567","url":null,"abstract":"A Protein-Protein Interaction(PPI) network clustering model and an algorithm based on the mechanism of the Artificial Immune System(AIS) were proposed to improve the identification accuracy. In this algorithm,the set of cluster centers was regarded as antigens and the neighbor nodes were regarded as antibodies. The antibodies were regarded as the memory cells of clusters by calculating the affinity between the antibodies and antigens. Then excellent antibodies were selected as vaccines,and they were injected into clustering modules to get update. Finally the memory cells were updated after comparing the fitness of the modules before injection. The simulation results on PPI datasets show that,compared with FLOW algorithm,the f-measure of precision and recall value of the new algorithm have got improved.","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69949541","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}
Pub Date : 2013-12-17DOI: 10.3724/SP.J.1087.2013.03580
Chunyan Li, Xuejie Zhang
{"title":"Performance evaluation on open source cloud platform for high performance computing: Performance evaluation on open source cloud platform for high performance computing","authors":"Chunyan Li, Xuejie Zhang","doi":"10.3724/SP.J.1087.2013.03580","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03580","url":null,"abstract":"","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69949637","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}
Pub Date : 2013-12-17DOI: 10.3724/SP.J.1087.2013.03522
Ruifeng Wu, Lu He, Dingyi Fang
{"title":"Automatic evaluation scheme for imperceptibility of natural language watermarking: Automatic evaluation scheme for imperceptibility of natural language watermarking","authors":"Ruifeng Wu, Lu He, Dingyi Fang","doi":"10.3724/SP.J.1087.2013.03522","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03522","url":null,"abstract":"","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69949155","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}
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