Pub Date : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073411
Asif Khan, S. Deep, Jian-ping Li, K. Kumar, R. Shaikh, Faraz Hasan
Content Based Image Retrieval is very hottest research area in computer vision and image processing. To perceive arbitrary natural scene from complex environment is a challenging issue in visual imaging and processing research area. Neural Network is a grid of “neuron like” nodes, in this paper we follow towards Neural Network (NN), is committed to contributing a new technical concept for the scene understanding and recognition by consolidating new intellectual visual features into the scene expression, which can be very crucial and provide cognitive intelligence to cloud robot. Inspired by Artificial Neural Network intelligence due to its dynamic nature, we make use of the attributes of the Gabor filter and Laplacian of Gaussian filter which is to be akin to robot visual perception, and apply the wavelet transform to inspect a new approach in complex environment natural scene perception and understanding for virtual phenomena. Through the study of Neural Network, the perception ability of the natural scene image from complex environment for cloud robot is enhanced with the integration of cognitive visual features and the scene expression.
{"title":"Vision prehension with CBIR for cloud robo","authors":"Asif Khan, S. Deep, Jian-ping Li, K. Kumar, R. Shaikh, Faraz Hasan","doi":"10.1109/ICCWAMTIP.2014.7073411","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073411","url":null,"abstract":"Content Based Image Retrieval is very hottest research area in computer vision and image processing. To perceive arbitrary natural scene from complex environment is a challenging issue in visual imaging and processing research area. Neural Network is a grid of “neuron like” nodes, in this paper we follow towards Neural Network (NN), is committed to contributing a new technical concept for the scene understanding and recognition by consolidating new intellectual visual features into the scene expression, which can be very crucial and provide cognitive intelligence to cloud robot. Inspired by Artificial Neural Network intelligence due to its dynamic nature, we make use of the attributes of the Gabor filter and Laplacian of Gaussian filter which is to be akin to robot visual perception, and apply the wavelet transform to inspect a new approach in complex environment natural scene perception and understanding for virtual phenomena. Through the study of Neural Network, the perception ability of the natural scene image from complex environment for cloud robot is enhanced with the integration of cognitive visual features and the scene expression.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128107946","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073354
Yucheng Liu, Hong Li, X. Cui, Mingquan Lu
GPS L2C signal is one of the modernized GPS civilian signals. With respect to GPS L1 C/A signal, L2C signal is more robust in harsh environment, since its tracking threshold would be lower by taking the advantage of new designed pilot channel. Up to now, some of current tracking methods are only based on the pilot channel or data channel of L2C signal and discard the other, which results in 50% power loss. Other methods are based on the joint tracking of the outputs of data and pilot channels' discriminators, which needs more additional computation resources and the integration time of tracking is limited by data. In this paper, a new method based on joint tracking of data and pilot channels but not discriminators is proposed. We know the navigation data of GPS signal, mainly consisted of ephemeris and almanac, are continuously and repeatedly broadcast. So the navigation data of GPS signal are predicable. Based on this, the proposed method removes the navigation data of data channel through a new designed navigation data predication module, before coherently combines the integration results of data and pilot channel. Then, the coherent integration result has both the energy of data channel and pilot channel. Compared with the previous methods, the proposed method has no power loss and it doesn't need additional computation resource. Furthermore, the integration time will not be limited by navigation data since they have been predictably removed and much better tracking performance is expected. Theoretical and simulation results demonstrated the results.
{"title":"A tracking method for GPS L2C signal based on the joint using of data and pilot channels","authors":"Yucheng Liu, Hong Li, X. Cui, Mingquan Lu","doi":"10.1109/ICCWAMTIP.2014.7073354","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073354","url":null,"abstract":"GPS L2C signal is one of the modernized GPS civilian signals. With respect to GPS L1 C/A signal, L2C signal is more robust in harsh environment, since its tracking threshold would be lower by taking the advantage of new designed pilot channel. Up to now, some of current tracking methods are only based on the pilot channel or data channel of L2C signal and discard the other, which results in 50% power loss. Other methods are based on the joint tracking of the outputs of data and pilot channels' discriminators, which needs more additional computation resources and the integration time of tracking is limited by data. In this paper, a new method based on joint tracking of data and pilot channels but not discriminators is proposed. We know the navigation data of GPS signal, mainly consisted of ephemeris and almanac, are continuously and repeatedly broadcast. So the navigation data of GPS signal are predicable. Based on this, the proposed method removes the navigation data of data channel through a new designed navigation data predication module, before coherently combines the integration results of data and pilot channel. Then, the coherent integration result has both the energy of data channel and pilot channel. Compared with the previous methods, the proposed method has no power loss and it doesn't need additional computation resource. Furthermore, the integration time will not be limited by navigation data since they have been predictably removed and much better tracking performance is expected. Theoretical and simulation results demonstrated the results.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129205818","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073440
Zhang Yong, Y. Hua
With rapidly analysis, no pollution, no damage, simple operation, low analysis cost, environmental protection and many other advantages, the near infrared spectroscopy (NIR) analysis has made breakthrough progress in the Chinese medicine field. In this paper, the near infrared spectrometry of extract of two kinds of astragalus is determined. Wavelet transform is used to compress the spectral variables, and the quantitative analysis models are carried on using artificial neural network technology in order to analyze the astragaloside content of extract of two kinds of astragalus. The simulation results show that, the prediction decision coefficient(R2) is 0.9863, the average relative error is 0.0354, the root mean square error of Cross-Validation(RMSECV) is 0.0258 in the astragalus extract samples (the ratio of material to liquid 1:2), and the predictive decision coefficient is 0.9798, the average relative error is 0.0425, and the root mean square error of Cross-Validation is 0.0301 in the astragalus extract samples (the ratio of material to liquid 1:5). The evaluation model can meet the need of practical application, and provide technical support for quantitative analysis to extract of astragalus and analysis of near infrared spectroscopy in traditional Chinese medicinal materials.
{"title":"Research on the quantitative analysis of near infrared spectroscopy of astragaloside based on artificial neural network and wavelet transform","authors":"Zhang Yong, Y. Hua","doi":"10.1109/ICCWAMTIP.2014.7073440","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073440","url":null,"abstract":"With rapidly analysis, no pollution, no damage, simple operation, low analysis cost, environmental protection and many other advantages, the near infrared spectroscopy (NIR) analysis has made breakthrough progress in the Chinese medicine field. In this paper, the near infrared spectrometry of extract of two kinds of astragalus is determined. Wavelet transform is used to compress the spectral variables, and the quantitative analysis models are carried on using artificial neural network technology in order to analyze the astragaloside content of extract of two kinds of astragalus. The simulation results show that, the prediction decision coefficient(R2) is 0.9863, the average relative error is 0.0354, the root mean square error of Cross-Validation(RMSECV) is 0.0258 in the astragalus extract samples (the ratio of material to liquid 1:2), and the predictive decision coefficient is 0.9798, the average relative error is 0.0425, and the root mean square error of Cross-Validation is 0.0301 in the astragalus extract samples (the ratio of material to liquid 1:5). The evaluation model can meet the need of practical application, and provide technical support for quantitative analysis to extract of astragalus and analysis of near infrared spectroscopy in traditional Chinese medicinal materials.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128573016","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073438
Hongyan Shi, Xiaowei Liu
Study on the prediction of stock price has great theoretical significance and application value. Traditional stock forecasting methods cannot fit and analysis highly nonlinear, multi-factors of stock market well, there are problems such as the prediction accuracy is not high, the slow training speed etc. In order to improve the accuracy of stock price forecasting, this paper proposes a prediction method of Elman neural network model based on principal component analysis method. In order to better compare results, establish structure same BP network and Elman network, forecast for stock data; then using principal component analysis filter factors of significant effect on stock prices, Elman neural network model based on principal component analysis method, and compared with single Elman network and BP networks prediction results. Result shows BP network convergence is relatively slow, train for a long time, and could converge to a local minimum; Elman network training time is short, the error bars for smoother and more stable performance; Elman neural network model based on principal component analysis with higher accuracy, faster network speeds.
{"title":"Application on stock price prediction of Elman neural networks based on principal component analysis method","authors":"Hongyan Shi, Xiaowei Liu","doi":"10.1109/ICCWAMTIP.2014.7073438","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073438","url":null,"abstract":"Study on the prediction of stock price has great theoretical significance and application value. Traditional stock forecasting methods cannot fit and analysis highly nonlinear, multi-factors of stock market well, there are problems such as the prediction accuracy is not high, the slow training speed etc. In order to improve the accuracy of stock price forecasting, this paper proposes a prediction method of Elman neural network model based on principal component analysis method. In order to better compare results, establish structure same BP network and Elman network, forecast for stock data; then using principal component analysis filter factors of significant effect on stock prices, Elman neural network model based on principal component analysis method, and compared with single Elman network and BP networks prediction results. Result shows BP network convergence is relatively slow, train for a long time, and could converge to a local minimum; Elman network training time is short, the error bars for smoother and more stable performance; Elman neural network model based on principal component analysis with higher accuracy, faster network speeds.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116259882","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073452
Baohua Chen, Na Zhao
In order to prevent user's data leaked when they submit their data to the cloud server, we use fully homomorphic encryption. We encrypt the data and submit it to the cloud server. The server processes the data, and then returned to the user. This paper introduces the principle of homomorphic encryption, then analyses some homomorphic encryption scheme and its improved algorithm used in cloud computing.
{"title":"Fully homomorphic encryption application in cloud computing","authors":"Baohua Chen, Na Zhao","doi":"10.1109/ICCWAMTIP.2014.7073452","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073452","url":null,"abstract":"In order to prevent user's data leaked when they submit their data to the cloud server, we use fully homomorphic encryption. We encrypt the data and submit it to the cloud server. The server processes the data, and then returned to the user. This paper introduces the principle of homomorphic encryption, then analyses some homomorphic encryption scheme and its improved algorithm used in cloud computing.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916306","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073362
Yanbin Hao, Xiao Guo, Naiding Yang
Granular computing is a useful method in the computational intelligence field. The current research on information granule is mainly granulation of information system object set and its properties. The concept of information granules of information system attribute set based on functional dependency is proposed, and then the concepts of information system structure and information system structure complexity are defined. The changing rules of information system structure are studied when function dependency or attribute changes. A new concept of attribute reduction is presented and efficient calculation method is given.
{"title":"Research on information system attribute set information granules based on functional dependency","authors":"Yanbin Hao, Xiao Guo, Naiding Yang","doi":"10.1109/ICCWAMTIP.2014.7073362","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073362","url":null,"abstract":"Granular computing is a useful method in the computational intelligence field. The current research on information granule is mainly granulation of information system object set and its properties. The concept of information granules of information system attribute set based on functional dependency is proposed, and then the concepts of information system structure and information system structure complexity are defined. The changing rules of information system structure are studied when function dependency or attribute changes. A new concept of attribute reduction is presented and efficient calculation method is given.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114653751","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073425
Hengtao Liu, Zhugang Yuan, Zhe Su
In order to solve the problem of the traditional intelligent mower, which cannot cover the lawn area and operate complexly, a visual, wireless, autonomous mower system via machine vision is designed. Firstly, collect the image information of locale dynamically by the real-time camera which erected on the high bracket, and display on the monitor of PC, then draw a few mowing range or mowing patterns of mower by mouse in the host computer software. Secondly, the host computer software analysis the data. Finally, convey the action signal to the actions required lawn mower to finish the mowing task. The experimental results show a high level of automation from the proposed lawn proposed lawn mower system, which has the function of avoiding obstacle automatically and covering the target lawn area completely.
{"title":"Design and realization of visual wireless autonomous lawn mower based on machine vision","authors":"Hengtao Liu, Zhugang Yuan, Zhe Su","doi":"10.1109/ICCWAMTIP.2014.7073425","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073425","url":null,"abstract":"In order to solve the problem of the traditional intelligent mower, which cannot cover the lawn area and operate complexly, a visual, wireless, autonomous mower system via machine vision is designed. Firstly, collect the image information of locale dynamically by the real-time camera which erected on the high bracket, and display on the monitor of PC, then draw a few mowing range or mowing patterns of mower by mouse in the host computer software. Secondly, the host computer software analysis the data. Finally, convey the action signal to the actions required lawn mower to finish the mowing task. The experimental results show a high level of automation from the proposed lawn proposed lawn mower system, which has the function of avoiding obstacle automatically and covering the target lawn area completely.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126585845","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073434
Zong-Wen Liang, Jian-ping Li
Identifying influential spreaders is an important and fundamental work in control information diffusion. Many methods based on centrality measures such as degree centrality, the betweenness centrality, closeness centrality and eigenvector centrality are proposed in the previous literatures, and it has proved that the k-shell decomposition plays overwhelming performance to find influential spreaders in networks. However, as the performance of former three methods is not satisfying enough and k-shell decomposition cannot rank nodes in the same k-core how to find the influential spreaders is still an open challenge. In this paper, we concerned about the influence of μ hop neighborhoods on a node and propose a novel metric, k-shell values of μ hop neighborhoods (μ-NKS), to estimate the spreading influence of nodes of each k- shell in networks. Our experimental results show that the proposed method can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.
{"title":"Identifying and ranking influential spreaders in complex networks","authors":"Zong-Wen Liang, Jian-ping Li","doi":"10.1109/ICCWAMTIP.2014.7073434","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073434","url":null,"abstract":"Identifying influential spreaders is an important and fundamental work in control information diffusion. Many methods based on centrality measures such as degree centrality, the betweenness centrality, closeness centrality and eigenvector centrality are proposed in the previous literatures, and it has proved that the k-shell decomposition plays overwhelming performance to find influential spreaders in networks. However, as the performance of former three methods is not satisfying enough and k-shell decomposition cannot rank nodes in the same k-core how to find the influential spreaders is still an open challenge. In this paper, we concerned about the influence of μ hop neighborhoods on a node and propose a novel metric, k-shell values of μ hop neighborhoods (μ-NKS), to estimate the spreading influence of nodes of each k- shell in networks. Our experimental results show that the proposed method can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123723305","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073356
Qian Wang, Xiao Yan, Kaiyu Qin
Based on the interpolated all-phase DFT, a new parameters estimation algorithm for the exponential signal is presented. The proposed algorithm utilizes the all-phase preprocessing unit to construct a new signal sequence by continuously cycle shifting signal samples and summing up N buffered exponential signal sample sequences, and estimate the parameters of the exponential signal based on the interpolated DFT spectrum of the signal sequence generated by all-phase preprocessing. The simulation results verify the effectiveness of the proposed algorithm in terms of estimation accuracy.
{"title":"Parameters estimation algorithm for the exponential signal by the interpolated all-phase DFT approach","authors":"Qian Wang, Xiao Yan, Kaiyu Qin","doi":"10.1109/ICCWAMTIP.2014.7073356","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073356","url":null,"abstract":"Based on the interpolated all-phase DFT, a new parameters estimation algorithm for the exponential signal is presented. The proposed algorithm utilizes the all-phase preprocessing unit to construct a new signal sequence by continuously cycle shifting signal samples and summing up N buffered exponential signal sample sequences, and estimate the parameters of the exponential signal based on the interpolated DFT spectrum of the signal sequence generated by all-phase preprocessing. The simulation results verify the effectiveness of the proposed algorithm in terms of estimation accuracy.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121838180","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 : 2014-12-01DOI: 10.1109/ICCWAMTIP.2014.7073431
Zhongwei Li, Chunlei Wu, Xuerong Cui
The drilling fluid pulse communication is a very popular technique in the measurement while drilling (MWD) field, because it has the simpler architecture compared to the continuous pressure ware systems. A noise suppression method for signal detection method based on the over complete dictionary is proposed in this paper. Considering the characteristics of the drilling fluid pulse signals, the theory of the sparse representation of those low duty cycle and spike-like pulses is adopted. Then guidelines to build different over complete dictionaries for signals are presented. Simulation results show that the PPM signals of drilling fluid pulse systems can be detected directly, and the AWGN noise can be reduced significantly by the proposed approach.
{"title":"Overcomplete dictionary based denoising and signal detection for drilling fluid pulse communication","authors":"Zhongwei Li, Chunlei Wu, Xuerong Cui","doi":"10.1109/ICCWAMTIP.2014.7073431","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2014.7073431","url":null,"abstract":"The drilling fluid pulse communication is a very popular technique in the measurement while drilling (MWD) field, because it has the simpler architecture compared to the continuous pressure ware systems. A noise suppression method for signal detection method based on the over complete dictionary is proposed in this paper. Considering the characteristics of the drilling fluid pulse signals, the theory of the sparse representation of those low duty cycle and spike-like pulses is adopted. Then guidelines to build different over complete dictionaries for signals are presented. Simulation results show that the PPM signals of drilling fluid pulse systems can be detected directly, and the AWGN noise can be reduced significantly by the proposed approach.","PeriodicalId":211273,"journal":{"name":"2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123211488","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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