This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst inte...This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.展开更多
When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive ...When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.展开更多
Because of the limited memory of the increasing amount of information in current wearable devices,the processing capacity of the servers in the storage system can not keep up with the speed of information growth,resul...Because of the limited memory of the increasing amount of information in current wearable devices,the processing capacity of the servers in the storage system can not keep up with the speed of information growth,resulting in low load balancing,long load balancing time and data processing delay.Therefore,a data load balancing technology is applied to the massive storage systems of wearable devices in this paper.We first analyze the object-oriented load balancing method,and formally describe the dynamic load balancing issues,taking the load balancing as a mapping problem.Then,the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are completed.Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node.According to the load information of each data storage node collected by the scheduler in the storage system,the load weight of the current data storage node is calculated and distributed.The data load balancing of the massive storage system for wearable devices is realized.The experimental results show that the average time of load balancing using this method is 1.75h,which is much lower than the traditional methods.The results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time,high load balancing,strong data processing capability,short processing time and obvious application.展开更多
With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this pap...With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.展开更多
Massive data covert transmission scheme based on Shamir threshold is proposed in this paper. This method applies Shamir threshold scheme to divide data, uses information hiding technology to cover shadows, and realize...Massive data covert transmission scheme based on Shamir threshold is proposed in this paper. This method applies Shamir threshold scheme to divide data, uses information hiding technology to cover shadows, and realizes massive data covert transmission through transmitting stego-covers. Analysis proves that compared with the natural division method, this scheme not only improves the time-efficiency of transmitting but also enhances the security.展开更多
The vegetation data of the Fengyun meteorological satellite are segmented according to the latitude and longitude, and can be written into 648 blocks. However, the vegetation data processing efficiency is low because ...The vegetation data of the Fengyun meteorological satellite are segmented according to the latitude and longitude, and can be written into 648 blocks. However, the vegetation data processing efficiency is low because the data belongs to massive data. This paper presents a data processing method based on RAM (h) for Fengyun-3 vegetation data. First of all, we introduce the Locality-Aware model to segment the input data, then locate the data based on geographic location, and finally fuse the independent data based on geographical location. Experimental results show that the proposed method can effectively improve the data processing efficiency.展开更多
Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this p...Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this paper for short-text sentiment analysis. The researches target microblog, product review and movie reviews. Words, symbols or sentences with emotional tendencies are proved important indicators in short-text sentiment analysis based on massive users’ data. It is an effective method to predict emotional tendencies of short text using these features. The model has noticed the phenomenon of polysemy in single-character emotional word in Chinese and discusses singlecharacter and multi-character emotional word separately. The idea of model can be used to deal with various kinds of short-text data. Experiments show that this model performs well in most cases.展开更多
In this paper, we consider the unified optimal subsampling estimation and inference on the lowdimensional parameter of main interest in the presence of the nuisance parameter for low/high-dimensionalgeneralized linear...In this paper, we consider the unified optimal subsampling estimation and inference on the lowdimensional parameter of main interest in the presence of the nuisance parameter for low/high-dimensionalgeneralized linear models (GLMs) with massive data. We first present a general subsampling decorrelated scorefunction to reduce the influence of the less accurate nuisance parameter estimation with the slow convergencerate. The consistency and asymptotic normality of the resultant subsample estimator from a general decorrelatedscore subsampling algorithm are established, and two optimal subsampling probabilities are derived under theA- and L-optimality criteria to downsize the data volume and reduce the computational burden. The proposedoptimal subsampling probabilities provably improve the asymptotic efficiency of the subsampling schemes in thelow-dimensional GLMs and perform better than the uniform subsampling scheme in the high-dimensional GLMs.A two-step algorithm is further proposed to implement, and the asymptotic properties of the correspondingestimators are also given. Simulations show satisfactory performance of the proposed estimators, and twoapplications to census income and Fashion-MNIST datasets also demonstrate its practical applicability.展开更多
基金supported by the National Key Laboratory of Wireless Communications Foundation,China (IFN20230204)。
文摘This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
基金supported by the New Century Excellent Talents in University(NCET-09-0396)the National Science&Technology Key Projects of Numerical Control(2012ZX04014-031)+1 种基金the Natural Science Foundation of Hubei Province(2011CDB279)the Foundation for Innovative Research Groups of the Natural Science Foundation of Hubei Province,China(2010CDA067)
文摘When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.
文摘Because of the limited memory of the increasing amount of information in current wearable devices,the processing capacity of the servers in the storage system can not keep up with the speed of information growth,resulting in low load balancing,long load balancing time and data processing delay.Therefore,a data load balancing technology is applied to the massive storage systems of wearable devices in this paper.We first analyze the object-oriented load balancing method,and formally describe the dynamic load balancing issues,taking the load balancing as a mapping problem.Then,the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are completed.Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node.According to the load information of each data storage node collected by the scheduler in the storage system,the load weight of the current data storage node is calculated and distributed.The data load balancing of the massive storage system for wearable devices is realized.The experimental results show that the average time of load balancing using this method is 1.75h,which is much lower than the traditional methods.The results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time,high load balancing,strong data processing capability,short processing time and obvious application.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2013RC0114111 Project of China under Grant No.B08004
文摘With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2007AA0825)
文摘Massive data covert transmission scheme based on Shamir threshold is proposed in this paper. This method applies Shamir threshold scheme to divide data, uses information hiding technology to cover shadows, and realizes massive data covert transmission through transmitting stego-covers. Analysis proves that compared with the natural division method, this scheme not only improves the time-efficiency of transmitting but also enhances the security.
文摘The vegetation data of the Fengyun meteorological satellite are segmented according to the latitude and longitude, and can be written into 648 blocks. However, the vegetation data processing efficiency is low because the data belongs to massive data. This paper presents a data processing method based on RAM (h) for Fengyun-3 vegetation data. First of all, we introduce the Locality-Aware model to segment the input data, then locate the data based on geographic location, and finally fuse the independent data based on geographical location. Experimental results show that the proposed method can effectively improve the data processing efficiency.
文摘Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this paper for short-text sentiment analysis. The researches target microblog, product review and movie reviews. Words, symbols or sentences with emotional tendencies are proved important indicators in short-text sentiment analysis based on massive users’ data. It is an effective method to predict emotional tendencies of short text using these features. The model has noticed the phenomenon of polysemy in single-character emotional word in Chinese and discusses singlecharacter and multi-character emotional word separately. The idea of model can be used to deal with various kinds of short-text data. Experiments show that this model performs well in most cases.
基金This work was supported by the Fundamental Research Funds for the Central Universities,National Natural Science Foundation of China(Grant No.12271272)and the Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin.
文摘In this paper, we consider the unified optimal subsampling estimation and inference on the lowdimensional parameter of main interest in the presence of the nuisance parameter for low/high-dimensionalgeneralized linear models (GLMs) with massive data. We first present a general subsampling decorrelated scorefunction to reduce the influence of the less accurate nuisance parameter estimation with the slow convergencerate. The consistency and asymptotic normality of the resultant subsample estimator from a general decorrelatedscore subsampling algorithm are established, and two optimal subsampling probabilities are derived under theA- and L-optimality criteria to downsize the data volume and reduce the computational burden. The proposedoptimal subsampling probabilities provably improve the asymptotic efficiency of the subsampling schemes in thelow-dimensional GLMs and perform better than the uniform subsampling scheme in the high-dimensional GLMs.A two-step algorithm is further proposed to implement, and the asymptotic properties of the correspondingestimators are also given. Simulations show satisfactory performance of the proposed estimators, and twoapplications to census income and Fashion-MNIST datasets also demonstrate its practical applicability.
