Based on the studies on downlink resource allocation in point to multi-point (PMP) mode in the 802.16e systems, an efficient downlink resource allocation algorithm with low complexity is proposed to maximize the syste...
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77GHz Automotive radar based on SiGe technique is the hot topics. The optimal matching in the RF part of the automotive radar should be solved and considered firstly. Left hand material(LHM) has special properties suc...
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77GHz Automotive radar based on SiGe technique is the hot topics. The optimal matching in the RF part of the automotive radar should be solved and considered firstly. Left hand material(LHM) has special properties such as low loss, negative phase velocity. The paper focus on designing a new transmission line which is made by LHM and RHM (right hand material) together. We analysis the theory of the TL (transmission line) based on compositing LHM and RHM together, then design the match circuits based on the TL. Moreover, the simulating results of the circuit based on the special TL are shown. Lastly, the possibility of using the TL to match at the automotive radar system is valued.
Dedicated Short Range Communication (DSRC) employs one control channel for safety-oriented applications and six service channels for non-safety commercial applications. However, most existing multichannel schemes requ...
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Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough ...
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Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by ...
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Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by taking chronic pharyngitis as an example: one-against-one method is used to realize multi-class;the problem of non-symmetrical samples of C-SVM is solved by giving positive and negative samples of different weights;a margin-based bound on generalization method is used to search parameters of the model. Finally, test results show that the classifier, which is realized and tested using vc++6.0, possess a very high recognition rate and can be applied to meridian diagnosis system.
Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP program...
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Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP programs based on Petri *** flow of programs are modeled according to the semantics of program control statements and directives of OpenMP programs;Data flow of programs are modeled by abstracting read and write operations related to shared *** two detection algorithms of data race and deadlock for OpenMP program are given based on the coverability tree of Petri ***,corresponding software tool is designed and implemented,and an OpenMP program example of the dining philosophers problem is analyzed to indicate the effectiveness of this method and tools.
Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computingsystems. In a cloud environment, service requ...
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Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in ident...
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Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.
In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, a...
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In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, and independently developed a parallel programming environment FC-TR-MPI based on FC-TR network. In FC-TR-MPI, a new method was applied to point-to-point communication that the network communications between processes in the same computing node were changed into memory operations;moreover, according to the underlying software and hardware features of FC-TR network, new algorithms were proposed to optimize the communication performance of some collective communications. Experimental results show that, compared with Sca MPI parallel programming environment, FC-TR-MPI has a higher parallel efficiency and speedup.
In recent years, the travel time prediction has been receiving sustained attention because of the prevalence of location based applications, smart city engineering and online car-hailing. In this paper, a Self-organiz...
In recent years, the travel time prediction has been receiving sustained attention because of the prevalence of location based applications, smart city engineering and online car-hailing. In this paper, a Self-organizing Graph Embedding Deep Network (SGED-Net) model is proposed to address the challenging Origin-Destination(OD) based travel time estimation (TTE). Specifically, SGED-Net comprises four modules, involving travel feature extraction, spatial association graph generation, graph embedding, and travel time deep learning prediction modules. First, we comprehensively extract three travel characteristics and feed them into a modified LightGBM component for learning the importance sort of features for different trips. Second, considering the lack of intermediate trajectories in the OD-based TTE, we employ a self-organizing feature mapping (SOM) approach to obtain the prominent nodes among pick-up and drop-off locations. Meanwhile clustering algorithm is used to divide a city into n-clusters districts of variant sizes. The topology learned by SOM is combined with the districts divided by clustering algorithm to obtain a district-based spatial association graph. Moreover, an improved SDNE algorithm is leveraged to gain a low-dimension spatial association representation while preserving the global and local structure of the graph. Then, we design a deep neural network for learning the captured spatial association representation. Finally, a series of experiments in two real-world large-scale datasets demonstrate the SGED-Net's excellent performance.
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