In this paper, we consider hybrid wireless networks with a general node density λ ∈ [1, n ], where n ad hoc nodes are uniformly distributed and m base stations (BSs) are regularly placed in a sq...
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In this paper, we consider hybrid wireless networks with a general node density λ ∈ [1, n ], where n ad hoc nodes are uniformly distributed and m base stations (BSs) are regularly placed in a square region A ( n , A ) = 1 , A × 1 , A with A ∈ [1, n ]. We focus on multicast sessions in which each ad hoc node as a user chooses randomly d ad hoc nodes as its destinations. Specifically, when d = 1 (or d = n − 1), a multicast session is essentially a unicast (or broadcast) session. We study the asymptotic multicast throughput for such a hybrid wireless network according to different cases in terms of m ∈ [1, n ] and d ∈ [1, n ], as n → ∞. To be specific, we design two types of multicast schemes, called hybrid scheme and BS - based scheme , respectively. For the hybrid scheme, there are two alternative routing backbones : sparse backbones and dense backbones . Particularly, according to different regimes of the node density λ = n A , we derive the thresholds in terms of m and d . Depending on these thresholds, we determine which scheme is preferred for the better performance of network throughput.
The degree of similarity between fuzzy numbers plays an important role in fuzzy information fusion. In this paper, improved ROG-based similarity measure developed from the current ROG method is presented. It is shown ...
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This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is pres...
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This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs; secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree; finally, an example is given to show the efficiency of our framework.
The Godson-3A microprocessor is a quad-core version of the scalable Godson-3 multi-core series. It is physically implemented based on the 65 nm CMOS process. This 174 mm2 chip consists of 425 million transistors. The ...
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The Godson-3A microprocessor is a quad-core version of the scalable Godson-3 multi-core series. It is physically implemented based on the 65 nm CMOS process. This 174 mm2 chip consists of 425 million transistors. The maximum frequency is 1GHz with a maximum power consumption of 15 W. The main challenges of Godson-3A physical implementation include very large scale, high frequency requirement, sub-micron technology effects and aggressive time schedule. This paper describes the design methodology of the physical implementation of Godson-3A, with particular emphasis on design methods for high frequency, clock tree design, power management, and on-chip variation (OCV) issue.
This paper proposes a novel face recognition algorithm inspired by the selective attention of Human Visual System (HVS). We record four observers' eye movements when they are viewing 100 FRGC [1] frontal view face...
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This paper proposes a novel face recognition algorithm inspired by the selective attention of Human Visual System (HVS). We record four observers' eye movements when they are viewing 100 FRGC [1] frontal view face images and find that the observers are highly consistent in the regions fixated. Inspired by the fact that fovea of HVS has a much higher spatial acuity than the periphery, a face recognition algorithm based on spatial variant sampling is proposed to simulate such foveated imaging phenomenon, where more information is reserved for the fixated regions. Moreover, information extracted from glance which adopts the low spatial frequency components of the image is integrated into the face recognition system to elicit a percept that occurs before any fixations. The experimental results on FERET database [2] demonstrate that the proposed method not only reduces the computational cost, but also achieves comparable performance, which shows that the characteristics of the HVS provide valuable insights into face recognition.
The network content security system of Internet information requires a controlling force over information flows which allows the access to some information and prohibits some other information. The complexity of Inter...
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The network content security system of Internet information requires a controlling force over information flows which allows the access to some information and prohibits some other information. The complexity of Internet information flows makes it difficult to control precisely over the Internet. Consequently, corresponding evaluation shall be conducted over all kinds of controlling methods. In the paper, precision rate and error rate metrics are proposed to evaluate the network content security system. Consequently we use the evaluation methods in intrusion detection and information retrieval for reference to evaluate the network content security system. We take a deep analysis for the evaluation methods from two aspects which are "monotony" of evaluation methods and the sensibility of evaluation methods and point out which of these methods are applicable to network content security controlling system. At last, we state briefly the application range of CID, ECC, NAMI and other metrics.
As an emerging portable service platform, OSGi is now taking a more and more important role in Ubiquitous computing environment. As a module management framework, OSGi provides the functions of dynamic loading and unl...
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ISBN:
(纸本)9781457702099
As an emerging portable service platform, OSGi is now taking a more and more important role in Ubiquitous computing environment. As a module management framework, OSGi provides the functions of dynamic loading and unloading software modules at runtime. But its use is limited because it is centralized and single programming language supported. In this paper we present LIR-OSGi, an extended framework of OSGi, which is designed to add the distribution and programming language independence to OSGi. Meanwhile it is easy to use for developers because of the transparence of service invocation and automation of distribution. Not like OSGi which can be applied only on the Java platform, LIR-OSGi makes programs which may be written in different programming languages be able to call each other transparently so that it can be applied in many different platforms such as DotNet Framework and so on. This brings much convenience to the program development in a distributed and heterogeneous environment.
Regularized Low-rank approximation with missing data is an effective approach for Collaborative Filtering since it generates high quality rating predictions for recommender systems. Alternative LS(ALS) method is one o...
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Regularized Low-rank approximation with missing data is an effective approach for Collaborative Filtering since it generates high quality rating predictions for recommender systems. Alternative LS(ALS) method is one of the commonly used algorithms for the CF problem. However, ALS did not work very well in some applications, due to the over-fitting to observations. This paper proposes a novel estimate-piloted regularization that uses a pre-estimate of the unobserved entries and uses the approximation errors to the pre-estimates as a regularize term. This new regularization can reduce the risk of over-fitting and improve the approximation accuracy of *** also proposed a fast implementation of the modified ALS method, which is also very suitable for parallel computing. The proposed algorithm PALS has higher accuracy than ALS for original model in three real-world data sets.
Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job ...
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ISBN:
(纸本)9781457702099
Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities; (2) a new method is proposed to caculate the confident scores of the contention samples; (3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM.
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