4963124
Blind equalization Using ν- support vector regressor for constant modulus signals
The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance of the proposed algorithm is analyzed by means of computer simulations.
22450367
Networking Business Model In Regional Medical Service- Case of Chikamori Medical Group in Kochi, Japan
Japan's Health Care System has experienced a number of changes over the last half a century. And the most heated aspect of current health policy debate focuses on the potential radical reform of the medical care system. In addition, the regional medical service organizations are in front of a more severe challenge because of the rapidly aging population and decreasing young population. Consequently, hospitals and other NHS organizations in local areas have been required to introduce reforms in their management structure for their survival This paper reports in depth on a case study of a private Japanese hospital, the Chikamori medical group, to introduce a key competence-based management model and differentiation marketing strategy. As one of the regional medical care support hospitals, Chikamori plays an active and important role in the regional medical care network. The case implies the effective business model based on positioning in the network architecture, which is significant not only for medical service field but also for other regional industries. The mutual correlations among financial strategy, human resource design, managing policy and collaboration manner with regional doctors are discussed in the paper.
27148951
Diffusion filtering in image processing based on wavelet transform
The nonlinear diffusion filtering in image processing bases on the heat diffusion equations. Its key is the control of diffusion amount. In the previous models, the diffusivity depends on the gradients of images. So it is easily affected by noises. This paper first gives a new multiscale computational technique for diffusivity. Then we proposed a class of nonlinear wavelet diffusion (NWD) models that are used to restore images. The NWD model has strong ability to resist noise. But it, like the previous models, requires higher computational effort. Thus, by simplifying the NWD, we establish linear wavelet diffusion (LWD) models that consist of advection and diffusion. Since there exists the advection, the LWD filter is anisotropic, and hence can well preserve edges although the diffusion at edges is isotropic. The advantage is that the LWD model is easy to be analyzed and has lesser computational load. Finally, a variety of numerical experiments compared with the previous model are shown.
17447352
Using Simulation to Characterize Topology of Peer to Peer Botnets
Future network intruders are constantly changing, improving and extending the capabilities of their botnets. Peer to peer bots are now under widespread development and are quickly evolving into a much tougher species to kill. However, partly due to the lack of understanding of the structural potential of command and control mechanism a botnets can have, countering peer to peer botnets has been ineffective. In this paper, we explore the characteristic of communication networks created by peer to peer botnets. We propose a simulation approach to characterize the structural properties and robustness of P2P botnets, corresponding to different paradigms of forming botnets. Through calculation and simulation for topology construction procedures, we show that extremely resilient peer to peer botnets can be formed to deliver attack code quickly. Such a theory would help predict botnets containment for a given topology and help develop strategies to improve defense against P2P botnets, because applying those strategies can always translate into some network topology transformation.
41632859
A Novel Frequency Offset Estimator over Frequency Selective Fading Channels by Using Correlative Coding
A new data-aided carrier frequency offset (CFO) estimation technique is presented for correlative coded OFDM systems in the presence of strong multipath. Different from traditional data-aided estimation techniques, the technique estimates CFO by detecting amplitude of pilots rather than their phase shift and removes effects on CFO estimation due to intercarrier interference by an iterative compensation method. A theoretical analysis of its performance has been derived and simulation results comparing the new technique with a traditional data-aided estimation technique are presented.
17905519
A Measurement and Modeling Study of P2P IPTV Applications
P2P IPTV applications start to be very popular on the Internet. Previous work on measuring and analyzing P2P IPTV traffic mostly focuses on a specific type of P2P applications or the behavior characteristics of P2P IPTV nodes acting as a client. In this paper, we have undertaken an in-depth measurement and modeling study of the most common P2P IPTV applications, specially focusing on the behavior characteristics of peers acting as a server. We measure and model the distributions of flow inter-arrival time, arrival rate, duration and size. Our results show the following. The statistics can fit equally well into distributions. Due to the different strategies, there exhibit some differences in the traffic characteristics. But, because of P2P IPTV users behaving similarly, they mainly have similar distributions and the traffic of them all have heavy-tailed phenomenon. Insights obtained in this study will be valuable for the identification of P2P IPTV traffic.
6780116
Joint MMSE Vector Precoding Based on GMD Method for MIMO Systems
We propose a geometric mean decomposition (GMD) based vector precoding (VP) for multiple input multiple output (MIMO) systems. Minimum mean square error (MMSE) criterion is used for the joint VP design. The application of GMD method eliminates the imbalance among subchannel gains and obtains a better perturbation vector than the conventional method. We then exploit the extended channel matrix for further performance improvement. Simulation results show the proposed schemes significantly outperform the existing VP schemes.
31231619
Optimizing the Location-aided Routing Protocol in Mobile Ad hoc Networks
27634891
Nonlinear Adaptive Blind Source Separation Based on Kernel Function
As the linear method is difficult to recover the sources from the nonlinear mixture signals, in this paper a new nonlinear adaptive blind signal separation algorithm based kernel space is proposed for general invertible nonlinearities. The received mixture signals are mapped from low dimensional space to high dimensional kernel feature space. In the feature space, the received signals form a smaller submanifold, and an orthonormal basis of the submanifold is constructed in this space, as the same time, the mixture signals are parameterized by the basis in the high dimensional kernel space. In the noiseless or noisy situation, the sources are rebuilt online processing by M-EASI and subspace tracking. The results of computer simulations are also presented.
15977207
Exploiting hierarchical CP-nets to increase the reliability of Web services workflow
Individual Web services can be composed together to form value-added composite Web services represented as business process workflow. However, the value of such composite Web services is directly influenced by the reliability of the composite services. Composing Web services is not an easy task and is error-prone, so there is a growing interest for the verification techniques which enable designers to find and repair design errors even before actual running of the service, or allow designers to detect erroneous properties such as deadlocks or livelocks and formally verify whether the service process design does have certain desired properties (such as consistency with the conversation protocols of partner service). In this paper, we present to analyze and verify Web services composition by using some kind of colored Petri nets (CP-nets) called hierarchical CP-nets, which combine the strengths of Petri nets with the expressive power of high-level programming and have sound mathematical semantics. Services composition proposals can be transformed by transformation rules into CP-nets, which can be simulated to analyze the performance and verified to investigate behavioral properties by exploiting existing CP-nets specialized tools
27471784
A Novel Greedy Bayesian Network Structure Learning Algorithm for Limited Data
Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm is a variant of K2 algorithm for learning BNs from limited datasets. MRMRG algorithm applies Max Relevance and Min Redundancy feature selection technique and proposes Local Bayesian Increment (LBI) function according to the Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that MRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.