36390607
Wavelet-based diffusion approaches for signal denoising
The efficiency of nonlinear diffusion filters is related to diffusivity. In the previous models, diffusivity depended on the derivatives of a signal, and hence it is easily affected by noise. This paper considers the nonlinear wavelet-based diffusion (NWD) methods for signal denoising in which the diffusivity is expressed by the wavelet transforms of received signal at several scales. Due to the multiresolution of wavelet transform, the proposed diffusivity with wavelet transforms efficiently reduces the influence of noise on the estimate of diffusion amount and improves nonlinear diffusion filters. Some numerical experimental results compared with the previous models are shown.
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.
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.
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.
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.
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.
11096456
Learning Bayesian Networks with Combination of MRMR Criterion and EMI Method
Currently, learning Bayesian Networks (BNs) from data has become a much attention-getting issue in fields of machine learning and data mining. While there exists few efficient algorithms for learning BNs in presence of incomplete data. In this paper, we present a scoring function based on mutual information for evaluating BN structures. To decrease computational complexity, we introduce MRMR criterion into the scoring function, which enables the computation of the scoring function to involve in only two-dimensional mutual information. When the dataset is incomplete, we use EMI method to estimate the Mutual Information (MI) from the incomplete dataset. As for whether a node ordering is manually given or not, we develop two versions of algorithms, named as MRMR- E1 and MRMR-E2 respectively and evaluate them through experiments. The experimental results on Alarm network show good accuracy and efficiency of our algorithms.
45720537
Bayesian Network Structure Ensemble Learning
Bayesian networks (BNs) have been widely used for learning model structures of a domain in the area of data mining and knowledge discovery. This paper incorporates ensemble learning into BN structure learning algorithms and presents a novel ensemble BN structure learning approach. Based on the Markov condition and the faithfulness condition of BN structure learning, our ensemble approach proposes a novel sample decomposition technique and a components integration technique. The experimental results reveal that our ensemble BN structure learning approach can achieve an improved result compared with individual BN structure learning approach in terms of accuracy.
26250215
Wideband Signal DOA Estimation Based on Modified Quantum Genetic Algorithm
A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.
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.
6983597
A Comparative Study of Parallel Prefix Adders in FPGA Implementation of EAC
Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and in- terconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.