In this paper, we introduced a novel storage architecture \"Unified Storage Network\", which merges NAC (Network Attached Channel) and SAN (Storage Area Network), and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN(Unified Storage Network). To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

This paper presents a new efficient algorithm for mining frequent closed itemsets. It enumerates the closed set of frequent itemsets by using a novel compound frequent itemset tree that facilitates fast growth and efficient pruning of search space. It also employs a hybrid approach that adapts search strategies, representations of projected transaction subsets, and projecting methods to the characteristics of the dataset. Efficient local pruning, global subsumption checking, and fast hashing methods are detailed in this paper. The principle that balances the overheads of search space growth and pruning is also discussed. Extensive experimental evaluations on real world and artificial datasets showed that our algorithm outperforms CHARM by a factor of five and is one to three orders of magnitude more efficient than CLOSET and MAFIA.

Peer-to-Peer systems are emerging as one of the most popular Internet applications. Structured Peer-to-Peer overlay networks use identifier based routing algorithms to allow robustness, load balancing, and distributed lookup needed in this environment. However, identifier based routing that is independent of Internet topology tends to be of low efficiency. Aimed at improving the routing efficiency, the super-proximity routing algorithms presented in this paper combine Internet topology and overlay routing table in choosing the next hop. Experimental results showed that the algorithms greatly improve the efficiency of Peer-to-Peer routing.

This paper describes a hierarchical architecture and a high-performance and interoperability protocol for centralized monitoring and controlling systems (CMCS). The protocol we proposed can interoperate different monitoring and controlling systems constructed by different companies, each with different functions and communication protocols. The protocol reduces the amount of traffic and has real-time and high-performance advantages. The protocol was implemented in CMCS for telecommunication power supply and air-conditioner used by the Telecommunication Bureau of Zhejiang Province. This paper deals with the hierarchical architecture and function of CMCS and packet format, command ID, and SDL description of its protocol. We also discuss the properties of the interoperability and performance of the protocol in this paper.

Due to e-business\'s variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network (QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

This paper proposes a new untraceable Partially Blind Signature scheme which is a cross between the traditional signature scheme and the blind signature scheme. In this proposed scheme, the message M that the signer signed can be divided into two parts. The first part can be known to the signer (like that in the traditional signature scheme) while the other part cannot be known to the signer (like that in the blind signature scheme). After having signed M, the signer cannot determine if he has made the signature of M except through the part that he knows. We draw ideas from Brands\' \"Restricted Blind Signature\" to solve the Untraceable Partially Blind Signature problem. Our scheme is a probabilistic signature scheme and the security of our Untraceable Partially Blind Signature scheme relies on the difficulty of computing discrete logarithm.

A simple channel estimator for space-time coded orthogonal frequency division multiplexing (OFDM) systems in rapid fading channels is proposed. The channels at the training bauds are estimated using the EM (expectation-maximization) algorithm, while the channels at the data bauds are estimated based on the method for modelling the time-varying channel as the linear combination of several time-invariant \"Doppler channels\". Computer simulations showed that this estimator outperforms the decision-directed tracking in rapid fading channels and that the performance of this method can be improved by iteration.

In this study, the author has designed new verifiable (t,n) threshold untraceable signature schemes. The proposed schemes have the following properties: (1) Verification: The shadows of the secret distributed by the trusted center can be verified by all of the participants; (2) Security: Even if the number of the dishonest member is over the value of the threshold, they cannot get the system secret parameters, such as the group secret key, and forge other member\'s individual signature; (3) Efficient verification: The verifier can verify the group signature easily and the verification time of the group signature is equivalent to that of an individual signature; (4) Untraceability: The signers of the group signature cannot be traced.

We present a simple and efficient scheme that combines multiple descriptions coding with wavelet transform under JPEG2000 image coding architecture. To reduce packet losses, controlled amounts of redundancy are added to the wavelet transform coefficients to produce multiple descriptions of wavelet coefficients during the compression process to produce multiple descriptions bit-stream of a compressed image. Even if a receiver gets only parts of descriptions (other descriptions being lost), it can still reconstruct image with acceptable quality. Specifically, the scheme uses not only high-performance wavelet transform to improve compression efficiency, but also multiple descriptions technique to enhance the robustness of the compressed image that is transmitted through unreliable network channels.

A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system\'s stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.

Study of the SISO mixed H₂/l₁ problem for discrete time systems showed that there exists a unique optimal solution which can be approximated within any prescribed missing error bound in l2 norm with solvable suboptimal solutions and solvable superoptimal solutions.

Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model.

This paper presents a method for robust tolerance design in terms of Process Capability Indices (PCI). The component tolerance and the suitable manufacturing processes can be selected based on the real manufacturing context. The robustness of design feasibility under the effect of uncertainties is also discussed. A comparison between the results obtained by the proposed model and other methods indicates that robust and reliable tolerance can be obtained.

Luquire et al.\'s impedance change model of a rectangular cross section probe coil above a structure with an arbitrary number of parallel layers was used to study the principle of measuring thicknesses of multi-layered structures in terms of eddy current testing voltage measurements. An experimental system for multi-layered thickness measurement was developed and several fitting models to formulate the relationships between detected impedance/voltage measurements and thickness are put forward using least square method. The determination of multi-layered thicknesses was investigated after inversing the voltage outputs of the detecting system. The best fitting and inversion models are presented.

The current standard Unified Modeling Language (UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A new UML profile that may customize UML for framework modeling was presented using the extension mechanisms of UML, providing a group of UML extensions to meet the needs of framework modeling. In this profile, the extended class diagrams and sequence diagrams were defined to straightforwardly identify the hot-spots and describe their instantiation restrictions. A transformation model based on design patterns was also put forward, such that the profile based framework design diagrams could be automatically mapped to the corresponding implementation diagrams. It was proved that the presented profile makes framework modeling more straightforwardly and therefore easier to understand and instantiate.

This novel method of Pedestrian Tracking using Support Vector (PTSV) proposed for a video surveillance instrument combines the Support Vector Machine (SVM) classifier into an optic-flow based tracker. The traditional method using optical flow tracks objects by minimizing an intensity difference function between successive frames, while PTSV tracks objects by maximizing the SVM classification score. As the SVM classifier for object and non-object is pre-trained, there is need only to classify an image block as object or non-object without having to compare the pixel region of the tracked object in the previous frame. To account for large motions between successive frames we build pyramids from the support vectors and use a coarse-to-fine scan in the classification stage. To accelerate the training of SVM, a Sequential Minimal Optimization Method (SMO) is adopted. The results of using a kernel-PTSV for pedestrian tracking from real time video are shown at the end. Comparative experimental results showed that PTSV improves the reliability of tracking compared to that of traditional tracking method using optical flow.

To find out the detailed characteristics of the coherent structures and associated particle dispersion in free shear flow, large eddy simulation method was adopted to investigate a two-dimensional particle-laden wake flow. The well-known Sub-grid Scale mode introduced by Smagorinsky was employed to simulate the gas flow field and Lagrangian approach was used to trace the particles. The results showed that the typical large-scale vortex structures exhibit a stable counter rotating arrangement of opposite sign, and alternately form from the near wall region, shed and move towards the downstream positions of the wake with the development of the flow. For particle dispersion, the Stokes number of particles is a key parameter. At the Stokes numbers of 1.4 and 3.8 the particles concentrate highly in the outer boundary regions. While the particles congregate densely in the vortex core regions at the Stokes number of 0.15, and the particles at Stokes number of 15 assemble in the vortex braid regions and the rib regions between the adjoining vortex structures.

The sedimentation of a single circular particle between two parallel walls was studied by means of direct numerical simulation (DNS) and experiment. The improved implementation of distributed Lagrange multiplier/fictitious domain method used in our DNS is a promising new way for simulation of particulate flows. The settling behaviors of the particle are presented ranging in Reynolds number from 0 to about 700, which showed that our results for low Reynolds numbers agreed well with that reported before. Nevertheless, for higher Reynolds numbers our results were different from theirs. The long-term mean equilibrium positions in our results were all on the centerline, but not at off-center position as reported before. In order to validate our simulation, experiments were also conducted. The results showed that the sedimenting behavior simulated in this paper agreed well with our experiment result.

In determining the replenishment policy for an inventory system, some researchers advocated that the iterative method of Newton could be applied to the derivative of the total cost function in order to get the optimal solution. But this approach requires calculation of the second derivative of the function. Avoiding this complex computation we use another iterative method presented by the second author. One of the goals of this paper is to present a unified convergence theory of this method. Then we give a numerical example to show the application of our theory.

In this paper, the authors propose a new model for active contours segmentation in a given image, based on Mumford-Shah functional (Mumford and Shah, 1989). The model is composed of a system of differential and integral equations. By the experimental results we can keep the advantages of Chan and Vese\'s model (Chan and Vese, 2001) and avoid the regularization for Dirac function. More importantly, in theory we prove that the system has a unique viscosity solution.