To overcome the bulk acoustic wave (BAW), the triple transit signals and the discontinuous frequency

band in the first generation surface acoustic wave's (FGSAW's) wavelet device, the full transfer multistrip coupler

(MSC) is applied to implement wavelet device, and a novel structure of the second generation surface acoustic

wave's (SGSAW's) wavelet device is proposed. In the SGSAW's wavelet device, the BAW is separated and eliminated in different acoustic propagating tracks, and the triple transit signal is suppressed. For arbitrary wavelet scale

device, the center frequency is three times the radius of frequency band, which ensures that the frequency band of

the SGSAW's wavelet device is continuous, and avoids losing signals caused by the discontinuation of frequency

band. Experimental result confirms that the BAW suppression, ripples in band, receiving loss and insertion loss of

the SGSAW' s wavelet device are remarkably improved compared with those of the FGSAW' s wavelet device.

The amplitude of frequency spectrum can be integrated with probabilistic data association (PDA) to distinguish the

target with clutter echoes, especially in low SNR underwater environment A new target-tracking algorithm is presented which

adopts the amplitude of frequency spectrum to improve target tracking in clutter. The probabilistic density distribution of fre

quency spectrum amplitude is analyzed By simulation, the results show that the algorithm is superior to PDA This approach

enhances stability for the association probability and increases the performance of target tracking.

A new restoration algorithm based on double loops and alternant iterations is proposed to restore the ob

ject image effectively from a few frames of turbulence-degraded images. Based on the double loops, the iterative re

lations for estimating the turbulent point spread function PSF and object image alternately are derived. The restora

tion experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estima

tions of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being

convincing.

Digital receivers have become more and more popular in radar, communication, and electric warfare for

the advantages compared with their analog counterparts. But conventional digital receivers have been generally considered impractical for bandwidth greater than several hundreds MHz. To extend receiver bandwidth, decrease data

rate and save hardware resources, three novel structures are proposed. They decimate the data stream prior to mixing and filtering, then process the multiple decimated streams in parallel at a lower rate. Consequently it is feasible

to realize wideband receivers on the current ASIC devices. A design example and corresponding simulation results

are demonstrated to evaluate the proposed structures.

To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted

voting (KD-DWV) is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to

operate in different measurement/ feature spaces to make the most of diverse classification informatioa The weights

assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables (DTs) are established in terms of the diverse feature sets. And

then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory (DST), from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output

The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results

show that the performance of the classification can be improved by using the proposed method compared with the

plurality voting (PV)

In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself (including fuzziness, randomness, incompleteness as well as roughness, etc). Hence it requires investigating the problem of uncertain information fusioa Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural

networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference

knowledge base and weighted fusion sectioa The simulation result demonstrates that the proposed fusion model

and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the

conventional Kaiman weighted fusion algorithm.

A marine INS/GP S adaptive navigation system is presented. GPS with two antenna providing vessel'

s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system.

T he Kaiman filter is the most frequently used algorithm in the integrated navigation system, which is capable of

estimating INS errors online based on the measured errors between INS and GPS. The standard Kaiman filter

( SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not

change, the Kaiman filter will give the optimal estimation. However GPS receiver will be disturbed easily and

thus temporally changing measurement noise will join into the outputs of GPS , which will lead to performance

degradation of the Kaiman filter. Many researchers introduce fuzzy logic control method into innovation-based a

daptive estimation adaptive Kaiman filtering (IAE-AKF ) algorithm, and accordingly propose various adaptive

Kaiman filters. However how to design the fuzzy logic controller is a very complicated problem still without a

convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion

for the proper computation of the filter innovation covariance and hence of the filter gain. Th e approach is di

rect and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE

A K F algorithm theoretically in detail, the approach is tested by the simulation based on the system error mod-

el of the developed INS/GP S integrated marine navigation system. Simulation results show that the adaptive

Kaiman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra

ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the

Kaiman filter.

Signals from multi-sensor systems are often mixtures of (statistically) independent sources by unknown

mixing method. Blind source separation(BSS) and independent component analysis(ICA) are the methods to identify/recover the channels and the sources. BSS/ICA of nonlinear mixing models are difficult problems. For instance,

the post-nonlinear model has been studied by several authors. It is noticed that in most cases, the proposed

models are always with an invertible mixing. According to this fact there is an interesting question: how about

the situation of the non-invertible non-linear mixing in BSS or ICA? A new simple non-linear mixing model is

proposed with a kind of non-invertible mixing, the folding mixing, and method to identify its channel, blindly.

As a result of the exponential growing rate of worldwide Internet usage, satellite systems are required to

support broadband Internet applications. The transmission control protocol (TCP) which is widely used in the Internet, performs very well on wired networks. However, in the case of satellite channels, due to the delay and

transmission errors, TCP performance degrades significantly and bandwidth of satellite links can not be fully utilized. To improve the TCP performance, a new idea of placing a TCP spoofing proxy in the satellite is considered.

A Novel Satellite Transport Protocol (NSTP) which takes advantage of the special properties of the satellite channel is also proposed. By using simulation, as compared with traditional TCPs, the on-board spoofing proxy integrated with the special transport protocol can significantly enhance throughput performance on the high BER satellite link, the time needed to transfer files and the bandwidth used in reverse path are sharply reduced.

The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The

fault dictionary of nonlinear circuits is constructed based on improved back-propagation (BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification

and deducibility.

The sliding mode control problem (SMC) is studied for a class of uncertain delay system in the presence

of both parameter uncertainties and external disturbances. A novel virtual feedback control method is presented.

Based on Lyapunov theory, sufficient conditions for design of the robust sliding mode plane are derived. Sliding

mode controller based on reaching law concept is developed, which is to ensure system trajectories from any initial

conditions asymptotically convergent to sliding mode plane. The global asymptotically stability of the closed-loop

system is guaranteed A numerical example with simulation results is given to illustrate the effectiveness of the

methodology.

The principle of uniform DFT filter bank is presented. Exploiting poly-phase structure, radar jamming

system samples the intercepted wideband radar signals through analysis filter bank by different channels and linearly

modulates the intercepted radar signal according to the theory of signal and system, then synthesizes the jamming

signal through the synthesis filter bank. The method merely requires lower sample frequency, reduces the computational complexity and the data quantity to be processed. The un-ideal filter's influence to the result of signals processing is analyzed by simulating the match filter in radar jamming system.

Multi-sensor system is becoming increasingly important in a variety of military and civilian applications.

In general, single sensor system can only provide partial information about environment while multi-sensor system

provides a synergistic effect, which improves the quality and availability of informatioa Data fusion techniques can

effectively combine this environmental information from similar and/or dissimilar sensors. Sensor management, aiming at improving data fusion performance by controlling sensor behavior, plays an important role in a data fusion

process. This paper presents a method using fisher information gain based sensor effectiveness metric for sensor assignment in multi-sensor and multi-target tracking applications. The fisher information gain is computed for every

sensor-target pairing on each scan. The advantage for this metric over other ones is that the fisher information gain

for the target obtained by multi-sensors is equal to the sum of ones obtained by the individual sensor, so standard

transportation problem formulation can be used to solve this problem without importing the concept of pseudo sensor. The simulation results show the effectiveness of the method.

The relationship between the importance of criterion and the criterion aggregation function is discussed,

criterion's weight and combinational weights between some criteria are defined, and a multi-criteria classification

method with incomplete certain information and polynomial aggregation function is proposed First, linear programming is constructed by classification to reference alternative set (assignment examples) and incomplete certain information on criterion's weights. Then the coefficient of the polynomial aggregation function and thresholds of categories are gained by solving the linear programming. And the consistency index of alternatives is obtained, the classification of the alternatives is achieved. The certain criteria's values of categories and uncertain criteria's values of categories are discussed in the method. Finally, an example shows the feasibility and availability of this method.

Remarks on a benchmark nonlinear constrained optimization problem are made. Due to a citation error,

two absolutely different results for the benchmark problem are obtained by independent researchers. Parallel simulated annealing using simplex method is employed in our study to solve the benchmark nonlinear constrained problem with mistaken formula and the best-known solution is obtained, whose optimality is testified by the Kuhn Tucker conditions.

To overcome the limitations of the traditional surrogate worth trade-off (SWT) method and solve the

multiple criteria decision making problem more efficiently and interactively, a new method labeled dual worth trade off (DWT) method is proposed. The DWT method dynamically uses the duality theory related to the multiple criteria decision making problem and analytic hierarchy process technique to obtain the decision maker's solution preference information and finally find the satisfactory compromise solution of the decision maker. Through the interactive process between the analyst and the decision maker, trade-off information is solicited and treated properly, the representative subset of efficient solutions and the satisfactory solution to the problem are found. The implementation procedure for the DWT method is presented. The effectiveness and applicability of the DWT method are shown

by a practical case study in the field of production scheduling

The basic concepts and models of weapon-target assignment (WTA) are introduced and the mathematical

nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The

present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA

models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms

of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively

so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic

WTA are discussed.

The fuzzy integration evaluation method (FIEM) is studied in order to select the best orbital elements

from the multi-group initial orbits determined by a satellite TT&C (Tracking, Telemetry and Control) center with

all kinds of data sources. By employing FIEM together with the experience of TT&.C experts, the index system to

evaluate the selection of the best initial orbits is established after the data sources and orbit determination theories

are studied. Besides, the concrete steps in employing the method are presented. Moreover, by taking the objects to

be evaluated as evaluation experts, the problem of how to generate evaluation matrices is solved. Through practical

application, the method to select the best initial orbital elements has been proved to be flexible and effective. The originality of the method is to find a new evaluation criterion (comparing the actually tracked orbits) replacing the

traditional one (comparing the nominal orbits) for selecting the best orbital elements.

The problem of robust stabilization for uncertain continuous descriptor system with state and control delay is

considered The time-varying parametric uncertainty is assumed to be norm-bounded The purpose of the robust stabilization is to design a memoryless state feedback law such that the resulting closed-loop system is robustly stable. A sufficient

condition that uncertain continuous descriptor system is robustly stabilizabled by state feedback law is derived in terms of line-

ar matrix inequality (LMI). Finally, a numerical example is provided to demonstrate the application of the proposed method

A new proportional-integral (PI) sliding surface is designed for a class of uncertain nonlinear state-delayed systems. Based on this, an adaptive sliding mode controller (ASMC) is synthesized, which guarantees the occurrence of sliding mode even when the system is undergoing parameter uncertainties and external disturbance. The resulting sliding mode has the same order as the original system, so that it becomes easy to solve the Hoo control problem by designing a memoryless Hoo state feedback controller. A delay-dependent sufficient condition is proposed in terms of linear matrix inequalities (LMIs), which guarantees the sliding mode robust asymptotically stable and has a noise attenuation level y in an iioo sense. The admissible state feedback controller can be found by solving

a sequential minimization problem subject to LMI constraints by applying the cone complementary linearization method This design scheme combines the strong robustness of the sliding mode control with the Hoo norm performance. A numerical example is given to illustrate the effectiveness of the proposed scheme.

The necessity of the use of the block and parallel modeling of the nonlinear continuous mappings with NN

is firstly expounded quantitatively. Then, a practical approach for the block and parallel modeling of the nonlinear

continuous mappings with NN is proposed. Finally, an example indicating that the method raised in this paper can

be realized by suitable existed software is given. The results of the experiment of the model discussed on the 3-D

Mexican straw hat indicate that the block and parallel modeling based on NN is more precise and faster in computation than the direct ones and it is obviously a concrete example and the development of the large-scale general model

established by Tu Xuyaa.

To achieve fast, smooth and accurate set point tracking in servo positioning systems, a parameterized design of nonlinear feedback controllers is presented, based on a so-called composite nonlinear feedback (CNF) control technique. The controller designed here consists of a linear feedback part and a nonlinear part. The linear part is responsible for stability and fast response of the closed-loop system. The nonlinear part serves to increase the damping ratio of closed-loop poles as the controlled output approaches the target reference. The CNF control brings together the good points of both the small and the large damping ratio cases, by continuously scheduling the damping ratio of the dominant closed-loop poles and thus has the capability for superior transient performance, i. e. a fast output response with low overshoot. In the presence of constant disturbances, an integral action is included so as to remove the static bias. An explicitly parameterized controller is derived for servo positioning systems characterized by second-order model. Practical application in a micro hard disk drive servo system is then presented, together with some discussion of the rationale and characteristics of such design. Simulation and experimental results demonstrate the effectiveness of this control design methodology.

The PD-type iterative learning control design of a class of affine nonlinear time-delay systems with external disturbances is considered. Sufficient conditions guaranteeing the convergence of the n-norm of the tracking error are derived. It is shown that the system outputs can be guaranteed to converge to desired trajectories in the absence of external disturbances and output measurement noises. And in the presence of state disturbances and measurement noises, the tracking error will be bounded uniformly. A numerical simulation example is presented to validate the effectiveness of the proposed scheme.

A unknown input observer (UIO) design for a class of linear time-delay systems when the observer error can't

completely decouple from unknown input is dealt with A sufficient condition to its existence is presented based on Lyapunov stability method Design problem of the proposed observer is formulated in term of linear matrix inequalities. Two

design problems of the observer with internal delay and without internal delay are formulated Based on He control theory

in time-delay systems, the proposed observer is designed in term of linear matrix inequalities (LMI). A design algorithm is

proposed The effective of the proposed approach is illustrated by a numerical example.

For a class of unknown nonlinear time-delay systems, an adaptive neural network (NN) control design

approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct

a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs,

such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness (UUB) of all the signals in the closed-loop system is proved

The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated

by an illustrative simulation example.

The problem of adaptive stabilization of a class of multi-input nonlinear systems with unknown parameters

both in the state vector-field and the input vector-field has been considered. By employing the control Lyapunov

function method, a direct adaptive controller is designed to complete the global adaptive stability of the uncertain

system. At the same time, the controller is also verified to possess the optimality. Example and simulations are

provided to illustrate the effectiveness of the proposed method.

The back-stepping designs based on confine functions are suggested for the robust output-feedback global stabilization of a class of nonlinear continuous systems; the proposed stabilizer is efficient for the nonlinear continuous systems confined by a bound function, the nonlinearities of the systems may be of varied forms or uncertain; the designed stabilizer is robust means that a class of nonlinear continuous systems can be stabilized by the same output feedback stabilization schemes; numerical simulation examples are given.

In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.

A multicast routing algorithm of multiple QoS constraints based on widest-bandwidth (MRQW) which

takes available bandwidth as the prime metric, considering the constraints of the surplus energy of the node, delay

and delay jitter, is presented. The process of routing based on MRQW is provided for as well. Correctness proof and

the complexity analysis of the MRQW are also given in the paper. Simulation results show that the MRQW has a

good performance in creating multicast trees. It not only satisfys multiple QoS constraints but also makes multicast

links have larger available bandwidth.

Unstructured P2P has power-law link distribution, and the random walk in power-law networks is analyzed. The analysis results show that the probability that a random walker walks through the high degree nodes is high in the power-law network, and the information on the high degree nodes can be easily found through random walk. Random walk spread and random walk search method (RWSS) is proposed based on the analysis result. Simulation results show that RWSS achieves high success rates at low cost and is robust to high degree node failure.

To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem, The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing. A small job shop scheduling problem is solved in DNA computing, and the "operations" of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.

The admission control issue in the design of a centralized bandwidth broker model for dynamic control and management of QoS provisioning is studied. A two-phase differentiated flow treatment based dynamic admission control scheme under the centralized bandwidth broker model is proposed. In the proposed scheme, the flow requests are classified into two classes and get differentiated treatment according to their QoS demands. We demonstrate that this admission control scheme can not only improve the resource utilization but also guarantee the flows' QoS. Furthermore, the admission control is divided into two phases: edge admission control and interior admission control. During the interior phase, the PoQ scheme is adopted, which enhances the call processing capability of

the bandwidth broker. The simulation results show that the proposed scheme can result in lower flow blocking probability and higher resource utilization. And it also reduces the number of QoS state accesses/updates, thereby increasing the overall call processing capability of the bandwidth broker.

Replicas can improve the data reliability in distributed system. However, the traditional algorithms for replica management are based on the assumption that all replicas have the uniform reliability, which is inaccurate in some actual systems. To address such problem, a novel algorithm is proposed based on dynamic programming to manage the number and distribution of replicas in different nodes. By using Markov model, replicas management is organized as a multi-phase process, and the recursion equations are provided. In this algorithm, the heterogeneity of nodes, the expense for maintaining replicas and the engaged space have been considered. Under these restricted conditions, this algorithm realizes high data reliability in a distributed system. The results of case analysis prove

the feasibility of the algorithm.

A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations (TPSs). The method has the ability to search all possible solution space by the genetic operators of elitist model and restrictioa The result of analyzing the error of this model shows that the accuracy of this model is precise enough to meet the need of calibration for navigation systems on ship, and the search space is only 0. 03% of the total search space, and the precision of heading determination is 4" in a general dock.

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise (LFSN) and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion (FBM) and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

End-to-end delay is one of the most important characteristics of Internet end-to-end packet dynamics, which can be applied to quality of services (QoS) management, service level agreement (SLA) management, congestion control algorithm development, etc. Nonstationarity and nonlinearity are found by the analysis of various delay series measured from different links. The fact that different types of links have different degree of Self-Similarity is also obtained. By constructing appropriate network architecture and neural functions, functional networks can be used to model the Internet end-to-end nonlinear delay time series. Furthermore, by using adaptive parameter studying algorithm, the nonstationarity can also be well modeled. The numerical results show that the provided

functional network architecture and adaptive algorithm can precisely characterize the Internet end-to-end delay dynamics.

Quality of service (QoS) routing and multicasting protocols in ad hoc networks are face with the challenge

of delivering data to destinations through multihop routes in the presence of node movements and topology changes.

The multicast routing problem with multiple QoS constraints is discussed, which may deal with the delay, bandwidth and cost metrics, and describes a network model for researching the ad hoc networks QoS multicast routing

problem. It presents a distributed QoS multicast routing protocol (DQMRP). The proof of correctness and complexity analysis of the DQMRP are also givea Simulation results show that the multicast tree optimized by DQM-

RP is better than other protocols and is fitter for the network situations with frequently changed status and the real

time multimedia applicatioa It is an available approach to multicast routing decision with multiple QoS constraints.

Group key distribution is faced with two important problems, i. e. reliability and scalability, to support security multicast for large and dynamic groups. With group member increasing, traditional reliable multicast protocol can not deal with them fully. Gossip-based group key distribution idea for wide-area dissemination was provided. It is based on an gossip-based loss recovery control mechanism. It can provide a probabilistic reliable guarantee for a information dissemination to reach every group member, which can achieve scalability and reliability. To achieve full reliability, three layers protocol model in group key distribution was provided. One is best effect layer,

which provides unreliable dissemination. Other is gossip-based loss recovery layer, which provides probabilistic reliable guarantee. Last is vsync-based layer, which provide deterministic loss recovery. We integrate probabilistic loss recovery method with deterministic one. The model possess scalability that probabilistic method has and full reliability prosthesis by vsync-based. To evaluate the effectiveness of gossip technique in scalable and reliable multicast protocols. We have compared gossip protocol with other reliable multicast protocols. Experimental result shows that gossip protocol has better scalability than other.