A construction method based on the p-plane to design
high-girth quasi-cyclic low-density parity-check (QC-LDPC) codes
is proposed. Firstly the good points in every line of the p-plane
can be ascertained through filtering the bad points, because the
designed parity-check matrixes using these points have the short
cycles in Tanner graph of codes. Then one of the best points from
the residual good points of every line in the p-plane will be found,
respectively. The optimal point is also singled out according to
the bit error rate (BER) performance of the QC-LDPC codes at
last. Explicit necessary and sufficient conditions for the QC-LDPC
codes to have no short cycles are presented which are in favor
of removing the bad points in the p-plane. Since preventing the
short cycles also prevents the small stopping sets, the proposed
construction method also leads to QC-LDPC codes with a higher
stopping distance.

A new method of unscented extended Kalman filter
(UEKF) for nonlinear system is presented. This new method is
a combination of the unscented transformation and the extended
Kalman filter (EKF). The extended Kalman filter is similar to that in
a conventional EKF. However, in every running step of the EKF the
unscented transformation is running, the deterministic sample is
caught by unscented transformation, then posterior mean of nonlinearity
is caught by propagating, but the posterior covariance of
nonlinearity is caught by linearizing. The accuracy of new method
is a little better than that of the unscented Kalman filter (UKF),
however, the computational time of the UEKF is much less than
that of the UKF.

Efficiency and linearity of the microwave power amplifier
are critical elements for mobile communication systems. A
memory polynomial baseband predistorter based on an indirect
learning architecture is presented for improving the linearity of
an envelope tracing (ET) amplifier with application to a wireless
transmitter. To deal with large peak-to-average ratio (PAR) problem,
a clipping procedure for the input signal is employed. Then
the system performance is verified by simulation results. For a
single carrier wideband code division multiple access (WCDMA)
signal of 16-quadrature amplitude modulation (16-QAM), about
2% improvement of the error vector magnitude (EVM) is achieved
at an average output power of 45.5 dBm and gain of 10.6 dB,
with adjacent channel leakage ratio (ACLR) of -64.55 dBc at offset
frequency of 5 MHz. Moreover, a three-carrier WCDMA signal and
a third-generation (3G) long term evolution (LTE) signal are used
as test signals to demonstrate the performance of the proposed
linearization scheme under different bandwidth signals.

A method that attempts to recover signal using generalized
inverse theory is presented to obtain a good approximation
of the signal in reconstruction space from its generalized samples.
The proposed approaches differ with the assumptions on reconstruction
space. If the reconstruction space satisfies one-to-one
relationship between the samples and the reconstruction model,
then we propose a method, which achieves consistent signal reconstruction.
At the same time, when the number of samples is
more than the number of reconstruction functions, the minimalnorm
reconstruction signal can be obtained. Finally, it is demonstrated
that the minimal-norm reconstruction can outperform consistent
signal reconstruction in both theory and simulations for the
problem.

A new forward scattering bistatic radar motion compensation
method based on spectral analysis and minimum waveform
entropy is proposed. In order to demonstrate effectiveness of the
presented method and ground vehicles imaging function of forward
scattering radar, a simple bistatic forward scattering radar
experiment system is set up on both sides of a road to collection
ground vehicles experimental data. Finally, experimental ground
vehicles imaging results confirm validity of the proposed motion
compensation method and the experimental imaging results are
identical with computer simulation results in the same parameter
and condition.

The high resolution radar target detection is addressed
in the non-Gaussian clutter. An adaptive detector is derived for
range-spread target based on a novel covariance matrix estimator.
It is proved that the new detector is constant false alarm rate
(CFAR) to both of the clutter covariance matrix structure and power
level theoretically for match cases. The simulation results show
that the new detector is almost CFAR for mismatch cases, and it
outperforms the existing adaptive detector based on the sample
covariance matrix. It also shows that the detection performance
improves, as the number of pulses, the number of secondary data
or the clutter spike increases. In addition, the derived detector is
robust to different subsets, estimated clutter group sizes and correlations
of clutter. Importantly, the number of iterations for practical
application is just one.

Ubiquitous radar is a new radar system that provides
continuous and uninterrupted multifunction capability within a coverage
volume. Continuous coverage from close-in “pop-up” targets
in clutter to long-range targets impacts selection of waveform parameters.
The coherent processing interval (CPI) must be long
enough to achieve a certain signal-to-noise ratio (SNR) that ensures
the efficiency of detection. The condition of detection in the
case of low SNR is analyzed, and three different cases that would
occur during integration are discussed and a method to determine
the CPI is presented. The simulation results show that targets
detection with SNR as low as −26 dB in the experimental system
can possibly determine the CPI.

Technology management is recognized as a key for
organizations to achieve competitiveness. How to promote an
organization’s technology management capability is of great significance
in creating efficiencies and achieving a competitive edge.
The knowledge essence of technology management capability
is introduced and then the correlation between knowledge diffusion
and the development of technology management capability
is discussed. Further, the basic and extended dynamic models of
the development of technology management capability are constructed,
and is applied into an enterprise. The results show that
the dynamic models can well explain how the knowledge improves
the development of technology management capability, and they
can be used as an useful tool by an enterprise to promote technology
management capability. Finally, the managerial implications of
the models are discussed.

Failure prediction plays an important role for many tasks
such as optimal resource management in large-scale system.
However, accurately failure number prediction of repairable largescale
long-running computing (RLLC) is a challenge because of
the reparability and large-scale. To address the challenge, a general
Bayesian serial revision prediction method based on Bootstrap
approach and moving average approach is put forward, which can
make an accurately prediction for the failure number. To demonstrate
the performance gains of our method, extensive experiments
on the data of Los Alamos National Laboratory (LANL) cluster is
implemented, which is a typical RLLC system. And experimental
results show that the prediction accuracy of our method is 80.2 %,
and it is a greatly improvement with 4 % compared with some
typical methods. Finally, the managerial implications of the models
are discussed.

Studies show that supply chain cooperation improves
supply chain performance. However, it remains a challenge to
develop and implement the realistic supply chain cooperation
scheme. We investigate a two-echelon supply chain planning
problem with capacity acquisition decision under asymmetric cost
and demand information. A simple negotiation-based coordination
mechanism is developed to synchronize production/order strategies
of a supplier and a buyer. The coordination scheme shows
how the supplier and the buyer modify their production and order
policy in order to find a joint economic lot sizing plan, which saves
the overall supply chain cost. The allocation of the cooperation
benefit is determined by negotiation. Due to the complexity of the
multiple periods, multiple level supply chain lot sizing with capacity
decision, a heuristic algorithm is developed to find coordination
solutions. Finally, the results of the numerical study indicate the
performance of supply chain coordination scheme.

The concept of rough communication based on bothbranch
fuzzy set is proposed, in which the loss of information
may exist, for each agent there has a different language and can
not provide precise communication to each other. The method of
information measure in a rough communication based on bothbranch
fuzzy set is proposed. By using some concepts, such
as |α|−both-branch rough communication cut, the relation theorem
between rough communication based on both-branch fuzzy
concept and rough communication based on classical concept is
obtained. Finally, an example of rough communication based on
both-branch fuzzy set is given.

Partial cooperation models are studied for many years
to solve the bilevel programming problems where the follower’s
optimal reaction is not unique. However, in these existed models,
the follower’s cooperation level does not depend on the leader’s
decision. A new model is proposed to solve this deficiency. It is
proved the feasibility of the new model when the reaction set of
the lower level is lower semi-continuous. And the numerical results
show that the new model has optimal solutions when the reaction
set of the lower level is discrete, lower semi-continuous and nonlower
semi-continuous.

An approach is proposed to solve the problem how to
obtain the priorities from interval fuzzy preference relations. Firstly,
another expression of interval numbers is given. Then, some basic
definitions on consistency and weak transitivity of real and interval
fuzzy preference relations are described. Based on these definitions,
a two-phase process for determining the priorities from
interval fuzzy preference relations is presented. Finally, two examples
are used to illustrate the use of the proposed approach.

In order to take full advantage of federated filter in faulttolerant
design of integrated navigation system, the limitation of
fault detection algorithm for gradual changing fault detection and
the poor fault tolerance of global optimal fusion algorithm are the
key problems to deal with. Based on theoretical analysis of the
influencing factors of federated filtering fault tolerance, global faulttolerant
fusion algorithm and information sharing algorithm are
proposed based on fuzzy assessment. It achieves intelligent faulttolerant
structure with two-stage and feedback, including real-time
fault detection in sub-filters, and fault-tolerant fusion and information
sharing in main filter. The simulation results demonstrate that
the algorithm can effectively improve fault-tolerant ability and ensure
relatively high positioning precision of integrated navigation
system when a subsystem having gradual changing fault.

For the improvement of accuracy and better faulttolerant
performance, a global position system (GPS)/vision navigation
(VISNAV) integrated relative navigation and attitude determination
approach is presented for ultra-close spacecraft formation
flying. Onboard GPS and VISNAV system are adopted and
a federal Kalman filter architecture is used for the total navigation
system design. Simulation results indicate that the integrated
system can provide a total improvement of relative navigation and
attitude estimation performance in accuracy and fault-tolerance.

Receding horizon H∞ control scheme which can deal
with both the H∞ disturbance attenuation and mean square stability
is proposed for a class of discrete-time Markovian jump linear
systems when minimizing a given quadratic performance criteria.
First, a control law is established for jump systems based on
pontryagin’s minimum principle and it can be constructed through
numerical solution of iterative equations. The aim of this control
strategy is to obtain an optimal control which can minimize the
cost function under the worst disturbance at every sampling time.
Due to the difficulty of the assurance of stability, then the above
mentioned approach is improved by determining terminal weighting
matrix which satisfies cost monotonicity condition. The control
move which is calculated by using this type of terminal weighting
matrix as boundary condition naturally guarantees the mean
square stability of the closed-loop system. A sufficient condition for
the existence of the terminal weighting matrix is presented in linear
matrix inequality (LMI) form which can be solved efficiently by
available software toolbox. Finally, a numerical example is given to
illustrate the feasibility and effectiveness of the proposed method.

Acquisition time of global position system (GPS) receiver,
which is the main factor contributes to time to first fix
(TTFF), can be shortened by estimating the Doppler frequency
shift through external inertial navigation system (INS) information
and almanac data and reducing the searching area. The traditional
fast acquisition is analyzed, the fast acquisition of the GPS receiver
aided is presented by INS information, and the signal is fine captured
by spectrum zooming. Then the algorithm is simulated by
sampled GPS intermediate frequency (IF) signal and the result
verifies that this acquisition can dramatically improve the capability
of GPS receiver and reduce its acquisition time.

A new method that stabilizes network-based systems
with both bounded delay and packet disordering is discussed under
the state feedback controller. A novel model, fully describing
the dynamic characteristic of network-based systems with packet
disordering, is constructed. Different from the existing models of
network-based systems, the number of delay items is time-varying
in the model proposed. Further, this model is converted into a
parameter-uncertain discrete-time system with time-varying delay
item numbers in terms of matrix theory. Moreover, the less
conservative stability condition is obtained by avoiding utilisation
of Moon et al.’ inequality and bounding inequalities for quadratic
functional terms. By solving a minization problem based on linear
matrix inequalities, the state feedback controller is presented. A
numerical example is given to illustrate the effectiveness of the
proposed method.

A robust dissipative control problem for a class of Itˆotype
stochastic systems is discussed with Markovian jumping parameters
and time-varying delay. A memoryless state feedback
dissipative controller is developed based on Lyapunov-Krasovskii
functional approach such that the closed-loop system is robustly
stochastically stable and weakly delay-dependent (RSSWDD) and
strictly (Q, S, R)-dissipative. The sufficient condition on the existence
of state feedback dissipative controller is presented by
linear matrix inequality (LMI). And the desired controller can be
concluded as solving a set of LMI. Finally, a numerical example
is provided to demonstrate the effectiveness of the proposed approach.

To solve the multi-class fault diagnosis tasks, decision
tree support vector machine (DTSVM), which combines SVM
and decision tree using the concept of dichotomy, is proposed.
Since the classification performance of DTSVM highly depends on
its structure, to cluster the multi-classes with maximum distance
between the clustering centers of the two sub-classes, genetic algorithm
is introduced into the formation of decision tree, so that the
most separable classes would be separated at each node of decisions
tree. Numerical simulations conducted on three datasets
compared with “one-against-all” and “one-against-one” demonstrate
the proposed method has better performance and higher
generalization ability than the two conventional methods.

To improve the error performance and the resource
utilization of cooperative systems, the optimum resource allocation,
i.e., power allocation and partner choice, for an adaptive
decode-and-forward (DF) cooperative diversity system based on
quadrature modulation is investigated. The closed-form expression
of the bit error rate (BER) system performance is derived and an
optimal power allocation (OPA) algorithm is proposed to optimize
the power allocation between the local and relayed signals under
the minimum BER criterion. Based on the OPA algorithm, a partner
choice strategy is proposed to determine the partner locations
specified by various cooperation gains. Simulation results show
that the proposed resource optimization algorithms are superior
to the unoptimized algorithms by significantly reducing the BER
and improving the cooperative gain, which is useful to simplify the
practical partner choice process.

Due to their characteristics of dynamic topology, wireless
channels and limited resources, mobile ad hoc networks
are particularly vulnerable to a denial of service (DoS) attacks
launched by intruders. The effects of flooding attacks in network
simulation 2 (NS2) and measured performance parameters are
investigated, including packet loss ratio, average delay, throughput
and average number of hops under different numbers of attack
nodes, flooding frequency, network bandwidth and network size.
Simulation results show that with the increase of the flooding frequency
and the number of attack nodes, network performance
sharply drops. But when the frequency of flooding attacks or the
number of attack nodes is greater than a certain value, performance
degradation tends to a stable value.

A novel particle filter bandwidth adaption for kernel particle
filter (BAKPF) is proposed. Selection of the kernel bandwidth
is a critical issue in kernel density estimation (KDE). The plug-in
method is adopted to get the global fixed bandwidth by optimizing
the asymptotic mean integrated squared error (AMISE) firstly.
Then, particle-driven bandwidth selection is invoked in the KDE. To
get a more effective allocation of the particles, the KDE with adaptive
bandwidth in the BAKPF is used to approximate the posterior
probability density function (PDF) by moving particles toward the
posterior. A closed-form expression of the true distribution is given.
The simulation results show that the proposed BAKPF performs
better than the standard particle filter (PF), unscented particle filter
(UPF) and the kernel particle filter (KPF) both in efficiency and
estimation precision.

Remote tracking for mobile targets is one of the most
important applications in wireless sensor networks (WSNs). A
target tracking protocol– exponential distributed predictive tracking
(EDPT) is proposed. To reduce energy waste and response time,
an improved predictive algorithm–exponential smoothing predictive
algorithm (ESPA) is presented. With the aid of an additive
proportion and differential (PD) controller, ESPA decreases the
system predictive delay effectively. As a recovery mechanism, an
optimal searching radius (OSR) algorithm is applied to calculate
the optimal radius of the recovery zone. The simulation results validate
that the proposed EDPT protocol performes better in terms of
track failed ratio, energy waste ratio and enlarged sensing nodes
ratio, respectively.

A new method for image fusion based on Contourlet
transform and cycle spinning is proposed. Contourlet transform is
a flexible multiresolution, local and directional image expansion,
also provids a sparse representation for two-dimensional piecewise
smooth signals resembling images. Due to lack of translation
invariance property in Contourlet transform, the conventional image
fusion algorithm based on Contourlet transform introduces
many artifacts. According to the theory of cycle spinning applied to
image denoising, an invariance transform can reduce the artifacts
through a series of processing efficiently. So the technology of
cycle spinning is introduced to develop the translation invariant
Contourlet fusion algorithm. This method can effectively eliminate
the Gibbs-like phenomenon, extract the characteristics of original
images, and preserve more important information. Experimental
results show the simplicity and effectiveness of the method and its
advantages over the conventional approaches.

In order to avoid staircasing effect and preserve small
scale texture information for the classical total variation regularization,
a new minimization energy functional model for image
decomposition is proposed. Firstly, an adaptive regularization
based on the local feature of images is introduced to substitute total
variational regularization. The oscillatory component containing
texture and/or noise is modeled in generalized function space div
(BMO). And then, the existence and uniqueness of the minimizer
for proposed model are proved. Finally, the gradient descent flow
of the Euler-Lagrange equations for the new model is numerically
implemented by using a finite difference method. Experiments
show that the proposed model is very robust to noise, and the
staircasing effect is avoided efficiently, while edges and textures
are well remained.