Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients, are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link additions, characteristic path lengths reduce rapidly and clustering coefficients increase greatly. A tree abstract, Cayley tree, is considered for the study of the navigation algorithm, which runs automatically in the small worlds of tree-based networks. In the further study, epidemics in the small worlds of tree-based wireless sensor networks on the large scale are studied, and the percolation threshold is calculated, at which the outbreak of the epidemic takes place. Compared with Cayley tree, there is a smaller percolation threshold suffering from the epidemic.

This paper proposes a scheme to construct timefrequency codes based on protograph low density parity check (LDPC) codes in orthogonal frequency division multiplexing
(OFDM) communication systems. This approach synthesizes two techniques: protograph LDPC codes and OFDM. One symbol of encoded information by protograph LDPC codes corresponds to one sub-carrier, namely the length of encoded information equals to the number of sub-carriers. The design of good protograph LDPC codes with short lengths is given, and the proposed protograph LDPC codes can be of fast encoding, which can reduce the encoding complexity and simplify encoder hardware implementation. The proposed approach provides a higher coding gain in the Rayleigh fading channel. The simulation results in the Rayleigh fading channel show that the bit error rate (BER) performance of the proposed time-frequency codes is as good as random LDPCOFDM codes and is better than Tanner LDPC-OFDM codes under the condition of different fading coefficients.

To correct the range walk through resolution cell in Doppler beam sharpening (DBS) imaging, a new DBS imaging algorithm based on Keystone transform is proposed. Without the exact values of the movement parameters and the look angle of the radar platform in the multi-targets environment, a linear transform on the received data is employed to correct different range walk values accurately under the condition of Doppler frequency ambiguity in this algorithm. This method can realize the coherent integration in azimuth dimension and improve the azimuth resolution. In order to reduce the computational burden, a fast implementation of Keystone transform is used. Theoretical analysis and simulation results demonstrate the effectiveness of the new algorithm. And through comparing the computational load of the fast implementation with several other algorithms, the real-time processing ability of the proposed algorithm is superior to that of other algorithms.

In multi-user multiple input multiple output (MU-MIMO) systems, the outdated channel state information at the transmitter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effective solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve (KL) expansion of timevarying channels. In this scheme, channel estimation is done at the base station (BS) rather than at the user terminal (UT), which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expansion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error (MSE) and ergodic capacity

Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Secondly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of
the advantages of M/K detector and integrating detector, a robust detector based on scatterer density (DBSD) is designed, which can reduce the probable collapsing loss or quantization error effectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indicate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD outperforms the existing scatterer-density-dependent detector.

The finite set statistics provides a mathematically rigorous single target Bayesian filter (STBF) for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded tracking performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Motivated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MMSTBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation (for generic models) and a Gaussian mixture implementation (for linear Gaussian models) are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of  dynamic state estimates.

The mismatch effect induced by the radial motion of a target is analyzed for linear frequency modulated (LFM) signals. Then, a novel integrated processing scheme is proposed to resolve the delay-Doppler coupling effect in LFM pulse compression. Therefore the range and radial velocity of the target can be simultaneously estimated with a narrowband LFM pulse. Finally, numerical simulation results demonstrate the effectiveness and good performance of the proposed method.

It is known that detecting small moving objects in astronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, provides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is reconstructed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the proposed approach on detection and localization.

A novel algorithm is proposed to solve the poor performance problem of the Tent chaos-based frequency modulation (FM) signal for range-Doppler imaging, which takes it into complex multi-segment system by increasing its segments. The simulation results show that the effectiveness of the proposed algorithm, as well as the performance of the improved Tent FM signal is obvious in a multipath or noise propagation environment.

A novel algorithm named randomized binary gravitational search (RBGS) algorithm is proposed for the set covering problem (SCP). It differs from previous SCP approaches because it does not work directly on the SCP matrix. In the proposed algorithm, the solution of SCP is viewed as multi-dimension position of objects in the binary search space. All objects in the space attract each other by the gravity force, and this force causes a global movement of all objects towards the objects with heavier masses which correspond to good solutions. Computation results show that the proposed algorithm is very competitive. In addition, the proposed algorithm is extended for SCP to solve the fault diagnosis problem in graph-based systems.

The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world problems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit (DMU) with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit (FSDEA). Five evaluation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches.

This paper is concerned with controller design of networked control systems (NCSs) with both network-induced delay and arbitrary packet dropout. By using a packet-loss-dependent Lyapunov function, sufficient conditions for state/output feedback stabilization and corresponding control laws are derived via a switched system approach. Different from the existing results, the proposed stabilizing controllers design is dependent on the packet loss occurring in the last two transmission intervals due to the network-induced delay. The cone complementary linearation (CCL) methodology is used to solve the non-convex feasibility problem by formulating it into an optimization problem subject to linear matrix inequality (LMI) constraints. Numerical examples and simulations are worked out to demonstrate the effectiveness and validity of the proposed techniques.

The globally exponential stability of nonlinear impulsive networked control systems (NINCS) with time delay and packet dropouts is investigated. By applying Lyapunov function theory, sufficient conditions on the global exponential stability are derived by introducing a comparison system and estimating the corresponding Cauchy matrix. An impulsive controller is explicitly designed to achieve exponential stability and ensure state converge with a given decay rate for the system. The Lorenz oscillator system is presented as a numerical example to illustrate the theoretical results and effectiveness of the proposed controller design procedure.

Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detection, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the differencevalue signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking convergent speed than the proportional-type (P-type) algorithm, but also calculates more simply than the proportional-derivative-type (PD-type) algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.

Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufacturing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heterogeneous models has brought great challenges to the modeling and simulation (M&S) science and technology. This paper presents a top-level modeling theory based on the meta modeling framework (M2F) of the COllaborative SIMulation (COSIM) theory of virtual prototyping to solve the problems. Firstly the fundamental principles of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special requirements in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the toplevel modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demonstrate the feasibility of the theory.

As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the existing adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satisfactory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which involves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generalization performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.

This paper introduces niching particle swarm optimization (nichePSO) into clustering analysis and puts forward a clustering algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and increases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clustering problem, which can be useful for the niches’ forming and searching. At last, a novel method that distributes samples to the
corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters automatically and accurately.

A new spectral matching algorithm is proposed by using nonsubsampled contourlet transform and scale-invariant feature transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency image. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity
matrix, a matching matrix (or matching result) reflecting the matching degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Smooth support vector machine (SSVM) changs the
normal support vector machine (SVM) into the unconstrained optimization
by using the smooth sigmoid function. The method can
be solved under the Broyden-Fletcher-Goldfarb-Shanno (BFGS)
algorithm and the Newdon-Armijio (NA) algorithm easily, however
the accuracy of sigmoid function is not as good as that of polynomial
smooth function. Furthermore, the method cannot reduce the
influence of outliers or noise in dataset. A fuzzy smooth support
vector machine (FSSVM) with fuzzy membership and polynomial
smooth functions is introduced into the SVM. The fuzzy membership
considers the contribution rate of each sample to the optimal
separating hyperplane and makes the optimization problem more
accurate at the inflection point. Those changes play a positive role
on trials. The results of the experiments show that those FSSVMs
can obtain a better accuracy and consume the shorter time than
SSVM and lagrange support vector machine (LSVM).