The case when the source of information provides precise belief function/mass, within the generalized power space, has been studied by many people. However, in many decision situations, the precise belief structure is not always available. In this case, an interval-valued belief degree rather than a precise one may be provided. So, the probabilistic transformation of imprecise belief function/mass in the generalized power space including Dezert-Smarandache (DSm) model from scalar transformation to sub-unitary interval transformation and, more generally, to any set of sub-unitary interval transformation is provided. Different from the existing probabilistic transformation algorithms that redistribute an ignorance mass to the singletons involved in that ignorance proportionally with respect to the precise belief function or probability function of singleton, the new algorithm provides an optimization idea to transform any type of imprecise belief assignment which may be represented by the union of several sub-unitary (half-) open intervals, (half-) closed intervals and/or sets of points belonging to [0,1]. Numerical examples are provided to illustrate the detailed implementation process of the new probabilistic transformation approach as well as its validity and wide applicability.

Two optimal power control (PC) schemes under the power constraint for space-time coded multiple input multiple output systems over the flat Rayleigh fading channel with the imperfect channel state information (CSI) are presented. One is based on the minimization of a bit error rate (BER), and the other is based on the maximization of a fuzzy signal-to-noise ratio. In these schemes, different powers are allocated to individual transmit antennas rather than equal power in the conventional one. For the first scheme, the optimal PC procedure is developed. It is shown that the Lagrange multiplier for the constrained optimization in the power control does exist and is unique. A practical iterative algorithm based on Newton’s method for finding the Lagrange multiplier is proposed. In the second scheme, some existing schemes are included, and a suboptimal PC procedure is developed by means of the asymptotic performance analysis. With this suboptimal scheme, a simple PC calculation formula is provided, and thus the calculation of the PC will be straightforward. Moreover, the suboptimal scheme has the BER performance close to the optimal scheme. Simulation results show that the two PC schemes can provide BER lower than the equal PC and antenna selection scheme under the imperfect CSI.

This paper studies a queueing model with the finite buffer of capacity K in wireless cellular networks, which has two types of arriving calls—handoff and originating calls, both of which follow the Markov arriving process with different rates. The channel holding times of the two types of calls follow different phase-type distributions. Firstly, the joint distribution of two queue lengths is derived, and then the dropping and blocking probabilities, the mean queue length and the mean waiting time from the joint distribution are gotten. Finally, numerical examples show the impact of different call arrival rates on the performance measures.

A minimum geometric power distortionless response beamforming approach against impulsive noise (including all α-stable noise) of unknown statistics is proposed. Due to that definite logarithmic moments require no priori knowledge of impulsive noise, this new beamformer substitutes the logarithmic moments for the second-order moments and iteratively minimizes the “geometric power” of the beamformer’s output snapshots, subjected to a linear constraint. Therefore, the proposed beamformer can provide significantly higher output geometric signal-to-noise-andinterference ratio. Moreover, the optimum weight vector is obtained by using a new iteration process. The simulation results prove that the new method is effective.

The second-order small slope approximation (SSA2) method is introduced to study the Doppler characteristics from time-evolving sea surfaces. Simulation results show better agreement between the SSA2 model and the numerical method for both vertical and horizontal polarizations, meaning that SSA2 gives a satisfactory prediction of the spectral difference between two polarizations; while such discrepancy cannot be captured using the lowest-order SSA (SSA1) model. In particular, the Doppler shifts and spectral widths for different incident angles, wind directions and polarizations are analyzed, demonstrating correct variations with respect to such parameters. Those observations prove that the SSA2 provides an efficient and relatively fast tool for sea surface Doppler spectral analysis.

A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade (GRG) into the particle swarm optimization (PSO). Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.

When the information of mutual coupling and shadowing effect of a conformal antenna array are unknown, the performance of direction of arrival (DOA) estimation will be seriously degraded by using some classical methods, such as the multiple signal classification (MUSIC) algorithm. Meanwhile it is difficult to measure or estimate the shadowing effect. The DOA estimation for a conformal uniform circular array (UCA) is studied. Firstly, the azimuthal angle is separated from all the unknown information by transforming the UCA from the element space to the mode space. Then the rank reduction (RARE) algorithm is applied in the estimation of the azimuthal angle. The π ambiguity existed in the RARE is solved by the beam forming. The main advantage of this method is that it does not need to measure the mutual coupling and the shadowing effect. Compared with the subarray method, it will not decrease the aperture of the array. Simulation results validate the advantages of the method.

Compared with the classical Markov repairable system, the Markov repairable system with stochastic regimes switching introduced in the paper provides a more realistic description of the practical system. The system can be used to model the dynamics of a repairable system whose performance regimes switch according to the external conditions. For example, to satisfy the demand variation that is typical for the power and communication systems and reduce the cost, these systems usually adjust their operating regimes. The transition rate matrices under distinct operating regimes are assumed to be different and the sojourn times in distinct regimes are governed by a finite state Markov chain. By using the theory of Markov process, Ion channel theory, and Laplace transforms, the up time of the system are studied. A numerical example is given to illustrate the obtained results. The effect of sojourn times in distinct regimes on the availability and the up time are also discussed in the numerical example.

Recently the integrated modular avionics (IMA) architecture which introduces the concept of resource partitioning becomes popular as an alternative to the traditional federated architecture. A novel hierarchical approach is proposed to solve the resource allocation problem for IMA systems in distributed environments. Firstly, the worst case response time of tasks with arbitrary deadlines is analyzed for the two-level scheduler. Then, the hierarchical resource allocation approach is presented in two levels. At the platform level, a task assignment algorithm based on genetic simulated annealing (GSA) is proposed to assign a set of pre-defined tasks to different processing nodes in the form of task groups, so that resources can be allocated as partitions and mapped to task groups. While yielding to all the resource constraints, the algorithm tries to find an optimal task assignment with minimized communication costs and balanced work load. At the node level, partition parameters are optimized, so that the computational resource can be allocated further. An example is shown to illustrate the hierarchal resource allocation approach and manifest the validity. Simulation results comparing the performance of the proposed GSA with that of traditional genetic algorithms are presented in the context of task assignment in IMA systems.

An optimal replacement model for gamma deteriorating systems is studied. This methodology uses a gamma distribution to model the material degradation, and the impact of imperfect maintenance actions on the system reliability is investigated. The state of a degrading system immediately after the imperfect maintenance action is assumed as a random variable and the maintenance time follows a geometric process. A maintenance policy (N) is applied by which the system will be repaired whenever it experiences Nth preventive maintenance (PM), and an optimal policy (N*) could be determined numerically or analytically for minimizing the long-run average cost per unit time. Finally, a numerical example is presented to demonstrate the use of this policy.

Aiming at the hybrid flow-shop (HFS) scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution (DE) is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.

The problem of delay-dependent exponential stability is investigated for impulsive stochastic systems with time-varying delay. Although the exponential stability of impulsive stochastic delay systems has been discussed by several authors, few works have been done on delay-dependent exponential stability of impulsive stochastic delay systems. Firstly, the Lyapunov-Krasovskii functional method combing the free-weighting matrix approach is applied to investigate this problem. Some delay-dependent mean square exponential stability criteria are derived in terms of linear matrix inequalities. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive effects. The obtained results show that the system will stable if the impulses’ frequency and amplitude are suitably related to the increase or decrease of the continuous flows, and impulses may be used as controllers to stabilize the underlying stochastic system. Numerical examples are given to show the effectiveness of the results.

A robust task space tracking scheme is proposed for the free-flying space manipulator system. The dynamic equations of the system are derived via the law of momentum conservation, and then a linear state space representation is formulated by local linearization. A parametric approach is applied by using the eigenstructure assignment theory and the model reference method. A feedback stabilizing controller and a feedforward compensation controller are built based on the approach. Then an optimization procedure is followed after that to obtain the desired requirement and characteristics. Simulation results are presented to show the effectiveness of the proposed method.

The mean-square exponential stability problem is investigated for a class of stochastic time-varying delay systems with Markovian jumping parameters. By decomposing the delay interval into multiple equidistant subintervals, a new delay-dependent and decay-rate-dependent criterion is presented based on constructing a novel Lyapunov functional and employing stochastic analysis technique. Besides, the decay rate has no conventional constraint and can be selected according to different practical conditions. Finally, two numerical examples are provided to show that the obtained result has less conservatism than some existing ones in the literature.

The fault detection problem for the nonlinear networked control system (NCS) with packet dropout and delay is investigated. A nonlinear stochastic system model is proposed to account for the NCS with random packet dropout and networkinduced non-uniformly distributed time-varying delay in both from sensor to controller (S/C) and from controller to actuator (C/A). Based on the obtained NCS model, employing an observer-based fault detection filter as the residual generator, the addressed fault detection problem is converted into an auxiliary nonlinear H∞ control problem. Then, with the help of Lyapunov functional approach, a sufficient condition for the desired fault detection filter is constructed in terms of certain linear matrix inequalities, which depend on not only the delay interval but also the delay interval occurrence rate and successful packet communication rate. Especially, a trade-off phenomenon between the maximum allowable delay bound and successful data packet transmission rate is found, which is typically resulted from the limited bandwidth of communication networks. The effectiveness of the proposed method is demonstrated by a simulation example.

The compressive sensing (CS) theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem. Because the theory is based on the assumption of that the location of sparse values is unknown, it has many constraints in practical applications. In fact, in many cases such as image processing, the location of sparse values is knowable, and CS can degrade to a linear process. In order to take full advantage of the visual information of images, this paper proposes the concept of dimensionality reduction transform matrix and then selects sparse values by constructing an accuracy control matrix, so on this basis, a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process. In comparison with similar methods, the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency. The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group (JPEG) compression and acquires the same display effect.

The application of protograph low density parity check (LDPC) codes involves the encoding complexity problem. Since the generator matrices are dense, and if the positions of “1” s are irregularity, the encoder needs to store every “1” of the generator matrices by using huge chip area. In order to solve this problem, we need to design the protograph LDPC codes with circular generator matrices. A theorem concerning the circulating property of generator matrices of nonsingular protograph LDPC codes is proposed. The circulating property of generator matrix of nonsingular protograph LDPC codes can be obtained from the corresponding quasi-cyclic parity check matrix. This paper gives a scheme of constructing protograph LDPC codes with circulating generator matrices, and it reveals that the fast encoding algorithm of protograph LDPC codes has lower encoding complexity under the condition of the proposed theorem. Simulation results in additive white Gaussian noise (AWGN) channels show that the bit error rate (BER) performance of the designed codes based on the proposed theorem is much better than that of GB20600 LDPC codes and Tanner LDPC codes.

A novel low-complexity weighted symbol-flipping algorithm with flipping patterns to decode nonbinary low-density parity-check codes is proposed. The proposed decoding procedure updates the hard-decision received symbol vector iteratively in search of a valid codeword in the symbol vector space. Only one symbol is flipped in each iteration, and symbol flipping function, which is employed as the symbol flipping metric, combines the number of failed checks and the reliabilities of the received bits and calculated symbols. A scheme to avoid infinite loops and select one symbol to flip in high order Galois field search is also proposed. The design of flipping pattern’s order and depth, which is dependent of the computational requirement and error performance, is also proposed and exemplified. Simulation results show that the algorithm achieves an appealing tradeoff between performance and computational requirement over relatively low Galois field for short to medium code length.

The software reliability testing has many disadvantages in practice, such as high complexity of constructing operational profiles and poor fault detection efficiency. Oppositely, the directed testing with a high fault detection rate is incapable of estimating reliability quantificationally. To solve this problem, a hybrid testing combining reliability and directed testing as well as a reliability model based on the order statistic (OS) model were presented by Mitchell. An extended research on Mitchell’s work is proposed. Firstly, the most proper distribution of the fault’s failure rate which tends to be lognormal is suggested, and a detailed form of the OS model based on lognormal and the corresponding parameter estimation method are proposed, respectively. Secondly, an implementing framework for the hybrid testing is proposed. Finally, the hybrid testing and the OS model are applied on a real website system. The experimental results indicate: the hybrid testing has more efficient fault detection power and lower testing cost than the reliability testing; compared with three traditional software reliability growth models, the OS model has a best or pretty estimation or prediction power for each data set; and for the failure data set collected from hybrid testing, the OS model also achieves an acceptable estimation result.

Existing studies about two-dimensional consecutivek-out-of-n: F system is dealt with two states (either component or system): working or failure. A new model for two-dimensional linear consecutive-k-out-of-n: F system is proposed, in which the components and systems can be at any of the three states: full working, derating working, and failure. Furthermore, two engineering concrete instances of the new system are given. By using the finite Markov chain imbedding approach, the system reliability is presented in a unified formula with the product of matrices for the case of independent and identically distributed component states which can be extended to the independent but non-identical case easily. Finally, an example is given to illustrate the effectiveness of the above approach and tractability of various problems.

The optimum design of equivalent accelerated life testing plan based on proportional hazards-proportional odds model using D-optimality is presented. The defined equivalent test plan is the test plan that has the same value of the determinant of Fisher information matrix. The equivalent test plan of step stress accelerated life testing (SSALT) to a baseline optimum constant stress accelerated life testing (CSALT) plan is obtained by adjusting the censoring time of SSALT and solving the optimization problem for each case to achieve the same value of the determinant of Fisher information matrix as in the baseline optimum CSALT plan. Numerical examples are given finally which demonstrate the equivalent SSALT plan to the baseline optimum CSALT plan reduces almost half of the test time while achieving approximately the same estimation errors of model parameters.