This paper presents an analytical saturation throughput model of IEEE 802.11 DCF (distributed coordination function) with basic access in ad hoc mode. The model takes into account freezing of the backoff timer when a station senses busy channel. It is shown that taking into account this feature of DCF is important in modeling saturation throughput by yielding more accurate and realistic results than models known from literature. The proposed analytical model also takes into account the effect of transmission errors. All essential features of the proposed analytical approach are illustrated with numerical results. The presentation of the model is proceeded by an overview of approaches to IEEE 802.11 network performance evaluation presented in the literature.

This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connectivity and efficiency of the CDS. Compared with Wu’s algorithm, the proposed algorithm can make full use of present network conditions and involves fewer nodes. Also it has better performance with regard to the approximation factor, message complexity, and time complexity.

In this letter, we present a modified vector-perturbation precoding scheme for the multiple-input multiple-output broadcast channel, where a perturbation vector is chosen to take into account both the instantaneous power and the instantaneous peak power of the transmitted signal. This perturbation vector is obtained by using the closest point search, with the aid of the lattice-reduction algorithm. Simulation results show that the proposed scheme yields a tradeoff among power efficiency, peak-to-average power ratio reduction, and complexity.

Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process: generation, diffusion, and dissipation of the congestion. In this account, we present a function that represents the extent of congestion on a given node. This approach is different from existing functions based on betweenness centrality. By introducing the concept of ‘delay time’, we designate an intergradation between permanent removal and nonremoval. We also construct an evaluation function of network efficiency, based on congestion, which measures the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability and packet generation speed on congestion propagation. Also, we uncover the relationship between the cascade dynamics and some properties of the network such as structure and size.

The local-world (LW) evolving network model shows a transition for the degree distribution between the exponential and power-law distributions, depending on the LW size. Cascading failures under intentional attacks in LW network models with different LW sizes were investigated using the cascading failures load model. We found that the LW size has a significant impact on the network’s robustness against deliberate attacks. It is much easier to trigger cascading failures in LW evolving networks with a larger LW size. Therefore, to avoid cascading failures in real networks with local preferential attachment such as the Internet, the World Trade Web and the multi-agent system, the LW size should be as small as possible.

This paper presents a pure vision based technique for 3D reconstruction of planet terrain. The reconstruction accuracy depends ultimately on an optimization technique known as ‘bundle adjustment’. In vision techniques, the translation is only known up to a scale factor, and a single scale factor is assumed for the whole sequence of images if only one camera is used. If an extra camera is available, stereo vision based reconstruction can be obtained by binocular views. If the baseline of the stereo setup is known, the scale factor problem is solved. We found that direct application of classical bundle adjustment on the constraints inherent between the binocular views has not been tested. Our method incorporated this constraint into the conventional bundle adjustment method. This special binocular bundle adjustment has been performed on image sequences similar to planet terrain circumstances. Experimental results show that our special method enhances not only the localization accuracy, but also the terrain mapping quality.

This paper presents a method to reconstruct symmetric geometric models from point cloud with inherent symmetric structure. Symmetry types commonly found in engineering parts, i.e., translational, reflectional and rotational symmetries are considered. The reconstruction problem is formulated as a constrained optimization, where the objective function is the sum of squared distances of points to the model, and constraints are enforced to keep geometric relationships in the model. First, the explicit representations of symmetric models are presented. Then, by using the concept of parameterized points (where the coordinate components are represented as functions rather than constants), the distances of points to symmetric models are deduced. With these distance functions, symmetry information, for both 2D and 3D models, is uniformly represented in the process of reconstruction. The constrained optimization problem is solved by a standard nonlinear optimization method. Owing to the explicit representation of symmetry information, the computational complexity of our method is reduced greatly. Finally, examples are given to demonstrate the application of the proposed method.

To solve the homogeneous transformation equation of the form AX=XB in hand-eye calibration, where X represents an unknown transformation from the camera to the robot hand, and A and B denote the known movement transformations associated with the robot hand and the camera, respectively, this paper introduces a new linear decomposition algorithm which consists of singular value decomposition followed by the estimation of the optimal rotation matrix and the least squares equation to solve the rotation matrix of X. Without the requirements of traditional methods that A and B be rigid transformations with the same rotation angle, it enables the extension to non-rigid transformations for A and B. The details of our method are given, together with a short discussion of experimental results, showing that more precision and robustness can be achieved.

This paper describes a vision-based system for blind spot detection (BSD) in intelligent vehicle applications. A camera is mounted in the lateral mirror of a car with the intention of visually detecting cars that are located in the so-called blind spot and cannot be perceived by the vehicle driver. The detection of cars in the blind spot is carried out using computer vision techniques, based on optical flow and a double-stage data clustering technique for robust vehicle detection.

Recently a new clustering algorithm called ‘affinity propagation’ (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two approaches are feasible and practicable.

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.

A novel kernel learning method for object-oriented (OO) software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was validated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experiments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.

This study proposes a recoverable stress testing algorithm (RSTA) for such special devices as compression/decompression card and encryption/decryption card. It uses a chaos function to generate a random sequence, and then, according to the random sequence, generates an effective command sequence. The dispatch of command obeys a special schedule strategy we designed for such devices, i.e., the commands are sent according to the command sequence, and the complete commands are put in a buffer for further result check. RSTA is used to test the HIFN compression acceleration card SAICHI-1000. Test results show that RSTA can make the card work continuously and adequately.

Considering the increasing use of information technology with established standards, such as TCP/IP and XML in modern industrial automation, we present a high cost performance solution with FPGA (field programmable gate array) implementation of a novel reliable real-time data transfer system based on EPA (Ethernet for plant automation) protocol and IEEE 1588 standard. This combination can provide more predictable and real-time communication between automation equipments and precise synchronization between devices. The designed EPA system has been verified on Xilinx Spartan3 XC3S1500 and it consumed 75% of the total slices. The experimental results show that the novel industrial control system achieves high synchronization precision and provides a 1.59-μs standard deviation between the master device and the slave ones. Such a real-time data transfer system is an excellent candidate for automation equipments which require precise synchronization based on Ethernet at a comparatively low price.

This paper presents a multi-mode control scheme for a soft-switched flyback converter to achieve high efficiency and excellent load regulation over the entire load range. At heavy load, critical conduction mode with valley switching (CCMVS) is employed to realize soft switching so as to reduce turn-on loss of power switch as well as conducted electromagnetic interference (EMI). At light load, the converter operates in discontinuous conduction mode (DCM) with valley switching and adaptive off-time control (AOT) to limit the switching frequency range and maintain load regulation. At extremely light load or in standby mode, burst mode operation is adopted to provide low power consumption through reducing both switching frequency and static power dissipation of the controller. The multi-mode control is implemented by an oscillator whose pulse duration is adjusted by output feedback. An accurate valley switching control circuit guarantees the minimum turn-on voltage drop of power switch. The prototype of the controller IC was fabricated in a 1.5-μm BiCMOS process and applied to a 310 V/20 V, 90 W flyback DC/DC converter circuitry. Experimental results showed that all expected functions were realized successfully. The flyback converter achieved a high efficiency of over 80% from full load down to 2.5 W, with the maximum reaching 88.8%, while the total power consumption in standby mode was about 300 mW.

In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global single-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

Recently, there have been more debates on the methods of measuring efficiency. The main objective of this paper is to make a sensitivity analysis for different frontier models and compare the results obtained from the different methods of estimating multi-output frontier for a specific application. The methods include stochastic distance function frontier, stochastic ray frontier, and data envelopment analysis. The stochastic frontier regressions with and without the inefficiency effects model are also compared and tested. The results indicate that there are significant correlations between the results obtained from the alternative estimation methods.

We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.

In this paper, we consider the set partitioning problem with matroid constraint, which is a generation of the k-partitioning problem. The objective is to minimize the weight of the heaviest subset. We present an approximation algorithm, which consists of two sub-algorithms—the modified Edmonds’ matroid partitioning algorithm and the exchange algorithm, for the problem. An estimation of the worst ratio for the algorithm is given.

As an important type of polynomial approximation, approximation of functions by Bernstein operators is an important topic in approximation theory and computational theory. This paper gives global and pointwise estimates for weighted approximation of functions with singularities by Bernstein operators. The main results are the Jackson’s estimates of functions f∈(W_w,λ)² and f∈C_w, which extends the result of (Della Vecchia et al., 2004).

In this paper, an implicit symmetry constraint is calculated and its associated binary nonlinearization of the Lax pairs and the adjoint Lax pairs is carried out for the modified Korteweg-de Vries (mKdV) equation. After introducing two new independent variables, we find that under the implicit symmetry constraint, the spatial part and the temporal part of the mKdV equation are decomposed into two finite-dimensional systems. Furthermore we prove that the obtained finite-dimensional systems are Hamiltonian systems and completely integrable in the Liouville sense.

There are some curved interfaces in ocean acoustic waveguides. To compute wave propagation along the range with some marching methods, a flattening of the internal interfaces and a transforming equation are needed. In this paper a local orthogonal coordinate transform and an equation transformation are constructed to flatten interfaces and change the Helmholtz equation as a solvable form. For a waveguide with a flat top, a flat bottom and n curved interfaces, the coefficients of the transformed Helmholtz equation are given in a closed formulation which can be thought of as an extension of the formal work related to the equation transformation with two curved internal interfaces. In the transformed horizontally stratified waveguide, the one-way reformulation based on the Dirichlet-to-Neumann (DtN) map is then used to reduce the boundary value problem to an initial value problem. Numerical implementation of the resulting operator Riccati equation uses a large range step method to discretize the range variable and a truncated local eigenfunction expansion to approximate the operators. This method is particularly useful for solving long range wave propagation problems in slowly varying waveguides. Furthermore, the method can also be applied to wave propagation problems in acoustic waveguides associated with varied density.