Two kinds of selection combining schemes including generalized selection combining (GSC) and generalized order selection combining (GOSC) are investigated. In the GSC scheme, L strongest diversity branches from a total of R diversity branches are selected and coherently combined by maximal ratio combining. GOSC means that the Lth strongest diversity branch from R diversity branches is selected for reception. Closed-form expressions for the average signal-to-noise ratios of maximum ratio transmission with GSC and GOSC are derived in Rayleigh fading channels.

We propose a reputation-based cooperative spectrum sensing scheme in cognitive radio (CR) networks to solve the uncertainty resulting from the multipath fading and shadowing effect. In the proposed scheme, each cooperative CR user has a reputation degree that is initialized and adjusted by the central controller, and used to weight the sensing result from the corresponding CR user in the linear fusion process at the central controller. A simple method for adjusting the reputation degree of CR users is also presented. We analyzed and evaluated the detection performance of the reputation-based cooperative spectrum sensing scheme. Simulation results showed that our proposed scheme alleviates the problem of corrupted detection resulting from destructive channel conditions between the primary transmitter and the CR user. The performance of our proposed scheme was improved compared to the average-based linear cooperation scheme, and was similar to that of the optimal linear cooperation scheme with feasible computational complexity. Moreover, our proposed scheme does not require knowledge of channel statistics.

Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity (SSIM) index could not predict the quality of the video sequence effectively. In this paper we propose a structural similarity quality metric for videos based on a spatial-temporal visual attention model. This model acquires the motion attended region and the distortion attended region by computing the motion features and the distortion contrast. It mimics the visual attention shifting between the two attended regions and takes the burst of error into account by introducing the non-linear weighting functions to give a much higher weighting factor to the extremely damaged frames. The proposed metric based on the model renders the final object quality rating of the whole video sequence and is validated using the 50 Hz video sequences of Video Quality Experts Group Phase I test database.

This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.

In this paper, a new three-dimensional Markov model is proposed for the estimation of saturation throughput of RTS/CTS (request to send/clear to send) scheme in an error-prone wireless local area network (WLAN) channel. The model takes account of the effect of bit error on all the frames, and station short and long retry limits. Saturation throughput was re-analyzed using the new Markov model and numerical results closely matched those from simulation, confirming the accuracy of the new model. Evaluation of the influence of different parameters on throughput showed that the saturation throughput is sensitive to channel bit error rates and packet length, especially in high bit error conditions.

Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between high- and low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.

A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering (DIBR) warped images. Current solutions include layered depth image (LDI), pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi’s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of filling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio (PSNR) statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.

Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.

Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as “pandas eat bamboo”. In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.

Finding all occurrences of a twig pattern is a core operation of extensible markup language (XML) query processing. Holistic twig join algorithms, which avoid a large number of intermediate results, represent the state-of-the-art algorithms. However, ordered XML twig join is mentioned rarely in the literature and previous algorithms developed in attempts to solve the problem of ordered twig pattern (OTP) matching have poor performance. In this paper, we first propose a novel children linked stacks encoding scheme to represent compactly the partial ordered twig join results. Based on this encoding scheme and extended Dewey, we design a novel holistic OTP matching algorithm, called OTJFast, which needs only to access the labels of the leaf query nodes. Furthermore, we propose a new algorithm, named OTJFaster, incorporating three effective optimization rules to avoid unnecessary computations. This works well on available indices (such as B⁺-tree), skipping useless elements. Thus, not only is disk access reduced greatly, but also many unnecessary computations are avoided. Finally, our extensive experiments over both real and synthetic datasets indicate that our algorithms are superior to previous approaches.

Satisfiability modulo theories (SMT) play a key role in verification applications. A crucial SMT problem is to combine separate theory solvers for the union of theories. In previous work, the simplex method is used to determine the solvability of constraint systems and the equalities implied by constraint systems are detected by a multitude of applications of the dual simplex method. We present an effective simplex tableau-based method to identify all implicit equalities such that the simplex method is harnessed to an irreducible minimum. Experimental results show that the method is feasible and effective.

Leakage of the private key has become a serious problem of menacing the cryptosystem security. To reduce the underlying danger induced by private key leakage, Dodis et al.(2003) proposed the first key-insulated signature scheme. To handle issues concerning the private key leakage in certificateless signature schemes, we devise the first certificateless key-insulated signature scheme. Our scheme applies the key-insulated mechanism to certificateless cryptography, one with neither certificate nor key escrow. We incorporate Waters (2005)’s signature scheme, Paterson and Schuldt (2006)’s identity-based signature scheme, and Liu et al.(2007)’s certificateless signature scheme to obtain a certificateless key-insulated signature scheme. Our scheme has two desirable properties. First, its security can be proved under the non-pairing-based generalized bilinear Diffie-Hellman (NGBDH) conjecture, without utilizing the random oracle model; second, it solves the key escrow problem in identity-based key-insulated signatures.

As technology scales down, the reliability issues are becoming more crucial, especially for networks-on-chip (NoCs) that provide the communication requirements of multi-processor systems-on-chip. Reliability evaluation based on analytical models is a precise method for dependability analysis before and after designing the fault-tolerant systems. In this paper, we accurately formulate the inherent reliability and vulnerability of some popular NoC architectures against permanent faults, also depending on the employed routing algorithm and traffic model. Based on this analysis, effects of failures in the links, switches and network interfaces on the packet delivery of NoCs are determined. Besides, some extensions to evaluate a fault-tolerant method and some routing algorithms are described. The analyses are validated through appropriate simulations. The results thus obtained are exactly the same as or very close to the analytical ones.

This paper presents an efficient method for globally optimizing and automating component sizing for rotary traveling wave oscillator arrays. The lumped equivalent model of transmission lines loaded by inverter pairs is evaluated and posynomial functions for oscillation frequency, power dissipation, phase noise, etc. are formulated using transmission line theory. The resulting design problem can be posed as a geometric programming problem, which can be efficiently solved with a convex optimization solver. The proposed method can compute the global optima more efficiently than the traditional iterative scheme and various design problems can be solved with the same circuit model. The globally optimal trade-off curves between competing objectives are also computed to carry out robust designs and quickly explore the design space.

Voltage source converter high-voltage direct current (VSC-HVDC) is a new power transmission technology preferable in small or medium power transmission. In this paper we discuss a new control system based on space vector modulation (SVM) without any voltage line sensors. Using direct power control (DPC) SVM and a new double synchronous reference frame phase-locked loop (DSRF-PLL) approach, the control system is resistant to the majority of line voltage disturbances. Also, the system response has accelerated by using a feed forward power decoupled loop. The operation of this control strategy was verified in a SIMULINK/MATLAB simulation environment. To validate this control system, a 5 kV·A prototype system was constructed. Compared to the original controllers, the current total harmonic distortion (THD), the active and reactive deviations and the DC voltage overshoot were lowered by 2.5%, 6.2% and 8%, respectively. The rectifier power factor in the worst condition was 0.93 and the DC voltage settling time was 0.2 s.

We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-explicit Runge-Kutta method for time integration. The resulting method retains the simplicity of the relaxation schemes. There is no need to involve Riemann solvers and characteristic decomposition. Even the computation of the eigenvalues is not required. This makes the scheme particularly well suited for the MCLWR model in which the analytical expressions of the eigenvalues are difficult to obtain for more than four classes of road users. The numerical results illustrate the effectiveness of the presented method.