We propose a parameterized multisurface fitting method for multi-frame super-resolution (SR) processing. A parameter assumed for the unknown high-resolution (HR) pixel is used for multisurface fitting. Each surface fitted at each low-resolution (LR) pixel is an expression of the parameter. Final SR result is obtained by fusing the sampling values from these surfaces in the maximum a posteriori fashion. Experimental results demonstrate the superiority of the proposed method.

Post-Silicon Tunable (PST) buffers are widely adopted in high-performance integrated circuits to fix timing violations introduced by process variations. In typical optimization procedures, the statistical timing analysis of the circuits with PST clock buffers will be executed more than 2000 times for large scale circuits. Therefore, the efficiency of the statistical timing analysis is crucial to the PST clock buffer optimization algorithms. In this paper, we propose a stochastic collocation based efficient statistical timing analysis method for circuits with PST buffers. In the proposed method, we employ the Howard algorithm to calculate the clock periods of the circuits on less than 100 deterministic sparse-grid collocation points. Afterwards, we use these obtained clock periods to derive the yield of the circuits according to the stochastic collocation theory. Compared with the state-of-the-art statistical timing analysis method for the circuits with PST clock buffers, the proposed method achieves up to 22X speedup with comparable accuracy.

In this paper, by jointly considering power allocation and network selection, we address the energy efficiency maximization problem in dynamic and heterogeneous wireless networks, where user equipments are typically equipped with multi-homing capability. In order to effectively deal with the dynamics of heterogeneous wireless networks, a stochastic optimization problem is formulated that optimizes the long-term energy efficiency under the constraints of system stability, peak power consumption and average transmission rate. By adopting the parametric approach and Lyapunov optimization, we derive an equivalent optimization problem out of the original problem and then investigate its optimal resource allocation. Then, to reduce the computational complexity, a suboptimal resource allocation algorithm is proposed based on relaxed optimization, which adapts to time-varying channels and stochastic traffic without requiring relevant a priori knowledge. The simulation results demonstrate the theoretical analysis and validate the adaptiveness of our proposed algorithm.

In this paper, we design a stealthy GSM phone identity catcher. As the GSM protocols do not mandate the authentication of BSes (Base Stations) to MSes (Mobile Stations), fake BSes can be implemented to lure victims to register with and thereby intercept crucial information of the user, including their identities. However, the straightforward implementation of GSM phone identity catcher can be easily perceived by users employing detection software due to such phenomena as phone interface changes and service interruptions. In this paper, we propose several effective mechanisms, such as smart configuration of the fake BSes, quick attachment/detachment and service relay, to make the catching process invisible to users and software. Real world experiments have been conducted and the results prove the efficiency and stealth of our proposed GSM phone identity catcher. We hope our work could help to enhance the effectiveness of IMSI catching attack and thereby alert the industry to design stronger authentication protocol in communication systems.

To detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.

In this paper, we propose a Modified nested sparse grid based Adaptive Stochastic Collocation Method (MASCM) for block-based Statistical Static Timing Analysis (SSTA). The proposed MASCM employs an improved adaptive strategy derived from the existing Adaptive Stochastic Collocation Method (ASCM) to approximate the key operator MAX during timing analysis. In contrast to ASCM which uses non-nested sparse grid and tensor product quadratures to approximate the MAX operator for weakly and strongly nonlinear conditions respectively, MASCM proposes a modified nested sparse grid quadrature to approximate the MAX operator for both weakly and strongly nonlinear conditions. In the modified nested sparse grid quadrature, we firstly construct the second order quadrature points based on extended Gauss-Hermite quadrature and nested sparse grid technique, and then discard those quadrature points that do not contribute significantly to the computation accuracy to enhance the efficiency of the MAX approximation. Compared with the non-nested sparse grid quadrature, the proposed modified nested sparse grid quadrature not only employs much fewer collocation points, but also offers much higher accuracy. Compared with the tensor product quadrature, the modified nested sparse grid quadrature greatly reduced the computational cost, while still maintains sufficient accuracy for the MAX operator approximation. As a result, the proposed MASCM provides comparable accuracy while remarkably reduces the computational cost compared with ASCM. The numerical results show that with comparable accuracy MASCM has 50% reduction in run time compared with ASCM.

Due to the growing popularity of High Dynamic Range (HDR) images and HDR displays, a large amount of existing Low Dynamic Range (LDR) images are required to be converted to HDR format to benefit HDR advantages, which give rise to some LDR to HDR algorithms. Most of these algorithms especially tackle overexposed areas during expanding, which is the potential to make the image quality worse than that before processing and introduces artifacts. To dispel these problems, we present a new LDR to HDR approach, unlike the existing techniques, it focuses on avoiding sophisticated treatment to overexposed areas in dynamic range expansion step. Based on a separating principle, firstly, according to the familiar types of overexposure, the overexposed areas are classified into two categories which are removed and corrected respectively by two kinds of techniques. Secondly, for maintaining color consistency, color recovery is carried out to the preprocessed images. Finally, the LDR image is expanded to HDR. Experiments show that the proposed approach performs well and produced images become more favorable and suitable for applications. The image quality metric also illustrates that we can reveal more details without causing artifacts introduced by other algorithms.

In this paper, a Stochastic Non-Homogeneous ARnoldi (SNHAR) method is proposed for the analysis of the on-chip power grid networks in the presence of process variations. In SNHAR method, the polynomial chaos based stochastic method is employed to handle the variations of power grids. Different from the existing StoEKS method which uses extended Krylov Subspace (EKS) method to compute the coefficients of the polynomial chaos, a computation-efficient and numerically stable Non-Homogeneous ARnoldi (NHAR) method is employed in SNHAR method to compute the coefficients of the polynomial chaos. Compared with EKS method, NHAR method has superior numerical stability and can achieve remarkably higher accuracy with even lower computational cost. As a result, SNHAR can capture the stochastic characteristics of the on-chip power grid networks with higher accuracy, but even lower computational cost than StoEKS.