An efficient identity-based group signature scheme over elliptic curves

Group signatures allow every authorized member of a group to sign on behalf of the underlying group. Anyone except the group manager is not able to validate who generates a signature for a document. A new identity-based group signature scheme is proposed in this paper. This scheme makes use of a bilinear function derived from Weil pairings over elliptic curves. Also, in the underlying composition of group signatures there is no exponentiation computation modulo a large composite number. Due to these ingredients of the novel group signatures, the proposed scheme is efficient with respect to the computation cost in signing process. In addition, this paper comes up with a security proof against adaptive forgeability

An efficient nonnegative matrix factorization approach in flexible Kernel space

In this paper, we propose a general formulation for kernel nonnegative matrix factorization with flexible kernels. Specifically, we propose the Gaussian nonnegative matrix factorization (GNMF) algorithm by using the Gaussian kernel in the framework. Different from a recently developed polynomial NMF (PNMF), GNMF finds basis vectors in the kernel-induced feature space and the computational cost is independent of input dimensions. Furthermore, we prove the convergence and nonnegativity of decomposition of our method. Extensive experiments compared with PNMF and other NMF algorithms on several face databases, validate the effectiveness of the proposed method

Rank Stability Radius for a Matrix with Structured Scalar Perturbations

In this paper, the rank stability radius problem is proposed for a real matrix under structured scalar perturbations and some interesting results are achieved based on polynomial analysis. In addition, a computable formula and a two-step procedure are obtained which nicely solves the problem in this simple set up. Finally, these results on rank stability radius are used to estimate the stability robustness of descriptor systems, and for a special class of symmetric descriptor systems, the rank stability radius is proved to be equal to the system stability radius

Multi-Scale Human Pose Tracking in 2D Monocular Images

In this paper we address the problem of tracking human poses in multiple perspective scales in 2D monocular images/videos. In most state-of-the-art 2D tracking approaches, the issue of scale variation is rarely discussed. However in reality, videos often contain human motion with dynamically changed scales. In this paper we pro-pose a tracking framework that can deal with this problem. A scale checking and adjusting algorithm is pro-posed to automatically adjust the perspective scales during the tracking process. Two metrics are proposed for detecting and adjusting the scale change. One metric is from the height value of the tracked target, which is suitable for some sequences where the tracked target is upright and with no limbs stretching. The other metric employed in this algorithm is more generic, which is invariant to motion types. It is the ratio between the pixel counts of the target silhouette and the detected bounding boxes of the target body. The proposed algorithm is tested on the publicly available datasets (HumanEva). The experimental results show that our method demon-strated higher accuracy and efficiency compared to state-of-the-art approache

Practical stability and controllability for nonlinear discrete time-delay systems

In this paper we study the practical asymptotic stability for a class of discrete-time time-delay systems via Razumikhin-type Theorems. Further estimations of the solution boundary and arrival time of the solution are also investigated based on practical stability. In addition, the proposed theorems are used to analyze the practical controllability of a general class of nonlinear discrete systems with input time delay. Some easy testing criteria for the uniform practical asymptotical stability are derived via Lyapunov function and Razumikhin technique. Finally a numerical example is given to illustrate the effectiveness of the proposed results

Low Resolution Face Recognition in Surveillance Systems

In surveillance systems, the captured facial images are often very small and different from the low-resolution images down-sampled from high-resolution facial images. They generally lead to low performance in face recog-nition. In this paper, we study specific scenarios of face recognition with surveillance cameras. Three important factors that influence face recognition performance are investigated: type of cameras, distance between the ob-ject and camera, and the resolution of the captured face images. Each factor is numerically investigated and analyzed in this paper. Based on these observations, a new approach is proposed for face recognition in real sur-veillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing tech-niques are employed to remove the illumination variations for the enhancement of image quality. The face im-ages are further improved through a novel face image super-resolution method. The proposed approach is proven to significantly improve the performance of face recognition as demonstrated by experiments

Dissipativity Analysis of Descriptor Systems Using Image Space Characterization

In this paper, we analyze the dissipativity for descriptor systems with impulsive behavior based on image space analysis. First, a new image space is used to characterize state responses for descriptor systems. Based on such characterization and an integral property of delta function, a new necessary and sufficient condition for the dissipativity of descriptor systems is derived using the linear matrix inequality (LMI) approach. Also, some of the earlier related results on dissipativity for linear systems are investigated in the framework proposed in this paper. Finally, two examples are given to show the validity of the derived results

H∞ Norm Computation for Descriptor Symmetric Systems

This paper deals with the problem of H∞ norm computation for general symmetric systems and descriptor symmetric systems. The computation of H∞ norm for state-space symmetric systems is extended to descriptor symmetric systems. An explicit expression is given based on the bound real lemma (BRL), and the generalized bound real lemma (GBRL). The results have obvious computational advantages, especially for large scale descriptor symmetric systems. Additionally, two numerical examples are presented to demonstrate the feasibility and effectiveness of the results

A new class of efficient piecewise nonlinear chaotic maps for secure cryptosystems

In this paper we construct a new class of nonlinear chaotic maps for secure cryptosystems. These maps can overcome the security holes brought by the "piecewise linearity" of the previous Piecewise Linear Chaotic Maps (PWLCM) due to a fact that the chaotic sequences generated by the derived iterative system based on the proposed maps are proved to have perfect dynamic properties, such as uniform invariant distribution, d-like autocorrelation function etc. Furthermore, the relative quantized two-value sequences also have perfect secure statistical characteristics. In terms of computing speed, the proposed maps have faster speed than the recently proposed nonlinear "piecewise-square-root" maps (PSRM), and they actually have equivalently the same computing speed with the linear PWLCM