Stability analysis of discrete-time switched systems: a switched homogeneous Lyapunov function method

This paper addresses the stability issue of discrete-time switched systems with guaranteed dwell-time. The approach of switched homogeneous Lyapunov function of higher order is formally proposed. By means of this approach, a necessary and sufficient condition is established to check the exponential stability of the considered system. With the observation that switching signal is actually arbitrary if the dwell time is one sample time, a necessary and sufficient condition is also presented to verify the exponential stability of switched systems under arbitrary switching signals. Using the augmented argument, a necessary and sufficient exponential stability criterion is given for discrete-time switched systems with delays. A numerical example is provided to show the advantages of the theoretical results.     

Online learning of dynamic multi-view gallery for person Re-identification

Person re-identification receives increasing attentions in computer vision due to its potential applications in video surveillance. In order to alleviate wrong matches caused by misalignment or missing features among cameras, we propose to learn a multi-view gallery of frequently appearing objects in a relatively closed environment. The gallery contains appearance models of these objects from different cameras and viewpoints. The strength of the learned appearance models lies in that they are invariant to viewpoint and illumination changes. To automatically estimate the number of frequently appearing objects in the environment and update their appearance models online, we propose a dynamic gallery learning algorithm. We specifically build up two datasets to validate the effectiveness of our approach in realistic scenarios. Comparisons with benchmark methods demonstrate promising performance in accuracy and efficiency of re-identification.     

Visual topic discovering,tracking and summarization from social media streams

Multimedia Tools and Applications - Nowadays, microblogging has become popular, with hundreds of millions of short messages being posted and shared every minute on a variety of topics in social...     

Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform

Multimedia Tools and Applications - Most of the existing image encryption algorithms based on compressive sensing are too complex to operate. An image compression-encryption scheme with simple...     

A transductive multi-label learning approach for video concept detection

In this paper, we address two important issues in the video concept detection problem: the insufficiency of labeled videos and the multiple labeling issue. Most existing solutions merely handle the two issues separately. We propose an integrated approach to handle them together, by presenting an effective transductive multi-label classification approach that simultaneously models the labeling consistency between the visually similar videos and the multi-label interdependence for each video. We compare the performance between the proposed approach and several representative transductive and supervised multi-label classification approaches for the video concept detection task over the widely used TRECVID data set. The comparative results demonstrate the superiority of the proposed approach.     

A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.     

Nanoparticle-based lift-off technique for ultra-thin nanoporous film preparation

Ultra-thin membrane with nanoscale through hole has great potential in biomedical applications, where precise controllability of porosity, pore size and film thickness is urgently required. The present work proposed a cost-effective way to prepare the ultra-thin nanoporous film with a promising controllability. Monodispersed nanoparticle, rather than photoresist, is used as the sacrificial material for this new lift-off process. By releasing the particles, holes can be achieved with predeter-mined characters. A 110 nm-thick nanoporous aluminum film with well-controlled pore＇s diameter was successfully fabricated to validate the technique. The technique has wider process window and better applicability than other nanofabrication methods.     

Efficient compression algorithm for hyperspectral images based on correlation coefficients adaptive 3D zerotree coding

The authors propose an efficient compression algorithm for hyperspectral images, which is based on the correlation coefficients adaptive asymmetric tree three-dimensional (3D) set partitioning in hierarchical trees (AT-3DSPIHT) coding of the asymmetric 3D wavelet transform (A3D-DWT) coefficients. According to the characteristics of the correlation coefficients between adjacent spectral bands, a binary tree spectral band grouping algorithm is carried out to divide the adjacent spectral bands into different mode groups. Along with this, an appropriate A3D-DWT with the corresponding decomposition levels and a proper AT-3D zerotree are determined adaptively. Several airborne visible/infrared imaging spectrometer images are used to evaluate the proposed algorithm. Compared with the state-of-the-art 3D-DWT based algorithms, the proposed adaptive AT-3DSPIHT achieves the best compression performance at lower bit rates. Moreover, at the low correlated adjacent bands, the proposed algorithm also outperforms the 2DSPIHT algorithm.