Proteomics in chronic kidney disease: The issues clinical nephrologists need an answer for

A growing number of patients are recognised to have chronic kidney disease (CKD). However, only a minority will progress to end-stage renal disease requiring dialysis or transplantation. Currently available diagnostic and staging tools frequently fail to identify those at higher risk of progression or death. Furthermore within specific disease entities there are shortcomings in the prediction of the need for therapeutic interventions or the response to different forms of therapy. Kidney and urine proteomic biomarkers are considered as promising diagnostic tools to predict CKD progression early in diabetic nephropathy, facilitating timely and selective intervention that may reduce the related health-care expenditures. However, independent groups have not validated these findings and the technique is not currently available for routine clinical care. Furthermore, there are gaps in our understanding of predictors of progression or need for therapy in non-diabetic CKD. Presumably, a combination of tissue and urine biomarkers will be more informative than individual markers. This review identifies clinical questions in need of an answer, summarises current information on proteomic biomarkers and CKD, and describes the European Kidney and Urine Proteomics initiative that has been launched to carry out a clinical study aimed at identifying urinary proteomic biomarkers distinguishing between fast and slow progressors among patients with biopsy-proven primary glomerulopathies.     

Weighted and extended total variation for image restoration and decomposition

In various information processing tasks obtaining regularized versions of a noisy or corrupted image data is often a prerequisite for successful use of classical image analysis algorithms. Image restoration and decomposition methods need to be robust if they are to be useful in practice. In particular, this property has to be verified in engineering and scientific applications. By robustness, we mean that the performance of an algorithm should not be affected significantly by small deviations from the assumed model. In image processing, total variation (TV) is a powerful tool to increase robustness. In this paper, we define several concepts that are useful in robust restoration and robust decomposition. We propose two extended total variation models, weighted total variation (WTV) and extended total variation (ETV). We state generic approaches. The idea is to replace the TV penalty term with more general terms. The motivation is to increase the robustness of ROF (Rudin, Osher, Fatemi) model and to prevent the staircasing effect due to this method. Moreover, rewriting the non-convex sublinear regularizing terms as WTV, we provide a new approach to perform minimization via the well-known Chambolle's algorithm. The implementation is then more straightforward than the half-quadratic algorithm. The behavior of image decomposition methods is also a challenging problem, which is closely related to anisotropic diffusion. ETV leads to an anisotropic decomposition close to edges improving the robustness. It allows to respect desired geometric properties during the restoration, and to control more precisely the regularization process. We also discuss why compression algorithms can be an objective method to evaluate the image decomposition quality.     

A dynamic video watermarking algorithm in fast motion areas in the wavelet domain

In this paper, we propose a video watermarking algorithm which embeds different parts of a single watermark into different shots of a video under the wavelet domain. Based on a Motion Activity Analysis, different regions of the original video are separated into perceptually distinct categories according to motion information and region complexity. Thus, the localizations of the watermark are adjusted adaptively in accordance with the human visual system and signal characteristics, which makes them perceptually invisible and less vulnerable to automated removal. In addition, contrary to traditional methods where the watermark remains at a fixed position on the screen, the watermark moves along with moving objects and thus motion artefacts can be avoid. The multi-frame based extraction strategy ensures that the watermark can be correctly recovered from a very short segment of video. Individual frames extracted from the video also contain watermark information. Experimental results show that the inserted watermark is not only less perceptible but also robust against common video processing attacks.     

Quasi-Monte Carlo methods for Markov chains with continuous multi-dimensional state space

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence of the method when the number of copies increases. We illustrate the method with numerical examples where the simulation accuracy is improved by large factors compared with Monte Carlo simulation.     

Multimodal fusion for multimedia analysis: a survey

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.     

Robust control of T‐S fuzzy systems with time‐varying delay using new approach

This paper aims to design a controller to robustly stabilize uncertain nonlinear systems with time‐varying delay and norm bounded uncertainties via Takagi–Sugeno (T‐S) fuzzy model. The stabilization conditions are given in the form of linear matrix inequalities using a single Lyapunov–Krasovskii functional (LKF) combining the introduction of some relaxation matrices and only one tuning parameter. In comparison with the existing techniques in the literature, the proposed approach has two major advantages. The first is the reduction of computational complexity when the number of IF‐THEN rules, r, is big. The second concerns the conservatism reduction. Several examples are given to show the effectiveness and the merits of the design procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

Editors’ note

Editorial Note

Editors’ note     

Building a peer-to-peer content distribution network with high performance,scalability and robustness

Content distribution networks (CDN) are fundamental, yet expensive technologies for distributing the content of web-servers to large audiences. The P2P model is a perfect match to build a low-cost and scalable CDN infrastructure for popular websites by exploiting the underutilized resources of their user communities. However, building a P2P-based CDN is not a straightforward endeavor. In contrast to traditional CDNs, peers are autonomous and volunteer participants with their own heterogeneous interests that should be taken into account in the design of the P2P system. Moreover, churn rate is much higher than in dedicated CDN infrastructures, which can easily destabilize the system and severely degrade the performance. Finally and foremostly, while many P2P systems abstract any topological information about the underlying network, a top priority of a CDN is to incorporate locality-awareness in query routing in order to locate close-by content. This paper aims at building a P2P CDN with high performance, scalability and robustness. Our proposed protocols combine DHT efficiency with gossip robustness and take into account the interests and localities of peers. In short, Flower-CDN provides a hybrid and locality-aware routing infrastructure for user queries. PetalUp-CDN is a highly scalable version of Flower-CDN that dynamically adapts to variable rates of participation and prevent overload situations. In addition, we ensure the robustness of our P2P CDN via low-cost maintenance protocols that can detect and recover from churn and dynamicity. Our extensive performance evaluation shows that our protocols yield high performance gains under both static and highly dynamic environments. Furthermore, they incur acceptable and tunable overhead. Finally we provide main guidelines to deploy Flower-CDN for the public use.     

Haar invariant signatures and spatial recognition using omnidirectional visual information only

This paper describes a method for spatial representation, place recognition and qualitative self-localization in dynamic indoor environments, based on omnidirectional images. This is a difficult problem because of the perceptual ambiguity of the acquired images, and their weak robustness to noise, geometrical and photometric variations of real world scenes. The spatial representation is built up invariant signatures using Invariance Theory where we suggest to adapt Haar invariant integrals to the particular geometry and image transformations of catadioptric omnidirectional sensors. It follows that combining simple image features in a process of integration over visual transformations and robot motion, can build discriminant percepts about robot spatial locations. We further analyze the invariance properties of the signatures and the apparent relation between their similarity measures and metric distances. The invariance properties of the signatures can be adapted to infer a hierarchical process, from global room recognition to local and coarse robot localization.