Central Aggregator Intrusion Detection System for Denial of Service Attacks

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed. The proposed system, central aggregator–intrusion detection system (CA-IDS), works as a security gateway for EVs to analyze and monitor incoming traffic for possible DoS attacks. EVs are registered with a Central Aggregator (CAG) to exchange authenticated messages, and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG. A denial of service (DoS) attack is simulated at CAG in a vehicle-to-grid (V2G) network manipulating various network parameters such as transmission overhead, receiving capacity of destination, average packet size, and channel availability. The proposed system is compared with existing intrusion detection systems using different parameters such as throughput, jitter, and accuracy. The analysis shows that the proposed system has a higher throughput, lower jitter, and higher accuracy as compared to the existing schemes.     

Effect of uncoated and coated diamond on the compressive properties of porous aluminium composites

Porous aluminium composites are structural and functional materials that have vast potential, due to their lightweight and high energy absorption capacity, especially in automotive and aerospace applications. In this study, the effect of varying content of uncoated and titanium coated diamond particles on the compressive properties of porous aluminium composite was investigated. The composites were developed using powder metallurgy technique and porosity was attained by using polymethylmethacrylate (30 wt.%) as space holder material. The morphology of the pores was found to replicate the shape and size of polymethylmethacrylate particles, that were uniformly distributed in the composites. X-ray diffraction analysis confirmed formation of aluminium carbide in uncoated diamond-based aluminium composites while negligible amount was present in titanium coated porous composites during sintering. The porosities of composites decreased with an increase in diamond content due to the incomplete decomposition of polymethylmethacrylate particles. Moreover, the maximum plateau stress and energy absorption capacity of 9.96 MPa and 1.7 Mj/m³ were obtained for the composites with 8 wt.% of titanium coated diamond particles. Thus, coating inhibits the formation of undesirable compounds and contributes to better interfacial bonding between matrix and reinforcement.     

Spectral Measurement of the Film-Substrate Index Difference in Proton-Exchanged LiNbO(3) Waveguides

We report the spectral characterization of proton-exchanged lithium niobate (PE:LiNbO(3)) waveguides in terms of the variation of the refractive-index difference between the waveguiding layer and the substrate. The dispersion of the extraordinary refractive-index increase (deltan(e)) is measured from 405 to 1319 nm with several light sources. Two types of proton-exchanged waveguide, prepared under different conditions, are studied. These measurements should be of use in the optimization of PE:LiNbO(3) waveguides for nonlinear optical applications, particularly in second-harmonic generation in the blue-green wavelength region.     

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.