On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines

In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques.This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.     

Determination of pyrethroids in chicken egg samples: development and validation of a confirmatory analytical method by gas chromatography/mass spectrometry

The aim of this study was to develop and validate an efficient analytical method based on gas chromatography/tandem mass spectrometry (GC/MS/MS) for detection and quantification of six pyrethroids residues (Phenothrin, Permethrin, Cyfluthrin, Cypermethrin, Deltamethrin and Fenvalerate) in chicken eggs. The method was based on a preliminary liquid–liquid extraction of albumen‐free yolk samples, followed by a clean‐up by solid‐phase extraction. GC/MS/MS analyses were carried out in the selected reaction monitoring mode. Validation parameters such as specificity, detection capability, decision limit, precision, recovery, stability and ruggedness were determined, resulting in compliance with Decision 2002/657/EC. No complicated apparatus are required; moreover, low volumes of organic solvents and a nonintensive manual labour are required. These low costs and simple procedure, based on rapid and safe operations, may represent a useful tool in the routine analysis of pyrethroids pesticides, in the place of the currently used conventional techniques.     

Chemical composition,sensory and cooking quality evaluation of durum wheat spaghetti enriched with pea flour

The goal of the study was to obtain spaghetti enriched with pea flour with sensory properties close to conventional pasta. To this aim, the study has been organised in two subsequent trials. In the first, the pea flour amount added to the spaghetti was continuously increased until the overall sensory quality reached its threshold value (pea flour concentration = 15%). In fact, the spaghetti samples supplemented with 15% pea flour (15%S‐P) showed less elasticity, unpleasant colour and higher firmness compared to the control sample (CTRL). The second step was aimed to improve the overall sensory quality of the 15%S‐P spaghetti by means of guar gum (GUAR). The sample with GUAR (15%S‐P/GUAR) showed a pleasant colour, odour and taste. Furthermore, the 15%S‐P/GUAR sample recorded a low starch digestibility value (i.e. 54) and a higher soluble fibre content with respect to the 15%S‐P sample.     

An LMI approach for H ∞ analysis and control of discrete-time piecewise affine systems

In this paper we investigate some analysis and control problems for discrete-time hybrid systems in the piece-wise affine form. By using arguments from the dissipativity theory for non-linear systems, we show that H X analysis and synthesis problems can be formulated and solved via linear matrix inequalities by taking into account the switching structure of the considered system. In this paper we address the generalized problem of controlling hybrid systems whose switching structure does not depend only on the state but also on the control input.     

Reversible Fragmentation and Self‐Assembling of Nematic Liquid Crystal Droplets on Functionalized Pyroelectric Substrates

Investigation on the behavior of nematic liquid crystals on functionalized polar dielectric crystal substrates is accomplished. Very interesting effects can be observed in maneuvering liquid crystal droplets on the substrate surface, driven by electric fields generated by pyroelectric effect. Reversible drops fragmentation and self‐assembling in different configurations can be achieved. The dynamics of the observed phenomena is studied and the repeatability of the process is full assessed.     

Simultaneous Microwave Extraction and Separation of Volatile and Non-Volatile Organic Compounds of Boldo Leaves. From Lab to Industrial Scale

Microwave extraction and separation has been used to increase the concentration of the extract compared to the conventional method with the same solid/liquid ratio, reducing extraction time and separate at the same time Volatile Organic Compounds (VOC) from non-Volatile Organic Compounds (NVOC) of boldo leaves. As preliminary study, a response surface method has been used to optimize the extraction of soluble material and the separation of VOC from the plant in laboratory scale. The results from the statistical analysis revealed that the optimized conditions were: microwave power 200 W, extraction time 56 min and solid liquid ratio of 7.5% of plants in water. Lab scale optimized microwave method is compared to conventional distillation, and requires a power/mass ratio of 0.4 W/g of water engaged. This power/mass ratio is kept in order to upscale from lab to pilot plant.     

General Framework for Rotation Invariant Texture Classification Through Co-occurrence of Patterns

The use of co-occurrences of patterns in image analysis has been recently suggested as one of the possible strategies to improve on the bag-of-features model. The intrinsically high number of features of the method, however, is a potential limit to its widespread application. Its extension into rotation invariant versions also requires careful consideration. In this paper we present a general, rotation invariant framework for co-occurrences of patterns and investigate possible solutions to the dimensionality problem. Using local binary patterns as bag-of-features model, we experimentally evaluate the potential advantages that co-occurrences can provide in comparison with bag-of-features. The results show that co-occurrences remarkably improve classification accuracy in some datasets, but in others the gain is negligible, or even negative. We found that this surprising outcome has an interesting explanation in terms of the degree of association between pairs of patterns in an image, and, in particular, that the higher the degree of association, the lower the gain provided by co-occurrences in comparison with bag-of-features.     

Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models

Maximum likelihood estimation of models based on continuous latent variables generally requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the adaptive Gaussian–Hermite (AGH) numerical quadrature approximation for a particular class of continuous latent variable models for time-series and longitudinal data. These dynamic models are based on time-varying latent variables that follow an autoregressive process of order 1, AR(1). Two examples are the stochastic volatility models for the analysis of financial time series and the limited dependent variable models for the analysis of panel data. A comparison between the performance of AGH methods and alternative approximation methods proposed in the literature is carried out by simulation. Empirical examples are also used to illustrate the proposed approach.